Bititci U S, “Modelling Performance Measurement Systems
1 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
MODELLING OF PERFORMANCE MEASUREMENT SYSTEMS
IN MANUFACTURING ENTERPRISES
Umit S Bititci
Department of Design Manufacture and Engineering Management, University of Strathclyde, James Weir
Building, 75 Montrose Street, Glasgow G1 1XJ
Abstract
In this paper the analysis, modelling and design of performance measurement systems is demonstrated
through a case study example. The paper starts off with reviewing the current developments in the area of
performance measurement. It then goes on to specify a set of requirements which future research on
performance systems should address. The remainder of the paper summarises an attempt to modelling
performance measurement systems using three views in a similar manner to that of information systems
modelling techniques commonly used today.
1. Introduction
Business Performance improvements arising from increased manufacturing integration
continues to be one of the primary competitive issues of the 90’s. Recent research in to
manufacturing systems integration [1] has identified the need for effective deployment
of business objectives down through the organisation and the subsequent measurement
of performance in critical areas as key elements of sustainable competitive advantage.
The objective of the current research is to provide industry with a comprehensive and
rigorous set of tools, techniques and procedures to allow auditing of existing
performance measurement systems against a reference model and consequently design
more robust , flexible and integrated performance measurement systems which could be
used as a management tool by strategic, tactical and operational levels of management.
This objective is being pursued through a collaborative research programme between
the academic and industrial research partners at the Universities of Liverpool,
Loughborough and Strathclyde. The deliverables of this research will provide
manufacturing industry with an approach to auditing their performance measurement
systems to ensure consistency and to provide reference models against which such
performance measurement systems can be benchmarked and designed.
The objective of this paper is to report the work carried out thus far at the University of
Strathclyde with particular emphasis on the tools and techniques used for the analysis
and modelling of performance measurement systems.
2. Background
The hypothesis behind the research described in this paper was borne directly from the
previous research aimed at developing integrating methods based on analysis of
information, material flow and organisational systems [1]. This previous programme
identified the value of Viable Systems Configurations Modelling [2, 3] as a frame work
Bititci U S, “Modelling Performance Measurement Systems
2 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
for analysis of manufacturing integration and the benefits of using performance
measurement to deploy business objectives and to pinpoint and monitor performance
improvements. Other researchers have also noted the links between performance
measures and strategic plans and/or critical success factors of the business. The research
by Neely [4], Grady [5] and Eccles and Pyburn [6] supports the same conclusions
drawn in the earlier research programme.
In summary, the need for an integrated set of performance measures which support
rather then contradict business objectives is now clearly established [7].
In addition, there is already considerable work being carried out by the accounting
profession on performance measurement. Indeed, most manufacturing organisations
have extensive performance measurement systems based on cost and financial
accounting practices. However, because these techniques are often based on old
fashion overhead absorption methods they fail to support the current business
objectives and do not enable continuous improvement [8]. More recent innovations
such as activity Based Costing improve allocation methods but still do not promote
continuous improvement and strategic orientation. Notable work has been carried out
by Kaplan [9, 10] and Johnson [11] in recognition of these weaknesses. Neely [12]
summarises the short comings of the current accounting practices with respect to
performance measurement in manufacturing enterprises.
Because , financial measures that are currently in place are not supporting the change
process, there is a case for new styles of measurement systems that are appropriate to
the needs of the modern manufacturing industry. Green [13], McNair et al [14,15],
Druker [16], Russell [17] and Kaplan [9] show that there is a need for alignment of
financial and non-financial measures that fit within a strategic framework.
Furthermore, based on research carried out by Bititci [18], Blenkinsop and Burns [19]
and Gelders et al [20] there is evidence that, even in companies where quality oriented
performance measures are employed, these are still being use in a manner which does
not promote integration. There has been several cases cited where the company’s
strategy, improvement projects and performance measures were in conflict.
Research by Yoshikawa et al [21] and Sakurai [22] illustrate that the Japanese research
shows a significant difference from the Western research, rather than emphasis on
performance measurement which tends to reinforce an “as-is” view, they tend to put
greater stress on “targets” and the measurement of the gap from the targets to facilitate
continuous improvement.
3. The Future
In the context of the above discussion future research on performance measurement
systems must place emphasis on the following points: Performance measurement systems must be researched in a holistic context applying
to all aspects of a manufacturing business in order to avoid sub optimisation.
Bititci U S, “Modelling Performance Measurement Systems
3 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
 Performance measurement systems must provide a vehicle for deploying business
objectives and strategies through all levels of the organisation to maximise
integration.
 Performance measurement systems must facilitate continuous improvement in line
with the strategic objectives of the business.
 Performance measurement systems should facilitate understanding of the structures
and relationships between various measures and promote conscious management of
inevitable conflicts.
 The traditional accounting based performance measures and the non-financial
performance measures should not be mutually exclusive but should be integrated
under one system of performance measures.
 Performance measurement system design should consider information systems
capabilities to provide the necessary level of data in terms of accuracy and reliability,
Kehoe et al [23].
 Performance measurement systems design should consider the organisational,
behavioural and cultural aspects of the organisation concerned. Some background
work on this area has been carried out by Rotter [24].
4. Analysis and Modelling Techniques
The performance measurement system within an organisation, with respect to its
structure and complexity, may be considered to be similar to an information system
because: An information systems consists of a number of data items, similarly the a
performance measurement system consists of individual performance measures.
 In an information system the individual data items are usually organised in the form
of a hierarchy, similarly the individual measures within a performance measurement
system are also structured in the form of a hierarchy corresponding to strategic,
tactical and operational aspects of the business.

In an information systems the data items may have simplex or complex relationships,
that is if one data item changes this may cause changes in one or more data items
elsewhere in the information system. This type of simplex or complex relationships
also exist between the individual measures within a performance measurement
system, i.e. an improvement in one measure may lead to changes (improvement or
deterioration) in one or more of the measures within the performance measurement.

In an information system the dynamic (i.e. time dependent) behaviour of the data
items are influenced by the underlying data processing process. Similarly, in a
performance measurement system the dynamic behaviour of the individual
performance measures are influenced by the underlying business process.
Bititci U S, “Modelling Performance Measurement Systems
4 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
Information systems analysis and design techniques model a information system from
three viewpoints [25]. These are: Process view - Modelling of the data process
 Data view - Modelling of the data structures
 Control view - Modelling of the data dynamics
When these three views are fully developed , defined and consolidated they provide a
complete analysis of the system. Any information system may be analysed in this
fashion to show the “system’s footprint”. The system’s footprint, shown in Figure 1,
represents the characteristics in terms of complexity of data relationships, data dynamics
and data processing, where the each of the axis denote increasing complexity for each of
three views.
Control View
(Data Dynamics)
Process View
(Data Process)
Data View
(Data Structures)
Figure 1. System’s foot print for information systems model.
The hypothesis of the work presented in this paper was that, since the performance
measurement systems exhibit similar characteristics to that of information systems they
also could be modelled using techniques similar to that used for information systems
analysis and modelling. This approach will result in the development of the same three
views for the performance measurement system, also resulting in a system’s footprint as
shown in Figure 2.
Control View
(PMS Dynamics)
Data View
(PMS Structure)
Process View
(PMS Process)
Bititci U S, “Modelling Performance Measurement Systems
5 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
Figure 2. System’s footprint for performance measurement systems model.
5. Modelling Performance Measurement Systems
In order to assess the validity of the hypothesis an attempt was made to model the
performance measurement system using real life data from a collaborating
manufacturing organisation. The case study company is a Scottish based subsidiary (cost
centre) of a major multinational specialising in the production, bottling, packaging and
world-wide distribution of white and brown spirits.
In this section the analysis methodology employed is described together with the
techniques used to model each one of the three views of the performance measurement
system. In summary the analysis methodology followed the following steps:



Identification of strategic performance measures.
Development of process models.
Development of the structure models.
Development of the control model.
5.1. Identification of Strategic Performance Measures
In order to identify the strategic measures of performance the simple total quality
concept of customer - supplier relationship was employed. The business as a whole was
treated as a single process and its key customers and their requirements were identified.
At this point it must be reminded that customers are not limited to those organizations or
individuals who purchase the business’s products and/or services, they most certainly
include employees, shareholders, government agencies and so on, i.e. stakeholders
(Druker 1990). To remain competitive and meet its business objectives the business must
recognize its diverse range of customers and meet their requirements. Therefore the next
step in the process was the identification of the customers requirements. For example the
parent company's requirements may be, return on investment, recovery rates, profitability,
etc. The commercial customers requirements may be quality, delivery and price oriented
and the employees requirements may be security, job satisfaction, career progress, and so
on. Having defined the requirements of each customer the next step was the identification
of measures which would provide objective information on the businesses ability to meet
its customers requirements.
During this process there was certain amount of rationalization in order to prevent the non
strategic performance measures being represented at the strategic level. In practice three
main customers and their requirements were focused upon. These being: The commercial customers
 The employees of the company
 The parent company / the shareholders
Bititci U S, “Modelling Performance Measurement Systems
6 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
Rationalization of the results at this level constituted the basis of the strategic performance
measures for the case study company . Table 1 presents a summary of key points of this
process.
CUSTOMER
STAKEHOLDER
Commercial
Customers
Employees
Parent company/
Shareholders
REQUIREMENTS
Product quality
Delivery reliability
Value for money
Product Image
Job Satisfaction
Security
Fair remuneration
Development / Investment
Progression
MEASURE
 % Orders shipped on time
 % Orders shipped with
alterations
 Number of complaints
 Average response time to
complaints
 New projects delivered on time
 New project lead-times
 Customer satisfaction index
 Employee satisfaction index
 Cashflow - Actual v Plan
Cost effective operations
 Cost of production per case
Effective
financial
produced
management
 Cost of production per case sold
Table 1. Strategic Performance Measures
5.2 Development of the Process Model
In this case the process model represents the underlying process which leads to
achievement of the strategic objective. It was decided to use Data-Flow-Diagramming
(DFD) technique to model the underlying process. Although it was possible to create a
single DFD model to encapsulate all activities which effect all the strategic performance
measures, it was simpler to create a DFD model of the process focusing on one measure
at a time. This resulted in considerable duplication between the models but it also
simplified the model making it more visible. Therefore, in this case study it has been
necessary to create a process model for each one of the strategic performance measures
identified. In this paper for illustration purposes the strategic measure “% Orders
Shipped On Time” has been selected for illustration purposes. Figure 3 shows the
process model developed for this particular strategic performance measure.
Bititci U S, “Modelling Performance Measurement Systems
7 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
Third Party
Warehouses
Quality
Control
Test
Wet Goods
Wet Goods
Spirit
Processing
Warehousing
Receipts for
Consolidation
Problem
Documentation
Shipping,
DOP,
Stock Control
Bottling,
Palletising
Documentation
Documentation
Dry Goods
Dry Goods
External
Supplier
BOM,
Materials
Short-Term
Orders
BOM
Product
Product
Customer
Invoice
Order
Order
Customer
Services
Order,
Complaints
Planning,
Procurement
Purchasing
Long-Term
Orders
Demand
Figure 3. The process model for “% Orders Shipped On Time”.
5.3 Development of the Structure Model
The development of the structure model follows on from the process model. Having
created a process model for the performance measure in question, in this step the
activities within each process were analysed in order to establish factors which may
contribute towards the particular performance measure. This was achieved through the
use of Cause-and-Effect analysis as shown in Figure 4. The Cause-and-Effect analysis
show how each process (shown as a major cause) may have an impact on the
performance measure.
For example, the Bottling and Palletising process can effect the performance of the
organisation with respect to “% Orders Shipped on Time” by its ability to fulfil the plan,
i.e. Bottling Plan Hit Rate. In turn the factors listed on the downward arrow, such as
quality, breakdowns, bottling plan accuracy, material supply, etc. can constrain the
Bottling and Palletising Process’s ability to fulfil the plan.
The Cause-and-Effect diagram as shown in Figure 4. Provides the structure view for that
particular performance measure. In addition it also leads to the identification of
performance measures for use at the tactical and operational levels. The measured
identified against each process, e.g. Bottling Plan Hit Rate, Order Processing Lead
Time, Vatting Hit Rate, Accuracy of Bottling Plans, etc. are considered to be tactical
performance measures corresponding to each process. The causes listed under each
process provides the basis for operational measures for that process, i.e. in the case of
Bititci U S, “Modelling Performance Measurement Systems
8 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
the Bottling and Palletising process operational measures consists of measures such as
downtime, material supply delays, labour shortage, etc.
External
Planning,
Procurement
Bottling,
Palletising
Accurate+Realistic
Plans
Bottling Plan
Hit Rate
Purchasing
Materials Ordered on
Time/in Correct Quantity
Line Choice
Transportation&
Shipping Delays
Orders not
Made Bookable
Unrealistic Line
Planning
Material
Supply
Material
Supply
Availability of Blend
Specification
Late Liquid
Receipts
Transportation
Problems
Spirit
Quality
Labour Supply/
Capacity
Breakdowns
Absence of
Daily Priorities
Labour Supply&
Line
Skills
Capacity
Spirit
Accuracy of
Planning Data Supply
Systems
Availability
Material Supply
Bottling
Plan
% Orders not
Shipped on Time
Loss&Damage
in Store
Spirit Quality
and Strength
Documentation
External Warehouses
Spirit Availability
Orders not Made Bookable
Inventory
Accuracy
Lack of
Capacity
Product
Knowledge
Data
Availability
Quality
Availability of
Planning Data
Material
Supply
Planning&
Cost Data
Changes to
Blend Program
Picking
Accuracy
Data Accuracy
Material
Availability
Product Mix
Forecast
Accuracy
Flexibility
Capacity Loading Plan
Inaccurate
Orders
Loading
Priorities
Stock Record
Accuracy
Failure to
Book Ship
Blend Hit Rate
Data Accuracy,
Timeliness
Vatting Hit Rate
Order Processing
Lead Time
Warehousing
BOM,
Materials
Spirit Processing
Shipping, DOP,
Stock Control
Transport
Availability
Figure 4. Cause-and-Effect Diagram providing the Structure view.
The use of the Cause-and-Effect analysis technique as described above provides a useful
approach to analysis and provides some form of guidance for modelling of the structure
of performance measures. It is possible to transpose the information contained in the
Cause-and-Effect diagram in to a more traditional structure chart commonly used in
information systems analysis. Figure 5 illustrates a traditional structure chart which
corresponds to the Cause-and-Effect diagram shown in Figure 4.
% orders shipped on time
Warehousing
Shipping, DOP
Stock Control
Blending plan
hit rate
Order Proc.
lead time
Stock Record
External
Warehouses
Accuracy
Transport
availability
Loading Plan
BOM
Materials
Loading
Process
Loading plan
Hit rate
Capacity
Planning
Procurement
Planning data
Bottling and
Palletising
Spirit
Processing
Bottling plan
hit rate
Vatting
Hit Rate
Purchasing
External
Material Supply
Material Supply
Planning and
cost data
Accuracy
Supply problems
Supply problems
Accuracy
Priorities
BOM
Materials
BOM
Materials
BOM
Materials
Bititci U S, “Modelling Performance Measurement Systems
9 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
Figure 5. Traditional structure chart.
5.4 Development of the Control Model
The control view is a model of the interactions between states and events of a real-time
system. The system responds to outside events and passes through a series of modes or
states. As events occur, they initiate changes in the system’s state. In software
engineering State Transition Diagrams (STD) are used to model the control view of a
system. Several attempts were made to create the control view of the performance
measures employing STD’s. But the only way of using an STD seems to be to describe
the control view of processes of an organisation. Figure 6 illustrates this on an example.
Using the STD as shown in Figure 6 can help to identify performance measures on the
functional level. It is displayed how failures could have an influence on the strategic
measures. In the example two failures can occur which affect the strategic measure “%
Orders Shipped on Time”. The first one is a failure in Bottling and Palletising which
leads to rework. Looking at this problem, a useful measure could be “% of Orders with
Bottling and Palletising Failures” or “Bottling Plan Hit Rate”. The other failure is that
there is no transportation available or not available for all goods. Therefore a second
measure could be “% of Orders with Transportation Problems”.
To create an STD which can be used to develop performance measures, it is necessary to
have a very good knowledge and understanding of the organisation’s operations,
processes and procedures. Without this, a sound STD cannot be developed. Creating an
STD was found to be a very time-consuming and difficult task. However, once it is
completed it could be a helpful means of identifying functional performance measures.
A possibility of creating the control view, which means to display how the measures are
influencing each other, is the use of a Quality Function Deployment (QFD) oriented
approach using a matrix diagram. Figure 7 illustrates how a QFD oriented matrix could
be employed. In the vertical column the influencing measures are written, perpendicular
to them the influenced measures. The levels are chosen according to the levels in the
structure view.
In the matrix the level of relationship between individual measures are illustrated in the
normal QFD fashion. That is:




++
+
Blank
--
Strong positive impact
Positive impact
No impact
Negative impact
Strong negative impact
For example, if the functional measure “Order Processing lead-time” improves, i.e., the
lead-time becomes shorter, this influences the strategic measure “% Orders Shipped on
Time” in a strong positive way.
Bititci U S, “Modelling Performance Measurement Systems
10 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
This QFD oriented matrix approach provides a valuable technique to describe the
dynamic view of a performance measurement system, because it is simple and
straightforward to understand.
Reworking Goods
C: Failure in Bottling &
Palletising
A: Rework
Bottling & Palletising
C: Bottling & Palletising Finished
Goods to Be Despatched Later
C: Bottling & Palletising Finished
Goods to Be Despatched Soon
A: Bring Goods to Cased Goods
Stock
A: Bring Goods to Despatch Hall
Goods in Cased Goods Stock
C: Goods Needed for Order
A: Bring Goods to Despatch
Hall
Goods Awaiting Despatch
C: Transportation not
Available
(for All Goods)
C: Transportation Available
A: Load Goods According to
Loading Plan
A: Waiting
Despatching Goods
C: Despatch Finished
A: Set Status “Despatch” to 1
Give Despatch “Information” to
Shipping Department
Shipping Goods
C: Shipping Finished
A: Send “Bill of Lading” to
Shipping Department
Figure 6. State Transition Diagram for controlling the despatch process.
Loading Plan Hit Rate
3. LEVEL
Not Made Bookable
Order Processing Lead-Time
2. LEVEL
3 % Orders Shipped without Alterations
% Orders Shipped with Correct Quantity
5 % Orders Shipped on Time
INFLUENCING MEASURE
1. LEVEL
INFLUENCED MEASURE
Bititci U S, “Modelling Performance Measurement Systems
11 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
1. LEVEL
% Orders Shipped on Time
% Orders Shipped with Correct Quantity
% Orders Shipped without Alterations
2. LEVEL
Order Processing Lead-Time
Not Made Bookable
++
++
3. LEVEL
Loading Plan Hit Rate
+
+
39
30
++
Figure 7. Control View using a matrix (not complete).
The use of the QFD approach also promotes deployment of priorities. between various
levels of the performance measurement system. Once the business objectives and
strategy is established this will allow prioritisation of the strategic performance
measures according to the these objectives and the strategy. Once the top level priorities
are in place these could be deployed to lower levels through the relationship matrix
using the relationship level as an deployment aid. This type of deployment approach is
well described in the widely available QFD material. For example, a strong relationship
may be rated “6” and a weak relationship may be rated “3”. In this case if the strategic
measures “% orders shipped on time” and “% orders shipped with no alterations” have
been prioritised as being “5” and “3” consecutively, then these priorities may be
deployed to the next level where the tactical measure “order processing lead time” has a
strong relationship with “% orders shipped on time and a weak relationship with the
measure “% orders shipped without alterations”. This will be achieved as follows:Priority = (5*6)+(3*3) = 39
However, in using this approach one must practice particular attention to ensure that
priority deployment is carried out between two distinct levels. In the example shown in
Figure 7 the priority deployment is carried out from level 1 to level 2, i.e. from the
Bititci U S, “Modelling Performance Measurement Systems
12 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
strategic level to the tactical level. Failure to follow this simple rule may lead distortion
of priorities.
6. Discussion and Conclusions
This paper started off with reviewing the current developments in the area of
performance measurement. It then went on to specify a set of requirements which future
research on performance systems should address. The remainder of the paper
summarised an attempt to modelling performance measurement systems using three
views in a similar manner to that of information systems modelling techniques
commonly used today.
It is the conclusion of the researchers that the value of this method of modelling is in the
method, i.e. creation of the three views, rather then in the particular tools and techniques
used. Figure 8 summarises the tools and techniques used to create the particular view in
this instance, it also identifies alternative methods which could be used to develop the
particular view. However, it does not offer any comments or guidance on the suitability
and robustness of the alternative approaches.
Control View
(PMS Dynamics)
• QFD Matrix
• State Transition Digrams
• Entity Relationship diagrams
Data View
(PMS Structures)
• Structre Diagrams
• Cause and Effect Diagrams
Process View
(PMS Process)
• Data Flow Diagrams
• IDEF 0,1, etc.
Figure 8. Tools and Techniques used to create the three views.
(Alternative techniques are shown in italics)
The approach to modelling as described in this paper goes some way towards fulfilling
the requirements expressed earlier in section three. This is achieved by:
 Considering all customers of the business as explained in section 5.1 leads to an
holistic approach to the modelling of performance measurement systems. This
approach includes financial and non-financial measures.
Bititci U S, “Modelling Performance Measurement Systems
13 in Manufacturing Enterprises”, International Journal of
Production Economics, vol. 42, April 1996, ISSN 0925-5273, pp 137-147.
 The structure view itself may be considered to be over complex and too detailed. But
the prioritisation and deployment techniques of the QFD based control view
illustrates how these could be narrowed down to identify the few critical measures.
This approach also ensures that operational processes are aligned to the
operations/manufacturing strategies.
 The QFD matrix oriented approach allows deployment of business objectives and
strategies through various levels of an organisation, thus promoting more structured
approach to continuous improvement programmes.
 The QFD matrix together with the structure view facilitates understanding of the
relationships between various measures thus maximising integration and promotes
conscious management of conflicts.
However, this approach in analysing and modelling mechanistic aspects of performance
measurement systems, fails to take in to account the information systems capabilities to
support the system ant the softer organisational, behavioural and cultural issues
associated with performance measurement systems.
In summary, it may be concluded that modelling performance measurement systems in
this fashion is a worthwhile exercise as it not only models the performance measures
currently used within the organisation, it also facilitates the identification and design of
a robust and integrated performance measurement system.
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