Performance Assessment and Optimization of Global …globalbizresearch.org/Thailand_Conference/pdf/T596_GBES.pdfThe paper uses the results of two independent surveys based on their

Proceedings of the International Conference on Global Business, Economics, Finance and

Social Sciences (GB15_Thai Conference) ISBN: 978-1-941505-22-9

Bangkok, Thailand, 20-22 February 2015 Paper ID: T596

1 www.globalbizresearch.org

Performance Assessment and Optimization of Global Supply Chains

Jagadeesh Rajashekharaiah,

SDM Institute for Management Development,

Mysore, Karnataka, India,

E-mail: [email protected]

___________________________________________________________________________

Abstract

Supply chains are assessed for their performance using various metrics and attributes that

help to compare and benchmark the performance across the globe. Several models have been

developed in the past but are limited to one particular approach and this paper develops a

model using both the metrics and the challenges faced by the global supply chains. This

allows the performance assessment against a supply chain’s capabilities to meet the

challenges. The paper uses the results of two independent surveys based on their applicability

and comprehensiveness and develops the model. The paper also describes how these metrics

can be used to optimize and compare using a weighted score model. The objective is to

provide a better decision making model using well established mathematical models.

___________________________________________________________________________

Key words: supply, chains, performance, metrics, optimization, global, assessment, criteria

JEL Classification:





1. Introduction

Supply chains constitute the backbone of business and economy. The increased attention

on supply chain management focusing on issues like supply chain competitiveness, risk,

networking and collaboration, vendor managed inventory, among other topics, prompts more

and more researchers to examine these issues. In the domain of Operations Management, the

supply chain management along with the logistics function is a key area management. Both in

engineering and management degree courses, the students study supply chain management

(SCM) as a core subject and acquires the necessary skills. The proliferation of the retail trade

enabled the SCM function to bloom and spread across various disciplines along with global

presence.

The SCM function involves a number of people and organizations who interlink and exchange

information, money or goods, (and the supply chain performance is dependent on several drivers,

as illustrated by a simple diagram shown in Figure 1, as given by Chopra & Meindl (2007).

However, it is necessary to properly integrate both the internal and the external supply chains

and be inter supporting to ensure supply chain success, (Bratić, 2011), as given in Figure 2.

Figure 1: Drivers of supply chain performance

Figure 2: External and the internal supply chain elements.

Supply chain priorities - Do they align with operations’ performance?

Operations managers constantly grapple with meeting multiple objectives and thus seek

optimal utilization of the resources. Considering the evolution of the production systems,

starting from handicraft or job systems to mass and flow systems, the operations priorities

varied to accommodate the changing strategies over time. This also prompted the operations





managers to develop "operations strategies" to successfully meet and beat the competition

offered by the global players.

It is understandable that the operations managers focused on key aspects while

manufacturing products and services and focused on 'critical success factors'. These factors

traditionally became the priorities and the challenge was to satisfy them to the maximum

possible extent. This also led to the practice of compromising whenever required because of

the inherent conflicts and constraints, (Boyer and Lewis, 2002). The three fundamental

success factors recognized as priorities are: quality, cost, and delivery, not necessarily in that

order but with equal importance. Later, three more factors namely flexibility, innovation, and

speed were added to expand the basket of success factors, (Ward, et al. 1998). It is obvious

that to realize these success factors a supportive supply chain should exist and enables to

realize the targets in each of the success factors considered.

This further requires the cooperation and coordination of all the supply chain partners

involved in the entire network. However, the strategic alignment between the partners is

difficult to measure and analyze, (Vachon, et al. 2009).

2. Measuring the supply chain performance – Literature Review

Measuring the performance of supply chains is a very popular area of research as

observed by the number of publication in the last two decades. While some researchers have

proposed different measures and performance metrics, some others have developed a

framework that enables performance assessment. Stewart (1995) illustrates the benchmarking

of the supply chain performance. Tan, et al. (1998) suggest assessment at different levels to

enable a better and comprehensive reporting.

Beamon (1999) identified three types of performance measures as necessary components

in any supply chain performance measurement system, and also recommends new flexibility

measures for manufacturing supply chains. Wong and Lee (2008) while arguing about the

complexities in performance assessment indicate how difficult the assessment could be

because the supply chain itself is a new field. Several researchers have developed frameworks

to help better assessment of the supply chain performance. For example, Gunasekaran, et al.

(2001) demonstrates a framework for measuring the strategic, tactical and operational level

performance in a supply chain.

Based on trust, terms like Supply Chain Event Management, Supply Chain Process

Management, and Supply Chain Execution Management are used interchangeably. Supply

chain monitoring must start with tight tracking of the many different processes involved in a

supply chain. As products and information flow through different parts of the supply chain, it

is necessary to capture the information and ensure that the end users’ requirements are

satisfied. Supply chain automation is a major trend in this direction that offers a variety of





tools and techniques to monitor and improve supply chain performance, (Huhns and

Stephens, 2001). Shepherd and Günter, (2006) have attempted a critical review of literature

pertaining to supply chain performance evaluation and have given some directions for further

research.

In another survey Gunasekaran and Kobu (2007) have provided an overview of measures

applicable for performance assessment of supply chains. Several researchers have

investigated the issue of performance measurement considering various aspects of supply

chain include. Chan (2003) introduces five other performance measurements like resource

utilization; flexibility; visibility; trust; and innovativeness., Bhagwat, and Sharma (2007)

developed a balanced scorecard for supply chain management (SCM) that measures and

evaluates day-to-day business operations from following four perspectives namely: finance,

customer, internal business process, and learning and growth. Wong and Wong (2007)

suggest two DEA (Data Envelopment Analysis) models– the technical efficiency model and

the cost efficiency models that are coupled with scenario analysis to enable improved

resources planning decisions.

A hierarchy based supply chain performance measurement system using the Analytic

Hierarchy Process is reported by Xu, et al. (2007). Brun, et al. (2009) provides a framework

for the selection of the right Performance Measurement System (PMS) for different supply

chain typologies. Further, supply chain performance measurement system implementation

(Charan et al. 2008) indices how the system is implemented. It is obvious from the literature

review that the performance measures have attracted the attention of the researchers and it is a

challenging task to develop an exhaustive performance measurement system considering all

the factors suggested or recommended across the world by researchers and practitioners.

3. Global supply chains - challenges and issues

Since the dawn of globalization in the early nineties, researchers across all disciplines

have studied the impact of going global and the associated success factors. The term

globalization is now deeply rooted in everyday business and general talk. Wikipedia

(www.wikipedia.com) defines globalization as “the process by which regional economies,

societies, and cultures are integrated through a global network of communication,

transportation, and trade. The term is used to refer specifically to economic globalization: the

integration of national economies into the international economy through trade, foreign direct

investment, capital flows, migration, and the spread of technology, (Bhagwati, 2004).

Globalization is usually recognized as being driven by a combination of economic,

technological, socio-cultural, political, and biological factors, (Sheila, 2004).The term can

also refer to the transnational circulation of ideas, languages, or popular culture through

acculturation. Alli, et al. (2007) has given a good interpretation of globalization and its





effects. According to them globalization is the interaction between economies, technologies

and politics which creates an environment that reduces state regulation of the market

promoting a more dominant role for large multinational corporations.

The advent of globalization made the operations mangers to look beyond the local

boundaries and start getting inputs from several places across the world and to look at the

whole world as their markets. Global supply chains with inbound and/or outbound logistics

are quite common today as the suppliers and customers could be located anywhere in the

world. Secondly, it is prudent to look for suppliers and customers far beyond the local

boundaries to realize several distinct advantages in terms of quality, quantity, price, variety,

currency fluctuations, regional policy matters, and to build balanced networks. On the other

hand global supply chains also have their limitations and challenges. A survey conducted by

McKinsey reveals the interesting responses as depicted in Figure 3. This paper proposes to

measure the supply chain performance against these perceptions to construct the supply

chains to meet these challenges. This ensures that the assessment of the supply chains are

with reference to the actual performance parameters taking into mind the challenges and the

realities across the globe.

4. Mapping of global supply chain challenges and supply chain

performance measures

Quality, cost, and delivery are the primary key metrics anytime applicable to assess the

supply chain performance. In addition as already informed, flexibility, innovation and speed,

constitute the expectations from the supply chains. Flexibility and speed refer to several sub-

factors like flexibility in terms of volume, variety, lead times, pricing, batch size, delivery

modes, packaging, distance traveled, shelf life and ability to handle last minute changes, and

several others. Similarly, speed of operations in terms of fast delivery, rapid changes in

design, ability to introduce new practices quickly, improved responsiveness, faster

turnarounds in inventory, and above all faster communication capabilities, will be helpful for

a comprehensive assessment.





Figure 3: Percent respondents agreeing to a given aspect of challenges

(Source: http://www.mckinsey.com/insights/operations/

the_challenges_ahead_for_supply_chains_mckinsey_global_survey_results)

Considering the moderate amount of literature dealing with the performance assessment of

the supply chains, the author proposes to adopt the model suggested by Anvari, et al. (2011)

based on the following considerations:

The proponents of this model have examined the various models of performance

assessment developed by different authors and have given those factors due

consideration





Firstly, the affecting factors on SC performance are addressed on the basis of

literature and elites' opinions; and later industrial connoisseurs' ideas were

gathered to identify the factors to be included in the questionnaire

The survey reveals the important factors

The list of factors is modified to reflect the changes in the environment

4.1 Mapping of factors and he challenges

The next step involves the mapping of the list of factors given by Anvari, et al. (2011)

and the challenges given by the McKinsey studies by Gorey, Jochim, and Norton. (2015)

reveal how the assessment can become more relevant to the industry requirements. Further

based on the respondents' perceptions and comments, the lists are ranked from most preferred

to least preferred parameters. Table 1 shows the assessment factors arranged in decreasing

order of importance. (The ranks below27 are not part of the ranks given by the respective

authors but included here for the completeness of the earlier list, and the ranks are just serially

given). Later using the McKinsey's report by Gorey, Jochim, and Norton (2015), Table 2

shows the challenges and the corresponding ranks.

Table 1: Assessment factors and corresponding ranks (Anvari, et al. (2011)

Assessment factors Rank

Purchase order cycle time 1

Order entry methods 2

Quality of delivered goods 3

Supplier ability to respond to quality problems 4

Buyer-supplier partnership level 5

Cycle Time 6

Delivery performance 7

Rejection rate 8

Effectiveness of distribution planning schedule 9

Customer satisfaction 10

Range of product and services 11

Order responsiveness 12

Fill rate 13

Warehouse cost 14

Accuracy of forecasting techniques 15

Lead time 16

Information sharing and availability 17

Frequency of delivery 18

Supplier assistance in solving technical problems 19

Flexibility to meet particular customer needs 20

Total Cash Flow Time 21

Supplier cost saving initiatives 22

Delivery reliability 23





Quality of delivery documentation 24

Inventory flow rate 25

Product development cycle time 26

Delivery lead time 27

Effectiveness of delivery invoice methods 28

Level of customer perceived value of product 29

Level of supplier's defect free deliveries 30

Master Production Scheduling 31

Rate of Return On Investment 32

Rate of unfilled orders 33

Variations against budget 34

Table 2: Global challenges ranked in Gorey, T., Jochim, M. and Norton, S. (2015)

Global Challenges Rank

Increasing volatility of customer demand 1

Increasing consumer expectations about quality 2

Increasing cost pressure in logistics/transportation 3

Increasing pressure from global competition 4

Increasing volatility of commodity prices 5

Increasingly complex patterns of customer demand 6

Increasing financial volatility 7

Increasingly global markets for labor and talent 8

Increasing complexity in supplier landscape 9

Growing exposure to differing regulatory requirements 10

Increasing environmental concerns 11

Geopolitical instability 12

4.2 Observations and remarks

The first challenge in Table 2 pertains to the "Increasing volatility of customer demand"

which refers to the unpredictability of the demand and makes the forecasting difficult. This in

turn demands applying sophisticated methods of forecasting to improve the accuracy. But,

from Table 1, "Accuracy of forecasting techniques" is ranked at 15 thereby showing a lesser

preference. This is an indication of mismatch between what the customers perceive and the

experts opine. Complex pattern of customer demand is a challenge, ranked at the middle of

the list almost corroborating to the lesser preference to the forecasting accuracy. However,

the conventional factors like quality, cost, and delivery, rank higher in both the customers' list





and list of the challenges. Similarly, factors like flexibility, innovation, and time related

parameters, are ranked almost at the same level of preferences in the two lists. However, the

two lists do not relate in any way in terms of the people surveyed or place of survey or

industries or the profile of the respondents. Hence, the lack coherence between the two lists

need not surprise in general, nevertheless shows some connectivity across the factors.

5. Weighted score model using the multiple criteria of performance assessment

Whenever a certain decision is based on multiple criteria a simple approach would be to

use a weighted score model. In the case of performance assessment of supply chain as shown

in Table 1, there are 34 factors established and hence a weighted score model would be

appropriate to simplify the decision of comparing the performance of the same supply chain

over a period of time or comparing a set of supply chains using similar criteria. The first step

in using the weighted score model is to convert the ranks to corresponding weights. The

criteria weights are developed by using the approach suggested by Alfares and Duffuaa

(2006), where a linear relationship specifies the average weight for each rank, assuming a

weight of 100% for the first-ranked (most important) factor. For any set of n ranked factors,

the percentage weight of a factor ranked as r is given by:

W(r, n) = 100 – Sn (r – 1)

Where, Sn = 3.19514 + (37.75756/n), 1<= r <= n, and r and n are integers

In the present case n = 34 and using a spreadsheet the weights are calculated and shown in

Table 3 along with their ranks.

Table 3: Rank and weights of the factors

Assessment factors Rank Weight in %

Purchase order cycle time 1 100

Order entry methods 2 95.69434353

Quality of delivered goods 3 91.38868706

Supplier ability to respond to quality

problems

4 87.08303059

Buyer-supplier partnership level 5 82.77737412

Cycle Time 6 78.47171765

Delivery performance 7 74.16606118

Rejection rate 8 69.86040471

Effectiveness of distribution planning

schedule

9 65.55474824

Customer satisfaction 10 61.24909176

Range of product and services 11 56.94343529

Order responsiveness 12 52.63777882





Fill rate 13 48.33212235

Warehouse cost 14 44.02646588

Accuracy of forecasting techniques 15 39.72080941

Lead time 16 35.41515294

Information sharing and availability 17 31.10949647

Frequency of delivery 18 26.80384

Supplier assistance in solving technical

problems

19 22.49818353

Flexibility to meet particular customer needs 20 18.19252706

Total Cash Flow Time 21 13.88687059

Supplier cost saving initiatives 22 9.581214118

Delivery reliability 23 5.275557647

Quality of delivery documentation 24 0.969901176

Multiplying the regular scores by the weights, the weighted scores can be established and

the composite score is calculated taking up the sum of all the weighted scores. The weights

assigned by the model follow a linear decrement. However, this model starts tapering to lower

values and eventually reaches close to zero when there are 24 factors. Another approach to

assign weights could be to use "learning curve" theory, which starts assigning weights from

100 to the first value and then decrements the values in a negative exponential manner.

However, these models are definitely worth examining further in order to justify the

methodology of assigning weights. Variance around mean is established and any model

selected should be having a minimum deviation from the central value. This paper will not

delve into the details as it would demand a separate analysis.

6. Conclusions and Recommendations

Performance assessment of supply chains is considered a vital aspect sine the last two

decades because of the immense importance of the supply chains in the global economy and

also due to the proliferation of the global supply chains. Many researchers and professional

bodies have developed a variety of measures to assess the performance of supply chains and

most of these assessment parameters seem to be agreeing with the conventional measures that

existed right from the days of prioritizing the operations requirements. In this paper the

factors as obtained through a comprehensive survey and the challenges identified by a well-

known research based professional agency, have been mapped to examine how the

assessment can be made with respect to the challenges. This serves the objective of assessing

against the challenges faced and hence tests the ability of the supply chains in meeting those

challenges. However, the model proposed here is limited by the fact that the two lists

containing the factors and the challenges are not based on the survey conducted on common





respondents nor the two lists have any other common factors. The paper demonstrates the

methodology to lead to a better model compared to simply ranking the factors and converting

the values to a single score says using the sum of the weighted scores. Further research on

similar lines but with a common participating group of respondens will yield reliable results.

References

Alfares, H.K., and Duffuaa, S.O., 2008. Assigning cardinal weights in multi-criteria decision

making based on ordinal ranking. Journal of Multi-Criteria Decision Analysis, 15(5/6), 123-

133.

Alli, A.M., Winter, G. S. and May, D. L., 2007. Globalization: Its Effects. International

Business & Economics Research Journal, 6(1), 89-96.

Beamon, B. M., 1999. Measuring supply chain performance. International Journal of

Operations & Production Management, 19(3), 275 - 292.

Bhagwat, R. and Sharma, M. K., 2007. Performance measurement of supply chain

management: A balanced scorecard approach. Computers and Industrial Engineering, 53(1),

43-62.

Bhagwati, J., 2004. In Defense of Globalization. Oxford, New York: Oxford University Press.

Boyer, K., and Lewis, M., 2002. Competitive priorities: investigating the need for trade-offs

in operations strategy. Production and Operations Management. 11(1), 9-20.

Bratić, D., 2011. Achieving a Competitive Advantage by SCM. IBIMA Business Review

Journal. 1-13.

Brun, A., Salama, K. F. and Gerosa, M., 2009. Selecting performance measurement systems:

matching a supply chain's requirements. European Journal of Industrial Engineering, 3(3),

336 – 362.

Chan, F.T.S., 2003. Performance measurement in a supply chain. International Journal of

Advanced Manufacturing Technology. 21, 534-548.

Charan, P., Shankar, R. and Baisya, R., 2009. Modeling the barriers of supply chain

performance measurement system implementation in the Indian automobile supply chain.

International Journal of Logistics Systems and Management. 5(6), 614-630.

Chopra, S., and Meindl, P., 2007. Supply chain management. Upper Saddle River, N.J.:

Pearson Prentice Hall.

Gunasekaran, A. and Kobu, B., 2007. Performance measures and metrics in logistics and

supply chain management: a review of recent literature (1995–2004) for research and

applications, International Journal of Production Research. 45(12),

Gunasekaran, A. Patel, C. and Tirtiroglu, E., 2001. Performance measures and metrics in a

supply chain environment. International Journal of Operations & Production Management.

21 (1/2), 71 – 87.





Gorey, T., Jochim, M. and Norton, S. (2015). The challenges ahead for supply chains:

McKinsey Global Survey results. [online] Mckinsey.com. Available at:

http://www.mckinsey.com/insights/operations/the_challenges_ahead_for_supply_chains_mck

insey_global_survey_results [Accessed 12 Jan. 2015].

Huhns, M. N., and Stephens, L. M., 2001, Automating Supply Chains. IEEE Internet

Computing, 5(5), 92-95

Shepherd, C. and Günter, H., 2006. Measuring supply chain performance: current research

and future directions. International Journal of Productivity and Performance Management.

55(3/4), 242 - 258.

Sheila, C. L., 2004. Globalization and Belonging: The Politics of Identity in a Changing

World. Rowman & Littlefield, US.

Stewart, G., 1995. Supply chain performance benchmarking study reveals keys to supply

chain excellence. Logistics Information Management. 8, (2), 38-44.

Tan, K.C., Lyman, S.B., and Wisner, J.D., 2002. Supply Chain Management: A Strategic

Perspective. International Journal of Operations & Production Management. 22 (6),

614-633

Vachon, S., Halley, A., and Beaulieu, M. 2009. Aligning competitive priorities in the supply

chain: the role of interactions with suppliers. International Journal of Operations &

Production Management, 29(4), 322-340.

Ward, P., McCreery, J., Ritzman, L., and Sharma, D., 1998. Competitive Priorities in

Operations Management. Decision Sciences, 29(4), 1035-1046.

Wong, W. P. and Wong, K. Y., 2007. Supply chain performance measurement system using

DEA modeling. Industrial Management & Data Systems. 107(3), 361 - 381.

Wong, W. P., Jaruphongsa, W. and Lee, L. H. (2008). Supply chain performance

measurement system: a montecarlo DEA-based approach. International Journal of Industrial

and Systems Engineering, 3 (2), 162 – 188.

Xu, L. X. X., Bin, M., and Lim, R. (2007). AHP Based supply chain performance

measurement system. In: IEEE Conference on Emerging Technologies and Factory

Automation, 25-28 September, Patras, Greece. pp. 1308-1315.

Documents

Performance Assessment and Optimization of Global …globalbizresearch.org/Thailand_Conference/pdf/T596_GBES.pdfThe paper uses the results of two independent surveys based on their