ACCA - P5 Study text - Emile Woolf INT

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Chapter 17: Alternative views of performance measurement

Standards

The second part of the framework for performance measurement suggested by

Fitzgerald and Moon relates to setting expected standards of performance, once the

dimensions of performance have been selected.

There are three aspects to setting standards of performance:

 To what extent do individuals feel that they own the standards that will be used

to assess their performance? Do they accept the standards as their own, or do

they feel that the standards have been imposed on them by senior management?

 Do the individuals held responsible for achieving the standards of performance

consider that these standards are achievable, or not?

 Are the standards fair (‘equitable’) for all managers in all business units of the

entity?

It is recognised that individuals should ‘own’ the standards that will be used to

assess their performance, and managers are more likely to own the standards when

they have been involved in the process of setting the standards.

It has also been argued that if an individual accepts or ‘owns’ the standards of

performance, better performance will be achieved when the standard is more

demanding and difficult to achieve than when the standard is easy to achieve. This

means that the standards of performance that are likely to motivate individuals the

most are standards that will not be achieved successfully all the time. Budget targets

should therefore be challenging, but not impossible to achieve.

Finding a balance between standards that the company thinks are achievable and

standards that the individual thinks are achievable can be a source of conflict

between senior management and their subordinates.

Standards should also be fair for everyone in all business units, and should not be

easier to achieve for some managers than others. To achieve fairness or equity, when

local conditions for the individual business units can vary, it is often necessary to

assess performance by relying on subjective judgement rather than objective

financial measurements.

Rewards

The third aspect of the performance measurement framework of Fitzgerald and

Moon is rewards. This refers to the structure of the rewards system, and how

individuals will be rewarded for the successful achievement of performance targets.

One of the main roles of a performance measurement system should be to ensure

that strategic objectives are achieved successfully, by linking operational

performance with strategic objectives.

According to Fitzgerald, there are three aspects to consider in the reward system.

 The system of setting performance targets and rewarding individuals for

achieving those targets must be clear to everyone involved. Provided that

managers accept their performance targets, motivation to achieve the targets

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will be greater when the targets are clear (and when the managers have

participated in the target-setting process).

 Employees may be motivated to work harder to achieve performance targets

when they are rewarded for successful achievements, for example with the

payment of a bonus.

 Individuals should only be held responsible for aspects of financial

performance that they can control. This is a basic principle of responsibility

accounting. A common problem, however, is that some costs are incurred for the

benefit of several divisions or departments of the organisation. The costs of

these shared services have to be allocated between the divisions or departments

that use them. The principle that costs should be controllable therefore means

that the allocation of shared costs between divisions must be fair. In practice,

arguments between divisional managers often arise because of disagreements as

to how the shared costs should be shared.

3.4 Applying the Fitzgerald and Moon framework

The actual measures of performance used by companies in service industries will

vary according to the nature of the service. Fitzgerald and Moon have used case

studies, however, to show how their framework can be used to assess performance

management systems.

One successful (and large) organisation reported as a case study by Fitzgerald was a

food retailing business with a large number of stores. Applying the Fitzgerald and

Moon framework, the performance management system was analysed as follows:

(a) Dimensions of performance

The company used four of the six dimensions of performance to assess the

performance of individual stores and the performance of each region.

Dimension of

performance

Measures used Comments

Profit

Profit by store

and by region.

Profit is seen as a very important

measure of performance.

The performance of each store and

region is publicised within the company,

by means of ‘league tables’, so that each

store manager knows how his store has

performed in comparison with others.

Competitiveness

Market share

(at company

level).

Prices of

competitors for

each local store.

The company places great importance on

monitoring the prices charged by

competitors. Prices are monitored for

each store, at the ‘local’ level.

Chapter 17: Alternative views of performance measurement

Dimension of

performance

Measures used Comments

Observation. Market share is assessed from published

market share statistics.

Managers of local stores may visit the

stores of competitors to see how full their

car park is, and compare this with the

number of cars in the car park of his own

store.

Quality of

service on

specific

transactions

Letters and other

messages from

customers.

Observation

The quality of service is monitored by

‘mystery shoppers’ – individuals hired

by the company to visit stores disguised’

as a customer, to observe the quality of

service provided.

Quality of

service overall

A range of

measures for each

store and

warehouse/depot

A number of different aspects of service

quality are monitored and measured,

and a performance league table for stores

and warehouses is published internally.

Resource

utilisation

Sales per square

metre.

Wastage rates

Flexibility

No formal

performance

measurement

However, managers are aware of the

need for flexibility. For example, when

there are staff shortages due to

absenteeism, store managers will

telephone part-time staff and ask them to

fill the vacancies at short notice.

Innovation

No formal

performance

measurement

The need to innovate continually is

recognised, however, and innovation is

discussed regularly at business planning

meetings.

(b) Standards of performance

The research found a significant difference between the level of ownership for:

 profit, and

 quality of service.

Managers participated in the process of setting profit targets for their store or

region, through the formal business planning process. However, standards for

quality of service were imposed by central management (head office). The

view of senior management was that quality standards must be the same at

every store in the country; therefore standards must be decided at head office

for the company as a whole.

‘Standards’ of performance were assessed for both profitability and quality of

service.

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Standard Comments

Profit Ownership

Managers were involved in setting profit

targets, as part of the discussions with head

office about annual targets and business

planning.

Achievability

The standards/targets were considered

achievable by the managers responsible for

achieving them.

Equity

In setting profit targets for each store,

allowance was made for the effect of

competition from local competitors to each

individual store.

Quality of

service

Ownership

Standards were imposed by head office.

Achievability

However, they were seen as achievable.

An aspect of standard-setting was the use

of internal benchmarks, and comparisons

of quality standards at different stores

within the company.

Equity

No allowances were made for different

local conditions. All stores throughout the

country were expected to achieve the same

standards.

The three elements of reward systems within the Fitzgerald and Moon

framework are:

 clarity of goals and targets

 how managers are motivated to achieve targets, and

 whether there are any problems about shared costs and controllability of

costs.

The research findings were as follows:

Clarity of goals

Managers were aware of company strategy,

and clearly understood how their

performance would contribute to the

achievement of strategic objectives.

Motivation: short-term financial

motivation

Store managers received a bonus for

achieving targets. In addition, there were

‘team’ bonus payments.

Motivation: non-financial aspects

Managers had pride in their position in the

league tables of stores, and were motivated

to improve or maintain their position in the

league.

Controllability

There were no problems with controllability

of costs or allocation of shared costs.

Chapter 17: Alternative views of performance measurement

(d) Conclusions

Conclusions drawn from this case study, based on the Fitzgerald and Moon

framework, were as follows:

 The company was highly successful.

 It had a clear statement of strategy that was well understood by

management in regions and local stores.

 Performance measures were consistent with that strategy.

 Performance measures were reported regularly, covering a range of

financial and non-financial aspects of performance.

 The performance measurements were clearly defined and communicated

to employees at all levels in the company, and there were regular reports

on actual achievements.

 The driving force for performance targets was the satisfaction of customer

needs.

 The performance measurement system was reinforced by a rewards

system to motivate managers and by a pride in performance.

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Predicting corporate failure

 Causes of corporate failure: two differing views

 Quantitative corporate failure prediction models: financial ratio analysis

 Quantitative corporate failure prediction models: the Z score model

 Altman: Z score model

 Predicting corporate failure: qualitative measures

 Using failure prediction models to prevent corporate failure

 Other indicators of failure

 Avoiding failure

4 Predicting corporate failure

4.1 Causes of corporate failure: two differing views

Corporate failure occurs when a company becomes insolvent and goes out of

business. Companies that fail have obviously performed badly. After a company has

failed, it should be possible to analyse the reasons why failure happened and what

went wrong.

Corporate failure prediction is concerned with trying to identify companies that are

at risk of failure, before the failure actually happens. If management can identify the

signs of failure in advance, they might be able to take action to deal with the

problems and prevent failure from happening.

There are two differing views about predicting corporate failure.

 One view is that corporate failure is caused by financial problems, such as losses

or an inability to pay creditors (liquidity problems).

 Another view is that the causes of failure are not financial. Financial problems

are the consequences of other problems, and failure is caused by these other

non-financial reasons.

Predicting the failure of companies may be based on either a quantitative approach

or a qualitative approach. If failure can be predicted by the existence of financial

problems, it should be possible to use financial ratios and quantitative analysis to

predict the failure. On the other hand, if the causes of failure are non-financial, it

might be necessary to use qualitative measures of performance – and judgement – to

predict failure.

4.2 Quantitative corporate failure prediction models: financial ratio analysis

Financial ratio analysis is widely used to assess financial performance. For example:

 ROCE is used to assess profitability in relation to the amount of capital invested.

A low ROCE or losses are an indication of a poor financial performance.

Chapter 17: Alternative views of performance measurement

 Financial gearing is used to assess the size of a company’s long-term debts in

relation to equity capital. A high gearing ratio might indicate that the company

could have difficulty in meeting its interest payment obligations.

 Liquidity ratios (the current ratio and quick ratio) can be used to assess the

liquidity of a company, and whether it might be at risk from a shortage of

liquidity (cash).

It is generally accepted that the key factors making the difference between firms that

fail and firms that survive are:

 Profitability

 Cash flow

 Return on equity, including dividends

 Gearing or debt levels

 Volatility in business revenue.

The purpose of quantitative corporate failure prediction models is to identify a

financial ratio (on its own) or a number of financial ratios that can be used to decide

whether a company is at risk of failure.

An early approach to the use of financial ratios to predict corporate failure was

suggested by William Beaver in the 1960s. He studied a sample of companies that

had failed and a sample of companies that had not failed. He studied whether the

differences in any one particular financial ratio between these two samples of

companies (failed and non-failed) were significant, so that the ratio would provide a

reliable guide for failure prediction.

Beaver concluded that of the financial ratios that he examined, the ratio of cash flow

to total debt was the most significant, and there were differences in this ratio

between most failed and most non-failed companies.

A weakness in Beaver’s approach was that he tried to identify whether any one

financial ratio on its own was a good predictor of corporate failure. In reality

however, individual financial ratios are probably not sufficient to provide reliable

predictions of corporate failure.

 It is difficult to assess when an individual financial ratio is at a dangerous level.

 The ratios of a company change over time.

 Financial ratios might also give contradictory signals: for example, if a company

has poor liquidity (a low current ratio) but good profitability (a high ROCE),

what is the probability of corporate failure?

4.3 Quantitative corporate failure prediction models: the Z score model

A quantitative corporate failure prediction model is a formula that can be used to

predict corporate failure. The formula consists of a number of key financial ratios

and each ratio is given a weighting. It produces a ‘score’ from the ratios, and this

score can be used to decide whether the company is at risk of corporate failure.

Paper P5: Advanced performance management

Creating a failure prediction model

A quantitative corporate failure prediction model is based on an objective statistical

analysis of historical financial data. There are several statistical methods that might

be used to construct a model, but the most commonly-used method is called

Multiple Discriminant Analysis or MDA.

Researchers have developed different MDA models for different industries, and for

different countries. The key financial ratios for predicting failure have been found to

differ between industries, countries and size of company.

MDA models are constructed in the following way:

 A sample is taken of a number of different companies of the same type. These

are grouped into two different categories: those that have failed and those that

have not failed (and continue in business).

 Historical financial data is gathered for all the companies in the sample, and a

variety of financial ratios are calculated for each company.

 Statistical analysis is used to identify the ratios that appear to discriminate most

between the two different groups or categories of company (failed companies

and successful companies).

 Weightings are given to these key financial ratios according to how significant

they appear to be in discriminating between failed and successful companies.

 A ‘Z score’ model is established. This is the score obtained from the model.

A Z score can be calculated for any company. Statistical analysis is used to establish

three categories of Z score.

 A Z score above a certain value indicates that the company is ‘safe’ and is not at

risk of failure.

 A Z score below a certain value indicates that the company has a high risk of

failure within a given period of time, such as within the next one or two years.

 A Z score between these two values (safe and high risk) indicates that the

company is in a ‘grey area’. There is some risk of corporate failure, but the risk is

not (yet) high.

4.4 Altman: Z score model

Altman was the first person to develop a corporate failure prediction model using

the MDA method. This model was developed in the 1970s, from a study of 22 US

companies.

His first Z score model used five key accounting ratios. The model was as follows:

Z score = 0.012X

+ 0.014X

+ 0.33X

+ 0.06X

+ 1.0X

where X

, X

and X

are the five financial ratios in the model.

= working capital/total assets

= retained earnings/total assets

Chapter 17: Alternative views of performance measurement

= earnings before interest and tax/total assets

= market value of equity/book value of total debt

= sales/total assets.

(You do not need to learn this formula. It is included here to illustrate the nature of

Z score models.)

With this model, Altman predicted that:

 a Z score of 2.7 or higher indicated that the company was not at risk of failure

 a Z score below 1.8 indicated a high risk of failure

 a Z score between 1.8 and 2.7 was in the ‘grey area’ where failure was a

possibility but not a high risk.

In 1982, Gritta applied the Altman model to the airline industry and successfully

predicted the failure of the Braniff airline company.

Other Z score models

Other researchers have developed corporate failure prediction models. In 1991, the

first industry-specific Z score model was developed for the airline industry. This is

called the AIRSCORE model but is based on the Altman Z score model. In the UK,

failure prediction models have been developed by Taffler and Tishaw.

Researchers generally agree that their models are good for predicting corporate

failure up to two years in the future, but are not reliable for longer-term predictions.

H score model

The H score model was devised by Company Watch. An H score is a percentage

score between 0 and 100. If a company has an H score of, say, 15 this means that

only 15% of companies have financial characteristics that are more indicative of a

company at risk of failure. Similarly an H score of 80 means that 80% of companies

have characteristics more indicative of a company at risk of failure. Company Watch

suggest that an H score of 25 or less puts a company into a high risk category.

The H score valuation is based on seven key financial ratios in three areas of

management:

 Profit management (changes in profitability)

 Asset management (measured by liquidity, working capital and current asset

cover

 Funding management (measured by adequacy of capital, reliance on debt and

reliance on current liabilities).

4.5 Predicting corporate failure: qualitative measures

A criticism of Z score models is that they focus on financial performance. It can be

argued that poor financial ratios are a symptom of corporate failure, but they are

Paper P5: Advanced performance management

not the cause of failure. Analysing financial ratios does not help management to

understand the nature of the problems that create the risk of corporate failure.

John Argenti (1976) commented: ‘Whilst these (financial) ratios may show that there

is something wrong … I doubt whether one would dare to predict collapse or failure

on the evidence of these ratios alone.’

The factors that could be the real cause of corporate failure might be:

 poor management for example, inexperienced management or autocratic

management)

 an inability to retain key staff

 ownership of the company in the hands of a small number of individuals

 poor management information systems

 the loss of a big client

 a large increase in interest rates

 the launch of a big project (that does not go to plan).

Argenti: ‘A score’ model

Argenti developed a model for predicting corporate failure based on qualitative

factors. His model produced a score that he called an ‘A score’. Like a Z score, an A

score could be used to predict corporate failure, particularly in small and medium-

sized companies.

Argenti assigned scores to qualitative factors under three main headings:

 Defects. These are weaknesses within the organisation and management

structure, such as:

Management weaknesses

- an autocratic or inexperienced managing director/CEO (8 marks)

- failure to separate role of chairman and chief executive officer (4 marks)

- a passive board of directors (2 marks)

- a lack of balance in the skills of the management team – such as financial and

marketing skills (4 marks)

- a weak finance director (2 marks)

- a lack of ‘management in depth’ (1 mark)

- poor ability to respond to change (15 marks).

Accounting deficiencies

- no budgetary control system (3 marks)

- no cash flow planning (3 marks)

- no costing system (3 marks).

The total mark for defects is 45, and Argenti suggested that a satisfactory score is

10 or less.

 Mistakes that have been made or ‘errors in action’. These are mistakes that the

company makes that could have been avoided. The three main mistakes are:

ACCA - P5 Study text - Emile Woolf INT - 2010

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