Financial planning and analysis at Philips Health ... - UvA

Financial planning and analysis at Philips Health

Systems Customer Services

Student: Pritam Sasmal

Student number: 10839593

Supervisor: Dr. Rui J. Oliveira Vieira Submission Date: 13th of September, 2015

Program: Master of Business Administration (MBA)

Institution: Amsterdam Business School, University of Amsterdam

Financial planning and analysis at Philips Health Systems Customer Services 2

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Statement of originality This document is written by Pritam Sasmal who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Amsterdam Business School is responsible solely for the supervision of completion of the work, not for the contents.


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Abstract:

Purpose- The purpose is to develop a framework and come up with suggestions to improve

the financial analysis and planning methods so as to ensure that the financial forecasts for

companies are more accurate and close to the actual results.

Approach/Methodology-To serve the purpose of the research, detailed information about

relevant financial parameters and key performance incentives were collected from various

financial reports. A thorough understanding of the various product categories and structures,

and, knowledge of the global market break up was also developed. Philips internal documents

and intranet system were also referred to get information about its financial system,

conventions, standards, best practices and processes. A series of semi-structured interviews

were conducted with director and senior financial analysts to know about the existing

processes and solicit suggestions on improving them. The theoretical framework focussed on

multiple aspects of forecast such as budgeting, variance analysis and also on decision making

guidelines for managers.

Findings-The research shows that in order to improve the accuracy of the financial forecast it

is important to understand the causes of past variances at the most granular level. The

research gives a clear view of the financial business hierarchy of the Health Systems

Customer Services division at Philips and the flow and distribution of information across the

hierarchy. A flowchart was devised to structure the processes of financial analysis and data

investigation based on various financial figures and the business hierarchy that would help the

management and financial analysts to identify the root causes of deviations between the

forecast and the actuals. On the basis of this information, the management may take necessary

actions to resolve the issues and/or adjust their forecasting models which would eventually

lead to improving the accuracy of their financial forecasts.

Limitations-The scope of the research is limited to the Health Systems Customer Services

division. The forecasting methods used in other units could be different, and therefore, the

findings of this research may not be completely applicable to all units across all organisations.

Value-This paper offers a comprehensive picture of the financial data investigation processes

followed at Philips Health Systems Customer Services division and provides suggestions and

guidelines to improve the financial analysis methods in order to have more accurate forecasts.


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Table of Contents

List of abbreviations, tables and figures ..................................................................................... 6

1. Introduction: ....................................................................................................................... 8

2. Theoretical Framework: ................................................................................................... 11

2.1. Budgeting: ..................................................................................................................... 11

2.2. Control process: ............................................................................................................. 11

2.3. Feedback: ...................................................................................................................... 12

2.4. Rolling budgeting (Forecast): ........................................................................................ 13

2.5. Stages in the budgeting process: ................................................................................... 14

2.6. Sales budget: ................................................................................................................. 17

2.7. Production budget and the budgeted inventory levels: ................................................. 17

2.8. Direct materials usage budget: ...................................................................................... 17

2.9. Direct materials purchase budget: ................................................................................. 17

2.10. Direct labour budget: ................................................................................................... 18

2.11. Factory overhead budget: ............................................................................................ 18

2.12. Selling and administration budget: .............................................................................. 18

2.13. Departmental budget: .................................................................................................. 18

2.14. Master budget: ............................................................................................................. 18

2.15. Cash budgets: .............................................................................................................. 18

2.16. Budgeting in Multinational Companies: ..................................................................... 19

2.17. Flexible budget: ........................................................................................................... 20

2.18. Variance Analysis: ...................................................................................................... 20

2.18.1. Sales variance ....................................................................................................... 21

2.18.2. Material variances ................................................................................................ 22

2.18.3. Labour variance .................................................................................................... 24

2.18.3.1. Wage rate variance ........................................................................................ 24

2.18.3.2. Labour efficiency variance ............................................................................ 24

2.18.3.3. Total labour variance ..................................................................................... 24

2.18.4. Overhead variances .............................................................................................. 25

2.18.4.1. Variable overhead variances ......................................................................... 25

2.18.4.1.1. Variable overhead expenditure variance ............................................................. 25

2.18.4.1.2. Variable overhead efficiency variance ................................................................ 26

2.18.4.2. Fixed overhead expenditure or spending variance ........................................ 26

2.19. Reconciling budgeted profit and actual profit ............................................................. 26

2.20. Using information to make better forecast .................................................................. 26

3. Research Methodology: .................................................................................................... 29

4. Case Study: ....................................................................................................................... 31


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4.1. About the company: ...................................................................................................... 31

4.2. About the division: ........................................................................................................ 31

4.3. Case Context: ................................................................................................................ 32

4.3.1. Profit warnings ....................................................................................................... 32

4.3.2. Taking the first step in the right direction .............................................................. 33

4.3.3. Existing Issues ........................................................................................................ 35

5. Discussion: ....................................................................................................................... 41

6. Conclusion: ....................................................................................................................... 52

7. References: ....................................................................................................................... 54


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List of abbreviations, tables and figures

List of abbreviations

Abbreviation Definition

BG Business group

BU Business unit

BW Business Warehouse

COO Costs of Organisation

CS Customer Services

CSA Customer Service Agreement

DI Diagnostic Imaging

EBIT Earnings before Interests and Taxes

EFR Enterprise Financial Reporting

FCO Field Change Order

FP&A Financial Planning and Analysis

GC General Consumption

GM Gross Margin

HISS Healthcare Informatics Solutions and Services

IGT Interventional Guided Therapy

IT Information Technology

ITM In The Month

KPI Key performance incentives

MAG Main Article Group

MAT Moving Annual Total

OCCO Other Costs of Organisation

OI Order Intake

OOH Order On Hand

ORU Organisational Reporting Unit

P&L Profit and Loss

PCMS Patient Care and Monitoring Solutions

QTD Quarter To Date

SAP Systems, Applications and Products

T&M Time and Material

US Ultrasound

YTD Year To Date


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List of tables

Table 1. Overview of interviews conducted ............................................................................. 29

List of figures

Figure 1. Strategic planning and control process ..................................................................... 12

Figure 2. Variable analysis for a variable costing system ........................................................ 21

Figure 3. Health Systems Customers services in the Philips hierarchy ................................... 32

Figure 4. P&L report model used at Philips earlier .................................................................. 34

Figure 5. Current P&L report model at Philips ........................................................................ 35

Figure 6. Forward looking forecast report ............................................................................... 39

Figure 7. Financial analysis framework ................................................................................... 43

Figure 8. Variance analysis and Mix impact illustration ......................................................... 45


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1. Introduction:

Over the years, the world economy has gone through major transformations, redesigning the

economic scenario, making it more and more difficult for companies to read and adapt to the

changes in their respective markets. Due to these volatile business conditions, the forecasting

processes, methods and tools employed at various organizations across the world are

becoming less effective, and therefore obsolete, resulting in poor forecasts. This fact is

substantiated by the surge in the number of profit warnings over the last few years. With

markets across the globe turning more impulsive and competition among the companies

becoming fiercer, the benefits that an improving economy may bring to the companies may

get offset. As quoted by Ernst and Young in the British newspaper Telegraph website

(25Jan2015):

“The six-year high in profit warnings might look incongruous next to improving growth, but it’s

a reminder that economic recovery isn’t a panacea for many companies,”

The finance managers should take cognizance of this fact and take necessary steps to battle

the malaise the financial processes are suffering from. A prudent and logical approach would

be to understand the reasons causing the poor forecast at the most granular levels and address

the issues by either eliminating the causes or adapting to the new market demands and

conditions. As mentioned by Player and Morlidge (2010, p.2):

“First, finance managers should recognize what ails them by identifying and addressing the

common symptoms of forecasting illness. Second, equipped with that understanding, finance

managers can implement cures that lead to healthier forecasting practices and, ultimately, more

flexible and profitable organizations.”

The first step is very crucial as that decides the direction for the way forward. With an

emphasis on improving the forecasting practices, this paper presents a guideline to navigate

through multiple levels of data hierarchy, gather information about the underlying issues,

assess the impact at the top level and set the platform to decide the necessary course of action.

Also, given the current dynamic market environment, organizations can no more afford to rely

on information from only the past to make predictions. They would also need information

based on the managers’ beliefs on the future and their anticipation about the outcomes, which

is developed during the forecasting processes. So, directly or indirectly, the forecast processes

adopted in an organization play a significant role in determining the accuracy of the forecasts.

Of late, poor forecasting practices in companies have resulted in financial losses and erosion

of shareholder value. Sometimes the financial managers have to alter between conflicting


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demands (making an accurate estimate or create a more optimistic forecast), and these

contradictory demands complicate the approach of their forecast activities and make them

inefficient to a certain extent (Player and Morlidge, 2010). If the companies want to avoid

wasting time on such inefficient activities they must start looking analytically at the reasons

behind the forecasting inaccuracy. One of the tools that the companies (may) use to do so is

variance analysis. It helps to scrutinize the aspects that caused the actual numbers to deviate

from the predicted ones and measures the accuracy of the predictions. This paper uses the

concepts of variance analysis to create a structured data analysis framework that could help

finance managers to recognize the reasons for fluctuations at different levels of business and

contribute meaningfully towards identifying the root causes of the forecasting failures,

thereby improving the forecasting processes and creating value for the companies. Moreover,

collecting information methodically and realistically would give the financial managers a

better visibility about possible results and the potential risks associated with them, and guide

them to come up with more accurate forecasts.

The purpose of this paper is served through a research done on the forecasting processes and

methods followed by the Financial Planning and Analysis team of the Global Health Systems

Customer Services division of Philips. This paper looks at the various factors (both internal

and external) driving the numbers of multiple financial KPIs in different reports and comes up

with suggestions to make the predictions of those numbers more accurate.

Though improving the forecasting processes and methods necessitates taking an array of

generic high level steps, every company (or business units within a company) should take an

approach that complements its distinctive mix of business processes, resources, culture and

people.

Coming to the structure of the paper, first, the theoretical framework will briefly explain the

concepts of budget, the budgeting processes, variance analysis and also discuss how manages

can use information to make better forecast. It then presents the research methodology that

tells about the various data collection methods and data sources. Then the case study

illustrates details about the company and the division, explains the importance of improving

forecasting methods in the current context, the current forecasting practices followed and the

scopes for improvement. The next section, discussion, explains in detail the ways to improve

the forecasting processes and the impact each of those improvements can have on the

accuracy of the forecasts. Lastly, the conclusion gives an overview of the research, presents


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its limitations and discusses proposals for further research related to improving forecast

accuracy.


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2. Theoretical Framework:

2.1. Budgeting:

Budgeting pertains to the application of the long-term plan for the year ahead. As the budgets

are done with a short time frame perspective they are very specific and detailed. Budgets are a

reflection of what is estimated to be accomplished during the budget period where as long

term plans are indicative of the board directions that the top management is supposed to

follow. Drury (2013, p.227) explains:

“The budget is not something that originates ‘from nothing’ each year – it is developed within

the context of on-going business and is ruled by previous decisions that have been taken within

the long-term planning process.”

Initially the activities that are approved and included in the long-term plan are done on the

basis of tentative estimates that are projected for many years. However, they must be

reviewed and revised to reflect the more recent business environment. This review and

revision process is often done as part of the annual budgeting process, and this may lead to

critical decisions about certain adjustments and modifications within the current budget period.

Drury (2013, p.227) further mentions:

“The budgeting process cannot therefore be viewed as being purely concerned with the current

year - it must be considered as an integrated part of the long-term planning process.”

The key process for controlling a budget begins with the preparation of annual budget when

managers must establish specific measurable budgetary goals. “Budgets are recognised both

as a plan for allocating resources and the process through which the plan is controlled.”

(Francisco, 1989, p.40)

2.2. Control process:

The next stages after budgeting process, as shown in Figure 1, compare the actual and

estimated results and respond to any deviation in the plan.

Planning is closely associated with control. Planning is a forward looking activity to decide

upon the actions necessary to achieve the goals of the organisation. Control is about looking

back to determine what the actual results are and then compare them with the planned ones.

Effective control is about taking the remedial action so as to match the actual results as

closely as possible with the planned results. Having mentioned that, it is also possible that the

plans have to be changed in case the comparisons show that the plans are no longer

achievable. The corrective action is shown by the arrowed lines in the figure below linking


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stages 5 and 2 and 5 and 3.They are the feedback loops. They tell us that the process is

dynamic and highlight the interdependencies between the various stages in the process

Figure 1. Strategic planning and control process

(Drury, 2013, p. 227)

The feedback loops between the stages show that the plans should be frequently revised, and

if they are no longer achievable then alternative course of action must be devised for meeting

the organisation’s targets. The arrow between 5 and 3 also indicates the corrective action that

may be taken so that actual outcomes conform to planned outcomes (Drury, 2013).

2.3. Feedback:

As Horngren, Datar and Rajan (2012, p.200) mention:

“Budgets coupled with responsibility accounting provide feedback to top management about the

performance relative to the budget of different responsibility centre managers.”

They suggested that variances, the differences between the forecast figures and the actual

figures, can be used to assist the management to implement and assess their strategies in the

following three ways –

Early warning: Variances give early warnings to managers about events which are not very

apparent thereby allowing managers to take corrective measures or use the opportunities to

their benefit.

Performance evaluation: Variances allow managers to assess the performance of the company

in implementing the strategies.


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Evaluating strategy: On certain occasions, variances inform the managers about how effective

their strategies are. For instance, a company trying to improve sales may figure out that it

achieved its objectives but that really didn’t improve their margin. This allows the

management to re-assess their strategies and take a different approach if necessary (Horngren

et al. 2012).

2.4. Rolling budgeting (Forecast):

The conventional approach for budget is that the manager of each budget/cost centre

formulates a thorough budget for a year, once in a year. The budget is categorised into either

12 monthly or 13 four-weekly periods for the purpose of control. A yearly budget has been

criticised strongly as it is considered too rigid and also because it coerces the company into a

12-month commitment which is highly risky given the volatile nature of the business

environment and the uncertain forecasts that it is based on.

A different methodology would be to break down the budget by months for the first three

months, and by quarters for the remaining nine months. The quarterly budgets are then

prepared or adjusted every month in the year. Drury (2103, p.230) explains:

“For example, during the first quarter, the monthly budgets for the second quarter will be

prepared; and during the second quarter, the monthly budgets for the third quarter will be

prepared. The quarterly budgets may also be reviewed as the year unfolds. For example, during

the first quarter, the budget for the next three quarters may be changed as new information

becomes available. A new budget for a fifth quarter will also be prepared. This process is

known as the continuous budgeting or the rolling budgeting, and ensures that a 12-month

budget is always available by adding a quarter in the future as the quarter just ended in

dropped.”

On the other hand, in case of the yearly budget, the period for which the budget is available

will shorten until the budget for the next year is ready. Unlike in the yearly budget, the

planning process in the rolling budgets is not a once-in-a-year activity. In fact, rolling budget

is a continuous process and managers are expected to constantly look forward and adjust the

future goals to suit the business demands. One more benefit is that the actual performance will

be compared vis-à-vis a more realistic target as the budgets are being constantly revised and

adjusted. The main area of concern for the rolling budget is that it can create uncertainty for

managers as it is adjusted and updated with the passing year (Drury, 2013). “Irrespective of

whether the budget is prepared on an annual or a continuous basis, monthly or four-weekly

budgets are typically used for control purposes.” (Drury, 2013, p. 231)


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2.5. Stages in the budgeting process:

Drury (2013, p.232) listed down the following important stages of budget:

“1. communicating details of budget policy and guidelines to those people responsible for the

preparation of budgets;

2. determining the factor that restricts output;

3. preparation of the sales budget;

4. initial preparation of various budgets;

5. negotiation of budgets with superiors;

6. coordination and review of budgets;

7. final acceptance of budgets;

8. Ongoing review of budgets.”

Communicating details of the budget policy-

Most of the decisions pertaining to the budget year are already taken as part of the long-term

planning process. So, the long-term plan is actually the starting point for preparing the annual

budget. It is therefore mandatory for the top management to communicate the policy changes

to the managers and teams working on the current year’s budget. There could be possible

changes in sales mix, or the expansion or contraction of certain activities. Any other guideline

that has an impact on the budget, such as price allowances, wage increases, expected

productivity changes, also has to be mentioned. Top management should also make the

managers, responsible for the budget, aware of the recent developments or changes in the

industry demands (Drury, 2013).

Determining the factor that restricts performance-

Every organisation has certain factors that restricts its performance in a given period. Sales

demand is the factor in most of the organisations. But production capacity may restrict

performance even when sales demand is in excess of the available capacity. It is up to the top

management to figure out the restricting factors prior to the preparation of the budgets as

these factors are critical for deciding the starting point of the annual budgeting process (Drury,

2013).


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Preparation of the sales budget-

The most important plan in the annual budgeting process is the Sales budget. “When sales

demand is the factor that restricts output, it is the volume of sales and the mix that determines

output” (Drury, 2013, p. 232). This budget is also the most difficult one to create as not only

the total sales revenue depends on the behaviour of customers but also the sales demand is

sometimes impacted by economic state and/or the action of competitors (Drury, 2013).

Initial Preparation of budgets-

Managers responsible for meeting the budgeted targets should make the budget for their

respective business areas or cost centres. Budget preparation should follow a ‘bottom-up’

approach. Basically, it should originate at the lowest levels of management, and, reviewed,

integrated and coordinated at the higher levels. This allows the managers to participate in the

budget making process, and hence, increases the chances of them accepting the budget and

striving to achieve the targets (Drury, 2013).

There is no particular method for coming up with the estimates in the budget. Typically, the

budget is prepared taking the past data as the starting point. However, it is incorrect to infer

that budget is based on the assumption that the past actions would be repeated in the future.

Past data may be used as reference, but future business environment and conditions ought to

be considered while preparing the budget. Also, the managers may refer to the guidelines

received from the top management to work on the budget numbers. As Drury (2013, p.234)

explains:

“For example, the guidelines may provide specific instructions as to the content of their budgets

and the permitted changes that can be made in the prices of purchase of materials and services.”

Negotiation of budgets-

Budgets should be a participative process. As mentioned earlier, the budget process should

start at the lowest level of management and managers at this level should submit their budget

to their superiors for approval. The mangers who prepare the budget and the superiors

negotiate on the budget and eventually come to an agreement on it. So, the final figures in the

budget report are actually results of the bargaining process between a manager and his or her

superior.

Also, it is important to note that the managers who prepare the budget do not try to come up

with budgets that are easily attainable. At the same time the superiors should not try to impose

extremely difficult targets assuming that a strict tactic will produce the desired results. The


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desired results may be achieved in the short term, but only at the cost of a loss of morale and

increased labour turnover in the future (Drury, 2013).

Coordination and review of budgets-

It is important to examine and review the individual budgets with respect to each other as they

move up the hierarchy structure in the negotiation process. It is possible that few budgets are

not in sync with each other and need adjustments so as to be compatible with the conditions,

restrictions and plans that are beyond a manager’s knowledge or control. As Drury (2013,

p.235) explains:

“For example, a plant manager may include equipment replacement in his or her budget when

funds are simply not available. The accountant must identify such inconsistencies and bring

them to the attention of the appropriate manager.”

Every change should be made by the responsible managers and this may necessitate moving

the budget move up and down the hierarchy a few times until it is coordinated and accepted

by all the parties involved. A budgeted profit and loss account, a balance sheet and cash flow

statement should be prepared during the coordination process to ensure that all the parts

combine to produce an acceptable whole. Otherwise, further adjustments and budget recycling

will be necessary until the budgeted profit and loss account, the balance sheet and the cash

flow statement prove to be acceptable (Drury, 2013).

Final acceptance of budgets-

When all the budgets are ready and in sync with each other, they are summarized into a

master budget consisting of a budgeted profit and loss account, balance sheet and a cash flow

statement. Once the master budget is approved, the budgets are then rolled across the

organisation to the respective cost centres. The approved master budget gives the manager of

each responsibility centre the authority to carry out the plans mentioned in the budget (Drury,

2013).

Budget review –

Agreement on the budgets should not be the last step in the budgeting process. The actual

results should be compared with the budget numbers periodically. Typically these

comparisons should be made every month and a report should be available in the first week of

the following month so that it can motivate the managers and team members to perform better.

This would help the managers to identify the areas where they could not achieve the desired

results and hence deep dive to investigate the reason for the differences. If the differences are


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within the control of management, corrective action can be taken to avoid similar

inefficiencies occurring again in the future. Drury (2013, p.236) explains:

“During the budget year, the budget committee should periodically evaluate the actual

performance and reappraise the company’s future plans. If there are any changes in the actual

conditions from those originally expected, this would normally mean that the budget plan

should be adjusted. This revised budget then represents a revised statement of formal operating

plans for the remaining portion of the budget period. The important point to note is that the

budgetary process does not end for the current year once the budget has begun; budgeting

should be seen as dynamic and continuous process.”

2.6. Sales budget:

The sales budget tells about the quantities of each product or amount of services that the

company plans to sell and the intended selling price. It gives an approximation of the total

revenue from which cash receipts from customers will be estimated, and also provides the

basic data for preparing budgets for production costs, and for selling, distribution and

administrative expenses. As mentioned by Drury (2013, p.236):

“The sales budget is therefore the foundation of all other budgets, since all expenditure is

ultimately dependent on the volume of sales. If the sales budget is not accurate, the other budget

estimates will also be unreliable.”

2.7. Production budget and the budgeted inventory levels:

The production budget is the next stage after the completion of the sales budget. The

production manager is responsible for this budget which is represented only in quantities. The

idea behind this budget is to ensure that the production is enough to meet the demands of the

sales and that the economic stock levels are maintained (Drury, 2013).

2.8. Direct materials usage budget:

The managers of different cost centres or departments will make an estimate of the required

material quantity to meet the targets of the production budget (Drury, 2013).

2.9. Direct materials purchase budget:

The purchasing manager is responsible for this budget as it is his/her responsibility to get the

estimated quantities of raw materials to satisfy production requirements. The goal is to

purchase them at the right time at the planned purchase price. The planned raw material

inventory levels must also be considered for this budget (Drury, 2013).


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2.10. Direct labour budget:

Managers of individual departments are responsible for preparing this budget. They make an

estimate of the labour hours, for their respective departments, needed to achieve the estimated

production. Different grades of labour, if present, should be mentioned specifically in the

budget (Drury, 2013).

2.11. Factory overhead budget:

The department managers are responsible for the preparation of factory overhead budget as

well. The individual overhead items costs, with respect to the expected production level, will

determine the total overhead budget. Analysis of the overheads is done depending on whether

they are controllable or non-controllable for controlling cost. Drury (2013, p.241) explains:

“The budgeted expenditure for the variable overhead items is determined by multiplying the

budgeted direct labour hours for each department by the budgeted variable overhead rate per

hour. It is assumed that all variable overheads vary in relation to direct labour hours.”

2.12. Selling and administration budget:

The sales manager and the chief administrative officer will have the responsibility for

preparing the selling budget and the administration budget respectively (Drury, 2013).

2.13. Departmental budget:

The direct labour budget, factory overhead budget and materials usage budget are pooled into

different departmental budgets for the purpose of cost control. Usually, these budgets are

divided into 12 monthly budgets, and the budgeted amount for each of the concerned items

are compared with the actual monthly expenditure. This comparison is useful in assessing the

effectiveness of managers for controlling expenditure they are responsible for (Drury, 2013).

2.14. Master budget:

Regarding the master budget, Drury (2013, p.242) states:

“When all the budgets have been prepared, the budgeted profit and loss account and balance

sheet provide the overall picture of the planned performance for the budget period.”

2.15. Cash budgets:

It is of paramount importance for any company to ensure that it has enough cash at its

disposal at all times to meet all levels of operations mentioned in various budgets. Given the

uncertain nature of cash budget, it is important to allocate more than the minimum necessary


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amount to allow for some margin of error during planning (Drury, 2013). Cash budgets

enables management to act proactively to invest surplus cash in short term investments to

avoid cash balances in excess of its requirements. Also, it can help to identify cash

deficiencies in advance, and therefore, allow management to take steps to ensure availability

of bank loans during temporary short-falls. Drury (2013, p.244) mentions that:

“The overall aim should be to manage the cash of the firm to attain maximum cash availability

and maximum interest income on any idle funds.”

2.16. Budgeting in Multinational Companies:

A business spread across multiple countries has its own set of advantages and disadvantages.

While a multinational company has access to different markets and resources, it has to face

the liabilities of being a foreigner while operating in unfamiliar business environments and is

also exposed to currency fluctuations. Multinational companies earn revenues and incur costs

in multiple currencies and have to convert their operating performance in a single currency so

as to report their results to their shareholders every quarter. This conversion is done based on

the currency exchange rate prevailing during the particular quarter. As a result, apart from

budgeting in different currencies the companies also have to prepare budget for foreign

exchange rates. This is difficult as the management accountants have to anticipate the

potential changes that may take place during the year despite the fact that the exchange rates

are constantly fluctuating, and therefore, are highly unpredictable. Finance managers, hence,

have to use different techniques such as forward, future and option contracts so as to reduce

the possible negative impact of the exchange rates and minimize the exposure to foreign

currency fluctuations. Moreover, the multinational companies also have to be aware of the

political, legal and in particular economic environments of the various countries they operate

in. Horngren, Datar and Rajan (2012, p.204) mention, “For example, in countries such as

Zimbabwe, Iraq, and Guinea, annual inflation rates are very high, resulting in sharp declines

in the value of the local currency.” Companies also have to consider tax issues due to

difference in tax regimes that may arise because of their transfer of goods and services across

different countries they do business in.

Budgeting is an essential tool for multinational companies operating in uncertain

environments. They adjust their budgets according to the changing business environments. In

such a scenario, the primary purpose of budget is not to assess the performance vis-à-vis the

budget, as it won’t make much sense given the highly volatile conditions, but to allow the

managers to learn to adjust their plans according to the changing conditions and to coordinate


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and communicate the actions that have to be taken within the organisation. Senior managers

assess performances more subjectively on the basis of how well lower managers perform in

unpredictable environments (Horngren et al., 2012).

2.17. Flexible budget:

Flexible budget is the most widely used flexible performance standard which is applied when

the targets have to be adjusted to factor in the variations in uncontrollable factors caused by

conditions not anticipated during the planning phase. In this budget, the volume effects on

cost behaviour that can’t be controlled are removed. It is important to take into account the

variability of costs while applying the controllability principle as some costs vary with the

level and type of activity (Drury, 2013).

The flexible budget is prepared after the actual results are available, and hence, the budgeted

revenues and budgeted costs are calculated based on the actual output in the budget period.

Kaplan (1994) explained how integrating flexible budgeting and analysis of actual expenses

with activity-based cost assignments and profitability information on products, customers, and

services, and, periodic reporting on actual activity demands and resource expenses can help

the managers to better understand the different categories or types of costs which would lead

them to make better decisions and more accurate predictions in the future. According to

Kaplan (1994, p.108), “Managers can easily see which expenses are expected to vary in the

short run and use this information in making short-run incremental pricing and product- and

customer-mix decisions.”

2.18. Variance Analysis:

Variance analysis means analysing the factors that caused the deviations between the actual

figures and the budgeted figures. It helps to distinguish between controllable and

uncontrollable items and identify the key performance incentives who are accountable for the

variances. For example, variances analysed by each type of cost, and by their price and

quantity effects, enable variances to be traced to accountable KPI’s and also to isolate those

variances that are due to uncontrollable factors (Drury, 2013).

Figure 2 presents a breakdown of the profit variance (difference between budgeted and actual

profit) into variances at different levels.


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Figure 2. Variable analysis for a variable costing system

(Drury, 2013, p.300)

2.18.1. Sales variance

A precise sales forecast would allow a company to offer high quality customer services. If the

demand prediction is accurate then the demand can be addressed in an efficient and timely

manner (Moon et al., 1998). To improve the accuracy of the sales forecast it would be

necessary to look at the past data and understand the causes of deviation from the forecast

numbers. In this context, the sales variance can provide significant information to the finance

managers. Drury (2013, p.311) mentions that:

“Sales variance can be used to analyse the performance of the sales function or revenue centres.

The most significant feature of sales variance calculations is that they are calculated in terms of

profit contribution margins rather than sales values.”

The total sales margin variance reflects the impact of the sales function on the difference

between budget and actual profit contribution. Drury (2013, p.312) defines it as:

“It is the difference between actual sales revenue (ASR) less the standard variable cost of sales

(SCOS) and the budgeted contribution (BC):

Total sales margin variance = (ASR – SCOS) – BC”

Sales variances are caused only because of changes in variables that are driven by sales

function, i.e., sales quantity and selling prices. The total sales margin variance can further be

analysed into two sub-variances: sales margin price variance, and, sales margin volume

variance (Drury, 2013).


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Sales margin price variance is used to analyse the impact of the sales price changes on the

difference between the budgeted and actual contribution margin by comparing the budgeted

sales price with the actual sales price. As Drury (2013, p.313) states:

“It is the difference between the actual selling price (ASP) and the standard selling

price (SSP) multiplied by the actual sales volume (AV):

Sales margin price variance = (ASP – SSP) * AV”

Sales margin volume variance is used to determine the impact of the sales volume changes on

the difference between the budgeted and actual contribution margin by comparing the

budgeted sales volume with the actual sales volume. According to Drury (2013, p.313):

“It is the difference between the actual sales volume (AV) and the budgeted volume (BV)

multiplied by the standard contribution margin (SM):

Sales margin price variance = (AV - BV) * SM”

2.18.2. Material variances

Material variances are related to the costs of the materials used in a manufactured product

which is calculated from two primary factors: the price of the materials, and the quantity of

used materials. So, there can be two possible cases - the actual cost will be different from the

standard cost as the actual price paid won’t be same as the standard price, and/or, actual

quantity of materials used will be different from the standard quantity. This leads us to two

more variances – Price variance (Material price variance) and quantity variance (Material

usage variance) (Drury, 2013).

Material price variance compares the standard price per unit of materials with the actual price

per unit. As defined by Drury (2013, p. 302):

“It is equal to the difference between the standard price (SP) and the actual price (AP) per unit

of materials multiplied by the actual quantity of materials purchased (AQ):

Material Price Variance = (SP –AP) * AQ”

An adverse price variance may be reflective of the failure of the purchasing department of the

company to find the most beneficial sources of supply. Having mentioned that it would be

incorrect to infer that the material price variance will always give a perfect idea of efficiency

of the purchasing department. The actual prices may differ from the standard prices because

of changes in the industry as a whole or changes in customer demands. In this case the price

variance would be beyond the purchasing department’s control (Drury, 2013).


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Material usage variance compares the standard quantity that should have been used with the

actual quantity that has been used. Drury (2013, p. 302) states:

“It is equal to the difference between the standard quantity (SQ) required for actual production

and the actual quantity (AQ) used multiplied by the standard material price (SP):

Material usage variance = (SQ – AQ) * SP”

The material usage variance is usually controllable by the manager of the respective cost

centres. Few common causes of material usage variances are sub-standard quality of material,

careless handling by production personnel, or, changes in production/manufacturing methods.

Material usage variances should be calculated separately for each type of material used and

allocated to each type of cost centres.

Variance analysis can help managers to understand the reasons behind the deviations which

can result from both internal (quality, procedures etc.) and external sources (price changes,

work load etc.). Flexible budget variance analysis affords the opportunity to quantify, with

line-item specificity, the dollars associated with a group of causes. These causes can be

differentiated into volume variance, a price variance and quantity or use variance. The key to

budget control is to be timely in reviewing variance, to correct negative behaviours and to

correct rates promptly. If variances are not investigated promptly, the budget serves only as a

planning tool and not as an aid to controlling operating costs. Improvements in budget

performance will depend on the actions taken as a direct result of effective variance analysis

(Francisco, 1989).

Variance analysis is particularly helpful in determining the causes of deviations between the

actual numbers and the projected numbers when there are multiple mix features or drivers for

different revenue, cost and margin KPI’s. Gaffney, Gladikh and Webb (2007, p.167)

developed a variance analysis framework, for managing distribution costs, that incorporates:

“(a) mix variance calculations where there is more than one mix factor within a single cost

element; (b) the impact of unplanned and unrealized activities; and (c) multiple nested mix

variance calculations.”

This framework can be generalized to other manufacturing and non-manufacturing costs that

have multiple mix features that may impact costs. This kind of framework can be used to

calculate the cost impact of an actual mix that deviates from the budget.


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2.18.3. Labour variance

2.18.3.1. Wage rate variance

The labour price paid and the labour quantity used determine the cost of labour. There could

be variances for both price and quantity. The price, or the wage rate, variance is determined

by taking the difference between the actual price per hour and the standard price per hour. In

the words of Drury (2013, p. 306):

“the wage rate variance is equal to the difference between the standard wage rate per hour (SR)

and the actual wage rate (AR) multiplied by the actual number of hours (AH):

Wage rate variance = (SR – AR) * AH”

This variance is one of the least controlled ones by the management. Often, the variance is

because of the wage rate standards not in line with the actual wage rates changes. Hence,

the managers normally can’t control this variance (Drury, 2013).

2.18.3.2. Labour efficiency variance

This variance shows the variance in quantity for direct labour. Drury (2013, p. 306) mentions

the following formula to calculate the variance:

“the labour efficiency variance is equal to the difference between the standard labour hours for

actual production (SH) and the actual labour hours worked (AH) during the period multiplied

by the standard wage rate per hour (SR):

Labour efficiency variance = (SH - AH) * SR”

Unlike the wage rate variance, this variance is usually controllable by the respective cost

centre managers. The reasons could be various such as introduction of new machinery and

tools and production process changes which affect labour efficiency, poor quality material

usage etc. However it may not be always controllable by production supervisor; may be

because of quality control standard changes or poor planning (Drury, 2013).

2.18.3.3. Total labour variance

As the name suggests, this variance gives the total variance prior to analysing the quantity and

price factors. The variance formula, as mentioned by Drury (2013, p. 307) is:

“the total labour variance is the difference between the standard labour costs (SC) for the

actual production and the actual labour cost (AC):

Total labour variance = SC - AC”


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2.18.4. Overhead variances

Overhead variances can be sub divided into two categories – Variable overhead variances and

Fixed overhead (expenditure or spending) variances.

2.18.4.1. Variable overhead variances

The total variable overhead as described by Drury (2013, p. 307) is as follows:

“the total variable overhead variance is the difference between the standard variable overheads

charged to production (SC) and the actual variable overheads incurred (AC):

Total variable overhead variance = SC - AC”

The variable overheads may vary due to input machine hours or direct labour but the total

variable overhead will be because of price variance caused by difference between actual

expenditure and budgeted expenditure, and/or, quantity variance caused by difference

between actual direct labour and the anticipated hours of input. These lead to two more sub-

variances, i.e., variable overhead expenditure variance and variable overhead efficiency

variance (Drury, 2013).

2.18.4.1.1. Variable overhead expenditure variance

It is important to twist the budget to compare actual overhead expenditure and budgeted

expenditure as it is assumed that variable overheads is a function of direct input labour hours.

As Drury (2013, p. 308) mentions:

“the variable overhead expenditure variance is equal to the difference between the budgeted

flexible variable overheads (BFVO) for the actual direct labour hours of input and the actual

variable overhead costs incurred incurred (AVO):

Variable overhead expenditure variance = BFVO - AVO”

Variable overhead is actually a summation of many individual factors such as electricity,

indirect materials etc. The variable overhead variance could be caused by changes in price of

any of the individual items. Hence, the variable overhead expenditure does not provide too

much of information on its own. A meaningful analysis would involve comparing the actual

expenditure of each item of variable overhead expenditure with the budget (Drury, 2013).


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2.18.4.1.2. Variable overhead efficiency variance

Drury presents (2013, p. 309) the following formula for this variance:

“the variable overhead efficiency variance is the difference between the standard hours of

output (SH) and the actual hours of input (AH) for the period multiplied by the standard

variable overhead rate (SR):

Variable overhead efficiency variance = (SH – AH) * SR”

2.18.4.2. Fixed overhead expenditure or spending variance

In case of direct costing system, fixed manufacturing costs are not combined and allocated to

items. The total fixed overheads for a period are instead shown as expense in the period they

are incurred in. These overheads are not expected to change in the short term with changes in

activity level. However, they may change due to other factors. As stated by Drury (2013, p.

311):

“The fixed overhead expenditure variance therefore explains the difference between budgeted

fixed overheads and the actual fixed overheads incurred. The formula for the fixed overhead

expenditure variance is the difference between the budgeted fixed overheads (BFO) and te

actual fixed overhead (AFO) spending:

Fixed overhead expenditure variance: BFO –AFO”

The total fixed overhead expenditure does not provide too much relevant information on its

own. A meaningful analysis would involve comparing the actual expenditure of each item of

fixed overhead expenditure with the budget (Drury, 2013).

2.19. Reconciling budgeted profit and actual profit

The causes for the difference between the actual profit being and the budgeted profit are

definitely of interest for the top management. By considering these variances in the budgeted

profit, and reconciling the budgeted profit with the actual profit, the management team gets a

broad picture that explains the major reasons for any difference between the budgeted and

actual profits (Drury, 2013).

2.20. Using information to make better forecast

Managers are involved in lots of preplanning activities prior to the execution of business.

However, uncertainties of markets and business environments that the companies face during

the execution necessitates taking corrective action. Though the actual figures are compared

with the budgeted figures of the activities and/or products, there are lot of uncertainties


___________________________________________________________________________

involved in the calculation of the budgeted figures. Managers responsible for different costs

centres get useful information from reports on costs and financial result. If the current issues

due to uncertainties are identified, then these reports would help the manager to take action to

resolve them.

Because of the uncertainties involved in the business, managers are expected to use the

available accounting information while preparing the budget so as to increase preplanning

capabilities and project control during business execution. Preplanning could be improved by

creating databases that provide more accurate cost estimation by factoring in the information

on relationships between actual costs and business circumstances. Control could be improved

if the actual costs information, which is compared to the budgeted costs, is more accurate.

An important aspect of the planning activities is that there are lot of uncertainties and

adjustments involved during the execution. Flexibility and improvisation are required.

A characteristic of road building activities is that there are many uncertainties. Uncertainties

arise because the actual working conditions are different from the planned one as it is not

possible to predict the exact conditions in advance. Uncertainties also involve operational

mistakes or errors such as suppliers responding late or machine breakdowns etc.

With proper information at hand, managers can assess the actual performance of the cost

centres against the expected performance and take actions, if necessary. Actual business

environments are different from the anticipated ones and operational mistakes can always

happen. Dealing with uncertainty is the core of cost management during the execution and the

managers use multiple sources of information to do so.

Budgets and plans do not contain clear information about uncertainties and risks that might

already be identified during the negotiation and work preparation phase.

Veeken and Wouters (2002, p.366) explain that:

“Information systems could incorporate risk management during all stages in the lifecycle of

projects. Starting at the calculation phase, the estimated risks can be part of negotiations with

the principal. At the work preparation phase, the second stage of the project lifecycle, risks

could be anticipated by developing a flexible resource plan and budget, based on best practice

scenarios. Information systems for cost management should facilitate such learning processes

and allow managers to benefit from a larger experience base.”

Information systems could collect information on issues that occurred in the business and the

circumstances under which they happened. These historical information would allow


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managers to anticipate certain problems that they haven’t encountered before and take actions

pro-actively so as to avoid those problems.

Managers can refer the information from the Information system to think about ways to solve

problems. The system could collect information on practical solutions for dealing with

uncertainty, results of earlier uses of these solutions and the conditions under which they were

tried. Such information could be used to come up with more reliable plans and to make more

accurate predictions (Veeken and Wouters, 2002).


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3. Research Methodology:

As the objective of the paper is to look at different avenues that lead to making better

predictions and more accurate forecast for the financial key performance incentives, the

research required collecting detailed information about different financial parameters used in

the financial reports, and therefore, involved extensive financial data analysis and

investigation. The methodology used is a qualitative research method which is an analysis-

driven approach involving systematic empirical investigation and which provides information

about people’s views from a financial perspective.

A thorough understanding of the various product categories and structures, and, knowledge of

the global market break up was developed. With most of the financial data residing on

electronic databases and platforms, relevant data was be collected from various IT sources

such as the relational database – Teradata, the SAP system and EFR. A number of company

internal documents and the company intranet system was referred to gather information about

the company’s financial system, conventions, standards, best practices and processes.

Table 1. Overview of interviews conducted

Interviewee Name Position Experience Duration (Approx)

1 Vincent Fleuret Director 8 years 60 min

2 Leo Brorens Senior Financial Analyst

26 years 30 min

3 Roel Gijsbers Senior Financial Analyst

17 years 30 min

4 Morgan Johnson

Senior Financial Analyst

12 years 30 min

5 Willem Kan Senior Financial Analyst

16 years 45 min

6 Yi Zhang Senior Financial Analyst

12 years 30 min

7 Enrico Bonasera


2 years 60 min

8 Andrianna Tzouveleki


3 years 30 min

9 Yanting Zhang Senior Financial Analyst

2.5 years 30 min


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Interviews were conducted with a director and eight senior financial analysts from different

financial planning and analysis teams to discuss about the research topic, know their

viewpoints, solicit advice and take suggestions from them. A specific procedure was followed

for the interviews that comprised certain guidelines that directed managing and conducting

the interviews. All the potential interviewees were first contacted in person to brief them

about the objective of the interview. And then, a meeting was set up through e-mail after

consulting them and finalising a time and place.

At the start of the meeting the interviewees were explained that the information they share

would be confidential and only the Universiteit van Amsterdam (UvA) professors will have

access to the information. The context for the interview was then set by briefly explaining the

purpose of the interview. The senior financial analysts were asked two primary questions –

“What are the driving factors for preparing the forecast report for the next period? What can

be done, in your opinion, so as to improve the forecast accuracy?” The director was asked an

additional question, apart from the ones asked to the senior financial analysts, - “What factors

led to poor forecasting that resulted in issuing profit warnings in the past?” The interviews

were semi-structured, and hence, I posed questions to the interviewees that followed up their

replies. The interview ended with me asking them if they had any further inputs to share that

could be helpful for the research paper. All of them agreed to reach out to them in case I

needed any further information from their end.

The interviews were conducted during the period 01-Jul-2015 – 31-Jul-2015. All interviews

were conducted face to face at Philips office in Eindhoven. Face to face interviewing is

considered to be the most appropriate method of interviewing as the researcher is able “to

observe body language to see how interviewees respond in a physical sense to questions.”

(Bryman, 2008, p. 457).

The first phase of the writing the paper involved jotting down all relevant information from

various sources and editing them to keep the relevant information intact. The second phase

aimed at reconciling the data collected earlier with the pieces of information from the

interviews. The third and last phase focussed on structuring the paper to ensure consistency of

the contents.


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4. Case Study:

4.1. About the company:

Royal Philips is a Dutch diversified health and well-being company, focused on improving

people's lives through meaningful innovation in the areas of Healthcare, Consumer Lifestyle

and Lighting. Headquartered in the Netherlands, Philips posted 2014 sales of EUR 21.4

billion and employs approximately 108,000 employees with sales and services in more than

100 countries.

The company is a global leader in cardiac care, acute care and home healthcare, energy

efficient lighting solutions and new lighting applications, as well as male shaving and

grooming and oral healthcare.

4.2. About the division:

The research was done in the Financial Analysis and Planning, Center of Expertise,

Commercial, Customer Service division of Philips Health Systems. Figure 3 shows the

position of this division in the Philips organizational hierarchy. The Customer Services unit

deals with the financial analysis and planning of the global post sales services for medical

equipment and had recorded annual revenues of approximately EUR 2.5 billion last year. It

primarily includes two types of services - ‘Contracts’ and ‘Time and Material’. Contracts has

two main areas – Extended warranty and Maintenance. An extended warranty, also

called service agreement or service contract, is a prolonged warranty offered to consumers in

addition to the standard warranty on a new items. The indemnity is to cover the cost of repair

and may include replacement if deemed uneconomic to repair. It is generally offered at the

point of sale. A maintenance agreement provides similar services but is offered at a non-point

of sale. In the Time and Material category, the customer pays Philips according to the work

performed by the technicians and the material used for the medical equipment. These kind of

requests are generally ad-hoc.

The unit performs an array of financial activities such as provide forward looking actionable

insights such as developing approach to generate insights to improve forecast accuracy and

drive growth, month end closure activities to identify and resolve financial deviations, collect

data for financial KPI’s from multiple sources and analyze the results for current financial

period, prepare the rolling forecasts reports, so on and so forth.

https://en.wikipedia.org/wiki/Warranty

https://en.wikipedia.org/wiki/Consumer


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Figure 3. Health Systems Customers services in the Philips hierarchy

4.3. Case Context:

4.3.1. Profit warnings

In the last few years, companies’ failure to make accurate forecasts and absorb the market

shocks resulted in the companies issuing profit warnings quite a few times which impacted

their share prices, and hence, their shareholder's trust. By definition, profit warning is a

warning declaration issued by a listed company to investors through a stock exchange. It

warns that the profit of the company in the coming quarter will obviously decline or even

have a loss with respect to that of the same quarter of previous year. Investors should be

aware of the possible loss when buying or selling its stock. Looking back at Philips’ history

from the recent past, it warned of sharply lower profit in 2011. It issued its second profit

warning in less than a year in 2012 citing weakness in its health care and lighting businesses

due to market conditions in Europe. More recently, in 2015, it issued another warning saying

adverse market conditions and delayed start-up of its factory in the US city of Cleveland hurt

its performance in the fourth quarter. In response to the question on the reasons for profit

warnings, the director of the global customer services FP&A team in the interview also made

a point on the Cleveland issue:

“Sometimes the management is optimistic about its performance and sets challenging targets

which are difficult to meet. The management was not satisfied with the quality of products

https://en.wikipedia.org/wiki/Listed_company

https://en.wikipedia.org/wiki/Investors

https://en.wikipedia.org/wiki/Stock_exchange

https://en.wikipedia.org/wiki/Profit_(accounting)

https://en.wikipedia.org/wiki/Fiscal_year

https://en.wikipedia.org/wiki/Decline

https://en.wikipedia.org/wiki/Income_statement

https://en.wikipedia.org/wiki/Income_statement

https://en.wikipedia.org/wiki/Stock


___________________________________________________________________________

manufactured at the Cleveland factory and proactively decided to shut it down. But it expected

the factory issue to be sorted out soon and was optimistic about sales of the machines and

equipment manufactured at this factory. The forecast made for the sales of those machines were

therefore a bit higher.”

Of late, profit warning has been a major area of concern for many companies, and therefore,

the management of the companies has been trying to revisit, and if necessary revise, the

processes within and guidelines followed by the Financial Planning and Analysis teams and

come up with solutions so as to improve their forecasts accuracy. The advantages of more

accurate forecasts are significant - from improved shareholder communications to more

effective planning to better decision-making. To achieve this, companies needs to ensure that

their forecasting and planning capabilities are current, fit for purpose, and able to adjust to

more dynamic times. Stakeholders, particularly investors, do not react favorably to surprises.

It reflects a lack of proper planning and control of business performance, even in volatile and

unpredictable business environments. At Philips, the management has raised serious concerns

about profit warnings and has been really keen on taking steps to avoid/minimize such

warnings in the future. A major part of the profit warnings has been attributed to the

inefficient and high level of forecast inaccuracy (big deviation between the forecast numbers

and the actual numbers). The management, therefore, wants to focus and look at the current

financial analysis and forecasting methods and processes, re-assess and fine-tune them so that

the forecasts are more realistic than the current ones and as close as possible to the actual

numbers, thereby, improving the accuracy of the forecasts.

4.3.2. Taking the first step in the right direction

The service P&L report is the most detailed, comprehensive and important source of

information that provides a complete overview of all the performance measures, key

performance indicators and financial parameters used in the services unit. Philips Healthcare

global services operations are spread across 17 markets worldwide, viz., North America

(Canada, USA), LATAM (Mexico, Brazil), DACH (Germany, Austria, Switzerland), UK &

Ireland, Benelux (Belgium, Netherlands, Luxembourg), Nordics (Sweden), France, IIG (Italy,

Israel, Greece), Iberia, CEE (Central Europe), Africa, Middle east & Turkey, Russia &

Central Asia, Japan, India & Subcontinent, ASEAN & Pacific (South east Asian Countries

and Australia) and China. And each of these markets span across a series of business lines.

Earlier the finance team of each of these markets had their own view of business model for

the P&L report and had their own set of financial parameters and KPI’s to measure the


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financial performance. They made their own choice of General Ledger accounts, which was

used to sort and store balance sheet and income statement transactions. Figure 4 shows the

P&L report of the Nordics market used earlier.

Figure 4. P&L report model used at Philips earlier

The finance teams from all the markets were only entitled to report the top-line margin and

EBIT for their respective market. This structure led to a lack of harmonization between the

reports from different markets, and therefore, it was extremely complicated and cumbersome

to investigate missing or incorrect data for different business lines. Because of the incoherent

data structure, it was very difficult to trace down the actual root cause for differences between

the actual numbers and the forecast numbers.

As a first step towards improving the accuracy of the financial forecast, Philips financial

management team decided to make a complete overhaul of the business models used for the

P&L reports. With an objective to make the data more transparent and the analysis process

more convenient, the management team modified the existing business models for the markets

and came up with a common business model for all the markets. Figure 5 shows the current

P&L Structure.

The whole idea was to combine the reporting processes in different markets and come up with

an integrated view of value chain for the customer or end user. As such, a service P&L

business model specific to the customer service was developed which is now used by teams

across all markets.


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Figure 5. Current P&L report model at Philips

4.3.3. Existing Issues

Financial forecasting for companies is an extensive process that is based on innumerable

factors and multiple drivers. To understand few of the underlying issues contributing to the

lack of accuracy of forecast, it is important to know the company’s business structure,

financial models used in the company and the processes followed by the financial teams. This

thesis attempts to highlight few of the fundamental issues with the forecasting processes

through the lens of the Philips Healthcare global services unit.

Philips Healthcare global services is categorized into multiple BG’s and each BG is sub-

categorized into multiple BU’s. Furthermore, each BU consists of various MAG’s which

further detail a business (individual healthcare equipment) and consist of one or more Article

Groups. Every month, financial numbers are published for each of the business groups and

business units based on periods such as ITM, QTD, YTD for an array of financial parameters

in the service P&L report. The report also provides the corresponding figures for the last year

and the last quarterly forecast so as to compare the current period’s performance against that

last year and against the forecast for the last quarter. For instance, the P&L report for the

month of July 2015 will have the actual ITM, YTD and QTD numbers for July 2015, July

2014 and the 2015 March forecast for various KPI’s such as Sales, margin etc.

The current practice is to look at the deviations (between actual and forecast), predominantly

for Sales and Margin, at a high level (Total Market, BG and BU) and understand which BG or

BU was responsible for driving up/down the Sales/Margin. The benchmark deviation amount

used is typically 100,000 Euros.


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The Financial Planning and Analysis unit at Philips follows the concepts of Flexible Budget

and Variance analysis to analyse and assess their financial performances, identify the root

causes for the deviations and accordingly modify their forecasting process to come up with

more accurate numbers.

The financial analysts look at the Sales figures at the total market level and compare them

with those forecasted for that quarter to check whether the actual Sales figures are favourable

against, unfavourable against or in line with the forecast. Similar analysis is done for margin

as well to determine whether they are favourable against, unfavourable against or in line with

the forecast. However, the analysis for margin is more detailed.

The deviation in margin is primarily driven by three factors – change in sales (volume),

change in Material costs and change in Cost of Organisation. The Material costs consist of all

costs related to the procurement of the material used for the products or services such as

Contract Material, Paid Repair Material, Factory Material, Goods Consumption or Returns,

Third Party Material, Physical Distribution, Change in Inventory, Installation, Warranty, FCO,

Training, Sales Support, so on and so forth. The Costs of Organisation include payment to

technicians for the contract hours and repair hours, salaries and wages, consultants, travel,

fixed assets related costs, distribution costs, IT/communication costs, advertising and

promotion costs etc.

Each market has different BGs and the management is interested to know what the impact of

the aforementioned KPI’s at the BG level is on the total margin. In other words, the overall

margin at the market level is impacted because of deviations in the sales volume, cost of

organisation and material cost at the individual BG levels. This is also referred as the ‘impact

of Mix’ (Mix of BG’s, and BU’s) on the margin which is determined by a variance analysis.

As mentioned earlier, Philips uses variance analysis to analyse the aspects that result in the

actual results differing from pre-determined budgeted targets due to dynamic market

conditions.

In essence, this variance analysis helps the management to understand and identify the factors

that are accountable for the margin deviation. This analysis is key to understanding the source

of the deviation and opens up avenues to make a deep-dive analysis to find out the reason for

deviation at a more granular level. For instance, if the deviation for the Cost of Organisation

at the total market level is high then the financial analysis team goes one level deep to check

which sub-categories (travel, distribution, advertising etc.) of this KPI were responsible for

driving the deviation high.


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A similar analysis is done at the BG level as well. The ‘Mix’ impact on the total margin at the

BG level is caused by the change in sales, cost of material and cost of organization at the BU

level. The management also looks at the BU’s with deviations of greater than 100,000 euros

that were key drivers of the overall sales deviations at the BG level.

The management’s current focus is mainly on the Sales and Margin (and higher levels of Cost

of Organisation and Material Costs) and is primarily limited at the BG and BU level. Though

the analysis at this level gives the management a good idea about the market conditions and

demand variations, the information is nevertheless insufficient. There is a multitude of areas

which are not explored unless there is a major issue and the management is forced to look at

them. Going further down the data hierarchy can provide much more information and

certainly be helpful in understanding the reasons for variations better which in turn would

allow analysts and team management to improve the accuracy of their forecasts.

While the findings of the service P&L report analysis is hugely helpful in making the forecast,

there are many other factors that the analysts consider while working on the forecast. Philips

has a rolling forecast practice, and therefore, makes and adjusts forecast every quarter. The

January forecast is done for the months of January, February and March and for the second,

third and fourth quarters. However, in the month of March, the forecast for the year is

adjusted based on the actual numbers in the month of January and February. So, the March

forecast report will have actual figures for January to March, and forecast for the months from

April to June and for the third and fourth quarter. Similar forecasts are made in the month of

June and September.

Analysts use separate standard models for each market for preparing forecast report. A model

can be defined as a framework made of two components – data and statistics. Every model is

based on the actual data collected from the past and certain statistical measures such as

percentage growth, trends, assumptions etc. The analysts extract the past data from multiple

sources, apply the statistical measures on them and make necessary adjustments for the future

period. They refer to historic figures to analyse the trend and change the forecast numbers

accordingly. They work in tandem with the local FP&A team of the respective markets to

collect more precise information about the market and make the forecast for different KPIs.

However, the model may still not produce accurate results, and, the reasons could be many.

First of all, the performance of the models for certain markets could be questionable. The

models may not be serving the purpose they were created for. For example, a model could be

based on the assumption that the sales growth on a year on year basis would be 5% based on


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historic data. But the actual sales growth in the current environment might be 2%. There is no

verification process in place to ensure the correctness of the models. Secondly, even if the

model is accurate, it may produce erroneous results. There could be huge deviations between

the forecast and the actual numbers because of two reasons. First, there could be a one-off

event which caused huge differences in the numbers and which couldn’t have been predicted.

For example, last year in the Africa market the sales were not invoiced until the end of the

year. So, at the year end, there was a huge surge in the actual sales volume which created a

big deviation between the forecast and actual sales. In the dynamic market environment these

kind of cases are possible and the analysts could do nothing about it. And the second reason

could be incorrect input data. The input data for forecast is extracted from multiple IT

databases. It is assumed that the data is correct and no cross verification is done to ensure data

correctness. However, there are chances of manual error while entering the figures in the

system. Also, the salary data, which is a major part of the cost of organisation, is provided by

the local market finance team. If the salary numbers are always overestimated or

underestimated, then the forecast would not be correct. In this case, although the model is

correct, it doesn’t make the correct forecast because of incorrect information. Moreover, lack

of sufficient information is also a reason for the inaccurate forecast figures. For example,

sometimes application specialist working cross country can report their hours on a Service

Work Orders (an official document to list down the number of hours of efforts and material

used), whilst no actual posting in the database is done. This means that a lot of cross border

activity remains uncharged to the contracts, and this eventually leads to big difference

between the forecast and the actuals.

The FP&A team also prepares a ‘Forward looking forecast’ report (Figure 6) that shows the

performance and accuracy of forecast for each market every month. This report is prepared

for each of the last two weeks of every month. Based on the information available until the

start of the week, a forecast for the month end is made for different KPI’s such as sales,

margin etc. for all markets using the models mentioned earlier.

At the end of the month, when the actual data is available, the forecast in each of the two

weeks is compared to the actuals and the percentage accuracy of the forecast is determined.

Therefore, this report, in a way, provides an overview of the issues in different markets to the

top management. Moreover, it also gives an overview of the performance of each model in

predicting the figures for the KPI’s. However, there is no clear action defined in case the

accuracy of a particular market is low. Also, high accurate forecast for a particular month


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doesn’t really ensure that the model is correct. The model could be highly inconsistent in

predicting the future outcomes for the market with high accuracy in few months and low

accuracy in others, and therefore, may not be reliable.

Figure 6. Forward looking forecast report

The economic scenario across the world has gone through a radical change in the past few

years. Evolving market vulnerabilities and differing monetary policies make the currencies

volatile. Price, policy and geopolitical uncertainties create huge potential for shocks.

Increasing pressure on pricing along the supply chain and the growing competition are one of

the biggest challenges companies have to face. Companies need to consider these ‘new

realities’ that are making the forecasting processes harder (thereby, resulting in inaccurate

forecasts) while developing the forecasting models. The underlying problem with the current

forecasting processes is ‘a lack of flexibility and adaptability to changing conditions in the

external marketplace. Organizations that fail to see external changes cannot alter their

forecasts to these new realities’ (Player and Morlidge, 2010, p.7). As is the case in most of the

companies, the forecasting processes are often complex and rigid. On the other hand, an

overly simple forecasting model has its own set of limitations and risks. It is therefore

important for the companies to understand the importance of the forecasting, challenge both

inherent and explicit assumptions frequently and decide how and when the forecast needs to

be updated. The current Planning and forecasting system at Philips are mostly based on

worksheet forecasting models which have risks and limitations per se. The forecasting models

do not embed uncertainty to a large extent. Not all the key drivers for the forecasting methods

are included in the models. These processes are time consuming, lack agility and may not be

able to deliver any significant insight about the company’s performance in the future.


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There is a growing interest among the stakeholders for evidence that the planning and

forecasting methods employed by the companies include deep-dive analysis for different

situations. As mentioned by Ernst & Young (2015, p.6):

“It’s not possible to avoid shocks, but careful up-front analysis will at least build an

understanding and allow companies to have contingency operational and communication plans

in place. This will provide the opportunity to mitigate adverse impacts on both results and

market sentiment.”

This is not the case with the forecasting models in many companies. The lack of such an

analysis at deeper levels not only results in lack of confidence among the stakeholders but

also creates an impediment to understanding the issues at the granular level thereby creating a

roadblock to improving the accuracy of the forecasts.

Sometimes lack of coordination among the higher management and the financial analysis and

forecasting team also leads to inaccuracy in forecast. Those who under perform in terms of

meeting the budget figures make promises to catch up while those who do a better job in

meeting the budgets try to play down their targets lest they may be set higher targets going

forward. It is therefore important for the management and the forecasting team to realise the

difference between the forecast and the target. Unless these are identified, the company would

continue to struggle to close the gap between them which eventually would result in

inaccurate forecasts.

In other cases, the management is obsessed about meeting the forecast targets, thereby,

creating a sort of ‘delusion of accuracy’. They invest a lot of time and effort to come up with

a flawless forecasting model and achieving the forecast numbers becomes their all-

encompassing goal. This results in organization losing sight of the actual goals, as the zeal for

improving forecasting accuracy takes precedence over the external market factors, and

therefore, losing out to competitors who focus more on adapting to market opportunities. The

underlying problem for companies is that they do not take into account all the volatilities of

the market. This results in the companies failing to manage risks and capitalize on

opportunities. (Ernst & Young, 2015)


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5. Discussion:

It is important to carefully consider why a company would need a plan or forecast, challenge

both explicit and implicit assumptions regularly, and determine when and how the forecast

update should be done. Most importantly, planning should be responsive, agile, current, and

require a proportionate amount of time and effort for the value and insight delivered.

Ultimately the company’s plan is, or should be, its best view of the future.

As mentioned by Ernst & Young (2015, p.5):

“Forecasts, and the activity of forecasting, are only useful if:

- They offer insights and inform important decisions

- They communicate plans in a way to drive behaviour

- They can be used to monitor financial performance, position and cash flow — driving

corrective actions and reducing risk

- They are used to guide market expectations and minimise surprises”

Effective financial planning and forecasting can be a source of competitive advantage through

the formation of timely insights, establishment of corrective actions and anticipation of future

events (Ernst & Young, 2015).

As mentioned earlier, Philips made a move in the right direction by adapting the new business

model for the P&L report. The new report was much more comprehensive and detailed, and,

provided a standard framework for the analysts of all markets to highlight the market

performance. However, the analysis done with the information provided by this document is

still very limited. There is a huge scope to expand the ambit of the analysis and to go deep

down into the data hierarchy to get a more accurate picture of the issues or reason for

variances, favourable or unfavourable. With the information at a very granular level, it would

be much easier for the management to focus on specific areas that impact their business,

understand the existing problems and take a bottom-up approach to fix the issues, thereby

paving the way for more accurate information for the future. Information at such a deep level

would also help the management to better understand the current business issues and

environment and make necessary changes in their forecasting processes to come up with more

accurate budget reports.

As of now the analysts mostly focus on the margin and the sales at the BG and BU level.

They could start looking at the deviations of other KPI’s that drive the margin and sales at


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levels further below the BU. To start with, analysts can start looking at the deviations (both

Sales and Margin) at the ORU level which would give them information about the regions

with high favourable and unfavourable sales. This would allow them to understand which

regions are driving the deviation at the market level, and therefore, focus on them and deep

dive into the financials for those regions so as to get to the root cause of the deviations.

Sometimes, a positive or a negative deviation doesn’t really give a clear picture of the

performance. It is important to look at the trend so as to get a better understanding of whether

the KPI’s such as sales and margin figures are fine. It is highly probably that at some point of

the year there would be huge changes in the KPI figures from the previous month or quarter.

This could be because of seasonality or regional factors which drive the numbers up or down

significantly. So, it would be helpful to use the MAT (total of the last 12 months) to calculate

the average for the month or the quarter and compare it with the actual figures. For instance,

we may have a case where the sales are unfavourable against the forecast. Based on this

information an analyst would immediately make a conclusion that the market sales is an area

of concern. However, when we look at the trend (average of the MAT) and compare the

actual figures with it we may observe that the sales numbers are favourable against the trend.

As the trend is an important factor in the forecast process, the comparison with the trend

informs the analyst that the sales figures are actually fine and the forecast was probably not

accurate, and they must pay more attention on the sales numbers while forecasting for the

next period. Such kind of remedial action is part of the control processes that would help

companies to reduce the variances between the actual results and the forecasted results.

The next, and the most important, step would be to look at the various components, at

different levels of hierarchy, for each of the KPI’s at the top level to get to the root cause of

the deviation. The key is to define a flowchart of analysis to navigate through all the KPI’s at

all levels which will provide a guideline for and, ease the process of, investigating at the most

granular level. For example, Sales has two categories – Contract Sales and Billable/Other

Sales. Contract sales as the name suggests are sealed deals and are easier to predict. If the

total period of the contract is 12 months, then the sales prediction from this contract for the

next month would be the monthly average. If the sales are unfavourable against the forecast

then the analyst would move to the next level to check which of the Contract Sales and

Billable/Other Sales contributed to the overall unfavourable sales. Once the cost component

with high deviations is recognised, further analysis can be done to look into the factors

driving this cost.


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Figure 7 shows the framework for analysing the financial results and navigating through the

data hierarchy to get to the root cause of the variances at the top level.

Figure 7. Financial analysis framework

The profitability is determined by the Revenue, Material Consumption, COO and the

Recoveries. Recoveries is the amount that the Services unit gets from the Equipment unit. The

Equipment unit uses the services of the technicians from the Services unit for different

purposes such as installation, Warranty, Training and FCO (a cost due to proactive

replacement of defective parts), and therefore, pays a certain amount for these services to the

Services unit. Therefore, the Services unit ‘recovers’ a part of the costs of technicians, that are

already included in its COO, from the Equipment unit.

Currently, the management largely looks at these four KPI’s to understand the variances in the

profitability. However, these figures don’t really tell what the actual cause of the variation

was and what issues contributed to the differences between the actual and forecast numbers.

The above framework can actually make a huge difference in understanding the source(s) of

the deviations and provide significant information which may be critical to improving the

forecast accuracy. Let us try to understand how this framework works. The whole framework

is based on the assumption that only variances of more than 100,000 euros are considered

favourable or unfavourable. Variances of less than 100,000 euros are considered in line with

the forecast.

In case of favourable or unfavourable revenue variance, the first step is to know whether the

variance was caused by the change in price or volume of the machines sold, or, the impact of

the ‘Mix’ (Mix of BGs and BUs). If the variance was because of the volume then further

analysis is done to check the Revenue trend and the OOH. The revenue trend is determined by


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looking at the historical data of the sales (what was the volume for a particular period in a

particular market). In case of a variation, the analyst tries to figure out whether the difference

was because of incorrect trend calculation or a sudden increase/decrease in volume due to an

on-off event. Order On Hand tells the number of orders/contracts the company has or is

expecting in the future. They are ideally recognised from their start date and calculated on a

monthly basis. In case of a big difference in this component the analyst has to check whether

it was because of missing contracts or few contracts not being signed/renewed.

For the price, Philips has an application named CS Price development which tracks the

changes in the actual prices of the contracts over a period of time. This informs the

management about the current prices of the contracts and whether they are going up or down

as compared to the past prices. There are multiple factors which drive these prices. For

example, while calculating the price of a contract, Philips takes into account the factor of

indexation which is related to the economy of a country. With the inflation rate going up, the

price is expected to go up. In other cases the exchange rate also contributes to the variation in

the prices. So, a contract signed a year ago may not produce the expected margin despite the

stable costs and volume because of the change in its inherent value.

The ‘Mix’ impact is one of the most important areas for variance analysis as this has a

significant impact on the margin in reality. The example below, with actual numbers from the

Services P&L report explains the concept of ‘Mix’ impact on margin clearly. As per the

convention used at Philips FP&A, the sales are recorded as negative and the costs are

recorded as positive. Figure 8 illustrates a report used for variance analysis and determination

of ‘Mix’ impact. The report is for QTD figures and pertain to the DACH market, which has 6

BGs - DI, IGT, US, PCMS, HISS, and Health Systems Undivided. As shown below, the

actual margin for the DACH market is favourable by 4.3 million euros against the forecast.

The total sales volume deviation is 1,309,556 euros favourable against forecast. The

forecasted cost of material for the overall market was 26.7%.

The volume impact on margin is the impact on margin only due to change in sales volume,

and hence, the percentage of Cost of Material (forecast) is assumed to be constant. So, the

volume impact on margin at the Market level is calculated as:

(1 – Cost of Material %) * (Actual Sales volume - Forecast Sales volume)

This gives a favourable volume impact of 960,296 euros.


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Similarly, the material cost impact on the margin is the impact only because of the change in

the material cost due to volume adjustments. Here, the volume (actual) is assumed to be

constant. So, the volume adjusted material cost impact is calculated as:

(Actual cost of material % - Forecast cost of material %) * (Actual Sales volume)

With the above formula, the volume adjusted material cost impact on margin is 471,981 euros

favourable.

Figure 8. Variance analysis and Mix impact illustration

The ‘Mix’ impact, which is the total of the above two impacts, is 1,432,276 euros favourable.

The cost of organisation is not impacted by change in volume and hence is calculated as a

simple difference between the actuals and the forecasted figures. All the three impacts taken

together gives the total deviation in margin.

In essence, analysing the ‘Mix’ impact is really important to understand which component

(volume/COO/Cost of Material) in which BG or BU contributed to the variances in the


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profitability, and, which area the management needs to focus on. This would greatly help

them to identify the underlying issues, work on them and enable them to predict the sales and

margin numbers more accurately in the future.

Material Consumption has five sub categories – Material consumption as % of sales, Contract

Profitability, third parties, number of high value parts consumed vs average, unused part

return % and value impact. The variances in the material consumption percentage can be

traced back to two sources – Time and Material, and, Contracts. The Time and Material costs

are the costs pertaining to the technician labour hours and machines and parts which are

incurred because of ad-hoc requests. This cost is difficult to predict and mostly relies on the

historic data or trend. The contract costs on the other hand are based on the contracts that have

been signed or renewed. The analysis for a deviation in this costs would be similar to the one

for OOH – check whether the historic data used for forecast was correct or were there any

contracts which were missed out or added, or, not renewed or recently signed. The next

element is the contract profitability which is determined from the BW Contract Profitability

report. This report provides details of all the contracts recognised in the current period with

actual sales and costs break up, and therefore, informs the management how profitable the

contracts are. If the reason for high margin variances are because of the increase or decrease

in the sales, costs or margin of the contracts, then the management can focus on this area to

get an in-depth information about the contracts, and use this information to investigate and

find the root cause of deviation. As a matter of fact, the contract revenue information from

this report contributes in determining the revenue trend (under Revenue category).

Deviations in the material costs could also be caused due to third party costs. These are the

costs of the materials or equipment that Philips buys from other manufacturers. Increase or

decrease in these costs over a period can have significant impact on the margin. For instance,

if the third party costs for a particular BU in a particular market turns out to be 10% where the

typical costs is around 3%, then the management has to look into this area and understand

what caused the increase in these costs. The analyst may then get in touch with the local

finance team to collect the details on the issue. With these details, the management can either

act proactively to avoid the issues or adjust their forecast so as to factor in the market

conditions that caused the costs to go up.

There are no specific reports for the number of high value parts consumed and the data is

collected from the IT SAP system. If the management finds that the high material

consumption variance is because of the increase in the high value part then they can work on


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identifying the issues with the machines that used the high value parts. The parts could have

been defective because of factory issues or they did not last their lifetime. This not only

allows the management to focus on resolving the issues but also gives them more information

that would help them to predict the usage of high value parts in the future.

The unused part returns is a financial as well operational KPI and refers to the costs

associated with the return of the unused spare parts. Though Philips gets its money back upon

return of these parts, it nevertheless, has to bear certain costs such as transportation and other

administrative costs. Higher the part returns, higher the costs, and hence, lower the margin.

High costs due to this component tells the management that the parts usage efficiency for

certain BU’s is poor which may be due to poor planning or some operational issue. If Philips

can work on these issues and fix them then the efficiency of the parts usage would improve

which would eventually result in a higher margin. This would, of course, also allow the

management to make better margin prediction, thereby, improving the accuracy of the

forecast.

The next KPI driving the margin, Cost of organisation, has four cost components – FTE,

Subcontracting, Distribution, Over-Under absorption percentage.

FTE provides all the information related to the salaries of the technicians working on the

machines. Margin deviations due to this component may be due to laying off or recruiting too

many technicians or due issues with their salaries. Too much salary deviation would prompt

the management to consider improving their human resource management and/or take

necessary actions to fix the issues related to salaries, thereby improving the overall margin.

Moreover, more accurate salary information would definitely result in more accurate forecast.

Subcontracting refers to the costs for third party labour hours utilised. Though Philips has its

own set of technicians, it sometimes has to hire technicians from third parties so as to provide

services to its clients. For instance, if Philips has a contract with a hospital to maintain all the

medical equipment and machines within the hospital, then it is responsible for machines

supplied not only by Philips but also by other companies. In case one of the machines from

the other supplier breaks down and Philips doesn’t have the expertise to deal with the problem,

then it hires technicians from third party companies to fix the machines. Also for certain

markets, Philips outsources the technician jobs to third parties. The subcontracting costs

details can also help to explain the variances in the total margin and inform management

about the issues pertaining to the third party labour costs.


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Distribution costs are the costs incurred for distribution and transportation of the machines

and equipment to customers. Just as in other costs, identifying the issues driving high

variances in distribution costs, which eventually impact the overall margin, could help the

management to reduce these costs and have a better idea about the costs to be incurred in the

future.

The Over-Under absorption percentage is related to the salaries of the technicians. This

basically identifies what percentage of the overall technician time or labour hours is over

utilised or underutilised and what are the costs associated with that. For instance, a technician

may be ideal for 40% of his total work hours because of improper schedule planning, or,

he/she may be spending 30% of his/her time travelling. In other cases, technicians may be

working extra hours and therefore are over utilised. High percentages of over or

underutilisation reflects uneven labour utilisation, and therefore, predicting these costs

becomes difficult. Deep diving into the labour hour utilisation numbers can help the

management to manage their resources more effectively and bring down their costs due to

under or over utilisation. With better utilisation percentage, the forecast would also be more

accurate.

The final driving KPI of the margin is the Recoveries component. When the margin variance

is high because of Recoveries, investigating the factors contributing to the Recoveries would

help the management to figure out the exact cause of the variance. For instance, if the

deviation was high because of the installation then the management can further analyse to

check the efforts put in by the technicians for this activity and what caused them to do so.

Identifying the cause may help the management to either fix the issue (if caused by

controllable factors) or adjust their budgets (if caused by uncontrollable factors), if required.

Eliminating the root cause would contribute to improving the margin and help the financial

analysis teams to come up with more accurate forecast.

The variances at each of the levels mentioned above can act as early warnings for managers

about issues that are not immediately relevant and prompt the managers to take actions

proactively to counter the effects of such issues or avoid them in the future. They also are a

reflection of how well the BG’s, BU’s, markets and the company as a whole has performed.

Of course, variances could be caused by both controllable and uncontrollable factors.

Variances due to uncontrollable factors such as the earthquake in Japan in 2011 or the more

recent economic situation in Greece can’t be avoided completely as it is almost impossible to

predict these kind of events. However, if the variances are due to controllable factors then a


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detailed analysis could lead the managers to understand the root of the issues, assess the

potential risks, take necessary actions to avoid such risks and come up with forecasts that are

as close as possible to the actual results.

Apart from following the analysis tree guideline explained above, the FP&A team can also

use the Forward Looking Forecast report to improve their forecast accuracy. As mentioned

earlier, this report informs the management about the current issues in the markets and the

percentage of accuracy of the predictions for the last two weeks of every month. The analysts

can use this information to understand the trend of the forecast accuracy of their respective

markets. As one of the analysts suggested in the interview:

“Activities such as plotting a graph of the percentage accuracy of the models over a period of

time would give us a clear and better view of their performance – whether they are improving in

predicting the sales, margin and other financial numbers or not. If not, then we have to further

investigate to find the reason for the poor performance of the models and work on it to improve

its efficiency.”

For instance, if the forecast accuracy for the Japan market is consistently low for 3 - 4 months,

then the analysts for the Japan market may contact the local FP&A team to understand if the

information they receive is accurate and sufficient. Sometimes, the local markets take a very

conservative approach and keep their prediction numbers low on purpose so that they can

achieve their budget targets. Such cases of incorrect information lead to inaccurate forecasting.

It would therefore be very helpful for the analysts to coordinate with the local market team

and ensure that the data they use for the forecast is accurate and complete. It is also important

to look at the forecast accuracy of the individual KPI’s so as to figure out which of them are

the major drivers of the delta. Another important point to note while making the forecast is to

consider seasonality and any specific trend that causes variances frequently. One of the

analysts pointed out in the interview:

“Typically, the forecast for the KPI’s that are highly volatile or unpredictable are based on the

average of the past data. However, we must keep in mind the seasonality factor, which causes

sudden increase or decrease in specific market demand in a particular period and which may

cause the actual numbers to vary significantly from the average.”

With the information about seasonality, they can adjust the forecast numbers which would

result in the forecast numbers close to the actuals. The analyst also should consider any

particular market behaviour that might be occurring frequently. For instance, if the analysts

observe that cost of material for a particular market is always significantly high in the last


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week, then they can have higher cost of material predictions while making the forecast. The

forecast numbers would then be more reflective of the market trend, and hence be more

accurate.

So, on the whole, studying the trend of the forecast would go a long a way in understanding

the model performance which in turn would lead the financial managers and analysts to make

further analysis about the data they receive from the local market and use for making forecast.

Having more accurate data would definitely make the predictions more justifiable and be

close to the actual numbers.

A more broad area of concern related to the forecasting is the amount of financial data

involved in the analysis. As the business expands, the volume of financial data involved in the

analysis and forecasting processes are expected to be larger as the financial managers seek

more financial details to understand the market conditions and build their budgets. As

mentioned by Player and Morlidge (2010, p.8):

“- Forecasts are regularly expanded to include more details, often down to the chart of account

level by month—a requirement that demands a massive amount of assumptions to be made to

populate the forecasts

- Demands for greater detail in variance explanations also increase, which requires a swarm of

finance staffers to pepper their operations colleagues with time-consuming questions about the

past (questions that prevent operations managers from focusing on the present and future)”

However, contrary to the belief, too much data creates a sort of “data noise” which makes the

analysis way too complicated. The excessively thorough analysis sometimes ends up being

ignored which renders the work done by the analysts unnecessary, and therefore, redundant.

This results in companies losing focus on the important drivers, creating confusions about

necessary steps to take to adapt to changing market conditions (Player and Morlidge, 2010).

This issue was pointed by one of the analysts in the interview:

“Lately, many changes have been implemented in the processes followed by the Philips FP&A

teams, and also, in the spread sheets and reports to include data on a more detailed level. This

has made the analysis and forecasting methods more cumbersome. Simplifying these processes

and having concentrated data could improve the efficiency of the financial data analysis. This

could help lead to a better forecast accuracy.”

Lastly, it is imperative to note that the single most important factor in the entire forecasting

and analysis methods is information. No matter how efficient the methods and processes are,

the forecast would be poor or inaccurate if the input data is incorrect. Therefore, the finance


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teams within a large multinational company such as Philips must coordinate and cooperate

with each other to ensure that the information exchange and flow is efficient and the data used

for analysis by different teams is accurate. The significance of working closely with other

finance teams was highlighted by one of the analysts in the interview:

“The local finance teams have more accurate financial as well as operational information

about the local market. For example, the CS director of the Africa market informed that the

economic situation in Tanzania is deteriorating, and hence, he does not expect to recognise the

sales made in this ORU in the current period. This kind of local market information helps us to

adjust the forecast models according to market conditions. This certainly helps to improve the

accuracy of the forecasts of the financial measures at the top level.”


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6. Conclusion:

The purpose of this research paper was to understand the current financial analysis and

planning processes and methods followed at Philips and to come up with possible ways to

improve them so as to have a better and more accurate forecast.

Though the management has made some fundamental changes to improve the data analysis

processes and financial models used, there is still a lot of scope to further fine-tune them and

make them more efficient and effective. This measure will eventually result in predicting

performances more accurately.

The key is to closely look at every level of the data hierarchy, understand the steps involved

in the investigation processes, perform a detailed variance analysis, and, make a thorough

deep dive analysis to get to the root of the issues driving the huge variances. Knowing the

actual causes of the issues will help the managers to understand uncertainties and risks

involved at various operational levels. They can either take necessary actions to get rid of the

issues and avoid the risks as much as possible if the risks are influenced by controllable

factors, or, adjust their plans in accordance with the present market and environmental

conditions and set targets that are more justifiable. Either way, the management will have a

clearer picture of what to expect from the market in the given circumstances, and therefore,

can formulate budgets with a high accuracy rate in the future.

With different financial models used to make forecasts, referring historical data and tracking

the performance of these models over a period would go a long way in comprehending and

improving the accuracy of the models thereby contributing to improving the forecast accuracy.

Though the research was quite extensive, the scope was limited only to the Philips Customer

Services division of the Health systems. Though Customer Services is one of the major

business units for Philips, accounting for almost 12% of the annual revenues, the scope of

overall financial budgeting is much more than that. The research could have been much more

comprehensive if it was done on the whole organisation rather than on one business segment.

That would have helped to understand the cross business and cross functional issues

impacting the financial performance of the company as a whole thereby setting the platform

to take a more inclusive approach to come up with solutions or suggestions to improve the

accuracy of forecast for the entire firm.

An interesting topic to consider for future research would be the role of advance analytics to

improve the forecast accuracy of the organisation. Philips has already taken a few initiatives


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in this direction and is trying to integrate IT with its financial analysis processes. The idea is

to creating a structured approach to data management in order to facilitate future financial

analysis with the help of IT. It would be interesting to research on how feasible automating

the data analysis and investigation processes would be, understand the challenges involved,

see the impact on the efficiency of the processes and observe the extent to which the forecast

accuracy can be improved.

Another area to venture in the future would be to see whether improving forecast accuracy

actually result in better performances and how obsessed the companies are with their forecasts.

Managers might be spending a lot of time and energy worrying over the development of the

perfect prediction model. In the process they might be using artificial measures to twist and

tweak information to achieve budget targets. However, improved forecast accuracy in such

cases would lead to sub-optimal performance losing out to competitors who invest more time

and energy adapting to marketplace opportunities. It would therefore be interesting to study

the behaviours of the managers and the practices they follow to build forecasting models and

what the eventual impact of those models is on the organization’s financial performance.


___________________________________________________________________________

7. References:

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http://www-01.ibm.com/common/ssi/cgi-


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http://www.telegraph.co.uk/finance/markets/11367963/Profit-warnings-hit-six-year-high.html

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