The economic impact of energy load shedding on small and

THE UNIVERSITY OF MISKOLC

FACULTY OF ECONOMICS

INSTITUTE OF MANAGEMENT SCIENCE

The economic impact of energy load shedding on small and medium-sized manufacturers in Richards Bay,

South Africa

Mfihlakalo Vusumuzi Mabuyakhulu

2020

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ABSTRACT

South Africa is facing an economic crisis that emanates from the shortage of electricity

supply. This power shortage arises from misguided government policy decisions and poor

management of the South African power utility, Eskom. The power shortage leads to

rotational power interruptions known as load shedding. The electricity supply shortage

remains a challenge as the current power generation infrastructure is aging and getting closer

to the end of life, while the demand in the country keeps rising. Eskom has to renew the

aging power generation plants, however, the utility is facing operational and financial

difficulties. The challenges faced by Eskom have a direct effect on electricity consumers,

particularly businesses in the manufacturing sector. The current study seeks to economic

impact of load shedding on small and medium-sized factories in Richards Bay, South Africa.

A mixed-methods approach was adopted for the research work which produced a qualitative

and quantitative study. Three studies were proposed initially, however, only two were

completed, an open-ended qualitative survey (study 1) and the subjective evaluation

methodology (study 2). The revealed preferences methodology (study 3) was not

implemented due to the unavailability of financial data from the factories, this data was

deemed to be highly sensitive and confidential. Results from study 1 show that the load

shedding has primary and secondary effects on the manufacturers. These effects are captured

on a model that shows the relationship between the effects. Study 2 estimates that the

economic impact of frequent power interruptions on small and medium-size factories is

worth 21% of their total assets. Study 3 was designed to collect data from backup power

generators, about the fuel consumed, energy delivered and maintenance performed to

estimate the true cost of "replacing" the grid supply with a backup generator in Rand.

From the two studies, the researcher recommends that stakeholders such as factory

shareholders, employees, suppliers, and customers work together closely to improve

communication, more especially when it comes to business challenges arising from load

shedding. The morale of the workers is affected by load shedding, professional help is

recommended. Eskom is encouraged to communicate load shedding schedules a week in

advance so that factories can plan their operations accordingly. Factories are encouraged to

consider moving their operating hours to the off-peak electricity demand period, between

22:00–06:00 and to negotiate preferential electricity tariffs for this move. The factories are

also encouraged to explore the possibility of setting a power generation plant jointly.

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ACKNOWLEDGMENT

Dr. Habil Berényi László: I appreciate your guidance and support throughout the thesis work

as my mentor. I am truly grateful for your dedication and professionalism.

Tempus Public Foundation: My appreciation goes to the Tempus Public Foundation for

awarding me a scholarship to pursue MBA studies in Miskolc, Hungary.

The South African Government: My sincere appreciation goes to the South African

Government, in particular the Department of Higher Education and Training for their

financial support throughout the studies and for granting me research funding.

Mabuyakhulu Family: From my parents, my siblings and extended family, I truly appreciate

your support and guidance from when I was awarded the scholarship, leaving South Africa

until the end of this work, you have been a source of strength and purpose.

Transnet Freight Rail: I appreciate the opportunity granted to me in the form of sabbatical

leave for two years to pursue my studies abroad, your support has been tremendous.

Zululand Chamber of Commerce and Industry: I appreciate your support provided during

the data collection of the thesis. This could have been a difficult exercise and long exercise,

your interventions made it short and seamless for me.

Thulani Dennis Mnyandu: My singular friend and brother, a very kindest, unpretentious,

affectionate, considerate, and empathetic, who gave me a strong support from start to the

end of our journey.

Laszlo Szasz: I am truly humbled to have worked with you coach on and off the athletics

track. It has been an honor and privilege to be coached by an Olympian. You have helped

me so much to balance my training, social life, and studies. You were not only an athletics

coach but a life coach to me.

God, I'm grateful for the intellect, courage, and blessings you had offered me in my entire

journey.

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TABLE OF CONTENTS

ABSTRACT .............................................................................................................................. ii

ACKNOWLEDGMENT .............................................................................................................. iii

TABLE OF TABLES ................................................................................................................. vii

TABLE OF FIGURES ............................................................................................................... viii

INTRODUCTION ....................................................................................................................... 1

1 RESEARCH PROBLEM ................................................................................................ 4

1.1 Research problem statement ............................................................................................. 4

1.2 What are the research objectives? .................................................................................... 4

1.3 Research methodology ...................................................................................................... 5

1.4 Research limitations ........................................................................................................... 6

1.5 Research layout .................................................................................................................. 6

2 LITERATURE REVIEW ............................................................................................... 7

2.1 The current economic situation in South Africa ................................................................ 7

2.2 The current energy situation in South Africa ................................................................... 10

What is Load Shedding? ............................................................................................................... 13

2.3 The impact of load shedding on electricity consumers in general and businesses in

particular ...................................................................................................................................... 13

Societal impact of load shedding ................................................................................................. 14

Indirect commercial impact of load shedding .............................................................................. 16

Direct commercial impact of load shedding ................................................................................. 17

3 METHODS USED TO MEASURE THE IMPACT OF LOAD SHEDDING ON ELECTRICITY

CONSUMERS IN GENERAL AND BUSINESSES IN PARTICULAR .................................................. 18

3.1 Different types of load shedding studies ......................................................................... 18

3.2 Methods for measuring the economic impact of load shedding ..................................... 19

4 CRITICAL ANALYSIS OF THE LITERATURE REVIEW .................................................. 23

5 RESEARCH QUESTIONS ........................................................................................... 26

6 CURRENT LOAD SHEDDING STATISTICS .................................................................. 27

7 RESEARCH METHODOLOGY .................................................................................... 29

7.1 The qualitative impact of load shedding on small and medium-size factories ................ 29

Unit of analysis and population ................................................................................................... 30

Sampling method and size ........................................................................................................... 30

Data collection process ................................................................................................................ 30

Analysis approach ........................................................................................................................ 31

7.2 The direct economic impact of load shedding on small and medium-size factories ....... 31

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7.2.1 The direct economic impact of load shedding employing the subjective evaluation

methodology ................................................................................................................................ 33





7.2.2 The direct economic impact of load shedding employing the revealed preferences

methodology ................................................................................................................................ 36





7.3 Survey design ................................................................................................................... 37

8 SOCIO-ECONOMIC CONDITIONS IN RICHARDS BAY , SOUTH AFRICA ....................... 39

9 SURVEY RESULTS ................................................................................................... 40

9.1 The qualitative impact of load shedding on small and medium-size factories ................ 40

Major themes ............................................................................................................................... 40

Degree of impact depends on various factors .............................................................................. 41

Operational impact of load shedding ........................................................................................... 42

Costs associated with load shedding ............................................................................................ 43

Revenue loss due to load shedding .............................................................................................. 44

Safety and security issues related to load shedding .................................................................... 45

Mitigation of load shedding impact ............................................................................................. 47

Stakeholders impacted by load shedding ..................................................................................... 48

Second-order effects of load shedding ......................................................................................... 49

Positive outcomes of load shedding ............................................................................................. 50

Graphical representation of major themes .................................................................................. 51

9.2 The quantitative impact of load shedding on small and medium-size factories employing

the subject evaluation methodology ........................................................................................... 52

Results from the survey ................................................................................................................ 52

Direct worth estimates of the impact of load shedding ............................................................... 52

Survey responses .......................................................................................................................... 53

9.3 The quantitative impact of load shedding on small and medium-size factories employing

the revealed preferences methodology ....................................................................................... 63

10 THE DISCUSSION OF THE RESULTS .......................................................................... 64

10.1 What is the qualitative impact of load shedding on small and medium-sized

manufacturing companies in Richards Bay, South Africa? ........................................................... 64

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10.2 What is the direct impact of load shedding on small and medium-sized manufacturing

companies in Richards Bay, South Africa? ................................................................................... 68

11 RECOMMENDATIONS .............................................................................................. 69

11.1 Recommendations for the manufacturers ....................................................................... 69

11.2 Recommendations for the power utility .......................................................................... 72

11.3 Limitations of the research .............................................................................................. 73

12 CONCLUSIONS ........................................................................................................ 74

12.1 What is the qualitative impact of load shedding on small and medium-sized

manufacturing companies in Richards Bay, South Africa? ........................................................... 74

12.2 What is the direct impact of load shedding on small and medium-sized manufacturing

companies in Richards Bay, South Africa? ................................................................................... 75

12.3 Limitations of the research and future research recommendations ............................... 76

REFERENCES ......................................................................................................................... 78

APPENDICES ......................................................................................................................... 81

Appendix A – Questionnaire approval ........................................................................................ 81

Appendix B – Survey questionnaire (qualitative) ......................................................................... 82

Appendix C – Survey questionnaire (quantitative) ...................................................................... 83

Appendix D – Pictures of Richards Bay......................................................................................... 87

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TABLE OF TABLES

Table 1: Three categories of impact resulting from load shedding (Goldberg, 2015) ....... 14

Table 2: Qualitative servey questionnaire. .......................................................................... 82

viii

TABLE OF FIGURES

Figure 1: The map of Richards Bay showing the strategic location of the town (Michelen, 2020)……………………………………………………………………………………….02 Figure 2: The economic sectors that contribute to the total GDP (Statistics South Africa, 2018)……………………………………………………………………………………….09

Figure 3: The frequency of load shedding stages experienced in Richards Bay from 01 December 2019 until 29 February 2020 (Eskom, 2020)…………………………………....27

Figure 4: The frequency of load shedding on the day of the week for the period 01 December 2019 to 29 February 2020 (Eskom, 2020)………………………………………………….28

Figure 5: Graphical representation of frequency of comments describing the impact of load shedding on factories………………………………………………………………………41

Figure 6: Major themes arising from the qualitative survey looking into the impact of load shedding on manufacturers………………………………………………………………...51

Figure 7: The distribution of responses on the availability of a procedure during a power outage……………………………………………………………………………………...53

Figure 8: Distribution of the working hours of the factories surveyed…………………...…54

Figure 9: The percentage distribution of factories with backup generators………………...54

Figure 10: The distribution of load shedding incidents per day and per week, Monday to Thursday…………………………………………………………………………………...55

Figure 11: The distribution of load shedding incidents per day and per week for Thursday to Sunday……………………………………………………………………………………..55

Figure 12: The percentage distribution of daily revenues lost due to power interruption incidents…………………………………………………………………………………....56

Figure 13: Percentage distribution of the costs incurred during the load shedding incidents…………………………………………………………………………………....56

Figure 14: The percentage distribution of load shedding impact on the equipment and machinery………………………………………………………………………………….57

Figure 15: The percentage distribution of factories that have backup power generators…...58

Figure 16: The percentage distribution of the capacity of backup power generators…….....58

Figure 17: The percentage distribution of the reliability of backup power generators….…..59

Figure 18: The percentage distribution of factories that outsource the backup power generators………………………………………………………………………………….60

Figure 19: The percentage distribution of costs associated with running the backup power generation system………………………………………………………………………….60

Figure 20: The percentage distribution of capacity provided by the backup power generators………………………………………………………………………………….61

Figure 21: The percentage distribution of the number of people who work in these factories……………………………………………………………………………………62

Figure 22: The percentage distribution of the area size of the factories surveyed………......62

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Figure 23: The percentage distribution of factories that have insurance as a mitigate against power interruptions………………………………………………………………………...63

Figure 24: Major themes arising from the qualitative survey looking into the impact of load shedding on manufacturers………………………………………………………………...74

Figure 25: The top view of the port of Richards Bay, the town’s export hub…………….....87

Figure 26: The inside of one of the medium factories producing plastic products in Richards Bay (Michelin, 2012)……………………………………………........…………87

1

INTRODUCTION

According to Van der Nest (2015), the immediate problem facing the South African

economy is the structural integrity of the electricity generation system. The lack of power

generation integrity compromises the entire electrical grid, where the demand on the

distribution side far exceeds the supply from the generation side. When the electricity

demand exceeds the supply, the South African power utility company, Eskom, is forced to

implement rolling blackouts to protect the power grid from collapsing known as 'load

shedding'. This is done regularly and the frequent power outages have become a norm for

the South African society and businesses. Nkomo (2005), says that the South African

economy is energy-intensive as the country is industrializing for development and growth.

The rolling blackouts have a devastating effect on the industrial business that are energy-

intensive in their operations. The power generation challenges are not new in South Africa,

they started in late 2007 according to (Goldberg, 2015). Goldberg (2015), further notes that

these power generation challenges are due to a combination of factors which include among

others, the maintenance backlog of Eskom's generation plants, the poor quality of coal

supplied to coal-fired power stations, skill shortages and insufficient generation capacity.

Eskom moved to construct two power stations to increase the generation capacity Medupi

and Kusile power stations (3 600 MW capacity each). However, these are behind the

construction schedule and over budget (Goldberg, 2015). The frequent power outages will

continue until the new power stations come online.

The current electricity supply challenges are not likely to be resolved anytime soon in South

Africa (Wright et al, 2019). The challenges facing the power utility are deep and structural,

they will take years to resolve according to (Wright, 2019). The power utility has a huge

sovereign debt of approximately 500 billion Rand and a host of technical challenges that are

largely related to skill shortages (Goldberg, 2015). Against this background, it is imperative

to understand how this systemic and entrenched problem is affecting businesses and the

economy of the country at large. The economic impact of load shedding on the

manufacturing sector will be studied, with a particular focus on small and medium-sized

manufacturers.

The manufacturing sector is the fourth largest contributor to the South African Gross

Domestic Product (GDP) in 2017, the sector contributed 12.2% to the total GDP (Statistics

South Africa, 2018). The small and medium-sized manufacturers contributed 69% to the

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12.2% total manufacturing contribution to the country's GDP (Statistics South Africa, 2018).

The small and medium-size factories employ about 65% of the total workforce in the

manufacturing sector (Statistics South Africa, 2018). After mining, the manufacturing sector

is the second most energy-intensive in the country. The manufacturing sector is highly

competitive in South Africa and Southern Africa. The sector depends largely on the

agriculture and mining sectors for primary sources of raw materials (Nkomo, 2015).

Electricity is not only important for factory operations, but it is also important in linking

mines and farms to the coastal factories located in Richards Bay, via railways. Manufacturers

are categorized into three groups based on the annual turnover, small (0 – 15 million Rand),

medium (15 – 51 million Rand) and large manufactures (51 million Rand and above)

(Statistics South Africa, 2018). The sustainability of the manufacturing sector relies heavily

on other stakeholders, such as raw material suppliers, electricity suppliers, and customer

confidence.

This research work aims to understand the economic impact of power interruptions on small

and medium-sized manufacturers in Richards Bay, South Africa. Richards Bay is an

industrial on the north coast of KwaZulu – Natal (KZN) province, South Africa. The town

is the third-largest economy in KZN and hub of small and medium enterprises (SME) in the

province. The economy of Richards's bay is rapidly growing and the town boasts the deepest

Port in Africa that is linked to the country by a state-of-art rail infrastructure, a regional

Airport, smelters, mining giants such as Rio Tinto, Exarro, and a whole host of

manufacturing and processing factories. Figure 1 shows the map of Richards Bay showing

the airport, highway in red and railway lines in yellow & white.

Figure 1: The map of Richards Bay showing the strategic location of the town (Michelin, 2020)

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Eskom is not solely blamed for the current power shortages currently experienced in South

Africa. The government has been aware of the potential power shortages since 1998 from a

white paper (WP) authored by the Department of Minerals and Energy back then (Goldberg,

2015). The WP noted that the surplus power generation capacity Eskom had at the time

would not be sufficient for the country's energy needs by the year 2007. The WP noted that

the demand in the country was rising rapidly and it recommended that timely action be set

in motion while taking into consideration lead times of some of the interventions required

(Goldberg, 2015). Senior government officials did not act accordingly on the contents of the

white paper regarding the energy forecasts, resulting in chronic power supply shortages

almost a decade later.

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1 RESEARCH PROBLEM

1.1 Research problem statement

Studies that estimate the cost of power interruptions on small and medium-size

manufacturers are not available in South Africa. Manufacturing is an important driver of the

country's economy contributing over 12% to the total GDP. Key stakeholders in the

manufacturing sector, including factory shareholders, factory employees, suppliers, and

customers, need reliable and scientific estimates of the economic impact that are induced by

frequent power interruptions on them for improved decision making.

1.2 What are the research objectives?

This research work seeks to contribute to the current body of knowledge describing the

economic impact of power interruptions. Three studies will be undertaken, as outlined

below. A mixed-method approach will be followed to carry out the research.

• Study 1: To study the qualitative impact of power interruptions on small and

medium-size manufacturers in Richards Bay, South Africa. The researcher will

design a qualitative survey to be distributed to financial, operational and maintenance

managers of these manufacturers.

• Study 2: To study the quantitative impact of power interruptions on small and

medium-size manufacturers in Richards Bay. Primary data will be collected from the

manufacturers using a survey questionnaire administered online.

• Study 3: To validate the results that will be obtained in study 2. Secondary data

regarding the marginal cost of backup power supply will be collected from the

respondents in study 2. This cost can be thought of as an indicator of the minimum

economic impact the frequent power outages have on manufacturers.

The proposed studies were adapted from Goldberg (2015) in a study that was looking at the

impact of load shedding on the retail sector. This research will build on the work laid by

Goldberg (2015), specifically looking at the impact on manufacturing operations of load

shedding. These will provide a country-specific case for South Africa. Similar studies that

have been done before, were done in developed countries internationally. The qualitative

results of study 1 will provide the soft aspects of the load shedding impact that affects human

beings in particular. The quantitative results of studies 2 & 3 will explain the hard aspects of

the load shedding impact on machinery, production and the bottom line.

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Pre-millennium (before the year 2000), South Africa had an abundance of electricity supply

that was stable and the electricity was sold at the most competitive tariffs worldwide

(Goldberg, 2015). The abundant and reliable power supply gave South Africa a competitive

advantage and the country attracted foreign, and local investments. Goldberg (2015), noted

that despite the current electricity challenges, South Africa is still an investment destination

as it is positioned strategically on the south coast and it serves as a gateway to Africa. Despite

the current electricity challenges, the country has over 200 years' worth of coal reserves in

Mpumalanga for coal-fired power stations (Goldberg, 2015). According to Goldberg (2015),

there is a huge potential for building up generation capacity fairly quickly in South Africa

as coal reserves are abundant and the gas industry is booming in East Africa.

This research work seeks to estimate the economic impact of power interruptions on small

and medium-size manufacturers in Richards Bay, KwaZulu - Natal. A systematic and

rigorous approach that is based on literature and theory will be followed. A descriptive

statistics based methodology will be followed in the study. This research will only be limited

to quantifying the costs of power interruptions on the manufacturing sector, focusing on

small and medium-sized factories in Richards Bay. The recommendations of the research

will be directed to the management of the manufacturers to minimize or mitigate the impact

of power interruptions in this sector. The recommendations will be directed to the power

utility company Eskom and the possibilities of expanding on the current research.

1.3 Research methodology

A multipronged approach will be followed when undertaking the current study. Three

proposed studies will be undertaken to address the set research objectives. The first study

will be qualitative focusing on the soft implications of load shedding on the factories. The

second and third studies will be quantitative focusing on the hard implications of load

shedding on the factories. Small and medium-sized factories will be the focus of this study

as categorized by their annual turnover. These factories generally operate at thin profit

margins, average 5.6% for small factories and 6.8% for medium-sized factories in the

financial year 2017/2018 (Statistics South Africa, 2018). With such profit-margins, any

drastic negative disturbance of these businesses is likely to throw the factories into

profitability challenges that may have huge implications for the employees in the short-run

and the economy of the country in the long-run.

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The Zululand Chamber of Commerce and Industry (ZCCI) is a chamber of businesses that

sits in Richards Bay, South Africa. It provides a platform for local business to come together

and share information and expertise. Businesses register with the chamber and pay annual

fees for services the chamber renders to them, including training and presenting

opportunities for collaboration. The chamber registers business in different sectors where

the businesses are operating, such as retail, manufacturing, agriculture, and services. Within

the sectors, there are different categories such as small-, medium- and large businesses. The

chamber is then able to link customers who have opportunities with their members in a

particular category. The chamber keeps the contact details of the members. The chamber

will be approached to assist with the data collection, it will serve as a link between the

researcher and the small and medium-size factories operating from Richards Bay.

The qualitative study will be conducted employing a survey questionnaire, that will consist

of ten open-ended questions and the survey will be conducted online. The first quantitative

study will focus on the primary or direct costs arising from a load shedding incident. The

second will seek to estimate the minimum cost of averting load shedding using generators.

1.4 Research limitations

The research work has the following limitations:

• The research work has a time constraint. An interview would have been the preferred

method of collecting data for the qualitative study instead of a questionnaire.

• This research work is limited to measuring the direct or primary costs associated with

load shedding. These costs arise directly from the incident such as loss of revenue.

• Budgetary constraints, ideally, it would be advantageous to carry out the research

countrywide to get a better understanding, as the impact may vary from city to city.

1.5 Research layout

Chapter 1: Introduces the research, sets objectives, outlines the research approach and

presents the limitations of the research. Chapter 2: Presents the relevant literature review

and contextualizes the research. Chapter 3: presents a critical analysis of the reviewed

literature. Chapter 4: outlines the research questions. Chapter 5: presents the current

statistics about the energy situation in South Africa. Chapter 6: shows the design of the

research. Chapter 7: presents the social and economic conditions of Richards Bay. Chapter

8: shows the results. Chapter 9: analysis and discusses the results. Chapter 10: makes

recommendations to various stakeholders and Chapter 11: concludes the research.

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2 L ITERATURE REVIEW

This chapter presents the South African economy in broad terms, firstly concerning the

global economy, continental economy and national economy. The contribution of the

manufacturing sector in the entire economy of the country will be highlighted. The energy

situation in the country will also be presented, focusing on the provision of electrical energy

to both domestic and commercial electricity consumers. The challenges faced by the energy

sector will be highlighted and discussed in detail. The implications of the challenged energy

sector on electricity consumers will be evaluated. A thorough review of methodologies

previously used to measure the cost of frequent power interruptions on electricity consumers

will be presented. A high emphasis will be placed on the implications of load shedding on

commercial activities.

2.1 The current economic situation in South Africa

Globally, the South African economy is regarded highly, the country is part of the Global

Twenty (G20) forum for economic cooperation (G20, 2020). The G20 comprises of 19

developing and developed countries and European Union (EU). The members of the G20

represent approximately 80% of the economic output in the world, two-thirds of the world

population and accounts for three-quarters of international trade (G20, 2020). The forum has

various committees, with the most prominent one made up of political leaders of member

states such as Prime Ministers, Head of States and Presidents. The leaders committee usually

convenes once annually at the beginning of each year to set the agenda for the whole year.

Thereafter, technical sub-committees made up of Finance Ministers and Central Bank

Governors operationalize the resolutions of the leaders committee. However, impromptu or

extra-ordinary meetings of the leaders can be called at any time when there is a pressing

matter that affects the majority of the major countries, like the special Corona Virus

Pandemic leaders summit, that was convened on 25 March 2020 via video conference (G20,

2020). According to (G20, 20) its main aim is macro-financial and socio-economic matters.

South Africa is also part of an international economic formation of emerging economies

which include Brazil, Russia, India, China, and South Africa (BRICS). The formation is

commonly referred to using the acronym BRICS. South Africa joined this important bloc in

December 2010 and has hosted two BRICS summits, the first one in 2013 and the second in

2018 (BRICS, 2019). According to BRICS (2019) the five countries represent 42% of the

world population, 23% of the world GDP, and 18% of global trade.

8

At the regional level, South Africa is considered one of the most advanced economies in the

continent of Africa. It is part of the African Union (AU) and the Southern African

Development Community (SADC) at the sub-regional level. South Africa is currently the

chair of the AU starting on 09 February 2020, His Excellency President Ramaphosa was

elected chairperson of AU for the year 2020 at the 33rd ordinary session of Heads State and

Government (AU, 2020). According to AU (2020), the primary objective role of the AU is

promoting unity, peace, economic growth, integration and cooperation of African states.

South Africa will be leading in setting the AU agenda for 2020 and linking the AU to the

world through the G20 and BRICS formations. The role of South Africa at the SADC level

is also prominent. Most SADC member states share borders with South Africa and are

largely the biggest trade partners of the country on the continent.

The South African economy is considered to be amongst the largest in Africa and the most

industrialized economy in Africa (Mare, 2011). According to Mare (2011) as one of the

most industrialized economies on the continent of Africa, the South African economy is

energy-intensive, dominated by heavy industries such as mining, processing, and

manufacturing. Even though the country has one of the largest economies on the continent,

there are still massive challenges related to economic growth which has been below 1% for

four years (2014-2018) and the high unemployment rate at 29.1% (Statistics South Africa,

2018). Some of the factors limiting economic growth in the country are external and some

are internal. The external factors related to the global economic slowdown as the

International Monetary Fund (IMF) has lowered the global economic rate forecast to 3.6%

for 2020 (South African National Treasury, 2020). The slow down of the global economy

severely affects the South African export market, as the demand for mineral resources

declines. Trade wars between major economies in the world that result in tariffs being

imposed on certain critical products do not aid the global economy. However, there are also

internal challenges that have stalled the economy from growing rapidly, such as the

weakening currency, energy challenges, and low business confidence.

Since the dawn of independence in 1994, the South African economy was driven by primary

sectors such as mineral resources and agricultural products (Brand South Africa, 2018).

Brand South Africa (2018) notes that the economy has evolved from the early 1990s from

relying heavily on the primary sectors to tertiary sectors such as financial services,

wholesale, manufacturing, and tourism. The economy is mainly based on technology, e-

commerce, and it is a vastly knowledge-economy (Brand South Africa, 2018).

9

The current structure of the South African economy is therefore largely dominated by sectors

such as manufacturing, wholesale, retail, transport, financial services, and tourism. Figure 2

below shows the sectoral distribution of the economy as of quarter three in 2017.

Figure 2: The economic sectors that contribute to the total GDP (Statistics South Africa, 2018).

It can be seen in Figure 2 that manufacturing contributed 13% to the total GDP in the third

quarter of 2017, this within the ball-pack of the 12.2% of the annual contribution of the

sector quoted earlier. The major contributor to the economy is by far the financial services

that are boosted by e-commerce, insurance, and the world-class banking system in South

Africa. The South African Central Bank is playing a critical role in regulating this sector

ensuring healthy competition and fraud detection. Trade comes in third at 15%, this includes

wholesale and retail trade. Statistics South Africa (2018) estimated that the actual trade

contribution is about 1% higher if the informal economy of street vendors is taken into

account. Manufacturing comes in at number 4 with a 13% direct contribution. It is estimated

that manufacturing contributes a further 5% to the South African GDP indirectly, through

tertiary sectors such as trade and financial services (Statistics South Africa, 2018). It is clear

from the statistics mentioned before that manufacturing is a critical sector in the economy.

10

Even though the South African economy has a modern structure and it is highly

industrialized compared to other economies on the continent, the economy is still not

growing at the expected rates. This could be due to some internal factors that will be

considered next.

The weak South African currency, the Rand, is affecting the import and export markets. The

internal factors are related to the structure of the economic policies the country is pursuing

and the management of key national assets, which include state-owned enterprises (SOE).

SOE's are large government-owned corporations in South Africa that are responsible for key

infrastructure and services. They include among others, Rand Water, an entity that is

responsible for ensuring that the country's present and future water needs are catered for and

it reports to the national government through the Department of Water Affairs. There is also

the South African Airways, the national carrier that flies domestically, regionally in Africa

and internationally to other continents. The primary objective of the national carrier is to link

South Africa to strategic partners in the world, prioritizing destinations such as London in

the United Kingdom, New York in the United States, Frankfort in Germany and Shanghai

in China, these countries and cities, in particular, have many people that own and run

businesses in South Africa. There is also Transnet that is responsible for the Ports and the

Railway infrastructure. However, the most critical of all the SOEs is the power utility

company Eskom. An economy cannot grow and be sustainable without a reliable, well-

priced and sustainable electricity supply (Goldberg, 2015).

2.2 The current energy situation in South Africa

Eskom is a South African power utility company that is responsible for the national power

system. The company owns 95% of the installed electricity generation infrastructure in the

country, manages the transmission grid and the reticulation network in non-metropolitan

areas (Eskom, 2020). According to Eskom (2020), the company owns and operations 30

power stations that include the following generation mix, 36 479 MW from coal-fired power

stations, 1 860 MW from a nuclear power station, 600 MW from hydro-stations, 2 724 from

pumped storage schemes, 2 409 from gas-fired power stations and this comes to a total

installed capacity of 44 172 MW of generation capacity. Eskom (2020) notes that the utility

never operates at the installed capacity, because some units are taken out of commission for

maintenance regularly, and the planned maintenance outage is 9 500 MW.

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On the power transmission and distribution side, the utility owns and operates 387 633 km

of high-, medium- and low-voltage lines and cables (Eskom, 2020). According to Eskom

(2020), the utility has a total of 6.2 million direct customers which include metropolitan

municipalities, mines, smelters, and the general public. The utility has 39 292 employees

spread across three operating divisions, which are power generation, power transmission,

and power distribution including new connections. The utility has a mandate to generate and

supply power to customers in South Africa and the SADC region, it exports power to

Zimbabwe, Botswana, the Kingdom of Lesotho and the Kingdom of Swaziland. Eskom was

regarded as a mighty power utility in the late 1990s and early 2000s, in the year 2005 it was

ranked the 11th best power utility in the world by world energy council (World Energy

Council, 2005). Soon after getting recognition from the World Energy Council in 2005, the

power utility started experiencing power supply shortages leading to rotational power

interruptions in the country in 2007 (Van der Nest, 2015).

According to Van der Nest (2015), the power supply challenges leading to load shedding

were only witnessed in 2007, however, they started much earlier in the late 1990s and early

2000s, approximately seven years before. Energy experts warned the South African

government through the Department of Minerals and Energy in a white paper authored in

1998 that the country's population and electricity demand were rising rapidly (Goldberg,

2015). The white paper forecasted that by the year 2007, the electricity demand is likely to

exceed the installed capacity at the time and this crucial expert advice was ignored by Eskom

and the South African government. Between 1998 when the white paper was given to the

government and 2007 when load shedding was first experienced, there was no investment in

upgrading or strengthening existing power generation infrastructure (Van der Nest, 2015).

This decision taken in 1998 was regretted a few years later when the power cuts started in

2007.

When the load shedding started, South Africa had already won the bid to host the 2010 FIFA

Football World Cup in 2010. The South African government gave the power utility a

mandate to do everything possible to keep the lights "on" to shield the country from

international embarrassment in the build-up to the World Cup in 2010. The already strained

power generation infrastructure was stretched to the limit, leaving almost no room for

periodic maintenance to keep the lights on. The 2008 global economic depression slowed

down the South African economy resulting in reduced energy demand for the years 2008,

2009 (Van der Nest, 2015).

12

The South African economy started rising again in 2010 as the country was building up to

staging the World Cup in June 2010 (The Economist, 2015). However, the rise in the

economy was largely due to the booming tourism and construction sectors at the time, and

both these sectors are not energy-intensive, therefore, the energy demand was still

manageable for the power utility (Goldberg 2015). After the World Cup the South African

economy continued to recover from the 2008 global depression and by 2011 most industries

that had shut down in 2008 were getting recapitalized and starting with operations again

(The Economist, 2015). The implication of this rising economy for the power utility was a

rise in energy demand and by early 2012 load shedding started again (Van der Nest, 2015).

According to Van der Nest (2015), the challenges facing the power utility in 2012 were far

greater than those that faced it in 2007. He further noted that the South African government

was forced to quickly invest in two new state of the art coal-fired power stations, Kusile and

Medupi in 2007. However, the lead times on the construction of these plants meant that they

were not available to come online in 2012 when they were needed the most. Van der Nest

(2015) highlights that the power stations are plagued by design and construction challenges

that caused massive delays, and they went over budget by billions of Rand.

Over and above the problem of load shedding, Eskom had the problem of the two new power

stations that were draining a lot of money and not bringing in any revenue. Both these mega-

projects were financed from money raised in the financial markets backed by a government

guarantee. The cost of servicing the interest on the debt is estimated to cost Eskom over 100

million Rand per month and as of the year 2015, the power utility had a sovereign debt of

approximately 450 billion Rand (The Economist, 2015). These financial difficulties are

coupled with technical challenges, where the current generation plants that were not

maintained properly from 2007 to 2010, are starting to breakdown often. This challenge was

also coupled with leadership instability at Eskom, where there were six different Chief

Executive Officers (CEO) in ten years from 2005 to 2015 (Van der Nest, 2015). All these

challenges and a skills exodus of experienced staff resulted in a deepening load shedding

situation. Whenever a new CEO came, they would change the structure and move employees

around and some employees did not like this leading them to resign. It is against this

background that South Africa is experiencing load shedding that is crippling the economy

and the lives of ordinary South Africans unpleasant with frequent power interruptions. A

deeper look at load shedding will show that this problem is structural, it is embedded in the

power utility. A meticulous approach would be needed to solve this problem at Eskom.

13

What is Load Shedding?

Load shedding is a systematic technique of managing the demand for electricity to prevent

the collapse of the grid. When the demand for electricity far exceeds the generation capacity

at a particular time, power utilities are forced to reduce the load by limiting the number of

supply points, opening circuit breakers that supply certain customers. When there is

insufficient power to meet the electricity demand and the power utility takes no measures to

reduce the load, this has the potential to collapse the grid (nationwide blackout). This would

be catastrophic as it would damage transmission infrastructure and it would take weeks to

restore the power countrywide (Van der Nest, 2015). As the surplus between the electricity

supply and demand reduces, load shedding becomes the only option to protect the grid and

it starts when the demand reaches 90% of the available supply at a time (Eskom, 2020).

Eskom priorities the stability of the national power grid. Therefore, they take preventative

measures when the electricity load rises rapidly, this includes requesting major customers to

reduce their load voluntarily. If this does not yield the required energy supply surplus, the

next step is to request all customers to reduce their demand by switching off all non-essential

electrical appliances and lights. If this call does not yield the required energy surplus in the

power system, Eskom activates peaking plants that open cycle turbines running on diesel to

counteract the rising demand. The diesel plants consume a lot of fuel and they are always

the last resort. If they also do not provide the necessary energy deficit or the demand keeps

on rising, Eskom then resorts to rotational power-sharing, where the available power is

shared rotationally until the demand reduces. There are various stages of load shedding,

depending on the size of the energy deficit between the supply and demand of electricity.

These will be discussed further in the subsequent chapter dealing with load shedding

statistics.

2.3 The impact of load shedding on electricity consumers in general and businesses in particular

Power outages have two main victims in South Africa which are domestic and commercial

electricity consumers. Domestic consumers generally use the electricity for non-commercial

purposes which includes activities to sustain life and for domestic entertainment. The

commercial consumers of electricity use it for business operations. The two victims do not

suffer the same fate from the load shedding and the differences will be outlined in the

subsequent sections of this report.

14

Table 1 shows three categories of the load shedding impact on electricity consumers. The

costs of a load shedding incident are classified according to three types which are the direct,

indirect and societal impacts. A loss in electricity utility qualifies as a loss in the context of

this study when this loss results from a frequent interruption of the electricity supply.

Electricity is such an integral part of people's lives today, the unavailability of the resource

results in mostly economic impact to society. Sanghvi (1991) notes that the nature of the

impact depends on several factors that include, among others, the duration of the load

shedding incident, the magnitude of the incident and the advance notification of electricity

consumers about the incident. When an electricity consumer that suffers from frequent

power interruptions can mitigate or minimize the impact thereof, this is considered when

determining or measuring the total cost of a power outage.

Table 1: Three categories of impact resulting from load shedding (Goldberg, 2015)

Direct commercial impacts Indirect commercial impacts Societal impacts

Loss of production The cost of income being postponed

Uncomfortable temperature Restart costs

Equipment damage The financial cost of losing market share

Loss of leisure time

Raw material spoilage Loss of investor confidence Risk to health and safety

Power interruptions do not only have direct costs they also have indirect costs that are much

more complex and difficult to accurately measure, and quantify. When the United States of

America (USA) government was quantifying the cost of the 1977 New York blackout, they

discovered that looting and arson account for 50% of the costs and they are defined as

indirect costs (Assistant Secretary for Energy Technology, 1978:2).

Societal impact of load shedding

Electricity supply is one of the crucial needs modern societies need daily to use for a variety

of needs. The impact of load shedding on the society depends on the nature and magnitude

of a load shedding event (Goldberg, 2015). The nature of a load shedding event includes the

time of the incident, the day of the incident and whether there was a prior notification about

the incident. The impact of load shedding also depends on the purpose that the society uses

the electricity for like, entertainment, maintaining comfortable temperatures or other home

chores like driving pool pumps, electric gates, energizing the electric fence, alarms,

powering closed-circuit cameras (CCTV), cooking and lighting.

15

In a study performed in Pakistan by Jamil and Faran (2018), they found that safety and

security was the biggest concern for the society they surveyed. The respondents in their study

expressed that they felt unsafe when load shedding occurred at night as the security lights,

the security cameras, and electric fence were not working during the load shedding incident

(Jamil and Faran, 2018). The surveyed respondents expressed that their neighborhood

experienced the most house burglaries during the nights when there is load shedding and this

was their biggest concern about load shedding. Jamil and Faran (2018) performed this study

in a middle-income neighborhood. They also surveyed a high-income neighborhood.

The high neighborhood expressed uncomfortable conditions, such as uncontrolled

temperatures due to lack of air-conditioning, lack of cold and hot beverages during the load

shedding and loss of internet, television and the non-circulation of water in their swimming

pools and fish ponds (Jamil and Faran, 2018). According to Jamil and Faran (2018), the

respondents further stated that the above reasons had health implications for the elderly when

the temperatures were high in the summer months and the were cultural implications, as they

cook lunch every day to enjoy together as family, the load shedding also makes it hard to

achieve this cultural practice. They also reported that the internet, sometimes the cellphone

reception would also get affected by the load shedding and the livelihood of the fish in the

pond would be affected by non-circulating water (Jamil and Faran, 2018).

It can be seen from the study of Jamil and Faran (2018) that the impact of load shedding also

varies depending on the class of society. The upper-class society expressed cultural effects,

health effects and entertainment effects resulting from the loss of internet and electronic

devices. They also expressed that they are unable to enjoy certain beverages when there is

load shedding these include cold beverages and hot beverages, and warm home-cooked

meals. The impact on the upper class can be summarized as cultural, health and leisure.

For the middle-class society, the impact is largely concerning safety matters and can be

described as life-threatening as there are house burglaries reported. This impact is not

directly due to load shedding, the incident does not pose danger to the lives of the society

members, however, it creates conditions that may pose danger to the livelihood of society

members. A similar environment was witnessed in New York during the 1977 blackout

where 50% of the impact was due to looting and arson (Assistant Secretary for Energy

Technology, 1978).

16

Indirect commercial impact of load shedding

For the South African economy, the most significant indirect cost is the loss of investor

confidence that results from unstable power supply or power shortages. Investors become

very skeptical of investing their money in economies where there is an unstable power

supply. A stable electricity supply is a major competitive advantage in attracting greenfield

foreign direct investments. Electricity tariffs and the reliability of electricity supply are a

huge factor when investors assess the viability of an investment destination (The Economist,

2015). Most investors are skeptical of investing in South Africa because of the electricity

challenges. There is no certainty as to what is the extent of the problem causing power

interruptions and the key stakeholders seem to be unable to grapple with the problem and

solve it permanently. The second impact that businesses and the economy face is the

postponement of income resulting in liquidity challenges.

For most small businesses, the biggest challenge arising from load shedding is the

postponement of income due to delayed deliveries of orders (Goldberg, 2015). This plunges

the businesses into liquidity crises where they are unable to meet their short-term financial

obligations such as rent, utility bills, wages and other costs of running their businesses.

Businesses accept sales orders based on their capacity to deliver under normal conditions

and load shedding conditions are not normal. Businesses are forced to consider the costly

option of keeping high product stock levels in warehouses and other storage facilities.

Storage is not ideal for businesses, over and above the cost of storage, there are quality issues

that may develop during storage, depending on the storage conditions. The challenge with

storage, it is difficult to determine the exact time the products are going to spend on the

storage facility as this depends on future orders. The most ideal method of conducting

business is the pull system, where a confirmed sales order activates the production process.

Businesses that can deliver despite load shedding challenges earn a competitive advantage.

When a particular business can stand out and deliver goods and services to customers despite

the challenges of load shedding, it is likely to have an increased market share (The

Economist, 2015). This competitive advantage could be due factors implemented internally

to mitigate against the impact of load shedding or contingency arrangements such as putting

away products in storage facilities. Goldberg (2015) noted that customers have preferred

businesses they are loyal to for many reasons, however, if they are unable to deliver at a

particular time, customers tend to look for alternatives, and this how the market share is lost.

17

Direct commercial impact of load shedding

South African businesses are constrained by the structural shortage in electricity supply and

this is severely affecting business operations (Ateba et al, 2019). The level and severity of

the load shedding impact on businesses is dependent on their core business. Sectors that are

energy reliant such as mining, manufacturing, rail transportation, telecommunications, trade,

and financial services get affected the most according to Nkomo (2015). The major impact

of load shedding results from loss of production or the halting of operations. The stopping

of business operations has direct implications on the business revenues and the costs of doing

business. Other direct costs arise from the load shedding that are industry-specific, however,

the abovementioned are common across many sectors of the economy.

Income revenue is one of the most important key performance indicators most businesses

track periodically to evaluate their performance. At the beginning of each financial year

businesses usually set out objectives and targets for the year ahead. Some businesses even

go further by setting, semi-annual, quarterly, monthly, weekly and daily targets that are

tracked using various tools. When business continuity is affected by load shedding, it affects

the throughput for that particular day. This may lead to the daily target being missed, which

has a direct bearing on the revenues. Targets are set based on forecasts of sales from the sales

& marketing department and the mossed targets have a bearing on the sales for that particular

period thus affecting the business income. Businesses sometimes make certain interventions

to recover from missed business targets. These interventions are usually over and above

provisions made for normal operations and they include extending business hours to cater

for the loss hours during load shedding and this has a cost implication for the businesses

The other major direct impact of load shedding on business is the higher costs of doing

business. The major cost component comes from overheads due to standing time and

overtime costs. The biggest cost component reported by business was overtime payments to

employees for working extra hours to recover the lost production due to load shedding

(Diboma and Tamo Tatietse, 2013). The common method of mitigating against missed order

obligations is to request workers to work extra hours to meet the daily target. However, when

workers work extra time, their employee costs double per hour, this is costly for businesses.

Another major cost component arises from workers being at working, however, not being

able to work because there is no electricity for operations, this is known as standing time.

18

3 METHODS USED TO MEASURE THE IMPACT OF LOAD SHEDDING ON

ELECTRICITY CONSUMERS IN GENERAL AND BUSINESSES IN PARTICULAR

There is a variety of methods in the academic literature that are employed to measure the

economic costs of load shedding. The methods vary according to the nature of electricity

customers being studied and the nature of power interruptions faced by the customers.

Therefore, before embarking on such a study, the researcher has to study the nature of

customers facing the load shedding and also understand the nature of load shedding being

experienced by the electricity customers. This information is crucial for selecting the most

appropriate method for the case that the researcher wishes to study. In the subsequent

sections, the research will review types of load shedding studies in the literature and the

methodologies used to measure the various types of load shedding.

3.1 Different types of load shedding studies

When analyzing the impact of a power outage incident it is important to consider the type of

incident being analyzed. Two categories of load shedding studies were noted in the literature.

The first category pertains to studies performed in high-income countries that have a high

percentage of supply reliability in the order of 99.98% (Sanghvi, 1991). The purpose of this

study was to understand the impact of random, rare and isolated incidents of power outages

in the high-income countries. Power utilities of the various countries commissioned the

studies themselves so that they could refine their operations to improve customer satisfaction

(Goldberg, 2015). Some high-income countries conduct power interruption studies to aid

policymakers in making informed policy decisions (Sanghvi, 1991).

The reliability of power supply in Germany is amongst the best in Europe, however, the

country is actively researching the economic impact of power interruptions. The country is

planning to decommission all nuclear power stations by 2022 and ramp up renewable energy

to 35% of the country's energy mix by the end of 2020 (Growitsch et al, 2014). An increase

in renewable energy sources poses challenges as there is no control of the meteorological

conditions in which the quantum of energy generated by these sources at a particular time.

There exists a serious risk of having energy shortages when the meteorological conditions

are not conducive to produce enough energy to meet the demand. This condition may

potentially lead to power interruptions in Germany, unless otherwise, there a peaking power

plants on standby, such as Gas turbines and Diesel generators, that can be called online when

there is insufficient energy from renewables.

19

The cost of power interruption studies has also been conducted for medium to low-income

countries. It is often difficult to distinguish between outages due to technical faults and the

shortage of power supply. If the power utility company is economical with the truth, it is

difficult to distinguish between the two. Technical faults may affect the generation capacity

temporarily, however, they do not cause systemic power supply shortages. Technical faults

are usually seasonal or occasional while power shortages are not sporadic at all. Oseni (2012)

noted that the power challenges in most African countries are due to poor demand

forecasting, generation efficiency and demand management (Oseni, 2012).

Power outages are a regular occurrence in poor countries and they have become a new

normal. Businesses have to factor in this condition when they plan their operations, the

likelihood of a power interruption is high. Power interruptions have both immediate and

future effects. In the present, they affect the production output of businesses and disrupt

operations. In the long term, this affects both customer and consumer trust as well as investor

confidence. The impact of technical faults can be absorbed by businesses, however, it is

difficult for businesses to absorb the cost of power interruptions. Regular faults usually affect

the profit margins and businesses view these as a once in a while anomaly. While power

interruptions affect the profitability of businesses in the short term, in the long term

sustainability of businesses is threatened by the power interruptions unless certain

interventions are made. Businesses that are struggling due to load shedding lead to a weak

or a declining country economy. Modern economies rely on a stable and reliable electricity

supply if this is not present there are primary and secondary economic effects experienced.

3.2 Methods for measuring the economic impact of load shedding

Various methods can be employed to measure the economic impact of load shedding. These

depend on the scope of the research, the type of electricity customer and the nature of load

shedding incidents. In the previous section, various load shedding types have been discussed.

This section will focus on the various methodologies used in the literature.

The first method that will be reviewed is the analytical methodology that uses secondary

data to estimate the cost of load shedding on electricity consumers. This method is only

applicable where there is historical data about the consumption of electricity for economic

activity. This method seeks to estimate the value of lost load (VoLL) per unit (Linares and

Rey, 2013). The VoLL estimation takes into account the economic sector of the business

being studied, the company size, the geographic location of the company and the nature of

20

the load shedding incident. The VoLL depends highly on the prior notification about the load

shedding incident. By estimating the utility value derived from having electricity supply,

this can be seen as a value lost when there is a power outage. The next methodology to be

reviewed is the revealed preferences methodology.

The revealed preferences methodology model is based on the understanding that businesses

are set up to make profits and pay dividends to shareholders. A company that experiences

load shedding frequently will develop mechanisms to first mitigate or secondly minimize

the impact of power outages, this may include getting a standby power plant. Any action that

a business takes to counteract the effect of load shedding will have financial implications.

Therefore, a thorough analysis of the advantages and disadvantages needs to be undertaken

before a decision is made to invest in any mitigating factor. Adenikinju proposes that

multiple options must be considered at first and after a thorough comparison, the best option

is chosen, which would offer the best total cost of ownership (TCO) (Adenikinju, 2003).

Businesses are always striving to make a profit, deciding on a power generating plant

investment is a managerial decision that considers, the cost of the plant, the power

requirements, the cost of interruptions and the economic value to be derived from the power

plant. Oseni (2012) noted that economics should be the ultimate deciding factor on whether

an investment on a backup generator is economically viable.

If a company purchases a backup generator to counter the effect of load shedding, the

information about acquisition costs would be available in the records of the company.

Modern backup generators log a lot of useful information that can be downloaded for

analysis. The biggest advantage of this methodology is the availability of data that is fairly

accurate as generators are smart to log all the important data themselves (Beenstock et al,

1997). The data collection is relatively cheap and quick, it involves a simple download from

the generator computer box. The data is very accurate as it is electronically logged compared

to survey responses where there is a potential for misrepresentations of facts. The

disadvantage of this methodology is that it is only applicable where there are backup

generators with data loggers installed. The next method to be reviewed is the use of customer

surveys to estimate the cost of load shedding on electricity consumers.

Customer surveys have been widely used in the literature to study the impact of power

interruptions on businesses. Sanghvi (1982) says this is the most widely used methodology

to measure the economic costs of load shedding on industries (Sanghvi, 1982). The onus is

left to the consumer of the electricity to estimate how much a power interruption costs them.

21

The method is referred to as the stated preference method, as the end-users of the electricity

estimate value that is lost due to a load shedding incident. Balducci (2003) says this is the

easiest methodology to measure the economic cost of power outages on the end-users of

electricity. The biggest challenge with this methodology is that it is prone to errors and

manipulation, as electricity consumers may make mistakes when responding to the survey

or deliberately misrepresent facts for some reason (Balducci et al, 2003). Customers are best

placed to determine the economic value of a load shedding incident, as it is something that

consumers experience, however, it is not simple to estimate the cost (Tollefson et al 1994).

The economic impact of load shedding can be measured at a micro-scale (small scale) and

then generalize the results to a larger population at a macro-scale (larger scale) if the sample

is representative of the population. Cheng (2014) says that customer surveys offer the

opportunity to get a statistical sample that spans across a wide range of respondents. The

design and execution of this survey is critical in soliciting the correct data from respondents.

The trick is to make the survey as simple as possible and to get as much data as possible

from the consumers of electricity (Cheng & Venkatesh, 2014). The next method to be

discussed is the conjoint analysis methodology.

The conjoint analysis methodology measures the relative values of attributes. Concerning

the matter of frequent power outages, the conjoint analysis would be used to estimate the

monetary value that the consumer of electricity would be willing to pay for increased

electricity supply reliability. The consumers of electricity are presented with a set of

hypothetical situations about power interruptions and their views are solicited. This is a

contrast to the CVM methodology that asks directly for the WTP and the WTA. The rating

and ranking scales are most preferred when implementing this methodology.

The researcher needs to understand the various kinds of impacts that a load shedding might

have to particular electricity consumer when designing the questionnaire. This will help the

researcher to create the required hypothetical conditions for consumers to rank or rate. If

these are poorly designed this method becomes ineffective and does not yield meaningful

results. The options to rank or rate must cover most, if not all possible scenarios so that each

respondent can be able to have something that is a priority to them or their business (Baarsma

& Hop, 2009). The next methodology to be reviewed is case studies.

22

The case study methodology is based on previous well researched and documented power

interruptions studies. The researcher reviews a practical case that happened in the past and

tries to match the conditions under which it happened to the current conditions to draw

parallels. The most famous case study that deals with the impact of power outages is the

1977 New City blackout. This black was extensively studied with funding from the US

government and a case study was developed from the results. The case study considers both

the economic and social impacts of the blackout (Corwin & Miles, 1978).

This method is extensive and requires a lot of resources and data. It needs the support of key

stakeholders for access to resources and data. The case went beyond the direct impact of the

blackout by looking into the indirect impact of the blackout. The major disadvantage of this

method is that no two power outages are the same according to Blien (2009). The case is

specific to a geographic location under specific conditions that may arise again (Bliem,

2009). The next method to be reviewed is the subject evaluation methodology.

The subject evaluation methodology places the task of quantifying the cost of power outages

on electricity consumers. The consumers use their experience, knowledge and own tools to

quantify the cost of load shedding on their businesses. The researcher presents the

respondents with outage scenarios and the respondents estimate the economic impact of each

scenario based on their experience (Kafeoglu & Lehtonen, 2015). Businesses are requested

to state economic losses suffered due to a specific or a set of power outages of the recent

past (Oseni, 2012). This method is also referred to as the direct worth methodology because,

consumers estimate their direct losses that they recorded (Tollefson, et al, 1994).

This methodology works well when the costs associated load shedding incidents are

quantifiable. The power outages interrupt the provision of services and production of goods

affecting the economic standing of firms and countries where these power interruptions are

experienced (Goldberg, 2015). Some of the important data that informs the subjective

evaluation methodology estimates in industrial operations losses due to load shedding

include idling resources, equipment damage, material spoilage costs, process restart costs

and the health and safety concerns (Sanghvi, 1982). These are usually recorded in the

industrial operations, therefore, industries can estimate these costs.

23

4 CRITICAL ANALYSIS OF THE LITERATURE REVIEW

Chapter two of this report has reviewed academic literature that is relevant to the current

research work. This chapter will provide an independent critique of the reviewed literature,

highlighting major strides made in the research field before and also identifying gaps that

the current research will seek to close. Approaches adapted for this research from the

literature will also be highlighted and contextualized.

The literature review begins by studying the South African economy, in the global, regional,

sub-regional and national contexts. South Africa is considered an emerging economy and

plays a prominent role in world economic forums such as the G20, BRICS, AU, and SADC.

The national context of the South African economy is currently gloom as the economic

growth has been below 1% for four years (2014-2018) according to Statistics South Africa.

This has been due to internal and external factors. It was noted by the National Treasury

(2020) that the global economy had a slower growth than anticipated, however, it was still

2% higher than the South African growth. Therefore, it is evident that the South Africa

economy is facing difficulties. The economy is still described as the most industrialized in

Africa. This is true, however, the challenges concerning economic growth will result in

South Africa losing the status of being an economic powerhouse on the continent. One of

the identified causes of the struggling economy is the management of state assets.

Industrialized economies are underpinned by a stable and reliable electricity supply. The

current energy situation in South Africa is not geared to support the industrialized economy

of the country. This is due to poor planning by the government by ignoring expert advice in

1998 about investments in future power generation and the mismanagement of the utility for

many years. The power utility had political pressure before the 2010 FIFA Wolrd Cup to

keep the lights on at all costs. This started in 2007 when load shedding started breaking out

until the World Cup was staged in June 2010. This meant that critical maintenance could not

be done to keep the lights. Like with any mechanical system, if maintenance is not done on

power plants the efficiency deteriorates which is the case with generation plants in South

Africa. In 2007 the government then rushed through two mega power projects that have been

delayed and over budget. These projects have not aided the energy situation in the country.

They have imposed major financial difficulties for the power utility, as the money used to

build these projects was sourced in the final markets, the power utility has a huge sovereign

debt estimated at 450 billion Rand (The Economist, 2015).

24

The challenges at the power utility have resulted in systemic power interruptions that are

known as load shedding. This severely affects business activities in the country as Nkomo

(2015) noted that there is a relationship stable power supply and economic growth in

industrialized or industrializing economies. With energy-intensive sectors such as mining,

transportation, and manufacturing contributing a significant percentage to the total GDP of

the country, it is imperative to understand the impact of load shedding on the sectors, so that

mitigating factors can be put in place and to guide policy development by policymakers.

The load shedding has different impacts on electricity consumers can be categorized into

three, direct commercial impact, indirect commercial impact, and the societal impact. The

impact on society is mostly associated with conditions that arise from the absence of

electricity such as health issues arising from uncontrollable room temperature and the

unavailability of security systems such as alarms. The conditions make the domestic users

of electricity inconvenienced as they become targets of criminals that place their lives at risk.

This according to the researcher is the major impact of load shedding on domestic electricity

users, which are usually experienced during the night. The other impacts include loss of

electronic devices and the internet connection, this can be described as mild impacts, as other

entertainment options are available that do not use electricity such as board games and

outdoor sport.

Direct and indirect commercial losses due to load shedding can be quite large and complex

to quantify accurately. In South Africa, power outages are not sporadic rather systemic as

alluded to earlier, these power interruptions started in late 2007 and they are still experienced

in 2019. This problem seems to be developing every day and this does not inspire investor

confidence in the South African economy. Currently, the power supply shortages challenges

appear to be worsening. It must be noted that investors do not only consider electricity supply

as the only factor when deciding to invest in South Africa, however, it would help the South

African to have a stable, reliable and reasonably priced energy supply. Although no study

has quantified the losses of both local and foreign direct capital investments in South Africa,

it is not unreasonable to assume that this loss is quite large.

It is not simple nor practical to accurately measure the true cost of a power outage incident.

The loss of investor confidence impact is not easily quantifiable. However, direct losses such

as revenues and cost escalations are measurable. These indicators can give a minimum

indication of the impact of load shedding on businesses and these can measured or estimated.

25

There is a variety of methods reviewed in the literature that are used to measure the impact

of load shedding on businesses. These have advantages and disadvantages and this section

seeks to highlight these and to recommend the most preferred methodology to be followed

in the current study.

Analytical methods offer an advantage of simplicity and cost-effectiveness when the

secondary data is readily available for analysis. Cheng et al (2014) noted that this

methodology uses historical energy consumption data to perform economic analysis of

energy load shedding. The major shortfall of this methodology is that it makes some

simplistic assumptions that are often invalid in reality and ignores indirect costs (Cheng &

Venkatesh, 2014:1). According to Balducci (2003), the secondary data is usually presented

at an aggregated level and cannot be used to make findings at the unit level, for example to

one factory or industry (Balducci, Roop, Schienbein, Desteese, & Weimar, 2003). Particular

details such as size, power consumption patterns, operational strategies and type of industry

are also important and this methodology does not have room to factor these into the model.

The revealed preferences methodology is reliable and cheaper than most methodologies that

are used to estimate the cost of a power outage (Beenstock et al, 1997). The disadvantage of

this methodology is the complexity that arises when the backup power plant does not supply

all the energy requirements during a power outage, where only a certain portion of the plant

is the backup power supply, the methodology becomes highly mathematical.

The case study methodology is not suitable for measuring the cost of power interruptions,

however, it is important to use it in demonstrating the importance of supply security, it gives

a good indication of how severe a power interruption can be (Chowdhury & Koval, 1999).

The most preferred methodology is the subjective evaluation methodology. This

methodology is ideal for this study, as there are financial and time constraints. In this

methodology, respondents will be the ones that estimate the impact of load shedding on

them. This methodology has disadvantages like any other methodology. The respondents

may misrepresent their losses or the impact of the load shedding to claim from insurance or

to get support from the government. The respondents may genuinely be unable to accurately

measure and report the losses from the load shedding, which is a possibility if they are not

experienced enough with the measurements. The researcher needs to be careful to guard

against bias and request further particulars where results seem to have outliers.

26

5 RESEARCH QUESTIONS

As previously mentioned, South Africa is suffering from load shedding due to the

constrained power generation capacity of Eskom. The limitations arise as the power

generation plants are aging and getting closer to the end of life. They have been poorly

maintained over the years leading to the 2010 FIFA World Cup. The power shortages have

an impact on both the society and businesses. This impact is not well understood, particularly

in the manufacturing sector. An individual factory may the impact on their business,

however, there is no sectorial analysis that details the impact across the sector. There is

consensus in the reviewed literature that load shedding hurts the economy, however, the

extent of the impact remains a point of debate in academia and society. A study was

formulated that consists of two studies to study the impact of load shedding. There is a

qualitative and a quantitative aspect of the study to better understand the impact of frequent

power interruptions. The results of this study will aid the captains of the manufacturing

sector and policymakers in making wise decisions. This research work seeks to add to the

current academic knowledge by answering the following questions.

The qualitative objective of the research will be addressed by answering question number 1

below and the quantitative aspect will be addressed by answering question number 2.

1. What is the qualitative impact of load shedding on small and medium-sized manufacturing companies in Richards Bay, South Africa?

2. What is the direct impact of load shedding on small and medium-sized manufacturing companies in Richards Bay, South Africa?

27

6 CURRENT LOAD SHEDDING STATISTICS

In South Africa, load shedding has various stages that are determined based on the energy

shortage at the time. Eskom has an installed capacity of 42 000 MW including peaking diesel

turbines. The nominal outage due to planned maintenance is 9 500 MW, meaning that at a

point in time, 9 500 MW is not available as the units are out of commission for maintenance.

Therefore, at a point in there is 36 500 MW available to meet the demand of the country.

Peaking diesel turbines account for 5 500 MW of the installed capacity, in essence, there is

31 000 MW available to power supply the country's energy requirements. South Africa has

a base load of 25 000 MW throughout the year. This rises in winter to about 28 000 MW due

to heating load and 26 500 MW during the summer due to air-conditioning load (Eskom,

2020).

When the generation system is healthy, there is a surplus of at 3 000 MW available and

Eskom exports this power to neighboring countries. The challenges start when there are

breakdowns and the system loses over 7 000 MW, load shedding begins to protect the

network. Anything under 7 000 MW, the peaking plant can provide. When the system is

short of 1 000 MW it is referred to as stage one load shedding. When the system is of 2 000

MW it is known as stage two and so forth. Figure 2 shows the frequency of load shedding

stages experienced in Richards from 01 December 2019 until 29 February 2020. Stage 3 is

not so frequent and it is the most devastating stage. The time of the day during which the

stage occurs is also shown in Figure 3 below.

Figure 3: The frequency of load shedding stages experienced in Richards Bay from 01 December 2019 until 29 February

2020 (Eskom, 2020).

28

When stage one occurs approximately 8% of the customers are affected by load shedding.

Major industries and factories are requested to reduce their demand first before the power

cuts ultimately are initiated. The worst stage of load shedding is stage 8 where the generation

system is 8 000 MW short (Eskom, 2020). This affects approximately 40% of the customers

are affected according to Eskom. The main challenge in South Africa is not the severity of

the power cuts, however, it is the frequency of power cuts that causes challenges for

manufacturers.

Figure 4: The distribution of power interruption incidents on the day of the week for the period 01 December 2019 to 29 February 2020 (Eskom, 2020).

Figure 4 shows the frequency of occurrence of each stage of load shedding on a particular

day of the week for the period 01 December 2019 to 29 February 2020 in Richards Bay. It

can be seen that stage 3 has only been experienced on Tuesdays, Wednesdays, and

Thursdays. This is when most businesses ramp up their operations and the demand rises in

the country. Therefore, the power grid becomes constrained when the generation capacity

cannot match the demand. To protect the grid, load shedding begins and the level rises when

the demand does not drop. Richards Bay experiences the highest number of load shedding

incidents from Thursday until Sunday. This is due to the heavy industries ramping up their

operations heavily starting on Thursday night into the weekend until Sunday night.

29

7 RESEARCH METHODOLOGY

The proposed methodology is multipronged consisting of three studies. The first study deals

with the qualitative impact employing the survey methodology, the second study deals with

quantitative aspect employing the subjective evaluation methodology through a customer

survey and the third study deals with the quantitative aspect employing the revealed

preferences methodology through a customer survey.

• Study 1: To study the qualitative impact of frequent power outages on small and

medium-size factories in Richards Bay, South Africa. A qualitative survey was

shared with managers of the sampled factories in Richards Bay.

• Study 2: To estimate the direct economic cost of frequent power outages on small

and medium-size manufacturers in Richards Bay, South Africa. This study will be

carried out using the direct worth methodology employing a survey questionnaire

that will be administered online. This study focuses on the direct costs of a load

shedding incident. Indirect costs are not covered in the current research scope.

• Study 3: To validate the results from study 2, secondary data will be collected from

backup power systems of the factories that have standby generators installed. The

results of this study will be compared with the results from study 2. The sampled

factories will be requested to share information about the total cost of ownership

(TCO) of their backup generators. This will provide the minimum estimate of what

the factories are willing to pay (WTP) to avoid load shedding.

7.1 The qualitative impact of load shedding on small and medium-size factories

There are no existing studies in the literature that look at the qualitative impact of load

shedding in the manufacturing sector in a developing country such as South Africa. The

literature was then used as a basis upon which an independent study was designed which

would apply to the South Africa context. There are two methods of implementing a

qualitative study, it can be in exploratory or inductive format. The exploratory format

explores situations to find out more about them and the inductive format studies existing

data to interpret (Saunders & Lewis, 2012). The inductive approach is appropriate to the

current research work because the operational and financial data is already available in the

factories, the load shedding incidents have happened. Grounded theory will be used to

extract major themes from the data collected during the qualitative survey.

30

Unit of analysis and population

Data will be collected at the factory-level, therefore, the unit of measure for this research is

a small or a medium-size factory that is operating in Richards Bay, South Africa. These

factories will be represented by management. The population is all small and medium-size

factories operating from the Richards Bay area with an annual turnover below 51 million

Rand. All factories with an annual turnover below 51 million Rand are regarded as small or

medium-size. Small factories have a maximum turnover of 15 million Rand.

Sampling method and size

A collection of all small and medium-sized factories in Richards Bay was not available from

the relevant authorities. An effort was made to gather a list of all factories in Richards Bay

operating from Richards Bay. It was not possible to get to all the factories, because some are

not listed companies, their turnover is not published. The next best bet was the Zululand

Chamber of Commerce and Industry, they have a unit for small and medium-sized

businesses, where some of the factories operating from Richards Bay are registered. These

factories were targetted as the sample of the study. Therefore, probability sampling was not

a possibility and as such non-probability sampling was used.

Purposive non-probability sampling was used to determine the sample. This kind of

sampling ensured that the final sample is representative, with an even distribution of small

and medium-sized factories represented in the final sample. When deciding on the sample

size for qualitative research, it is important to consider the saturation point, where no new

themes arise from more data collection. The researcher had a sample of 38 managers from

both small and medium-size factories who were requested to participate in the qualitative

study. There were 12 medium-size factories and 26 small size factories that were to take part

in the survey.

Data collection process

The data collection process was done employing a survey questionnaire. The qualitative

survey questionnaire was administered electronically online. The questionnaire consisted of

ten open-ended questions. Table 3 shows the sample of the questions that were shared with

the factory managers to solicit qualitative data. The survey had ten questions in total. As

previously mentioned 38 managers from both small and medium-size factories were

approached to complete the survey.

31

Table 2: Qualitative research survey questions (Self-creation, 2020)

What is the impact of load shedding on your business and operations and brand?

How are the revenues of your business affected by load shedding?

How are the costs of doing business affected by load shedding?

How is safety in your factory affected by load shedding?

How does load shedding affect your staff morale and attitude?

What is the impact of load shedding on the Administration side of the factory?

If you have installed backup power supply, what challenges do you still experience, if any,

despite having backup generators in place?

Are there any opportunities that result from load shedding?

What are the knock-on effects experienced due to load shedding?

What do you do to manage and mitigate the load shedding risk?

Analysis approach

The data for the qualitative part was analyzed manually to get the various themes and this

was possible as the number of responses was only 22. For a high number of responses,

typically a software package like Atlas Ti would be required to analyze the responses to

identify common concepts and cluster them for analysis. For this analysis, the most common

themes were grouped manually. This made it possible to understand the most pertinent issues

arising from load shedding from a qualitative perspective.

7.2 The direct economic impact of load shedding on small and medium-size factories

These two quantitative studies are designed to be descriptive, to allow respondents to

describe their experience of load shedding. These two studies are quantitative and they seek

to estimate the direct economic impact of power outages on the factories. The studies are

designed to be descriptive to enable the factories to describe the economic impact they suffer

because of load shedding. Saunders and Lewis (2012) say that a descriptive study produces

an accurate account or representation of persons, events, and situations (Saunders & Lewis,

2012). This study will provide a systematic and scientific approach to estimating the direct

economic impact of load shedding on small and medium-size factories in Richards Bay.

32

The main aim of these quantitative studies is to come up with a numerical value that will

represent the cost of power outages on the small and medium-size factories operating from

Richards Bay, South Africa. These two studies will have as a financial result a numerical

value or a percentage representation of the economic impact of load shedding on factories.

From the various methodologies reviewed from the literature, two important considerations

were noted when a methodology is chosen for the studies. The first consideration is the type

of power outages and the various types of impact arising from the outage.

In a country that has frequent power interruptions, there are two categories of victims, the

first are businesses and the second is the society in general. The types of impacts that a load

shedding incident may have various electricity consumers are listed below.

• Direct economic impact

• Indirect economic impact

• Societal impact

The costs associated with indirect economic costs and societal costs are significant, their

study is beyond the scope of this research work. These would require a separate methodology

to estimate and a case study is best suitable for this kind of research work according to

(Corwin & Miles, 1978). Due to the limited resources in terms of time and financial

limitations, the case study is beyond the scope of the current research. It will be

recommended for future research work. The total costs of power interruptions will be higher

than what will be measured by these two studies as they will only focus on the direct costs.

The other important consideration when studying the economic impact of power outages is

their nature and their frequency. In South Africa power outages are experienced daily,

however, the stages vary as previously mentioned. This depends on the power deficit

between the generating capacity and the demand at a particular time. During the high peak

periods, the higher stages of load shedding are usually experienced which are mornings and

evenings, when household activity is at the highest. South African society has accepted that

load shedding is a part of their lives and business operations. When there is a prior

notification of the load shedding, it is possible to plan alternative activities that do not require

the use of electricity or to organize alternative power supply for the continuation of activities

that require the use of electricity. Several studies looking into the cost of power interruption

have noted that the impact of a power outage significantly reduces when there is a prior

notification of the electricity consumers about the load shedding incident (Kim et al, 2015).

33

There are four studies from the literature that will be used as a basis upon which this study

will be developed. All these four studies use electricity consumer surveys to estimate the

economic impact of load shedding on consumers. Below is a list of the studies, countries

where they were performed and the people who performed the studies (Goldberg, 2015).

• Cameroon (2013), Diboma and Tamo Tatietse employed the direct worth methodology

to estimate the economic impact of power outages on industrial consumers employing a

customer survey.

• Various African countries (2012), Oseni employed the secondary data from a World

Bank survey to estimate the economic cost of power interruptions in 12 countries in

Africa.

• Nigeria (2003), Adenikinju employed customer surveys to estimate the economic impact

of frequent power interruptions. The marginal cost of the backup power supply was also

estimated from the data from customer surveys.

• South Korea (2016), Nam et al employed customer surveys to estimate the direct cost of

power interruptions on heavy industrial electricity consumers. The direct worth

methodology was used for the estimations.

The studies mentioned above make use of customer surveys to solicit information required

for the economic cost estimation of power interruptions. This technique is widely used in

similar circumstances to the current research estimate the economic impact of power

outages. The subjective evaluation method is used to estimate the direct costs and the same

method will be adopted for this research. The marginal cost of backup systems is estimated

using the revealed preferences method, and this approach will also be adopted in the current

research. The environment where the above methods have been applied is similar to the

Richards Bay case. A structural shortage of electricity is common to all four studies and this

is also the case experienced in Richards Bay by factories.

7.2.1 The direct economic impact of load shedding employing the subjective evaluation methodology


Data will be collected at the factory level. It will either be a small or medium-size factory

that is operating from Richards Bay, South Africa. A customer survey with quantitative

questions will be sent to the factories electronically for them to estimate the economic impact

of energy load shedding. The impact of load shedding will be normalized to cost per hour.

34

Where load shedding incidents lasted for part of an hour, the impact will be normalized to

one hour, assuming that the impact is linear over the total load shedding period. This

assumption is not accurate for industries dealing with steel and other metals, where the

processes involve molding and turning of the metals, longer load shedding periods results in

much higher costs.

The population for this survey is 38 small and medium-sized manufacturers in Richards Bay,

which are registered with the Richards Bay Chamber of Commerce. Load shedding patterns

are not the same in summer months (October – March) and the winter months (April –

September). The electricity demand rises in South Africa during winter, due to increased

heating loads and this increases the frequency and severity of load shedding (Eskom, 2020).

It would have been ideal to do this research in winter, however, the only opportunity

available was the summer, between December 2019 and February 2020, in South Africa.

The results from the study do not represent the worst-case conditions.


Purposive non-probability sampling was used for this study. The population is 38 small and

medium-sized factories. The entire population was taken as the sample. This was done to

ensure that the sample is representative of both small and medium-sized factories. The

purposive non-probability sampling was used together with stratified random sampling

because there are two groups within the population. The literature suggests stratified random

sampling for studies of this nature when there are different groups within the population,

like households, some are wealthy and others are not wealthy (Kjolle et al, 2008).

Since the population of small and medium-size factories operating from Richards Bay is

38 according to the Zululand Chamber of Commerce and Industry (ZCCI). There are

26 small size factories and 12 medium-size factories. A minimum of 13 small-size

manufacturers' responses and a minimum of 6 medium-size manufacturers responses' are

required for the stratification principle to be satisfied (Kjolle et al, 2008).


An online administered questionnaire was employed for data collection. The electricity

consumer was requested to assess how load shedding impacts their factory financially and

economically. The survey was designed to be flexible to allow the consumer to explain their

estimations or provide a methodology of how they arrived at a particular estimation. This is

necessary to test and evaluate the methodology to understand the assumptions that factories

35

make when they do their estimation. This methodology is known as the direct worth

approach to studying the economic impact of power outages.

There are costs associated with volume production losses and costs associated with

overheads and related expenses and some savings made due to the disturbance of the

operation. This is captured by equation 1 below (Godberg, 2015).

�� = �� + � − �� Equation 1

DW is the total estimate of a power interruption cost. VLP represents the losses incurred to

production disturbances. ORC referrers to other VLP is the value of lost production, and

ORC and ORS is the outage related costs and outage related savings respectively (Godberg,

2015). The subjective evaluation methodology presents the consumer with options that

represent certain scenarios that they may have experienced in the past. The researcher must

have a good understanding of the industry and possible impact when designing the scenarios.

Analysis approach

The factories provided details of a particular load shedding incident, including the time it

started and duration. The impact was then broken down into intervals of one hour. This is

illustrated below using an example, where an incident starts at 10:00 until 13:00.

• 10:00 – 11:00

• 11:00 – 12:00

• 12:00 – 13:00

Where a load shedding incident lasted less than one hour or a certain number of hours and

part of an hour, the part was normalized to a cost per hour using equation 2, instead of cost

per minute. Let the impact of experienced by a business as a result of a power outage for a

part of an hour be I. Let the cost be denoted C and the time in minutes be donated TM.

Therefore, the normalized impact will be as follows.

=�

��∗ 60��/1ℎ� Equation 2

After determining I in equation 2, it is then possible to determine the percentage of revenue

lost per day in hours. The revenue lost per day in hours is called the Revenue Loss Per Hour

(RLPH) and it is calculated using equation 3.

�� = !"#$%'("%)*+%!,-%!(.(/0(-12)#11!.3

45,6#"(/7(5"+!.2$#.-"!( Equation 3

36

The RLPH is considered with other factors to come with a Customer Damage Function

(CDF). The CDF varies with factors that make up the nature of the load shedding incident

experienced. The factors are the hour of the day, day of the week Duration of the load

shedding incident, the hour of the week (Goldberg, 2015).

The CDF changes as any one or more of the previously mentioned factors change. This

function makes it possible to track and trace the changes as the factors change. The factors

are independent variables and the CDF depends on them. An assumption was made that a

factory's revenue could be affected by load shedding during trading hours only, i.e. hours

when there is production in the factory. Of course, some factories that do not run production

24 hours a day and 7 days a week, however, they do have refrigerated storage facilities that

operate 24 hours a day and 7 days a week from the electricity supply and are affected by

power outages outside of production hours. This effect is beyond the scope of the current

research, it will be recommended for future work.

7.2.2 The direct economic impact of load shedding employing the revealed preferences methodology


The energy delivered in kilowatt-hour (kWh) from the back power generating systems or

plant will be used as a unit of analysis in this study. The revealed preferences methodology

will be used to estimate the cost of a kWh of energy from a backup power plant relative to

the cost a kWh from the power utility company Eskom. The population is the small and

medium-size factories that have backup power systems. This included only those small and

medium-size factories that are registered with the Zululand Chamber of Commerce and

Industry as Chamber's database was used to identify and contact the relevant factories.


The population of small and medium-size factories operating from Richards Bay that have

backup power generators are limited, therefore, purposive sampling employed to identify

those factories in the small and medium-size category, which have backup power systems or

plants installed. With purposive sampling, it is possible to ensure that the sample covers both

the small and medium-size factories. No definite financial data was obtained, all

manufacturers deemed this data confidential and highly sensitive. However, comparative

figures (backup power plants costs vs utility power costs) in percentage were supplied. The

size of the sample was 12 factories in total, 8 are small size and 4 are medium size.

37


The data was collection carried out by approaching the manufacturing organizations

electronically employing a survey questionnaire. Acquiring financial data from all the

factories was a huge challenge. Again absolute figures proved to be sensitive for most

factories, therefore a percentage system was used. The survey asked specific questions

regarding backup power systems.

Analysis approach

This study will analyze the cost of investment in a backup power system taking into

consideration the acquisition costs and the running costs. A calculation will be performed

that will come with the cost of supplying a kWh from a backup system and it will be

compared with the cost of a kWh supplied by the power utility. The total cost of ownership

of the backup power system will be determined and this shall serve as the minimum cost of

load shedding on that factory. This exercise becomes highly mathematical and complex

when the backup power system supplies a certain of the factory, further information is

required to make the calculation and estimations.

7.3 Survey design

The questionnaire was prepared initially and pre-tested using a small number of factory

managers in Richards Bay. The survey provided factory representatives with possible

scenarios relating to load shedding incidents and they were requested to estimate how these

affected their business before it was experienced or how it would affect their business.

During the pre-testing of the survey with five factory managers, the following was

discovered.

The representatives of the five factories that were chosen to pre-test the questionnaire were

only comfortable to estimate losses that they had experienced. The hypothetical scenarios

did not apply to some of their factories and they could not imagine the impact if they had to

experience the scenario. No scenario was common to all the five factories that took the pre-

testing questionnaire. It was discovered during the pre-testing that giving respondents a set

of scenarios was not the best option for the current research work. It was then decided that

the respondents would be requested to pick a load shedding incident from the recent past and

estimate the economic impact it had on their factory.

38

For an accurate estimation, the respondents were asked to recall a recent power interruption

incident that they had recently experienced. Respondents were requested to briefly describe

the incident, the stating the time, the time it ended, the day on which it happened and to

estimate the impact in terms of revenue losses and costs escalation. The biggest advantage

of this approach was that the factory representatives still had the incidents fresh in their

minds as they had experienced and could remember the incident well, meaning their direct

worth estimate was likely to be accurate.

The respondents consider the revenue losses associated with production losses to be of

utmost importance compared to other losses related to the power outage. The qualitative part

of the survey in study 1 provides a deeper understanding of other elements that can be

affected by load shedding other than revenues. In a factory setting, the highest loss will come

from the loss of production. Equation 1 shows that the VLP is the first variable to consider

when computing the CDF, it has the highest contribution to the total losses. The emphasis

was put on measuring the revenue losses associated with a power outage.

Factory managers experienced a difficulty in spelling out in monetary value the cost of

power interruptions, a percentage estimation was decided to be accurate enough. This

information is considered highly sensitive by all the trial respondents. The next best measure

to absolute values is the percentage estimation and all respondents were comfortable with

this proposal. It proved to be a challenge to secure managers for a physical interview to

understand the qualitative aspect load shedding has on manufacturing, therefore, it was

decided that the qualitative part will form part of the survey in the form of open-ended

questions.

After taking into consideration the concerns raised during the pre-testing of the

questionnaire, a final questionnaire was developed. Based on the subjective evaluation

methodology the survey questionnaire asked factory managers to think of a recent power

outage incident that they have recently experienced, which is perhaps still fresh in their

memory. The managers were also asked to estimate the direct economic loss associated with

the incident. The qualitative impact of load shedding on the manufacturing business was

captured using part 2 of the survey shown in Appendix A. Section 3 of the questionnaire

considers the quantitative impact of the load shedding scenario. Section 4 of the survey

pertains to the backup power generation system where applicable and section 5 covers

particulars of the company such as demographics, size and staff complement.

39

8 SOCIO-ECONOMIC CONDITIONS IN RICHARDS BAY , SOUTH AFRICA

Richards Bay is a coastal town on the North Coast of KwaZulu-Natal Province, South Africa.

The town started as a small town for fishing for people on holiday. It has since evolved to

be a major economic hub, not only in the Province of KwaZulu-Natal but in the whole of

South Africa. The port of Richards Bay is a key national asset that is owned by the South

African government. The port has a world-class coal terminal for coal export and six other

terminals that are used for import and export of other various commodities and cargo. Some

of the commodities that are exported include ilimite, rutile, zircon and pig iron (Nel, 2018).

The port also services a host of farmers that export various fruits, vegetables, and timber to

the foreign markets. The area around Richards Bay is fertile for timber plantations and

sugarcane farms. These are the largest agricultural feeds. The evolvement of the town is a

result of the government policy on industrial decentralization that enacted in 1998. This

down is considered to be one of the most successful government projects on industrial

decentralization (Arinuth & Barns, 1998). The development of the deepwater port and the

state of the art railway system linking the port to various economic activities inland was a

major game change for Richards Bay. The town has several international corporations setting

up manufacturing operations around it, because of the convenience of export finished goods

or the import of raw material. This has seen the economy of the town rise rapidly over the

past few decades. Richards Bay is now the city with the third-largest economy in KwaZulu-

Natal and the tenth in South Africa. The future of the town looks bright as the local

government is trying, by all means, to attract more investments into the town.

UMhlathuze Local Municipality that houses Richards Bay and the nearby town of

Empangeni is home to 1 600 000 residents (Nel, 2018). According to Nel (2018), the

unemployment rate in Richards Bay is 22%. The port of Richards Bay is a major export hub

in the KwaZulu – Natal Province. Many people who are employed in Richards Bay, work

for the industries that are located around the port. These industries process agricultural

products from inland into finished goods and export them into the foreign market. They also

receive imported raw material that is processed into finished products and then distributed

to the country for consumption using the railway system linked to the port. The major

industries have a huge impact on the local economy, however, the small factories, employ

more people compared to the big factories or smelters as mentioned in the introduction.

Therefore, the impact of power losses on these small industry players affects several people

and has a far-reaching implication for the GDP of the Municipality as well as the Province.

40

9 SURVEY RESULTS

9.1 The qualitative impact of load shedding on small and medium-size factories

Qualitative data was collected using a survey with open-ended questions that were

administered online. This survey was sent to factory managers to solicit the data, they were

requested to describe the impact of load shedding in open-ended questions.

Major themes

When a qualitative study is undertaken with a larger group, meaning that there is no focus

group, the analysis technique involves identifying themes emanating from the data. It is

highly recommended that the data should reach a saturation point for accurate research.

When new themes do not appear from the analysis of the data, the data is deemed to be

saturated. In the current research work, the sample and population could not provide enough

data to reach saturation. However, the collected can still be analyzed to get a view of the

factories surveyed. The outcome of this study cannot be generalized as the available data is

not sufficient. The analysis of the qualitative data yielded nine major themes that are going

to be described further to understand the qualitative impact of frequent power interruptions

on manufacturers. The major themes are the following:

The above themes are discussed in the subsequent sections. The major themes emanate from

the analysis of the qualitative survey questionnaire responses. These themes will be further

explained in the following subsections. Where appropriate, a survey questionnaire responses

will be quoted to help explain the theme.

41

Degree of impact depends on various factors

The economic impact of power outages varies from factory to factory and it depends on

measures each factory takes to mitigate against the impact of power outages. A Likert scale

was used to graphically represent the responses of factories on the level of impact the power

outages have on them. The scale starts from negative 5 to positive 5. Negative 5 represents

the worst impact and positive 5 represents a positive impact of the load shedding incident or

event. A rating of '0' means that there is a neutral impact of load shedding on that particular

factory. Figure 5 on the next page shows the graphical representation of the frequency of

responses stating either positive, neutral or negative impact of a power outage.

Figure 5: Graphical representation of the frequency of comments about the impact of power outages on factories.

The severity of the impact of a power outage incident is largely dependent on two factors,

internal and external to the factory. The internal factors relate to actions and measures that

the factory takes to mitigate and minimize the impact of a load shedding incident. They

include checking the published load shedding schedules from Eskom and setting up internal

mechanisms to deal with load shedding incidents. The comments from factories below give

some examples of the measures factories take. The external factors have to do with the

notification about the incident, duration, and stage of the load shedding incident.

• Survey Response: We are in the business of molding steel, we melt it and then mold.

If the energy goes out, the melting has to be done all over again and it is energy-

intensive, our electricity bill shoots up while the production does not.

There are critical processes in manufacturing that have a devastating effect if they are

stopped midway. Over and above production losses, there are significant energy costs

associated with rework of what was already done or in progress during the load shedding

incident for some factories.

4

6

3 3

1

2

1

2

1

0 00

1

2

3

4

5

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-5 -4 -3 -2 -1 0 1 2 3 4 5

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• Survey Response: Our factory is largely affected by the loss of production during a load

shedding incident. The raw material is largely not affected as we producing wood-chips

from timber. We are just crushing it to make chips. Our crushers suffer the most from

the stop and start. If are notified on time about the load shedding, we suspend our

operations before the load shedding starts and switch off our equipment.

Some factories are not impacted by rework, however, they are affected by loss of production

mostly. Unplanned load shedding poses a lot of challenges for their equipment. It is possible

to take precautions and reduce the impact on the plant when the load shedding is

communicated well in advance.

• Survey Response: Load shedding usually hits us in the morning when our staff is

energic and eager to push to meet our daily target. It demotivates them as they have to

potentially work longer hours. In the afternoon, the workers tend to think they can just

go home if the load shedding is anticipated to last until knock-off time. We always have

to negotiate with our staff and offer them transport home and overtime pay.

The time of the day during which a load shedding event happens has a huge implication on

the attitude of employees. Employees are more productive in the morning or at the beginning

of their shift.

Operational impact of load shedding

Electrical systems form the backbone of any manufacturing business enabling operations to

be carried out using electrical machinery. An electricity outage does not only affect the

production of goods, it has administrative and safety implications for manufacturers. The

halting of operational processes was the major operational impact expressed by factories.

• Survey Response: We plan our operations around the published load shedding

schedule. We halt our operations during the outages.

• Survey Response: We center our operations around the load shedding schedule.

• Survey Response: We plan our operations, even start or knock-off times based on the

load shedding schedule.

• Survey Response: We only do what we can. Something that doesn't need electricity.

• Survey Response: Try to plan around the load shedding schedule and replenish diesel

in the backup generator after every load shedding incident.

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• Survey Response: We try to schedule our operations around the announced load

shedding schedule, sometimes it is not followed and we experience unplanned load

shedding which is catastrophic.

• Survey Response: We are producing perishable dairy products, our storage facilities

need energy all day and every day. Therefore, we have a backup power plant for our

storage facility.

Above 90% of the surveyed factories plan their operations the published load schedule by

the South African power utility company, Eskom. If the load shedding is communicated well

in advance and the load shedding is carried out as per the published schedule, the factories

try to minimize the impact, by starting work later than usual if the incident is early in the

morning. If the incident is anticipated in the late afternoon, work commences earlier than

usual. If it happens around midday, the company finds training activities for the employees

to do that do not require electricity. Backup power generation is used to minimize the impact

of a power interruption.

Costs associated with load shedding

A power outage has several costs associated with it for factories that experience it. The costs

are associated directly with a particular power outage or the phenomenon of frequent power

outages. A power outage increases the overtime costs, in terms of extra overtime worked,

damage to equipment and spoilage of raw materials. Costs associated with the phenomenon

of power outages include investments in emergency lights and alternative energy sources.

• Survey Response: The costs of doing business are rising rapidly. We have raw material

spoilage costs that have shot up. We can recycle some of the half processed products,

however, this is energy intense and pushes up our utility bill.

• Survey Response: We have decided to commission a professional firm to perform a

study on the alternative energy sources available and the nature of capital that could be

required. Backup generators consume a lot of diesel, we cannot have sustainable

operations if the generators are to be run very often and for long periods.

• Survey Response: The overhead costs on standing time and overtime are reaching

unsustainable levels. This is a major cost contributor for us arising from load shedding

incidents.

• Survey Response: The costs are rising, most products that are produced during load

shedding do not pass our internal quality testing mechanisms. The cost of rework is

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rising, we are pondering completely halting operations during load shedding incidents

to cut down on the rework costs.

• Survey Response: We are paying penalties from left, right and center, our clients are

penalizing us heavily for missed deliveries. We have had to renegotiate a lot of our

service level agreements with clients. They also have obligations to their clients, it is a

precarious situation. We are more careful about which contracts we take on, typically it

is far less than our capacity. We are losing business to our competitors if you cannot

deliver clients tend to take the business elsewhere.

• Survey Response: It is so difficult to contain costs, they are running away. We have

decided together with our management team to invest in a backup power plant. This will

require a huge capital investment. We are going to have to train our maintenance team

on the maintenance of the plant, fuel costs and we need to justify the investment to the

board of directors. Our bottom line has to improve after this investment.

The costs of load shedding on business can be classified into two categories, fixed and

variable costs. The fixed costs can often be associated with large capital outlay required

which is difficult for small and medium-size factories. If a factory can afford the initial

capital outlay to provide backup power generation, there are still operational costs associated

with load shedding. The variable costs associated with the running and maintenance of

generators increase significantly with increasing load shedding incidents. Labor costs are

one of the most significant for most factories, as they have to pay employees for not doing

any work as long as they are at work. The overtime bill also increases for some factories, as

they have to extend hours to meet production targets.

Revenue loss due to load shedding

Manufacturers rely on a stable and reliable power supply for production, air-conditioning,

administrative activities, lighting and for security reasons. The major contributor to the

economic impact of load shedding on small and medium-size factories is the loss of revenues

as a result of production losses.

• Survey Response: Revenues are down by 28% per week, we simply cannot afford to

take a huge number of orders. The uncertainty around the electricity supply makes it

difficult to make commitments to our customers. Customers do not take excuses they want

us to deliver and when we don’t they lose confidence in our abilities and capabilities.

45

• Survey Response: About 20% of revenues would come from non-traditional customers,

that we do not have contracts with. They would just call and ask for the availability of

the product, request a quotation and buy. When these customers enquire, we always have

to tell them, sorry we do not have shelf stock. We are barely meeting the obligations of

contracted orders.

• Survey Response: The revenues are declining so quickly. It is so devastating when we

get hit by a sporadic load shedding incident that was not on the published schedule. It

sometimes leads to the cancellation of some of our customer orders, which is the most

unfortunate thing for any business to do. We have built good relationships with our key

customers, however, it is more difficult to manage the relationship now because of the

load shedding resulting in reduced production output.

• Survey Response: They have been plummeting down and the situation is becoming

untenable. We need solutions to this problem and we need it quickly. My factory barely

gives 70% of the design capacity, it is a sorry state of affairs.

• Survey Response: The revenues are declining mainly due to lower production volumes

and the number of rejected products. After a load shedding incident, the quality of the

first few products is bad. We have to recycle these products and this also eats on our

throughput, the number of products that leave our factory daily is declining and this

affects our revenues heavily.

The inability of certain factories to continue with production during load shedding incidents

affects their throughput and consequently the revenues decline. Factories are unable to

produce at full capacity due to power outage incidents. Factories endeavor to plan for load

shedding incidents that are announced on the schedule by the Power Utility, Eskom.

However, when an unplanned incident occurs, it has that element of surprise to factories and

they become reactive instead of being proactive. When factories produce less than what they

used to produce before, they tend to sell less, and the revenues decline compared to before.

Safety and security issues related to load shedding

Administrative systems are critical for tracking orders, deliveries, and production

monitoring. The processing of payments to suppliers is done through an electronic system

for most manufacturers. Sensormatic and closed-circuit television (CCTV) systems get

affected by load shedding, this exposes the factory to looting and theft by criminals. Electric

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fences also get de-energized during the load shedding making the premises less secure, this

is a huge concern for manufacturers dealing with high valued products.

• Survey Response: The impact of load shedding on security is quite severe, all security

systems like electric gates, automated clocking of employees, electric fences do not

function putting both our employees at risk and our facility in its entirety.

• Survey Response: We need ventilation to keep our factory habitable. We use extractor

fans to circulate the air within the building. Load shedding makes it impossible to

continue operating as the PPE of the employees is not enough and natural ventilation is

not enough.

• Survey Response: Safety is compromised when there is an electricity outage. We have

an automated assembly bay that has limited human involvement and a manual assembly

bay that requires 100% human involvement. The manual assembly bay requires

vigilance as there are mechanical hand tools used to assemble the furniture.

• Survey Response: We need ventilation in our factory, it is the most major form of safety

precaution over and above personal protective equipment. We use the extraction

mechanism for our ventilation system that is electrically driven. Lighting does affect

security more especially our stockpile area.

• Survey Response: It is difficult to maintain safety without electricity, actually the whole

factory environment becomes hazardous when there is an electricity outage. We

immediately withdraw our personnel and turn-off all equipment from supply points

during a power outage.

• Survey Response: Safety is a huge concern for us. We have electric sensors that

measure gas concentration levels throughout the factory when there is a power outage

these become dysfunctional and it is dangerous to occupy the factory space when the gas

levels are not known. As a precaution, we send our employees semi-annually for medical

check-up. This has a cost implication for us, however, our people come first.

Safety and security gets compromised during load shedding incidents in most factories.

Some even state that their factories become a health hazard when there is insufficient

ventilation and lighting in their factories. Employees have to be withdrawn until the power

is restored and the environment is made suitable again for human occupation. Routine

medical check-ups of employees are carried out to ensure that the health of the employees

does not deteriorate because of the working conditions.

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Mitigation of load shedding impact

Richards Bay factories are exploring and putting in place a few measures to combat the

economic impact that power interruptions have on their factories. These are difficult business

decisions that require financing and consultation with various stakeholders. This is a tough

balancing act that involves looking at the pros and cons of each scenario. The ultimate goal

is to reduce the costs and increase revenues, which in turn increases the profit.

Investment in backup power generators remains one of the most popular mitigating move by

the majority of the factories. In most cases, the backup power generator is not sufficient to

supply the entire factory's energy needs, therefore, an improvision is made to prioritize the

high impact sections. Sometimes it is not possible to mitigate the impact entirely, therefore,

factories try to minimize the impact by planning their operations around the published load

shedding schedule, the start of production is delayed if load shedding is anticipated in the

morning. Some factories had manual production processes before moving to automation.

They did not decommission the manual production means, during load shedding they revert

to manual production process. Some factories organize other activities for employees to do

that do not require electricity, like coaching, training and workshop housekeeping.

• Survey Response: Firstly, we plan our operations around the published load shedding

plan. Secondly, we have a backup power plant. Thirdly, we are incorporating the risk of

load shedding when negotiating and signing contracts with our clients, and we strive for

a mutually beneficial outcome for all parties.

• Survey Response: We center our operations around the published load shedding

schedule. If we have to suspend operations, we do it before the load shedding starts or if

we have to delay the start, we also do that to minimize its impact.

• Survey Response: Our operations are linked to the load shedding schedule, we have

even set our working hours to coincide with the published schedules.

• Survey Response: Unfortunately we halt our primary operation during a load shedding

incident. However, we then do other activities that do not require the use of electricity.

• Survey Response: We try to schedule our operations around the announced load

shedding schedule, sometimes it is not followed and we experience unplanned load

shedding.

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Most small and medium-sized factories in the Richards Bay area, schedule their production

operations based on the published load schedules. In this case, they switch off and disconnect

their equipment before the power falls away, and they start it once the power has been

restored and stabilized. This protects the equipment and makes the planning manageable. In

cases where there is an unplanned load, the implications are catastrophic for the

manufacturers, as they are caught unaware and tend to be reactionary.

Stakeholders impacted by load shedding

The survey responses gave rise to four major stakeholders in the manufacturing sector that

are directly impacted by power outages incidents. The first stakeholder is the owners of the

factory who are shareholders. The second stakeholder is the customers or clients of the

factory who buy the products of the factory. The third stakeholder is the employees of the

factory, from top management to the lowest employee. The fourth stakeholder is the

suppliers of the raw materials to the factory.

• Survey Response: Our staff is demoralized as the work stops and starts more often due

to the load shedding. We more often than not, fail to meet our daily targets that have an

implication on the employee incentives and bonuses.

• Survey Response: Staff morale is at an all-time low for us. The workers feel the effects

of load shedding on their pockets, we are used to having production bonuses and lately,

we never meet our targets.

• Survey Response: Load shedding has hit us the most on our people. Our staff are the

most precious resource we have in this factory. The morale is declining at an alarming

rate, we have sought to find professional help from psychologists. All our future is

uncertain under these conditions.

• Survey Response: We, as the management of the factory, have had serious discussions

amongst ourselves about going off-grid. This would have both short & long term

implications. The short-term implications include the huge capital outlay required from

the shareholders and the costs associated with the maintenance of the plant until we

break-even.

• Survey Response: We have developed a transparent relationship with our clients, where

we keep them informed of the disturbances we experience and give them evidence to back

up everything assertion we make. We are upfront with the challenges load shedding is

causing for us and we highlight these during our contract negotiation process.

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• Survey Response: Our suppliers are big stakeholder and the success of our business

relies heavily on their ability to deliver. We have established an open and transparent

line of communication with our suppliers. We have a unit that focuses on our suppliers

and supports them when they encounter challenges. Some suppliers need support.

• Survey Response: In the next few days, we will be taking a proposal to our shareholders,

where we want to explore the use of LPG for energy generation. We are busy finalizing

the business and technical case. We have the space in our premises and the LPG is

available closeby in Mozambique.

• Survey Response: We have open ourselves up, our clients can see our capacity in terms

of equipment, expertise and staff compliment. They are convinced without a doubt that

if we fail to deliver it is due to external factors, internally good systems.

Second-order effects of load shedding

The frequent power interruptions are experienced by everyone and everywhere in South

Africa. This a national problem in South Africa which lowers the risk of brands having their

names tarnished by the load shedding as it is a reality for everyone in South Africa. Therefore

a factory's brand being damaged by load shedding is low from a public perception point of

view. These load shedding incidents are deemed to be beyond the control of the factory or

manufacturer. The brand is enhanced if a particular factory can stand out and be proactive

to avert or minimize the impact of load shedding. This makes the factory distinguished

among the competitors and drives a lot of business towards it. The ability to provide backup

power during load shedding gives a factory a lot of competitive advantage amongst

competitors who are not able to produce during this time. This increases the market share.

• Survey Response: Our clients tell us that they come to us because our competitors are

not able to deliver the volumes they require because of load shedding related incidents.

• Survey Response: We have been making surveys, asking our customers to rate our

service and getting their thoughts on our brand. The majority think that our brand is still

intact, load shedding is something that is not within our control and ambit. We are only

in a position to mitigate or minimize its impact, we cannot stop it.

• Survey Response: I do not think that our brand is tarnished by the load shedding

incidents. Generally, brands are tarnished by a lack of honesty and trust and matters

within our purview. This is a national problem that is beyond our ambit. We suffer from

the loss of market share as customers take their business elsewhere, where they have

means of production even during load shedding.

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• Survey Response: We have recently invested aggresively on backup power systems and

this will surely have a positive impact on our brand, market share, and competitiveness.

There is a huge pressure from our shareholders to ensure that we break-even as possible

on the investments on backup power generators. It costs a lot of money to keep a brand

shining and to maintain reputation.

Load shedding invokes a lot of conflicts between various stakeholders and this poses a lot

of complications for businesses. Some factories have a single customer that they supply and

it is easier to re-negotiate contracts as and when conditions of trade change. However, most

factories that play in the open market have to note what competitors are doing to gain a

competitive advantage. The South African economy is struggling to grow, therefore markets

are price sensitive. It is difficult to pass on the effect of load shedding to the consumer and

the manufacturers cannot afford to absorb the cost entirely. It is a tough balancing act that

requires a meticulous approach.

Positive outcomes of load shedding

The economic impact of power outages on factories is severely negative. However,

respondents were able to identify some positives arising from the power outages in their

factories. The identified positives can be grouped into categories which are increased

competitiveness in the manufacturing sector and robust learning by factories as they tackle

the reality and challenges of load shedding.

• Survey Response: We have learned to appreciate every kWh that is available to us for

use. We have developed procedures of disconnecting electrical equipment when not in

use. We have upgraded all our lights to energy-saving ones. We are looking in the market

for more efficient motors that consume less energy. In the long run, I think these load

shedding challenges will bring about positive change, even though it is not entirely

voluntary change.

• Survey Response: This challenge has forced us to think outside the box as

manufacturers, and have realized that it cannot be business as usual. We now working

on a collaboration with what would be “our competitors”, however, we are now having

conversations about a local power generating plant in the Richards Bay area that will

have the capacity to supply all of us.

• Survey Response: We are asking ourselves what else could go wrong on the same scale

as load shedding. Our risk analysis has changed and we now consider all, and all

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possible eventualities and we try to at least plan what to do when the challenge

experience. Crisis moves fast and there is no time to plan during a crisis, one has to

move with the crisis or play catch up later. Load shedding has to teach us to think ahead

and be anticipatory in running our business.

• Survey Response: We are always imagining that the worst will happen, which has

brought some innovation in our business. We try to foresee what would ordinarily be an

unforeseen circumstance or event.

• Survey Response: As a Maintenance Manager, I struggle to get the plant out of

commission to maintain it during non-load shedding times, production takes priority.

Therefore, any load shedding incident provides an opportunity maintenance window for

me. My equipment is in a far more healthy state now than before, as I would only get to

maintain it once a week, now I get at least two hours a day to work on the machine for

maintenance.

• Survey Response: Another interesting thing is that we are looking at various methods

of powering electrical appliances and equipment. They putting solar panels on the top

of their roofs and getting solar lights. This is on a minor scale, it is not for production

purposes but it does help lower the utility bill at the end of the month.

Graphical representation of major themes

When a qualitative study is undertaken with a larger group, meaning that there is no focus

group, the analysis technique involves identifying themes emanating from the data. It is

highly recommended that the data should reach a saturation point an accurate result. The

collected data did not saturate, however, it analyzed and summarised in a model in Figure 6.

Figure 6: Major themes arising from the qualitative survey looking into the impact of load shedding on manufacturers

(Self-creation, 2020).

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9.2 The quantitative impact of load shedding on small and medium-size factories employing the subject evaluation methodology

Online surveys were conducted where factory managers were requested to quantitatively

estimate and describe the impact of power interruptions on their factories. This method of

collecting data cost estimations from the factories is known as the subject evaluation

methodology, because the subject or the person who has experience on what is being studied,

gives an opinion based on experience. This methodology has three components in the

manufacturing sense and these are described below.

• VLP: The value of losses in production

• ORC: Power outage related costs

• ORS: Power outage related savings

The above information can be used to construct a customer demand function (CDF) that

shows how the impact of load shedding varies with certain factors (Goldberg, 2015).

Results from the survey

The electronic survey was distributed to 38 small and medium-sized manufacturing

companies that are registered with the Zululand Chamber of Commerce and Industry

(ZCCI), in Richards Bay, South Africa. Over two months, 22 responses were received

electronically. The distribution of the survey responses was 8 medium-sized and 14 small

manufacturers. The survey was completed by senior management of the factories, like

Managing Directors, Financial Directors, and Technical Directors. More factories are

operating in the Richards Bay area that are small or medium size, however, they do not reveal

this information. Therefore, they were not approached to be part of the survey to protect the

integrity of the data. This could have potentially created a bias in the data, as only those

manufacturers with a known size status were approached.

Direct worth estimates of the impact of load shedding

The survey asked factory managers to recall a specific power outage incident that they had

experienced in the recent past. They were requested to describe the incident, the day on

which the incident happened and the duration of the incident, stating the start and end times.

The survey further asked the respondents to estimate the impact on the daily revenues of the

load shedding incident. The estimates of factory managers were normalized to hourly

impact. Where a power outage incident lasted for less than one hour, this was normalized to

53

hour impact assuming linearity. The customer demand function is then constructed using the

hourly estimates. Consider a three-hour power outage incident on a Friday as an example.

• Friday 10:00 to 11:00

• Friday 11:00 to 12:00

• Friday 12:00 to 13:00

The entire three-hour incident is broken down into three incidents of one hour each. The

impact was assumed to be linear over three hours. A measure of the hourly impact is defined

and named "Revenue Loss Per Hour" (RLPH). The RLPH estimates the revenue lost per

hour due to a particular load shedding incident. When solving the above example using the

defined RLPH, it can be seen that a revenue decrease of 30% over three hours gives rise to

a 10% RLPH for each of the three hours. The RLPH is defined by equation 3.

�� = !"#$%'("%)*+%!,-%!(.(/0(-12)#11!.3

45,6#"(/7(5"+!.2$#.-"!( Equation 4

Survey responses

Respondents were presented with a quantitative survey with a predetermined set of outcomes

and they had to choose one that best describes their situation. During the pre-testing of the

survey, it was discovered that most manufacturers were uncomfortable to give exact figures

to questions, therefore, percentage estimations were preferred. These following results will

be interpreted in this section, however, an analysis of the results in the context of the research

follows on in section 8.

Figure 7: The distribution of responses on the availability of a procedure during a power outage.

54

It can be seen from Figure 7 that 50% of the respondents have a procedure that is followed

during a power interruption. This shows a proactive approach from the manufacturers, as

load shedding is a big part of their operations, it is wise to have a procedure that everybody

would under and follow during load shedding. It is also encouraging that 41% of respondents

are planning to develop a procedure for load shedding incidents. The remaining 9% that does

not have a procedure and is not planning on developing on, it is wise to develop one.

Figure 8: Distribution of the working hours of the factories surveyed.

Figure 8 shows the distribution of business operating hours of the factories surveyed. It can

be seen that the majority of the factories work between 6 – 8 hours per day, with 45.5% of

the factories surveyed falling into this bracket. The second bracket of factories operate for

9–12 hours per day, these factories account for 27.3% of the total factories surveyed. The

third bracket of factories has operations that run for 20 – 24 hours per day and these account

for 27.3% of the factories surveyed.

Figure 9: The percentage distribution of factories with backup generators.

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Figure 9 summarises the percentage distribution of factories that have a backup power

system installed in their factories. From Figure 8 it can be seen that only 18.2% of the

surveyed factories have backup power systems available and 81.8% of the factories do not

have backup power plants. Perhaps the initial investment required to install a backup power

plant is the hindrance for most factories as they are operating in difficult economic times.

Figure 10: The distribution of load shedding incidents per day and per week, Monday to Thursday.

Figure 11: The distribution of load shedding incidents per day and per week for Thursday to Sunday.

Figures 10 & 11 show the distribution of load shedding incidents experienced by the

surveyed factories in the months between December 2019 and February 2020. The color-

coding in Figure 9 shows the time of the day during which the incident occurred. A glance

at the two charts shows that either the factories experience more load shedding during the

week (Monday to Friday) or some factories do not operate during the weekend, therefore,

the weekend load shedding incidents are not experienced by these factories. The most likely

explanation for the drop in load shedding incidents during the weekend, is the low demand

as most businesses do not operate during the weekend. Most incidents are experienced in the

morning or after, this is when the load demand is highest in the country.

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Figure 12: The percentage distribution of daily revenues lost due to power interruption incidents.

Figure 12 shows the percentage distribution of revenue lost due to various load shedding

incidents by the surveyed factories. About 41% put the revenue losses between 11-20%,

which compares well with a global trends at 22% (Oseni, 2002). The 27.3% of factories that

have less than a 10% revenue loss either have backup generators or they worked extended

hours and they incurred an extra cost. About 4.5% of the factories surveyed experienced a

catastrophic loss of over 51% of their daily revenues, this was most likely unplanned load

shedding incidents that had such a devastating effect.

Figure 13: Percentage distribution of the costs incurred during the load shedding incidents.

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Figure 13 shows the distribution of costs arising from load shedding incidents. The costs are

due to raw material spoilages, overtime payments to employees, penalties arising from

missed contractual obligations, standing time payments, re-start costs. About 31.8% of the

surveyed factories had a cost escalation of 0 – 10%. About 27.3% of the suffered between

11 – 20% losses. About 4.5% of the factories suffered catastrophic losses in the region of

31 - 50% of the losses. The major cost drivers are the cost of backup power systems and the

penalties suffered from contractual obligations.

Figure 14: The percentage distribution of load shedding impact on the equipment and machinery.

Figure 14 shows the percentage distribution of the estimated life cycle reduction of the

machinery due to load shedding. Certain production machinery is not designed for frequent

starting and stopping. The stopping and starting of the machinery frequently affects the life

expectancy of the machines. Most factories, at 45.5%, estimate that the life cycle of their

machines is reduced by 6 - 10% per year. About 40.9% of the factories estimate that the life

cycle of their machines is reduced 0 – 5% per year. About 4.5% of the factories estimate that

the life cycle will be reduced by 11 – 15%. The remaining 9.1% of the factories estimates a

catastrophic life cycle reduction of equipment of above 16%. The biggest contributor to the

life cycle reduction of manufacturing machinery is unplanned load shedding and the

frequency of stopping and starting the machines.

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Figure 15: The percentage distribution of factories that have backup power generators.

Figure 15 shows the percentage of factories that have backup power generators installed in

their facilities. It can be seen that only 40.9% of the factories do have power generating

plants installed in their factories. The rest of the factories do not have backup power

generators installed, however, 40.9% of these factories are planning to install the backup

power generators. The biggest hindrance in setting up backup power generating plants is the

initial capital required and the running costs. The generators consume a lot of diesel and they

cannot be run as a primary source of energy, they can only be run for a short time.

Figure 16: The percentage distribution of the capacity of backup power generators.

Figure 16 shows the distribution of various backup power system capacities in factories that

have backup power generators installed. The capacity ranges from a 10kW generator that is

used to power the administration area and for lighting purposes. The capacity of backup

power generators goes up to 45 000 kW which powers production areas and storage facilities

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that require refrigeration. Six of the nine surveyed factories have backup capacity that is

beyond 1 000 kW which is quite significant for small or medium-size factories. The bigger

the backup power plant, the higher is the running costs as they consume more fuel. However,

in the long run, the massive power plants tend to be efficient as they are not operated at full

capacity, usually, they are operated at 80% of the full capacity. This is the most optimal

region for the best efficiency in terms of fuel consumption and this ensures the life cycle of

the power plant is not shortened by overuse.

Figure 17: The percentage distribution of the reliability of backup power generators.

Figure 17 shows the percentage distribution of how reliable are the backup power systems

installed by the factories surveyed. The reliability is estimated based on how frequently the

backup plant fails to come online in the anticipated time when it is required to come online.

The majority of the factories, at 55.6% find their backup power plants to be between 81

- 100%. This compares well with international trends, as most back up power systems have

a reliability of 78% (Sangvhi, 2008). About 22.2% of the factories estimated the reliability

of their backup power plants at 61 - 80%, a portion of this still falls within the international

trends, albeit partially according to Sagvhi (2018). The rest of the factories sited issues such

as the inability of a power plant to come online automatically when there is a power outage.

This required the factory to contact the service provider supplying the backup power plant

to dispatch a Technician to manual start the generator. Crucial production time was lost

during the waiting period and 22.2% of the surveyed factories estimated the reliability to be

between 41 – 60%.

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Figure 18: The percentage distribution of factories that outsource the backup power generators.

Some factories manage their backup power system and some outsource this service to an

external provider. About 66.7% of the surveyed factories are outsourcing this service to a

service provider. The only reason this service is preferred, the factories do not need to

develop skills and expertise in the operation and maintenance of the backup power plant.

However, the downside of this arrangement is that there is a potential for huge delays when

the service provider is not onsite and the power plant has to be operated. The liability rests

with the service provider and the factory only pays for the service. The rest of the factories

invest in the training of staff in the operation and maintenance of the power plant. The

biggest advantage of this move is that skills reside in-house and the reaction time is much

smaller compared to the activation of a service provider who does operate offsite.

Figure 19: The percentage distribution of costs associated with running the backup power generation system.

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Figure 19 shows the percentage distribution of the cost estimations associated with the

running of backup power systems. Factories were asked to do a cost estimation of running

their factory using a backup power plant versus the utility supply. The respondents were

given a set of pre-determined options and all factories estimated that the cost of running the

backup power system costed more than the utility supply when comparing the cost of a kWh.

About 22.2% of the factories estimate that the backup power system costs them between

21 – 30% more to run compared to the utility supply. The majority of factories, 55.6%

estimated that the backup power supply costs them between 31 – 40% more and this

compares well with international trends, with an average of 32% (Sangvhi, 2018). Some

factories estimated that the backup power system costs them between 51 - 100%, this is an

abnormally and the backup plant is not sustainable.

Figure 20: The percentage distribution of capacity provided by the backup power generators.

Figure 20 shows the distribution of load that is supplied from backup power systems. The

factories were then asked to estimate at what capacity they can operate on the backup power

plant. It must be noted that all factories derive some form of operation from the backup

power system. The majority of the factories, 66.6%, operating at a capacity of between 21 –

60% on the backup power. This highlights the cost of backup power, it has been noted in

Figure 16 that it costs more to run on the backup power system. The majority of the factories

do not even operate at full capacity when operating from the backup power plant. Therefore,

it is not desirable to operate on the backup generators, it costs more and there is less

production output. Perhaps, the benefits of meeting contractual obligations and preserving

the company's brand outweigh the costs of operating on backup power.

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Figure 21: The percentage distribution of the number of people who work in these factories.

Figure 21 shows the percentage distribution of the workers employed by the surveyed

factories. The smallest factory has only 5 workers and the largest factory has 1232 workers.

The factory with only five people is highly automated and outsources most of its services.

The factory is owned by Italian nationals and uses highly sophisticated technology. The

largest factory runs 24-hour operations with three shifts of eight hours.

Figure 22: The percentage distribution of the area size of the factories surveyed.

Figure 22 summarises the percentage distribution of the size of the factories surveyed. The

factories supplied the size of their main production area, for example, the smallest factory

has a production area of about 100 square meters. However, the total area of the factory

premises is much bigger. The largest factory has over 67 000 square meters of space.

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Figure 23: The percentage distribution of factories that have insurance as a mitigant against power interruptions.

Figure 23 shows that only 9.1% of the surveyed factories have purchased insurance to

mitigate the impact of load shedding. The impact of load shedding is felt but not yet

understood, factories are not sure how the insurance will protect them. However, 50% of the

factories intend to get insurance in the future. The rest must be encouraged to consider the

insurance option.

9.3 The quantitative impact of load shedding on small and medium-size factories employing the revealed preferences methodology

Information about the financial implications of having backup power generators was not

made available to the researcher. This information was deemed to be highly confidential and

sensitive information by the factory managers. The data from the qualitative and quantitative

studies show that the cost of operating from the backup power supply is much higher

compared to the utility electricity supply. Factories tend to use the backup power supply for

essential elements of their operations. The factories can apply the revealed preferences

methodology internally to determine the cost of substituting the utility supply with the

backup power supply.

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10 THE DISCUSSION OF THE RESULTS

This research work had three objectives that were set out in the objectives section at the

beginning of this report. The first objective was to understand the qualitative aspect of the

economic impact of power interruptions on small and medium-sized manufacturers in

Richards Bay, South Africa. The second objective was to estimate the direct economic

impact of power interruptions on factories using the subjective evaluation methodology. The

third objective was to validate the estimates in objective two by using backup power data to

estimate the minimum economic impact of load shedding using the revealed preferences

methodology. All objectives were achieved except for the third one.

Study 1 consisted of a qualitative survey of open-ended questions administered online.

Physical interviews were the preferred method of collecting qualitative data, however, due

to the timing being a festive season, most managers were not available for physical

interviews. Therefore, the next best solution was an electronic survey questionnaire. The

survey was probing the qualitative impact of frequent power outages on the factories.

Study 2 focused on estimating the direct economic impact load shedding has on the small

and medium-size factories in Richards Bay. The subjective evaluation methodology formed

the basis for this study and the analysis of the results.

Study 3 was not implemented as the factories deemed this data to be highly confidential and

they were not willing to share it with the research. Even after proposing to sign non-

disclosure agreements and only sharing the analysis of the results, not the results. The

factories were unable to provide this data. However, a methodology was developed and an

attempt was made to collect the data. The revealed preferences methodology was found to

be suitable for this kind of analysis, as it is widely used in literature.

10.1 What is the qualitative impact of load shedding on small and medium-sized manufacturing companies in Richards Bay, South Africa?

The pie-chart in Figure 4 shows how often a particular comment appears in the responses

describing the impact of power interruptions on the factories. It can be seen from the

responses that the impact is overwhelmingly negative on the factories. A small margin of

the respondents considered the impact to neutral and surprisingly, a positive impact has been

identified that led to new business opportunities for the factory concerned. The cases where

neutral or positive impact has been experienced are quite rare and the factories concerned

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have a unique advantage. One factory has a huge piece of land on-site that is unused and

they are planning to explore the possibility of setting a renewable energy system on site.

Figure 6 shows the outcome of an analysis that was performed in study 1. Nine major themes

came out of the analysis resulting in the model shown in Figure 5. The themes describe the

various challenges that factories experienced due to power outages. The degree of impact

was found to vary according to the primary product that the factory is producing. Factories

dealing with steel are the ones that suffer the most when there are load shedding incidents,

usually, these factories are energy-intensive and the steel is melted before molding. Re-

melting cost is one of the biggest costs experienced by steel factories.

Power outages disrupt manufacturing operations. Before the frequent power outages were

experienced, South Africa had an abundance and a stable supply of electricity. Factories are

designed and built for a stable and reliable power supply. Factories rely on electricity supply

for production and the manufacturing equipment is not designed for frequent restarting. The

most frequently cited operational challenge was the raw material spoilage and the need for

recycling the spoiled products into raw material again. The reduction of life expectancy of

the machinery was also cited as a major concern for most factories. As the machines are not

suitable for frequent restarting. The operations are planned around the load shedding

schedule that is published by Eskom. This influences the starting and the stopping of the

operations. If the schedule is not followed, this has a devastating impact on the factories.

Power outages have an effect of pushing costs of doing business for manufacturers. From

the qualitative model shown in Figure 6, it can be seen that the 'costs associated with load

shedding' occupy a big block in the graphical model. The biggest cost driver was the

overhead costs arising from working overtime and paying employees while they are not

productive. The second major cost component was the raw material spoilage. Some factories

mention that if the load shedding lasts for two hours or less, the costs remain within a

manageable region. If the load shedding period goes beyond two hours, it becomes difficult

to salvage anything from the spoiled raw material. For factories that have the backup power

supply, the costs of this energy was also cited as a major cost. The costs rising from missed

contractual obligations are also a cost component factories face.

The most catastrophic economic impact of power interruptions on factories is the loss of

production that results in revenue loss. It can be seen from the qualitative model in Figure

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that 'revenue loss' has a bigger block compared to the other themes. Revenue losses have

more impact when manufacturers produce less than what they can produce, in other words,

there is an investment in capacity but the volumes do not match the installed capacity. When

factories produce less, they able to sell less. Due to the constrained capacity arising from

load shedding, factories sign fewer contracts with customers which affects the revenues.

Existing contracts are also renegotiated in terms of volumes.

Some factory environments become hazardous when there is load shedding as they rely

heavily on electrical ventilation to keep the factory habitable. Therefore, personnel are

withdrawn immediately when there is a power interruption. Security systems such as closed-

circuit television cameras (CCTV) and Sensormatic systems get affected during a load

shedding incident. Lighting becomes a huge problem and this exposes factories to theft.

Over 90% of the factories rely heavily on the published load shedding schedule to plan their

operations. Scheduling their operations around the load shedding incident minimizes the

impact as there is no element of surprise when the power outage occurs. Factories can plan

and prepare for the outage. The factories can plan other activities the employees can do while

there is load shedding, like training or coaching or housekeeping activities in the workshop

or cloakrooms. This keeps employees active while there is a power outage and prevents the

possibility of having zero activity for the duration of the load shedding incident. Some

factories resort to manual production using mechanical means to keep the production line

going, like the furniture assembly factory. A small percentage of factories have backup

power generators installed in their premises that are activated when there is a power outage.

The challenge with backup power is that it costs significantly more than the utility supply.

Four major stakeholders came out from the analysis of study 1. Firstly it is the shareholders

of factories, secondly, it is the employees of the factories, thirdly it is the customers of the

factories and fourthly the suppliers of the factories. The shareholders of factories are the

final decision-makers on issues about investments into the backup power supply,

investments into developing suppliers to cope with load shedding and deciding on staff

requirements depending on the business direction. The second stakeholder is the employees.

The employees are the most important assets of the factories. They are the ultimate drivers

of production and they suffer the most when a power interruption is experienced. Employees

always wish to meet their daily targets because it has a bearing on their wages, incentives,

and bonuses. Some employees are loyal and work for one factory for a very long time, they

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always want their factory to succeed, therefore they put in extra hours to ensure that the

business meets its obligations. They get demotivated when they cannot work because of load

shedding and factories have been getting professional help to keep the morale of the

employees high, despite the challenging circumstances.

Customers of the factories surveyed are usually other businesses that sell the products to

consumers. Sometimes the products of the surveyed factories act as raw material for other

factories. They ultimately have obligations to their customers or consumers to deliver,

therefore, they are under a lot of pressure when load shedding is experienced. They are

restructuring the contracts with factories to protect themselves and their consumers. The

final stakeholder identified was the suppliers to the factories.

Suppliers play a critical role in enabling factories to meet their production targets and

objectives. Factories closely monitor the impact of load shedding on their suppliers and offer

support where possible. The supplier relationship management is more critical as factories

cannot afford to stop production due to load shedding and possibly lack of raw material or

other critical supplies. Some factories that have the possibility of stockpiling raw materials,

go for this option. However, this comes with challenges of theft and looting.

Most of the factories surveyed tended to focus mostly on the direct impact of power

interruptions. The second-order effects were not a major concern for most factories, it has a

small weighting on the model in Figure 6. The major concern for most factories is the effect

of load shedding on the manufacturer's brand. Most manufacturers think that the frequent

power outages affect their credibility and ultimately their brand. This is because they are

unable to meet some of their obligations to customers. Some factories mentioned the loss of

market share as one of the second-order effects, as customers take their business elsewhere

if one factory cannot deliver. A small number of factories mentioned that their customers

understand that frequent power interruptions are not within their control.

The positive outcomes arise as factories change the way they operate where it is business,

power interruptions disrupt this and cause discomfort. This discomfort forces factories to be

innovative and think out of the box. These skills of innovation are then only used to combat

the impact of load shedding, however, they are used in business to improve operations and

risk analysis. The factories that can stand out and provide products during the difficult period

of severe power interruptions, they tend to experience a surge in new customers and their

revenues increase sharply. The factories view the experience of dealing with power

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interruptions as a positive thing, as they can use the similar tools learned or develop during

the power interruption crisis to solve other business problems.

10.2 What is the direct impact of load shedding on small and medium-sized manufacturing companies in Richards Bay, South Africa?

The subjective evaluation methodology is the most prevalent methodology that employs

customer surveys to study the economic impact of power interruption on consumers of

electricity. This methodology is particularly applicable in environments that have frequent

power outages. The biggest disadvantage of this methodology is the credibility of

information provided by the factories. Sometimes factories do not answer honestly to avoid

overstating their challenges or not portray a picture where they are almost immune to load

shedding (Kufeoglu & Lohtonen, 2015:591).

However, this methodology was able to estimate that the impact of load shedding on

manufacturers at 21% of their total assets, on average. The total value of the assets is not

known, therefore a numerical value is not given. This takes into account the revenue losses

and cost escalations. The impact varies with the size and resources of the factory. The

challenge with the current estimates is that they ignore the primary product of the factory

and assume that each day of the week contributes the same losses to the weekly losses of

revenues and costs. In reality, each day of the week has to be assessed individually as the

revenue losses are not uniform per day. This will be further discussed in the next section.

The survey population was 38 small and medium-sized factories. A total of 22 responses

were recorded. To perform a more accurate study more responses would be required. One of

the major disadvantages of the study of this nature is the time and cost associated with

carrying out such a study. Kahneman (1979) notes that some respondents are sometimes not

exactly sure of the impact they are meant to describe in the surveys.

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11 RECOMMENDATIONS

This section presents recommendations arising from the research for key stakeholders

affected by the load shedding. These are the manufacturers and the South African power

utility. Limitations of the research and the possible future expansion of the work are also

proposed, which is aimed at improving the current contribution.

The load shedding situation in South Africa is a huge challenge for all electricity consumers.

The power interruptions arise from the poor planning by the power utility company Eskom

and the South African government. The country has noted growth in population and

economic activity, however, the electricity generation capacity remained the same. This has

resulted in inherent power interruptions that severely affect business operations and

profitability. The impact of power interruptions is felt by all consumers, however, very few

consumers know the exact impact or able to accurately quantify the impact. This is also true

for factories operating from Richards Bay, they are impacted by load shedding as well. This

study has sought to determine the impact of load shedding from a mixed approach

considering both the qualitative and quantitative aspects.

The manufacturing sector in Richards Bay employs over 200 000 people and the sector

nationally contributes over 12% to the total GDP. Richards Bay is an export town and

producing from here makes the export logistics cheap as the port is also in Richards Bay.

Most manufacturing companies that are servicing the export industry are moving to Richards

Bay and setting up factories here. The availability of state of the art railway infrastructure

linking the port and major hubs inland remains a competitive advantage for the port as those

products not destined for the export market find market inland, and the railway system

connects the factories with consumers. South Africa has an aspiration of becoming the most

industrialized economy in Africa. This aspiration is unlikely to be realized if the electricity-

related challenges are not addressed aggressively and speedily by all stakeholders including

the South Africa government and industry captains.

11.1 Recommendations for the manufacturers

Manufacturers take the impact of power interruptions and it is a difficult task dealing with

this impact as there are few options available to the manufacturer to deal with this challenge.

The manufacturer has the option of passing the higher costs of production arising from load

shedding challenges to the consumer. The factories can try to minimize the impact of power

interruptions and absorb the minimized impact that reduces their profits. The third option is

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for the factories to collaborate with all key stakeholders in the sector to come up with a set

of sustainable and long-lasting solutions.

It is a fact that it costs factories more to produce the same quantum of goods when there is

load shedding compared to producing the same quantum when there is no load shedding.

The cost of production per item increases, also known as the unit cost. The costs arise from

several contributors including overtime payments, standing time payments and standby

power where there are backup generators installed. The factory has to carefully consider how

to deal with the increased cost. It is recommended that factories should not pass 100% of the

extra costs to the consumer or customers, as this may be unsustainable and reduce market

share. It is recommended that the factory absorbs a certain portion of the costs. This should

be done while ensuring that the profitability of the factory is not compromised, which may,

in turn, affect the sustainability of the factory and possibly affecting the workforce which is

not desirable at all. The risk of the factory absorbing all the extra costs is unprofitability and

unsustainability of the business. This may likely reduce the market share if the competitors

do not increase prices significantly.

Factories are advised to develop a comprehensive load shedding procedure if it has not been

developed already. The procedure should be comprehensive taking into account planned and

unplanned load shedding circumstances. The procedure should be a blueprint that is clear to

the employees that have to implement it. It must have clear roles and responsible persons.

The plan should take into consideration the different stages of load shedding. The load

shedding plan should ultimately reduce the impact and cost of load shedding to a point where

the factory can absorb without compromising profitability on their bottom line. This would

give factories the benefit of keeping customers and keep delivering to their expectations.

The major risk of this approach is short-term losses while the plan is still on the trial phase.

However, the plan must be modeled and tested with different scenarios in a simulation first.

Factories that do not have 24-hour operations are advised to consider changing their

operating hours to the following period or part of the following period, 22:00 – 06:00. During

this period the frequency of load shedding is at the minimum, according to the statistics from

Figure 3. This period would ensure a long period of uninterrupted production for

approximately 8 hours. Before the decision can be made, the following must be taken into

consideration, the basic conditions of employment act, the occupational health and safety

act, and the cost of changing operational requirements. The condition of employment act

outlines the rights of employees and the minimum basic conditions under which they must

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execute their duties. If the working times of the employees have to change, they must be

consulted individually or in a union. The necessary shift or night working allowances must

be applied. Heightened safety and security measures should be taken into account. The risks

of working during the night are many, employees may be fatigued after not having rested

enough during the day. This could be because other family members are active during the

day, including children where applicable. Employees may be tempted to steal certain

company assets because the night presents an opportunity. The safety and health of

employees may be at risk if they work on heavy-duty machinery while fatigued. The cost of

operating during the night may be higher than that of operating during the day after taking

all consideration into account including the load shedding, in this case, the operating hours

should not be changed. The factories could consolidate their efforts and come up with plans

to minimize the impact of load shedding.

One of the measures factories in Richards Bay can take collectively, is to approach the power

utility to negotiate preferable energy tariffs to operate during the off-peak period. These

tariffs should be able to offset the costs that will arise from operating during the offpeak

period. This would require the small and medium-size factories to collectively lobby the

power utility company. The security of production is a huge factor in this option of operation.

The factories could explore this option immediately. A detailed cost-benefit analysis has to

be developed. Another option is to build a generation plant in Richards Bay.

Some factories are proposing a collaborative effort in building a power generation station

that will supply factories locally in Richards Bay. This would require a huge capital budget

and probably have a complex ownership structure, depending on the investment level of each

factory. The power generation facility would be set up such that it can supply all the small

and medium-size manufacturers in Richards Bay. There is a huge natural gas storage facility

available in Richards Bay. Mozambique which is 300km North of Richards Bay has an

abundance of natural gas. It is possible to set up a gas power generation plant in Richards

Bay. It must be noted that this a huge project that would require huge capital and carry a lot

of risks for the factories. The financing and ownership structure would be complex and

would require a lot of negotiations. The operation of the plan would also pose challenges, as

there would be operational costs to sustain the power plant. Necessary approvals would be

required from the government for self-generation of energy. The returns on investment in

this kind of project would take a long time, this could compromise the liquidity of the small

factories.

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11.2 Recommendations for the power utility

The power utility is facing a multitude of challenges that include capacity, operational and

financial difficulties. The resultant of the aforementioned challenges is the load shedding

that is experienced in the country. The following set of recommendations are proposed for

the power utility to assist in minimizing the impact of load shedding. The first suggestion

arises from Figure 2 the daily load shedding frequencies. The second proposal arises from

the planning and communication of load shedding schedules.

From Figure 3 it can be seen that the frequency of load shedding is at the lowest between

22:00 – 06:00. This is a period when the demand on the national grid is at the lowest. Most

small and medium-sized factories are closed during this time, corporate businesses are also

closed during this time. Most households are only using electricity for lighting,

entertainment purposes such as television sets, desktops and other electronic devices.

Usually, around this time, the high energy-consuming components such as geysers, pool

pumps, electric gates, electric stoves, and kettles are not working. This significantly lowers

the demand on the national grid. The mining and other heavy industries ramp up their

operations during this time. These industries can run their operations largely unconstrained

by power supply and power interruptions. These industries get preferential electricity tariffs

from the power utility for operating at peak during the off-peak period.

The preferential electricity should be extended to small and medium factories if they are to

operate during the off-peak period of power demand. This would help ease the pressure and

demand on the grid during the day. This applies to those factories that do not have 24-hour

operations. For example Figure 7 shows that 73.7% of the surveyed factories operate

between 6 – 12 hours per day. The off-peak period is approximately 8 hours. The factories

that operate for 12 hours could schedule their operations such that the 12 hours includes the

8 hours of off-peak. The incentive given to mining companies and heavy industries may

prove to be unsustainable if it is extended to small and medium factories. The power utility

would have to make estimates to determine the feasibility of this option. The power utility

must also consider the possibility or the risk of having the demand curve shifting

significantly if this incentive were to be extended. The second suggestion that emanates from

the responses of the survey is regarding the planning and communication of load shedding

incidents.

The power utility has tools that predict or anticipate the change in demand and the

availability of generating capacity. The utility can predict with a good degree of certainty as

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to when will load shedding be required and which areas will be affected, and for how long.

The utility does weekly and monthly forecasts of both the demand and generation capacity.

Only the weekly plan is shared with electricity consumers like smelters, mining companies,

and other heavy industries for them to plan around it, they are the ones that are required to

reduce demand first when the grid is strained. The rest of the customers only get a two-day

plan that is supposedly more accurate according to the power utility. This does not give the

factories enough time to adjust and plan around the load shedding incidents. However, they

still plan, even though it is not the most optimal planning. The incidents are better managed.

The power utility should try to minimize as much as possible unplanned power interruptions.

If these were unforeseen, they should rather be directed to domestic customers in residential

areas as most of them do not have economic activity. Their loss is only comfort and leisure

time. About 90% of the surveyed factories plan their operations around the load shedding

schedule of the power utility, any deviation has a devastating impact on the factories.

11.3 Limitations of the research

There are small and medium-sized factories in the whole of South Africa. However, due to

resource limitations, only a specific population was considered, which are factories operating

from Richards Bay. Ideally, for a study to be represented as possible, the entire population

of small and medium-size factories in South Africa would have to be considered. This would

require considerable resources and time to accomplish this task. This research was limited

in terms of the population from which the sample was drawn, only the factories registered

with the Zululand Chamber of Commerce and Industry (ZCCI) were considered for the

sample. ZCCI has a database of small and medium-size manufacturers operating from

Richards Bay. Some factories may be small or medium operating from Richards Bay,

however, their status is not known because it is not declared anywhere publicly. These

factories could be approached individually to ascertain if they are small or medium size to

increase the population.

The second major limitation of the research was the unavailability of data necessary to

implement the revealed preferences methodology to estimate the impact of load shedding on

the factories. This was because this data was deemed highly sensitive and confidential by

the factories. This methodology would have been used to compare and contrast with the

results from the subject evaluation methodology that was implemented. It is expected that

the revealed preferences methodology was going to confirm the estimations of the subject

evaluation methodology.

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12 CONCLUSIONS

South Africa is currently facing systemic power interruptions that are due to constrained

generating capacity and rising demand for electricity. The impact of power interruptions on

business entities is complex and this is true for manufacturers as well. The manufacturing

sector contributed 12.2% to the South African GDP in 2018. The sector employs 17% of the

total workforce in the country directly and creates a 5% jobs for the suppliers and retailers.

Research has been undertaken to understand the qualitative, as well as the quantitative

economic impact of power interruptions on small and medium-size, manufacturers in

Richards Bay. Initially, three studies were proposed, however, only two were undertaken

due to the unavailability of the third study which was deemed highly sensitive and

confidential by the respondents.

12.1 What is the qualitative impact of load shedding on small and medium-sized manufacturing companies in Richards Bay, South Africa?

An open-ended questionnaire was shared with managers of factories electronically to solicit

information about load shedding that affects their businesses. The qualitative study was

undertaken to understand the soft impact of load shedding on the factories, from employees,

external clients, and their brand. Figure 23 below shows the major themes that emanated

from the analysis of the qualitative results. The block size depicts the size of impact as

described by the respondents and the position shows the interaction of the blocks, as an

example revenues interacts with operations.

Figure 24: Major themes arising from the qualitative survey looking into the impact of load shedding on manufacturers

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The impact of power outages on the factories was found to vary according to factors such as

the primary product being produced and the nature of the power outage incident. Advance

notification was found to significantly reduce the impact of power outages on the factories.

Factories were better prepared when notified in advance. The longer the duration of a power

outage incident, the more severe was the impact. Load shedding severely affects the

operational activities of factories which are essentially production activities in the main. The

factories are unable to harness the full capacity of their factories as there is frequent stopping

of production due to load shedding.

The other two major themes at the core of the model are the cost escalations and the revenue

losses. Costs have a direct implication for the bottom line of any business. Cost escalations

and the revenue losses were found to be also major themes following closely from

operational losses. The operations have a direct bearing on the costs and revenues. This

negatively affects the profitability of the factories. The two major cost drivers are overhead

costs arising from extended working hours and the cost of having a backup power system

for those factories that have it installed. Revenues depend on the quantum sold, and the

quantity of the sold products depends on the volume of production, when the factory

produces less, they sell less and have less revenues.

The primary stakeholders affected by load shedding include factory shareholders,

employees, customers, and suppliers. All these stakeholders need to support each other to

survive the impact of load shedding, shareholders have to approve investments into

developing skills of workers and suppliers. Employees need to be prepared to put in long

and extended hours. Suppliers need to be transparent and upfront with challenges faced and

let the factories know in time, with actions they are taking or proposing to remedy the

situation. Customers need to understand some challenges are beyond their control.

12.2 What is the direct impact of load shedding on small and medium-sized manufacturing companies in Richards Bay, South Africa?

The subjective evaluation methodology was applied to estimate the impact of load shedding

on small and medium-size manufacturers in Richards Bay. It is estimated that the impact on

manufacturers is worth 21% of their total assets, on average. The revenue losses suffered by

factories due to a load shedding incident are not linear, therefore, a more accurate estimation

would be non-linear. The estimation would also have to take into consideration the primary

product of the factory and seek to quantify the indirect costs of load shedding such as loss

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of credibility. A larger population and sample would help to reinforce the findings and

perhaps reveal other dimensions that the current study may have not revealed.

The revealed preferences methodology was not implemented due to the unavailability of

data. Factories deemed this data highly sensitive and confidential. Generators provide

accurate information about the delivered kWh, fuel consumed and other consumables are

taken over time to keep the machine running. It is fairly simple to accurately work out the

cost of running a backup power supply and use this cost to estimate the impact of load

shedding. This method gives a more accurate estimation as generators are intelligent, they

record the fuel intake, power delivered and the service intervals, based on hours of

operations. This information can be consolidated to determine the cost in numerical terms

that would represent the impact of a power interruption on the factory.

12.3 Limitations of the research and future research recommendations

One major constraint of both studies is the sample size. A bigger sampled size would have

revealed more dimensions and perspectives. The qualitative data did not reach saturation,

however, it still provided an interesting analysis. This can either be solidified or questioned

by commissioning another study with a larger sample. The study was focused on the

Richards Bay area and presents a biased view of the Richards Bay factories, the same may

not be true for other factories throughout the country. This was done due to the time

constraints and the limitations of the budget. The revealed preferences methodology could

not be implemented because data could not be obtained from the factories.

The overwhelming majority of factories surveyed mentioned that load shedding has a

severely negative impact on them economically. The frequency and the severity of load

shedding are unlikely to come to an end soon, therefore, a plan has to be developed to

mitigate against load shedding or minimize the impact. Study 1 shows that the revenues,

costs, and production is affected the most.

Stakeholders in the manufacturing sector need to understand that it is possible to

quantitatively estimate the economic impact of frequent power interruptions. The performed

study followed a widely accepted methodology that has been used extensively in similar

environments to measure the economic impact of power interruptions on electricity

consumers. The subjective evaluation methodology was employed to estimate the economic

impact on factories, by factory managers. Key stakeholders need to understand the true

impact of power interruptions to effect mitigating mechanisms.

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An expansion of this work would include the implementation of the revealed preferences

methodology, after reaching an understanding with factories regarding data privacy. The

impact of the load shedding should be analyzed at the level of the product produced and not

at an aggregated cost level. A larger sample is recommended to countrywide view of the

load shedding impact to avoid the bias of the study. The data collection could be extended

countrywide to reach saturation for the qualitative study.

The two main objectives of the research were to understand the qualitative and quantitative

impact of the frequent power interruptions in Richards Bay. Two studies were undertaken,

it was found that the qualitative impact of load shedding affects the brand of the organization

and the morale of the employees of the factories. Quantitatively, the economic effect load

shedding is devastating as it negatively affects production, revenues and increases the cost

of doing business. This affects the profitability and sustainability of the factories.

The qualitative model in Figure 23 summarises the results of study 1. The model shows the

most pertinent issues facing the manufacturing sector in Richards Bay as a result of frequent

power outages. The quantitative results of study 2 show that the impact is estimated at 21%

of the total asset of the factories. The revealed preferences methodology was not

implemented due to the unavailability of data that was deemed to be highly sensitive and

confidential. It is hoped that this work will aid stakeholders in making informed decisions in

the future.

78

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APPENDICES

Appendix A – Questionnaire approval

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Appendix B – Survey questionnaire (qualitative)

Table 2: Qualitative servey questionnaire.

What is the impact of load shedding on your business and operations and brand?

How are the revenues of your business affected by load shedding?

How are the costs of doing business affected by load shedding?

How is safety in your factory affected by load shedding?

How does load shedding affect your staff morale and attitude?

What is the impact of load shedding on the Administration side of the factory?

If you have installed backup power supply, what challenges do you still experience, if any, despite having backup generators in place?

Are there any the opportunities that result from load shedding?

What are the knock on effects experienced due to load shedding?

What do you do to manage and mitigate the load shedding risk?

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Appendix C – Survey questionnaire (quantitative)

Do you have a standard procedure that is followed during load shedding in the

factory? * • Yes • No • No, but we plan to develop it.

How long is your daily business operation in hours? * Less than 6

• 6 - 8 • 9 - 12 • 13 - 15 • 16 - 19 • 20 – 24

Have you ever experienced load shedding while in operation with no backup

generators available? * • Yes • No • Other:

Please think of particular load shedding incidents that you recently experienced. Please mark when it happened! Please mark only once, e.g Sunday – Night

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Please describe how long did it take and what was the main challenge of the incident! *

Please estimate the percentage revenue that was lost due this load shedding

incident on the day? * • 0 - 10% • 11 - 20% • 21 - 30% • 31 - 40% • 41 - 50% • 51% and above

Please estimate the percentage costs that were incurred due to the load

shedding incident on the day? Raw material spoilage costs, disposables costs,

restart costs, etc. *

• 0 - 10% • 11 - 20% • 21 - 30% • 31 - 40% • 41 - 50% • 51% and above

Please estimate in percentage, how much load shedding reduces the life cycle of

your production equipment in one year? * • 0 - 5% • 6 - 10% • 11 - 15% • 16% or above • Other:

What is the power (kW) requirements for your business to operate at full

capacity? *

Does your factory have backup power generator(s)? * • Yes • No

• No, however, planning to install

What is the capacity (kW) of the your backup system? *

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What is the reliability of your backup power generation system in percentage? * • 0 - 20% • 21 - 40 % • 41 - 60% • 61 - 80% • 81 - 100% • Other:

Do you manage your own power backup system or this service is outsourced? *

• Managed Internally • Outsourced Service • Other:

In percentage, how does the cost of running your factory on back up supply for

two hours compare to the cost of running it from the utility supply for two hours?

The cost of running on backup is usually higher, please indicate how high it is for

your factory. *

• 0 - 10% • 11 - 20% • 21 - 30% • 31 - 40% • 41 - 50% • 51% and above • Other

Please explain how did you make the estimation above!

Please describe which parts of your factory are powered by the backup power system during load shedding? e.g. administration area, production area, storage area, warehouse, etc *

Please estimate at what capacity are you able to operate your factory on the

backup power system? * • None • 1 - 20% • 21 - 40% • 41 - 60% • 61 - 80% • 81 - 100%

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How may people work in your factory? *

What is the size of your factory (in square meters)? *

What are your main products? *

Is your factory categorized as small or medium-sized? * • Small • Medium

Do you have insurance to mitigate the impact of load shedding? * • Yes • No • No, however, I am planning to purchase one • Other

Is there any other information that you would like to share with the researcher?

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Appendix D – Pictures of Richards Bay

Figure 25: The top view of the port of Richards Bay, the town’s export hub (Michelin, 2020).

Figure 26: The inside of one of the medium factories producing plastic products in Richards Bay (Michelin,2012).

Documents

The economic impact of energy load shedding on small and