Productivity Measurement Approaches

Critical Analysis of Different Productivity Measuring Approaches

Muhammad Mushtaq Mangat

Abstract

Productivity growth is the endurance point for any firm at micro level and for any

economy at macro level. Even its significance cannot be uncared at individual

levels. Productivity Measurement is the most complex phenomenon. There are lot

of multiplicity in the methods to evaluate productivity. In Productivity Measurement

process the most important factors are, who measure productivity and at what level

the productivity is measured. There are number of ways suggested by different

authors to measure productivity. Many approaches were used to assess productivity

of different firms and industries. There are many factors, which participate in

selection of productivity measurement technique. The range of measurement

approaches and measurement tools is quite large. The choice of an appropriate tool

depends on the nature, scale, level and phase of the investigation. There are even

‘political’ considerations in the selection of Productivity Measurement technique.

Ddifferent people are interested in Productivity Measurement i.e. Economists,

Industrial Engineers, Environmental Engineers and Accountants. Every one has his

own reasons to study productivity. They have diverse purposes in studying

productivity. Due to such diversification in their objectives they cannot use a

common tool to assess the productivity. So every group defines its own tools to

measure productivity. Furthermore, in the same group different authors proposed

different approaches to measure the productivity. This is mainly due to the changes

in business practices with the passage of time, and this is also due to difference of

opinion. This opinion difference is basically because of the academic approaches,

which are based upon variation in their Productivity Measurement objectives. The

subject matter of this paper is to have a critical analysis of different Productivity

Measurement approaches and models. It is found that there is no consensus on

any model rather there is a lot of variation in the models and every model is

suitable in a particular state of affairs. It is mandatory that model assortment should

be given due importance since productivity measurement objectives can only be

achieved by using a most apposite model.

1I warmly welcome your comments [email protected]

Classification of Productivity Measuring Approaches

It is a popular game among the researchers to find a suitable measure for denoting

the effectiveness of a set of manufacturing circumstances and using this measure

to monitor the changes (Stark & Bottoms, 1980, p. 100). From the above statement,

it is clear that selection of method in productivity measurement is the most crucial

step. This problem has been explained well by McKee (2003, p. 138) in the following

words, “The range of measurement approaches and measurement tools is quite

large. As with other productivity tools, the choice of an appropriate tool depends on

the nature, scale, level and phase of the investigation. There are even ‘political’

considerations”.

As it has been discussed in previous pages that there are number of ways to

measure productivity. Varity of opinions is a proof that many people are concerned

to this topic due to its significant nature in current business scenario. There are

number of factors affecting selection of any Productivity Measurement approaches.

Theses may include, objective of Productivity Measurement, level of Productivity

Measurement, data available for Productivity Measurement etc. However, it is a

fact that there is no consensus on any approach. In the following pages there is a

detailed discussion about the Productivity Measurement approaches and models

proposed by different authors. These models, approaches and methods will be

studied analytically.

Summary of Models by Singh et al

Singh; Motwani & Kumar (2000, p. 238) have collected different models used for

Productivity Measurement from literature and summarised all collected models in

the following table, which tells about the review of researches by various authors to

measure productivity in diverse times of dissimilar industries.

Table: 1

Summary of the Empirical Research on Productivity Measurement

Author(s) (year) Approach Industry/ Findings

2

application

setting

Fuller (1988) Index

measurement

Computer

manufacturing

Shows how using a

productivity loss index

helps in enhancement of

productivity and quality

Sengupta (1988) Linear

programming

Manufacturing A robust minimax

approach is used to

measure productive

efficiency

Conrad (1989) Econometric

models

Manufacturing An extended framework

was developed to reflect

the efficiency aspect of

productivity gaps in

terms of cost

disadvantages

Pritchard et al.

(1989)

Index

measurement

Manufacturing A new approach to the

measurement an

enhancement of

organizational

productivity is described

and evaluated.

Omachonu et al.

(1990)

Index

measurement

Technical

installation

A methodology for

measuring the

productivity of

Engineering and

Technical Organizations

is developed

Yousif and Dale

(1990)

Index

measurement

Hardware

manufacturing

Total and partial

productivities are similar

when calculated using

fixed and current prices.

Pritchard and Econometric Manufacturing Inclusion of non-

3

Roth (1991) models linearity’s result in more

valid productivity

composites

Brown and Gobell

(1992)

Index

measurement

Research centres

manufacturing

An R&D measurement

system can be as

complex or as

streamlined as the

manager’s wish.

Mady (1992) Index

measurement

Manufacturing An integrated, easy-to-

implement model for

presented.

Ray and Sahu

(1992)

Index

measurement

Manufacturing Suggests a combination

of production factors

with which management

would b able to increase

the productivity of the

products.

Radovilsky and

Gotcher (1992)

Econometric

models

Electronic

equipment

The main loss factors in

terms of productivity

improvement are

ineffective technology

design, overstocked

inventory, poor product

quality, and wrong work

standards.

Prasad (1993) Index

measurement

Aircraft industry Uses the M-type

interactive procedure (a

time series method) to

monitor the labour

productivity index over

time.

Bogetoft (1994) Linear

programming

Manufacturing An illustration of how to

design optimal incentive

4

schemes based on DEA

frontiers is provide.

Eimuti and

Kathawala (1994)

Index

measurement

Production planning

and control

The traditional

productivity index

measurement show a

positive impact of

participation in the

employee productivity

Saha (1994) Index

measurement

Chemical Describes the

application of TOPROD,

a software for total

productivity

measurement in a

chemical processing

plant

Sueyoshi

(1995)

Linear

programming

Telecommunicatio

ns

A new DEA application to

production analysis in different

time periods is illustrated

Balvers and

Bergstrand

(1997)

Economic

models

OECD countries

and US

Offers insight into the robust

cross-sectional relationship

between relative per capita GDP

and relative national price levels.

Birechee and

Konzelmann

(1997)

Economic

models

Corn, processing,

steel, paper and

coal

Increasingly

aggressive/adversarial labour

relations characterize firms that

have chosen to follow the low-

wage path

Ford and

Pittman

(1997)

Economic

models

Technical institute Created a unique program and

CI mode that has significantly

increased productivity and

profitability

Wilson (1994) Linear Manufacturing Presents an improved goal-

5

programming oriented method for Productivity

Measurement and monitoring

of performance of manufacturing

firms

Jablonski

(1995)

Index

measurement

Broadwoven

fabrics

Technological change was the

most important factor underlying

multi-factor productivity growth

in the period

Kieiner et al.

(1995)

Economic

models

Manufacturing Production-related residual

grievances was correlated to

increased managerial monitoring

(that had a positive impact on

productivity)

Nohria and

Gulati (1995)

Econometric

models

Production

planning and

control

An inverse u-shaped relationship

exists between organizational

slack and innovation in

multinational firms

Sueyoshi

(1995)

Linear

programming

Telecommunicatio

ns

A new DEA application to

production analysis in different

time periods is illustrated

6

It is clear from the above table that different researchers used different models to

assess productivity. Selection of the most appropriate tool is the most difficult step

in measuring productivity. It depends upon many factors, such as, type of industry,

objective, data available etc.

Salinger and Productivity Measurement Models

7

Salinger (2001, p. 09) has divided Productivity Measurement models into three

following categories:

Growth models attribute increased economic growth either to accumulation

of physical or human capital or to increase efficiency of their use

Neoclassical growth models view technical progress as exogenously

determined

Endogenous models consider a range of structural and policy variables which

contributes to differences in technology endowment, investment, and

knowledge accumulation among countries

As it has been said in previous pages that productivity increase is possible in many

ways, even war is a tested way to have more than others. It gives prosperity to the

nations. In addition, in some cases sudden availability of some natural resources

like oil can increase income of the nation. But it will not increase labour productivity

of the nation. Even then this will give prosperity to the nation, which is one of the

desired outcomes in improving productivity.

Parsons Approaches

Parsons (2001, p. 21) has given following seven different methods/approaches to

measure productivity, applicable to service sector and white collar/knowledge

worker environment:

o Control panels

o The objective matrix-OMAX

o The balance scorecard

o Economic value Added-EVA

o Productivity Accounting

o Integrated Business Control

Parsons has also proposed the following model, which is similar to Sumanth model.

Total Factor Productivity (TFP) = Gross Output_________________________ Labour+ capital+ Materials+ Energy+ Others

Lawlor Classification

8

Lawlor (1985, p. 10) has put forward two main classes of productivity measuring,

macro and micro. According to Lawlor (1985, p. 10), “macro is used for comparison

between countries while micro is used for individual organisations, units or for

employees”. Lawlor (1985, p. 10) has further given more kinds of productivity

measuring. Summary of Lawlor’s (1985, p. 10) work is given as under:

1-Simple and compound measuring

Where outputs and inputs are stated in the same terms will be called simple

measuring and where output and input are stated in different terms is called

compound measuring e.g. sale per employee

2-First and second order indices

First order measurement involves only one index and second order involves two

where connected indices are used. GDP divided by Number of Employees (NE) is

called first order and GDP/NE of one divided by other country’s index is called

second order.

Mawson, Carlaw & McLellan Classification

Mawson; Carlaw & McLellan (2003, p. 06) have classified productivity measurement

approaches into four categories:

The growth accounting approach

The index number approach

A distance function approach

Econometric approach

Here is a brief discussion on these approaches.

1-Growth accounting

Growth accounting enables output growth to be decomposed into the growth of

different inputs (typically capital and labour) and changes in total factor productivity

are marked. Growth accounting requires the specification of a production function

that defines what level of output can be produced at some particular time given the

availability of a certain level of different inputs and total factor productivity.

This growth accounting approach is based upon following four assumptions:

9

The technology or total factor productivity term, At is separable

The production function exhibits constant returns to scale

Producers behave efficiently in that they attempt to maximise profits

Markets are perfectly competitive with all participants being price-takers who

can only adjust quantities while having no individual impact on prices

2-Index number approaches to measuring productivity

The majority of statistical agencies that produce regular productivity statistics use

the index number approach. For example, the Australian Bureau of Statistics

calculates market sector multifactor productivity using the index number approach

based on a Törnqvist index, as does the US Bureau of Labor Statistics.

The index number approach to calculate productivity involves dividing an output

quantity index by an input quantity index to get a productivity index.

3-A distance function based approach

The distance function based approach to measure TFP seeks to separate TFP into

two components using an output distance function. More generally, the distance

function (which is the dual of the cost function) is discussed in the consumer and

production literature where duality concepts are used. In principle, this technique

enables a change in TFP to be decomposed into changes resulting from a

movement towards the production frontier and shifts in the frontier. The output

distance function measures how close a particular level of output is to the

maximum attainable level of output that could be obtained from the same level of

inputs if production is technically efficient. In other words, it represents how close a

particular output vector is to the production frontier given a particular input vector.

The Econometric Approach to Productivity Measurement

The econometric approach to productivity measurement involves the estimation of

parameters of a specified production function (or cost, revenue, or profit function,

etc).

Often the production function is expressed in growth rate and then estimated to

yield an estimate of the parameter that reflects the growth in technological

progress, which is typically interpreted as a measure of productivity growth.

10

One major advantage of the econometric approach is ability to gain information on

the full representation of the specified production technology. In addition to

estimates for productivity, information is about other parameters of the production

technology is also obtained.

It is not possible to generate this additional information using the growth accounting

or index number approaches. Moreover, because the econometric approach is

based on information of outputs and inputs, so there is a greater flexibility in

specifying the production technology. For example, it is possible to introduce other

forms of factor-augmenting technological change other than the Hicks-neutral

formulation implied by the growth accounting and index number approaches, and to

make allowance for adjustment cost and variation in input utilisation. Within the

econometric framework it is possible to test the validity of assumptions that

underpin the growth accounting and index number approaches because of the

sampling properties of the production technology. For example, it is possible to test

the assumption of constant returns to scale that is often used in the growth

accounting approach to productivity measurement.

Gharneh Classification

Gharneh (1997, p. 31) has classified Productivity Measurement in two main

categories:

1-Production function and index numbers

2-Accounting models

In next lines there is a brief discussion about the classification proposed by Gharneh

(1997, p. 31)

1-Production function and index numbers

Production function is one way to assess productivity and is widely used by

economists. Many models are available to assess the productivity of any production

function.

2-Accounting models

11

According to Gharneh (1997, p. 31) accounts models are used to assess

productivity. As it has been discussed in previous pages that accountants are more

concerned about different financial ratios.

Ali Classification

Ali (1978, p. 48) has classified different Productivity Measurement approaches on

the basis of different measuring levels. According to him productivity is measured at

four different levels:

International

National

Industry (one sector of economy)

Firm or organisation

As it has been discussed in previous pages that in Productivity Measurement

process the most important is who measures productivity and at what level it is

measured. Ali (1978, p. 41) has proposed four levels of Productivity Measurement

and at every level different model is used since productivity-measuring objectives

are different.

Mahadevan Classification

As discussed by Mahadevan (2002, p. 05) the concept of Total Factor Productivity

(TFP) growth dates back to the work of Tinbergen, Abramovtiz, Solow, and

Jorgenson Griliches among many others. A significant number of studies thereafter

have often focused on the non-frontier approach to calculate TFP growth; Farrell (as

cited in Mahadevan, 2002, p. 05) first initiated the frontier approach to TFP

measurement. However, it was not so until the late 1970s that this approach was

formalized and used for empirical investigation.

Mahadevan (2002, p. 06) has classified measuring of TFP into two main approaches:

Frontier approach

Non-Frontier approach

12

Figure 1

Classification Productivity Measuring approaches

13

Source: Mahadevan, 2002, p. 06

In the above-mentioned figure the main categorization is on the basis of frontier

and non-frontier approach. It is imperative to discuss these two terms used by

Mahadevan (2002, p. 07). According to Mahadevan (2002, p. 07),

frontier refers to a bounding function, or more appropriately, a set of best

obtainable positions. Thus a production frontier traces the set of maximum outputs

obtainable from a given set of inputs and technology, and a cost frontier traces the

14

minimum achievable cost given input prices and output. The production frontier is

an unobservable function that is said to represent the 'best practice' function, as it

is a function bounding or enveloping the sample data. The frontier and non-frontier

categorization is of methodological importance since the frontier approach identifies

the role of technical efficiency in overall firm’s performance whereas the non-

frontier approach assumes that firms are technically efficient.

Figure 2

Technical Progress

15

Above figure shows that F1 and F2 are production frontiers. Movement from A to B is

referred to the accumulation of knowledge due to the learning-by-doing process,

diffusion of new technology. AB represents technical inefficiency. Shift B to C represents

technical progress and TFP growth.

Non- Frontier TFP Growth = Technical Progress

Frontier TFP growth = Technical progress + Gains in technical efficiency

(Shifts of the production frontier) + (Shifts towards Frontier)

The Non Frontier Approach

Output Growth = Input Growth + TFP Growth

TFP Growth = Output Growth - Input Growth

In this model the non-frontier approach uses the standard growth accounting framework.

According to the model the output growth is equal to the sum of input growth and the

TFP growth, while TFP growth is the difference between output growth and input growth.

Average Response Function

According to Mahadevan (2002, p. 10):

The non-frontier parametric estimation takes the form of the average response function

using data from the production or cost side. By far the most important aspect of this

method is the selection of an appropriate functional form that ranges from the simple

Cobb-Douglas to the more flexible Tran slog form.

In this regard following example can be taken:

Log Y = a + b Log K + c Log L

Where Y = valued added output

K = capital used

L = labour employed

b = capital share and c = labour share.

In this equation Cobb-Douglas production function has constant returns to scale

technology and thus b + C = 1 alternatively,

Equation can be expressed as:

Log (Y/L) = a 1 + b 1 Log (K/L)

Figure 3

Average Response Function

16

The Frontier Approach

According to Mahadevan (2002, p.09):

Unlike the non-frontier approach, the frontier approach is able to decompose output

growth not just into input growth and TFP growth; it goes a step further to decompose

TFP growth into various efficiency components such as technical progress and gains in

technical efficiency.

It can be expressed in the following way:

Output Growth = Input Growth + TFP Growth

= Input Growth + Technical Progress + Gains in Technical Efficiency

In this equation the horizontal axis represents a typical industry's inputs and the vertical

axis represents its output. More explanations are required to use the figure for

17

explanation. Mahadevan (2002, p. 13) has expressed his views and the summary of his

observation is as under.

Assume that the industry faces two production frontiers, F1 and F2, the ‘efficient

production technologies' for periods 1 and 2, respectively. In period 1, if the industry is

producing with full technical efficiency by following the best-practice techniques, its

realized output will be y1* at the x1 input level. However, because of various

organizational constraints, such as the lack of a proper incentive structure for workers,

the industry may not be following the best-practice techniques and therefore may be

producing at less than its full technical efficiency. This means that the realized output y1

is smaller than the maximum possible output y1*. Technical Efficiency, TE1, measures

this gap by the vertical distance between y1 and y1*. Now, suppose there is technical

progress due to the improved quality of human and physical capital induced by policy

changes, then an industry's potential frontier shifts to F2 in period 2. If the given industry

keeps up with technical progress, more output is produced from the same level of input.

Therefore, the industry's output will be y1** from the x1 input level, as shown in the

Figure 1.4. Technical progress is measured by the distance between two frontiers (F2-F1)

evaluated at x1. Now the industry is generally induced to increase its levels of input in

period 2. Its maximum possible output is y2** for new levels of input x2, and its realized

output is y2. The vertical distance between y2 and y2* is measured as TE2. Therefore,

the contribution of the change in technical efficiency to output growth between the two

periods is measured by the difference between TE2 and TE1. When this difference is

positive, it means that there is improvement in the industry's technical efficiency and

vice versa. Positive difference shows improvement in the industry’s technical efficiency

and negative difference shows otherwise.

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Figure: 4

Decomposition of output growth and TFP growth

.4

19

This is an important model for more accurate policy formulation based on two different

sources i.e. technical progress and technical efficiency. For More details see Mahadevan

(2002).

Boussemart; Briec; Kerstens & Poutineau (2003, p. 391) have made a comparison in TFP

of different countries. Their main focus is on the use of input variables. They have

expressed their views in the following words:

For many years productivity growth measures have identical total factor

productivity growth with a shift in technology. Productivity growth measure have

been evaluated via continuous time production functions on macro or micro

economic data, whereby output variations that are left unexplained by input

variations—the famous Solow residual ----are interpreted as technological

change. In last two decades there is growing awareness that ignoring inefficiency

in input usage or output production yield a biased measure of productivity

growth.

Borger & Kerstens (2002, p. 304) have assessed Malmquist productivity index with

different angle. They have concern about the technical efficiency and plant utilization.

According to them, “one potentially issue ignored in Malmquist productivity index is that

change in technical efficiency may be partially due to change in utilization of production

capacity”.

Bayesian Approach

20

The Bayesian approach, which is a relatively recent development in productivity growth

analysis, provides robustness to model and parameter uncertainty, thus guarding against

drawing strong conclusions from weak evidence (as cited in Mahadevan, 2002, p. 16).

Figure: 5

Types of parametric production frontiers

This is one of the latest methods that have been suggested in the literature to determine

the growth. The result of this approach has a degree of 95% accuracy.

Sink classification

Sink (1985, pp. 94, 138, 189) has given three types of Productivity Measurement.

Summary of Sink (1985, pp. 94, 138, 189) ideas is as under:

Measurement is a natural part of analysis, control, evaluation and management

process.

There are three basic techniques to measure productivity:

I. Normative productivity measurement methodology (NPMM)

II. Multifactor productivity Measurement Model

21

III. Multicriteria Performance/productivity measurement Techniques

Parsons Approach

Parsons (1980, p. 60) has given a new kind of approach called, “Profitability analysis in

inter-firm comparison: a new approach”. Parsons (1980, p. 60) developed new system

with the help of National Productivity Institute. This measuring model is called “Resource

Allocation Strategists” (REALST).

REALST adopts standard costing approach by using information drawn from income

statements and production record. The results are presented in a variance matrix, which

measures the contribution to profit variance of differences in a capacity utilisation,

efficiency and price recovery. REALST is significantly different from the standard costing

approach. REALST correctly identifies the additional benefit flowing from the firm’s higher

labour productivity, which in standard costing remains buried in the measurement of

resource price variance.

REALST also disaggregates the bottom line profit impact of capacity utilisation, efficiency

and price recovery to show the contribution per resource element. By contrast, standard

costing decomposes only efficiency variance into the contribution per resource element.

This approach was used by the Parsons (1980, p. 06) to assess the productivity of South

African Sugar Mills Industry.

It is quite clear from the above discussion that it is hard to reach a consensus on the

classification of productivity. Different authors classified Productivity Measurement

approaches into different categories. This variation is just because of different objectives

of measuring productivity. Numbers of models are available in the literature to assess

productivity. Every model is suitable depending upon the measuring objectives and

availability of data. Also it depends upon nature of the industry and level of productivity

measuring. Some models have been selected from the host of models for critical

analysis.

Critical Examination of Selected Productivity Measurement Approaches and

Models

22

In previous pages main discussion was to elaborate different classification of Productivity

Measurement approaches. In this part of chapter main topic of discussion is the critical

analysis of some approaches. It is not possible to discuss all approaches; however most

common approaches or models will be viewed analytically. In previous pages, it has been

discussed in detail that there is a high diversification in Productivity Measurement

approaches and there are many reasons of such diversification. In this part of the chapter

main focus is to view different approaches only proposed for Productivity Measurement

at company level.

Sink Model

Sink (1985, pp. 94, 138, 189) suggested three different methods/techniques to assess

productivity. Sink (1985, pp. 94, 138, 189) has discussed each one in detail (as discussed

in previous pages). In the following paragraphs there is a brief discussion on Sink modes.

1- The Normative Productivity Measurement Methodology (NPMM)

2- Multifactor Productivity Measurement Model (MPM)

3- Multicriteria Performance/Productivity Measurement Technique (MCP/PMT)

1-Normative Productivity Measurement Methodology (NPMM)

Drs. William Morris and George Smith at The Ohio State University (as cited in Sink, 1985,

p. 94) first studied this methodology in 1975. These two researchers headed a project

sponsored by the National Science Foundation in which they tried to develop innovative

productivity measurement systems for administrative computing and information

system. Two important processes used in NPMM are the nominal group technique and

the Delphi technique, both were used to develop synchronised measures of productivity

for a given organisation system.

2-Multifactor Productivity Measurement Model

This model does not incorporate involvement in any major form in the collection of data.

It is more a macroscopic measurement approach. It also structures the input data in such

a way, as to adhere, automatically, to strict general definition of productivity.

23

3-Multicriteria Performance/Productivity Measurement Techniques

Multicriteria Performance/Productivity Measurement Techniques (also called the

objective) allows for measurement and valuation of performance (the broader issue) or

productivity and most importantly, it provides a mechanism to developing an aggregate

performance or productivity index.

Lawlor’s approach

Lawlor (1985, p. 76) has classified the Productivity Measurement into three categories:

Micro and Macro: Macro is used for comparison among the countries and

Micro is used for individual organisations.

Simple and Compound analysis: In Simple Analysis output and inputs are

stated in the same terms while in Compound Analysis other ratios are

used which do not have common terms, like sales per employee etc.

First and Second order indices: in first order measurement involves only

one index while in second order two connected indices are used.

Lawlor (1985, p. 76) has divided his Productivity Measurement approach into primary

and secondary categories.

Primary Productivity Measurement

As cited by Lawlor (1985, p.76) late Harold Martin studied over many years to develop a

primary measurement of productivity to satisfy the following requirements:

o Attainment of primary objectives

o Explanation of output/input ratio which relates to the primary objectives

Lawlor (1985, p. 77) has specified the earning as primary objective. Lawlor (1985, p. 77)

believes that the earning is the ultimate objective of the firm. More earning means high

productivity. Lawlor (1985, p. 77) has suggested the following formula:

Earning Productivity = Total earning Conversion Cost

Profit Productivity Index = Total earning – Conversion Cost Conversion Cost

24

This model is a simple representation of the total earning and the total expenses incurred

for this earning. Lawlor (1985, p. 77) has further explained that earning is a difference

between sales and cost of sales.

Lawlor (1985, p. 77) has further proposed as expression to make a relationship between

total earning and added value concept. According to him:

T = S-M

AV = S-X

AV= T-PS

Where:

T = Total Earning

AV = Added value

S = Sales

M= Total Material

PS = Purchase Services

X = total outside purchases, including through material “M” and purchased services “PS”

Lawlor (1985, p. 81) has focused on financial performance of the organisation and has

suggested some productivity indicators based upon the financial ratios like, Conversion

Utilisation Productivity as the ratio of the total time or cost incurred on production

Secondary Productivity Measurement

In Lawlor’s Productivity Measurement approach primary measurement rate and quantity

is discussed and in the secondary measurement the potential that can be achieved is

highlighted. The Lawlor suggests this in the following way:

Resource or conversion utilisation = Cd__ C

Where:

Cd = time or cost incurred on productivity and ancillary work

C = Total time available or total conversion cost, which includes idle time He has further

explained that:

Resource Productivity = Ce /C

Where:

Ce = time or cost incurred on purely productive work

25

C = total time available or total conversion cost, which includes ancillary work and idle

time

Lawlor (1985) has discussed calculation of the potential productivity in detail, which

other authors have not discussed. This approach looks more comprehensive than

previous approaches. The main difficulty with this approach is the availability of the

required information. Collection of such information needs in depth study of the

organisation.

Financial Ratios

Financial Ratios approach is one of the most common, simple and easy ways to assess

the performance of any organisation. Every organisation prepares its annual account

statements and on the basis of these statements its performance is measured. These

ratios are the basic criteria for its share value in stock exchange. One should be clear

that such ratios are indicators of performance and productivity. However this approach is

widely used in the industry and very easy to understand.

Centre for Inter Firm Comparison UK (CIFC) has developed 103 different ratios to assess

the performance. CIFC also publishes Inter Firm Comparison (IFC) of its members. This

IFC helps the individual organisation assess its position in the market. But as it has been

said it does not depict the productivity of the organisation.

Following factors are used to assess the performance of the firms:

1. Total Capital Employed (Fixed assets, Sales Profits)

2. Different Ratios (Current Ratios, Quick Ratios etc)

The main theme of these ratios is to make a relation between different out comes of the

firms. This is a valid way to check the performance of any firm. In account these ratio are

used to assess the present health as well as the future of the firms.

Chen; Liaw Shu-Yi & Chen (2001, p. 379) have proposed following 15 financial ratios to

assess productivity of any firm:

1- After-tax return on net worth

2- Before-tax return on net worth

26

3- Return on total assets

4- Total assets turnover

5- Inventory turnover

6- Days-inventory turn

7- Fixed assets turnover

8- Net worth turnover

9- Net operating cycle

10- Fixed assets growth ratio

11- Operating income per share

12- Sales per share

13- Pre-tax income per share

14- Earnings per share

15- Effective tax ratio

The above-mentioned 15 ratios cover maximum financial activities; by using these ratios

one can assess productivity of any firm. But note such approaches do not show the real

picture about resource utilisation, rather these ratios tell about the financial health of the

firms.

Unit Cost Approach

As cited by Sumanth (1990, p. 119) this approach was advocated by Adam et al in 1981.

It covers the unit cost of processing and re-works separately. Such measurement is

called Quality- Productivity Ratio (QPR).

It is clear from the above expression that this approach is more concerned with quality

rather than quantity. Firms to assess the quality level not productivity can use it.

Sumanth’s Model, 1990

Sumanth (1990, p. 153) developed Total Productivity Model and defines model as under:

Total Factor Productivity = Total tangible output Total tangible input

27

This model is similar to that of Craig and Harris (as cited by Sumanth, 1990, p. 101).

The only major difference is more clarity about the input and out put. According to

Sumanth (1990, p. 153) there are following five major outputs:

o Value of finished unit produced

o Value of partial units produced (work in process)

o Dividend from securities

o Interest from bonds

o Other income

Sumanth (1990, p. 153) has further explained following five major inputs:

Human (labour cost)

Material

Capital

Energy

Other expense

This model is more comprehensive as compared to previously discussed models, and

looks more applicable. Carlaw & Lipsey (2003, p.457) have criticised TFP approach in

another way. They express their views in the following words:

We argue that TFP is not a measure of technological change and only under ideal

conditions does it measure the super normal profits associated with technological

change. The critical driving force of economic growth is not the super normal

profits that technological change generates rather the continuous creation of

opportunities for further technological development.

In the above statement it is obvious that TFP is not a right approach to assess

productivity rather it becomes a base for further technological development. Many

authors have appreciated this model, like Ali (1978, p. 43). But there is one deficiency in

this model and that is the value of intangible output in not accounted for. There is a

common observation that the intangible outputs are also the result of productivity of the

organisation, like, brand value, creditworthiness of the organisation etc.

Färe et al (1994)

28

Färe & Grosskopf (1994, p. 83) introduced their model to calculate productivity of two

different firms. Summary of their work is mentioned here:

The model introduced by Färe et al (as cited by Färe & Grosskopf, 1994, p. 83)

only requires data on inputs (factors of production) and outputs in the

measurement of productivity. The model allows for multiple inputs as well as

multiple outputs. These inputs and outputs are assumed to be measured for each

firm and for each period. There may be any number of firms and any number of

periods.

In order to give a simple introduction to their model, it is assumed that there are

two firms A and B, which use one input (x) to produce one output (y). Inputs and

outputs are observed at each of two periods t = 0, 1. Thus (x0A, y0A) denotes

firm A’s input and output in period 0. Similar notation is used for the other period

and firm.

In period t = 0, firm A’s average productivity y 0A /x 0A equals 2/3, which is

higher than firm B’s which equals y 0B x/x 0B = 3/5. Although B produces more

output than A, its average productivity is lower, thus B is inefficient when

compared with A. The question is how much inefficient is B? They calculate it by

asking how much more output should B produce to equalize its productivity with

that of A. They have answered this question by proposing a quite complicated

model.

ProMES Measuring Model

Tuijl (1997, p. 295) has put forward ProMES (Productivity Measurement and

Enhancement System) a method by which the essence of group performance can be

made measurable. Pritchard et al. (as cited by Tuijl, 1997, p. 295) developed this method

in the late 1980s for the US Air Force. According to Pritchard (as cited by Tuijl, 1997, p.

295) it has been applied to numerous other countries and other organizational settings.

The measurable group performance serves as an input for regulatory activities of a group

aimed at the continuous improvement of its performance. Therefore, the method is

extremely suitable in providing self-managing teams with necessary control instruments.

29

The above-mentioned model is most suitable for self-management teams. However this

model cannot be used just to measure productivity of any organisation without fulfilling

its pre–requisites of the model. The main pre-requisite is the level of team skill and

commitment of them.

Diverse Productivity Measurement Models

Afzal (2004, p. 14) has discussed following productivity measuring models in detail:

Norman and Bahiri’s approach

Kendrick & Creamer Model, 1965, 1973

Craig and Harris Model

Hines Model

The American Productivity Centre (APC) Model

Mundel Model

Taylor – Davis model

Production Function approach

Farrell Model

Bhatia Model (Similar to Kendrick and creamer).

Ramsay Model

Tsujimura Model

Cobb-Douglas Production Function

CES Production Function or Arrow Model

Ernst ModelBitran and Chang Model

Faraday’s approach

Kadota Model

Husban & Ghobadian Model

Adam Model

Mali Model

Kurosawa Model

Hershaure and Ruch Model

IFC Model Inter-Factory (Firm) Comparison Dewitt Model

In next lines there is a brief discussion about some of these models. Models will be

viewed critically. Main focus is to elaborate their advantages and disadvantages.

30

1-Norman and Bahiri Approach

Norman & Bahiri have suggested the following two models/methods to measure

productivity at organisational level:

Accountant method

Industrial Engineer method

According to these methods, accountants are mainly concerned with the ratios and their

main focus is to measure the performance of the organisation by using different financial

ratios. These ratios are principally derived from the financial statements. Industrial

Engineers, on the other hand, are mainly dealing with system analysis, resource

utilisation, waste control etc. The main objection to this model is that these are just

performance indicators. Management can use these indicators to make decisions and

they can also help to understand the strengths and weaknesses of the organisation. In

fact these ratios are not the productivity of the organisation. These ratios can be

considered as performance and efficiency of the organisation.

Norman and Bahiri have used these methods to measure partial productivity and ignored

the concern of other people who are also interested in measuring productivity. Also no

solid recommendation can be proposed on the basis of these ratios since these are only

indicators. To conclude, Norman and Bahiri failed to evaluate and criticise productivity

and made no recommendation to improve the productivity.

2-Kendrick & Creamer Model

According to Kendrick & Creamer Model company’s productivity can be measured with

the help of three types of productivity indices.

Total Productivity Index= Total output

All associated inputs

Total factor productivity Index =Net output_____ Labour + Capital

Partial productivity Index = Output_________ One factor of input

31

Kendrick & Creamer have defined the output as intermediate goods and services. They

have taken labour and capital in total input factors and disregarded other factors like

material etc. They have properly guided about the calculation of TFP and Partial Factor

Productivity (PFP) of material, labour and capital. Their emphasis is that TFP and PFP can

be helpful in improving the productivity after being properly analysed. The main

objection to this model is the method of calculation of TFP and PFP. In both cases they

have used net output as numerator, which is confusing.

3-Craig and Harris Model

Craig and Harris have suggested the following Total Productivity model.

TP= OT_______ L + C+ R +Q

Where:

TP= Total productivity

OT= Total Output

L= Labour input factor

C=Capital input factor

R= Raw material input factor

Q= other miscellaneous goods and services input factor

In the above-mentioned model the authors have taken all input factors. This looks one of

the best or the best applicable model. Husband and Ghobadian (as cited in Afzal, 2004,

p. 33) have used this model. This model is particularly useful for medium size

organisations. There are some deficiencies in this model, like, it does not consider any

technological change or change in the human resource skill. Also notable is that there

are some intangible gains for every organisation and surprisingly no models consider the

intangible factors. Every brand has its value. Furthermore, this value is based upon the

productivity, performance and effectiveness of the organisation. This is in fact an

outcome of all inputs. However this model is the most suitable among all the available

models to assess productivity of a small organisation.

4-Hines Model

Hines (as cited in Afzal, 2004, p. 21) has proposed the following model. It looks modified

shape of the Craig and Harris model. In this model Hines has taken all outputs as

32

numerator and all inputs as denominator. But the major difference of this model from

Craig and Harris model is definition of inputs and outputs. Hines has defined the inputs in

the following manner:

Oi = ∑ Pj Ui j

Where:

Oi = output for period i (the current period)

Pj = price/ unit for item j in the base period

Ui, j = Number of production units of item type “j” produced in period “i”.

Input has been defined as follows:

Labour input:

Li = ∑ ni, k, Wk,

Where

Li= Labour input measured in period i

ni, k = number of employees in category “k” in period i

Wk = base period wage for category k include fringe benefits

Capital Input:

Ci= ∑ Ci, j where

Ci, j = uniform annual cost for item “j” in period “i”.

This type of capital cost concept is quite different from the book value concept.

There are lots of similarities between Craig and Harris and Hines model except the

formula used and disregard of the miscellaneous inputs by Hines, which is the main

weakness of this model.

5-The American Productivity Centre (APC) Model

APC model is based upon the relationship of profitability with productivity and the price

recovery factor. This model is presented as under:

Profitability = Sales

33

Cost

= Productivity * price recovery factor

This model makes a link among productivity, profitability and price recovery factor. This

is the most suitable model for the managers who are interested to know about the profits

of the organisation rather than productivity. This model is most suitable for the investors

of the organisation. Due to its comprehensive approach it is much applicable and most

commonly used. This model also helps in reducing the resistance created by the

managers in Productivity Measurement.

6-Mundel Model

Mundel proposed two productivity indices to measure productivity and those are as

under:

PI = OMP/IMP * 100

= Current performance index/ base performance index * 100

PI = OMP/OBP * 100 = Output Index/ Input Index * 100

IMP/IBP

Where:

PI = Productivity Index

OMP = aggregate output of measured period

BOP = aggregate outputs of base period

IMP = Inputs of measured period

IBP = inputs of base period

This model is based upon the growth of productivity with reference to base period. The

main weakness of this model is the method of calculation of productivity and breakdown

of the input and output factors. Also Mundel has not taken into account the intangible

output which has been discussed in case of Sumanth model.

34

7-Taylor – Davis model

Taylor and Davis have proposed Total Factor Productivity (TFP) model to assess the

productivity of any organisation. According to their model;

TFP = (S + C + MP) - E [(W + B) + (Kw +Kf). Fb. df]

Where:

S = net adjusted sales

C = Inventory change

MP = manufacturing plant (internal maintenance and repair, internally produced

machinery and R&D)

E = Exclusion and depreciations

W = wages and salaries

B = all benefits

Kw = working capital

Kf = Fixed capital

Fb = investor contribution, expressed as a percentage

df = price deflator

Taylor and Davis have ignored the significance of material as input on the premise by

saying that it is the “fruit” of someone else’s labour. Taylor and Davis have recognized

the significance of raw material, supplies, depreciation and rentals, so they add them to

both outputs and inputs to obtain what they call an “Inclusive Model”, which is a real

total productivity.

8-Production Function Approach

This model can be explained with a simple case in which a production system produces a

single output from two inputs. In this case production of a single product with the

combination of two inputs is called “efficient production function”.

If a curve is drawn then it can be observed that at different points the efficiency is

different. On some points the combination is different since the ratio of these two inputs

varies along the line. At different points efficiency of one input is different from the other

one. Some one can find a point on the curve where the efficiency of these two inputs is

35

maximum. This approach can be considered as quantitative measurement of the

efficiency of the same output.

This approach is difficult in case of multiple inputs and multiple out puts which are quite

common in the industry. And furthermore this approach cannot be taken as a total

measurement of firm’s productivity.

9-Bhatia Model (Similar to Kendrick and creamer)

Where:

P=Productivity

Q=Weighted index of output of products 

I1, I2, …. In =are the indices of various production inputs

W1, W2, W, =weights attached to each input.

In this model productivity is not calculated simply by dividing output with input, rather

every input and output is given a proper weight. There is no set method to give weigh to

any input or output. It is arbitrary and can create much confusion. Different values can

give different results. This is the main weakness of this model.

10-Tsujimura Labour Productivity Model

This model mainly deals with the labour output. It is much useful to assess partial

productivity of labour.

Physical productivity = Q L

Where:Q = Quantity of production

L = Labour expenses

11-Cobb-Douglas Production Function Model

nn2211 IW......IW,IW

QP

teKALQ

36

Where

Q = Output

L = Labour

K = Capital

t = Time

e = Exponential rate of technological progress

A, , and = parameters to be estimated

This is one of the most popular models available in the literature. Many authors have

given their comments on this model. This model is used where time series data is

available. This model cannot be used for this study due to non-availability of time series

data.

12-Bitran and Chang Model

Where:

PI = Productivity index

I = Set of all input indices

j = Set of all output indices

qti = Quantity of input i employed in period t.

Qtj = Quantity of output j produced in period t.

wi = Conversion factor (or weight) of input i.

wj = Conversion factor (or weight) of output j.

i = A subset of I

j = a subset of J

i

tiI&i

jtj

J&jI qwQwP

37

This model provides an index of productivity. In previous model the main concern was to

calculate a ratio of output to input. But in this model the main concern is to get a ratio of

two productivity values of two different periods. This index will tell the real growth in two

different periods. This model is more useful to assess long term tends in productivity

change.

13-Faraday’s approach

Faraday had made a distinction between two different measurements of productivity at

organisation level:

Total productivity: the ratio of output to the total inputs of labour, material and capital

Partial productivity: the ratio of output to any of input such as labour or capital

The major problem with this approach is the ambiguity about the prescribed output and

the method to measure the output. Though he has specifically defined the input like

labour or capital. Yet he has missed the other input factors like energy, rentals, and

many other expenses. In conclusion there is no proper guidance in this approach to

measure the productivity.

14-Kadota Model Where:

P = Productivity of Labour

U = Utilization of Labour time (in man-hours)

= Efficiency of Labour (in man-hours)

T = elapsed time

t = Idle and unproductive time

S = Standard time accomplished (in man-hour

This model provides labour productivity with reference to their time utilisation against

standard required time to do a certain job. This model also provides the efficiency of the

labour. This is most suitable model in case of a job where much work is done manually,

like loading, un-loading and even in garment manufacturing.

tT

S.

T

tT.UP

38

15-Adam Model

P= Actual Pay Standard Pay

This model gives a true picture of the labour expenses for a certain job. This model is

most useful to assess the labour expenses share in the total cost. In every process

labour cost is estimated and on the basis of all estimations final cost is calculated and

prices are set. This model provides a ratio of actual pay and the standard pay. In

conclusion it can be said that this model is useful to assess partial productivity of

wages/salary.

16-IFC Model

Inter-Factory (Firm) Comparison Dewitt Model

This model helps us estimate the contribution of capital, facilities and personnel.

1. Personnel Productivity 

a. Revenue per employees

b. Operating income per employee

c. Net earning per employee 

2. Capital Productivity 

Following Ratios are suggested 

a. Revenue per stockholder’s equity dollar

b. Operating income per stockholder’s equity dollar

c. Net earnings per stockholders’ equity dollar 

3. Facilities Productivity 

a. Revenue per plant and equipment dollar (invested)

b. Operating income per plant and equipment dollar (invested)

c. Net earning per plant and equipment dollar (invested)

This model is much useful for benchmarking. As it has been said earlier that productivity

is a phenomenon for comparison. There is a need of benchmark for comparison. This

model suggests that comparison is made with other firms in the market. This model is

much useful in finalising the position of the firm in the market. This model provides many

indicators to be compared. With the help of this model one can assess the position of the

39

firm and consequently this comparison can help management to formulate strategy to

improve its position among the competitors.

40

Summary of Productivity Measurement Approaches

It is obvious from all above-mentioned approaches that a lot of divergence exists in

different Productivity Measurement approaches. Brummer; Glauben & Thijssen (2002, p.

628) have expressed their views about productivity growth measurement in the following

ways, “in last twenty years, the literature on productivity growth measurement has been

extended from standard calculation of TFP towards more refined decomposition

methods”. No one can be

considered as competent enough to be used for all conditions. Selection of the most

suitable approach mainly depends upon the following factors:

The purpose of Productivity Measurement

The resources available for the Productivity Measurement

Capabilities of the people involved in Productivity Measurement

Organisational set up

Types of product and composition of market segments

Available Data

This was further advocated by Singh et al (2000, p. 240). They have given their

conclusion in the following words:

The theoretical and empirical sections of this paper clearly point out that there is no one

method for every company. However, in general, productivity measurement, as well as

indexes and comparisons, can provide an objective source of information about long

term operating trends, draw attention towards the problems of performance and inspire

a useful exchange of ideas”.

Dwyer (1996, p. 13) has used 12 different Productivity Measurement methods to assess

productivity at plant level. According to him Dwyer (1996, p. 13), “ all measures of

productivity considered are credible in the sense that highly productive plants,

regardless of measure, are clearly more profitable, less likely to close, and grow faster”.

The main point in the above discussion is that if plants were highly productive, no matter

in which way you measure its productivity, results would be same. Every measure will

prove that this plant is making profit, which is the ultimate goal of the plant owners. But

there is a need of careful selection of the tool to assess productivity. And this all depend

upon objectives, capability, and data/resources available. Dwyer (1996, p.53) has

favoured regression models application as a better predictor of plant growth and survival

than factor shared-based measure of TFP.

41

Malley; Muscatelli & Woitek (2003, p. 98) have done an international comparison

taking TFP into account. They have compared TFP across the G7 countries. According to

them:

This paper has produced new international comparative data on total factor

(multiple-input) productivity measures, comparing various manufacturing sectors

across the G-7 economies. By collecting sectoral data on the use of intermediate

inputs, we can calculate gross output measures of TFP growth, and of TFP levels.

This provides a more accurate view of underlying TFP growth across the G-7

economies. Existing value-added and cyclically unadjusted measures tend to

overestimate TFP growth, and hence the effect of underlying technical progress.

Given the importance of the accurate measurement of underlying productivity

growth for economic policy (see Her Majesty’s Treasury (2000), this emphasizes

the need to obtain more robust measures of TFP, by using the techniques

outlined in this paper.

From the above statement it is clear that TFP model has been used for assessing

TFP level across the different economies and finally TFP of different economies

was compared. In this report same model has been used to asses TFP of the

PKGI. But comparison is not possible since there is no TFP data available of any

other industry.

Conclusion

Productivity Measurement is one of the most important functions in any

organization. This function is carried out in different ways in different firms. There

is a strong need of accurate, suitable and most appropriate method for accurate

results. Selection of tool to assess Productivity Measurement is the most sensitive

and crucial step. There is no single or agreed method to measure productivity. It

depends upon the objectives, data available, and circumstances that which tool

or method can give better results. But this is sure that firms fail in selection of an

appropriate model cannot measure productivity in a better way and finally they

cannot improve since improvement is based upon the accurate measuring of the

42

current position. However, firms can use different models at same time to assess

productivity from different angles.

43

References

Afzal, Muhammad. (2004). Measurement of Productivity in the Large Scale

Manufacturing Sector of Pakistan: Unpublished Ph.D. Thesis, The University

of Lahore, pp. 14-33

Ali, K. J. (1978). Labour Productivity in Iraqi Economy. Unpublished Master’s

Thesis, University of Manchester Institute of Science and Technology, UK, pp.

41-48

Borger De Bruno & Kerstens Kristiaan (2000). The Malmquist Productivity Index

and Plant Capacity Utilization. Scand. J. of Economics 102(2), p. 304

Boussemart Jean-Philip, Briec Walter, Kerstens Kristiaan & Poutineau, Jean-

Christophe. (2003). Luenberger and Malquist productivity indices: Theoretical

comparisons and empirical illustration. Bulletin of Economic Research 55:4,p. 391

Brummer Bernhard, Glauben Thomas & Thijssen Geert. (Aug 2002).

Decomposition of productivity growth using distance function approach: The case

of dairy farms in three European countries. American Journal of Economics 84(3),

p. 628

Carlaw Kenneth I. & Lipsey, Richard G. (2003). Productivity, Technology and

Economic Growth: What is the Relationship? Journal of Economic Surveys Vol.

17, No.3 Blackwell Publishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK

and 350 Main St, Malden, MA 02148, USA, p.457.

Chen, Liang-Hsuan, Liaw Shu-Li & Chen, Yeong Shin. (2001). Using financial

factors to investigate productivity: an empirical study in Taiwan. Industrial

Management & Data Systems.101/7 [2001] # MCB University Press

[ISSN 0263-5577], p. 379

44

Dwyer, W. Douglas (Dec, 1996). Productivity Races I: Are some productivity

measure better than other? William M. Mercer Incorporated, 1166 Avenue of the

Americas, New York, NY 10036 2708, p. 13-53

Färe Rolf and Grosskopf Shawna. (1994). Measuring Productivity: A Comment.

International Journal of Operations & Production Management, Vol. 14

No. 9, © MCB University Press, 0144-3577, p.83

Gharneh, Shams Naser. (1997). The measurement of productivity and

performance in Textiles: The UK and Iran. Unpublished Thesis, University of

Manchester Institute of Science and Technology. p. 31

Lawlor, Alan. (1985). Productivity Improvement Manual. Gower Aldershot, UK, pp.

10-81

Mahadevan, Renuka. (2002). New Currents in productivity analysis: Where to

Now. Asian Productivity Organization, Tokyo, pp. 05-16

Malley Jim, Muscatelli Anton and Woitek Ulrich. (2003). Some new international

comparisons of productivity performance at the sect oral level. J. R. Statist. Soc. A

(2003) 166, Part 1, p. 98

Mawson Peter; Carlaw Kenneth I & McLellan Nathan. (June 2003). Productivity

measurement: Alternative approaches and estimates. New Zeeland Treasury

Working paper 03/12, p. 06

McKee, David (2003). Productivity tools: ‘‘horses for courses’’. Work Study Vol.

52. Number 3. 2003, MCB UP Limited. ISSN 0043-8022

DOI 10.1108/00438020310471935, p. 138

45

Parsons, John. (1980). Profitability Analysis in inter-firm comparison: a new

approach. In Bailey, David & Huber, Tony (Eds). Productivity Measurement:

An international Review of Concepts, Techniques, Programmes and Current

Issues, Gower, UK, p. 60

Parsons, John. (2001). Current Approaches to Measurement with in

Service Sector. Paper presented in APO Symposium on Productivity

Measurement in Service Sector, p. 21

Salinger, Lyn. (2001). Productivity Comparative advantage and competitiveness

in Africa. US Agency for International Development, Bureau of Africa office of

Sustainable Development, Washington, DC 20523-4600,p. 09

Singh Harinder, Motwani Jaideep & Kumar Asjok. (2000). A review and analysis

of the state-of-the-art research on productivity measurement. Industrial

Management & Data Systems 100/5[2000], MCB University Press

[ISSN 0263-5577], pp.238-240

Sink, D. Scott. (1985). Productivity management: Planning, Measurement and

Evaluation, Control and improvement. John Wiley & Sons. Canada, pp.94-189

Stark, John & Bottoms, Charles. (1980). Monitoring activity performance.

In Bailey, David & Huber, Tony (Eds). Productivity Measurement: An

International Review of Concepts, Techniques, Programmes and Current Issues,

Gower, UK, p. 100

Sumanth David, J. (1990). Productivity Engineering and Management. Delhi

India: Tata McGraw-Hill Edition, pp.101-153

46

Tuijl Harrie F.J.M. van. (1997). ProMES: a method for “accepted control loops.

Leadership & Organization Development Journal 18/6 [1997] © MCB

University Press [ISSN 0143-7739], p. 295

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