International journal of computer integrated manufacturing , tập 23, số 7, 2010

International Journal of Computer Integrated Manufacturing
Vol. 23, No. 7, July 2010, 585–602

The barriers to realising sustainable process improvement: A root cause analysis
of paradigms for manufacturing systems improvement
B.J. Hicks* and J. Matthews
Innovative Design and Manufacturing Research Centre, Department of Mechanical Engineering, University of Bath, UK
(Received 2 February 2010; final version received 8 April 2010)
To become world-class, manufacturing organisations employ an array of tools and methods to realise process
improvement. However, many of these fail to meet expectations and/or bring about new less well understood
problems. Hence, prior to developing further tools and methods it is first necessary to understand the reasons why
such initiatives fail. This paper seeks to elicit the root causes of failed implementations and consider how these may
be overcome. The paper begins by reviewing various paradigms for manufacturing systems improvement including
design/redesign-, maintenance-, operator-, process-, product- and quality-led initiatives. In addition to examining
the knowledge requirements of these approaches, the barriers to realising improvement are examined through
consideration and review of literature from the fields of manufacturing, management and information systems.
These fields are selected because of the considerable work that deals with process improvement, change
management, information systems implementation and production systems. The review reveals the importance of
fundamental understanding and highlights the lack of current methods for generating such understanding. To
address this issue, the concept of machine-material interaction is introduced and a set of requirements for a
supportive methodology to generate the fundamental understanding necessary to realise sustainable process
improvement is developed.
Keywords: manufacturing improvement; tools and methods; knowledge requirements; generating understanding

1. Introduction
In today’s highly competitive global markets product
quality and cost, and manufacturing efficiency and
flexibility are critical factors in an organisation’s
commercial success (Roth and Miller 1992, ManarroViseras et al. 2005, Swink et al. 2005). The dimensions
associated with production and in particular quality,

efficiency and flexibility ultimately define the unit cost
of the finished product, and are therefore a central
focus of any organisation’s business plan and performance monitoring. However, the three factors of
quality, efficiency and flexibility are heavily interrelated and attempts to optimise one factor can have a
potentially detrimental effect on the other. It is
therefore important to consider the collective effect of
these dimensions on the organisation’s manufacturing
capability (cf. Figure 1(a)).
Within a manufacturing context, quality refers to
the perception of the degree to which the product or
service meets the customer’s expectations. For any
manufacturing process to be capable it must be able to
produce a quality product. As the customer requirements for quality increase the manufacturing capability must also evolve. Manufacturing efficiency is

*Corresponding author. Email:
ISSN 0951-192X print/ISSN 1362-3052 online
Ó 2010 Taylor & Francis
DOI: 10.1080/0951192X.2010.485754


effectively a measure of the profit or return realised
from the manufacturing system or process (Hansen
2005). At the manufacturing system level this can
equate to the time it takes to complete a given task or
the number of staff members needed to facilitate the
production of a particular item. The aim of flexibility
in a manufacturing system is to change the mix,
volume and timing of its output and essentially
describes the ability to process variant products
(Matthews et al. 2006). When considering the overall

manufacturing capability, flexibility has the two
dimensions, range and response. The range flexibility
states what a manufacturing system can adopt in terms
of number of different products and output levels –
termed product flexibility and volume flexibility; the
response flexibility describes the ease with which a
system can be adapted from one state to another –
termed delivery and mix in Slack (2005). This response
flexibility must be considered in terms of time, cost and
organisational disruption. In general flexibility offers
the manufacturer some degrees of freedom to take
advantage of demand opportunities and simultaneously provide an ability to reduce losses (Bengtsson
2001).


586

Figure 1.

B.J. Hicks and J. Matthews

Manufacturing capability, the organisation and the business environment.

While attempts to improve particular aspects of,
for example, the product design or the manufacturing
process can lead to improvements in the areas of either
quality, efficiency or flexibility, it is ultimately the sum
of all systems, actors and inputs associated with the
realisation of the product that determine levels of
quality, efficiency and flexibility. Hence, manufacturing capability is dependent upon an organisation’s

people, its processes, its products and its practices (cf.
Figure 1(b)). Achieving a high level of manufacturing
capability and the attainment of high levels of
performance within each of the these areas is
frequently associated with the notion of ‘World-Class
Manufacturing’ (Maskell 1991). While at a given point
in time an organisation may be performing at a high
capability level it is the ability to sustain an optimal or
near optimal level that is the characteristic of a truly
world-class organisation. Hence, the notion of worldclass manufacturing and ‘world-class’ organisations is
more about the ability of an organisation; its people,
processes, products and practices (cf. Figure 1(b)), to
adapt, improve and evolve within the context of the

changing business environment (cf. Figure 1(c)) (Riek
et al. 2006). This ability to respond and adapt is
becoming of increasing importance as product complexity increases (Sommer 2003); customer demand for
product variety increases (Jiao and Tang 1999);
product lifecycles shorten (Christopher and Peck
2003); legislation concerning areas such as materials
(European packaging and packaging waste directive
2004/12/EC), emissions (Ambient air quality assessment EC Directive 96/62/EC) and Health and Safety
(European Machine Safety 98/37/EC) tighten; supply
chains and customers become global (Gelderman and
Semeijn 2006).
As a consequence of the influence of people,
products, processes and practices on an organisation’s
manufacturing capability there exists a wide variety of
tools, methods and approaches to deliver targeted
improvements in a particular area. However, in many

cases the improvement projects fail to meet expectations and in extreme cases can fail to deliver any
improvement or bring about new less well understood
problems (Hicks et al. 2002). Furthermore, of those


International Journal of Computer Integrated Manufacturing
that do deliver improvements many are short-term
(Keating et al. 1999) and the improvements are lost
when there is, for example, a change of staff, variation
in materials or process inputs, altered practices, the
introduction of new equipment or yet another initiative. From an organisation’s perspective these programmes not only require an investment of many tens
or hundreds of thousands of pounds (Chapman et al.,
1997, Sterman et al. 1997, Keating et al. 1999) but in
the case of failed initiatives incur an indirect cost which
can represent a magnitude of cost and lost opportunity
which far exceeds the cost of the original improvement
programme. For example, optimising set-up and
process parameters could make the manufacturing
system sensitive to variation in inputs, e.g. materials,
and result in significant downtime.
For these reasons and to ensure long-term success,
manufacturing organisations need to possess a functional and holistic understanding of the production
systems and the variety of tools, methods and
approaches for improvement (cf. Figure 1(d)) in order
that they may be successfully applied and reapplied
within the context of the changing business environment. Furthermore, as previously stated, it is the
ability of an organisation; its people, processes,
products and practices to adapt, improve and evolve
within the context of the changing business that
enables it to be ‘World Class’. A prerequisite for

achieving this is the means or capability to generate the
fundamental understanding necessary to respond
appropriately. It is the critical dimension of understanding and the creation of methods for generating
the necessary understanding that is addressed in this
paper.
This paper first explores the motivations for
manufacturing improvement and examines in detail
the principles and underlying knowledge requirements
of a range of common improvement paradigms. The
barriers to realising sustainable improvement are then
discussed and the importance of generating and
communicating a fundamental understanding is highlighted. The need to support organisations in reinforcing and extending their fundamental understanding is
further argued and the deficiencies in existing supportive techniques are described. In order to overcome
these deficiencies the concept of machine-material
interaction is introduced and its relationship to
‘function’ and fundamental understanding is discussed.
The paper concludes with the development of a set of
requirements for a new supportive methodology which
enables machine–material interactions to be investigated, and the necessary fundamental understanding
to be developed and contextualised with respect to the
knowledge requirements of a range of common
improvement paradigms.

2.

587

Improvement paradigms

There are a wide variety of approaches and philosophies associated with the improvement of manufacturing and production systems. These higher level

paradigms generally involve a range of tools and
methods to target, plan and implement an improvement programme. For the purpose of considering
these various philosophies and their corresponding
tools and methods (Brassard and Ritter 1994), the
approaches and the methods can be grouped under
the seven areas of: equipment design/redesign, maintenance, operator-led, process-control, product modification and new product introduction, quality, and
tooling design and changeover. The various manufacturing paradigms and the corresponding tools and
methods that can be associated with each of these
seven areas are illustrated in Figure 2 and described
in detail in Tables 1 and 2. Of particular interest in
this work are the underlying knowledge requirements
necessary to successfully apply the various tools and
methods. These requirements are developed in Tables
1 and 2 from an analysis of the aims and underlying
principles of the various tools and methods, which are
now summarised.
(1) Process control. As levels of automation
increase and in particular, the automation of
changeover and machine set-up, so does the
need to possess the understanding necessary to
explicitly define set-up rules and parameters.
Intelligent monitoring and control has been
successfully applied in Component manufacture (Uraikul et al. 2000, Murdock and HayesRoth 1991) and Machining processes (Hou
et al. 2003, Liang et al. 2004) but requires indepth knowledge of the relationship between
product variation and process variation - both
upstream and downstream. Central to the
success of these methods is the need to understand and describe the acceptable variation in
product attributes during all stages of
production.
(2) Operator-led. One of the key elements to the

effective operation and improvement of a
production system is the successful training of
the operating staff (Woodcock 1972). Training
is imperative to ensure changes to working
practices and operating procedures are effectively taken-up. For effective training to be
delivered the trainer needs to possess an indepth understanding of the content (Davis and
Davis 1998), which in the case of manufacturing improvement concerns both the tools and
methods for improvement and the production


588

B.J. Hicks and J. Matthews

Figure 2.

Manufacturing improvements paradigms and their corresponding tools and methods.

system(s). Further, the content and learning
outcomes of the training have to reflect goodpractice or at least improved practices, which
must be determined in advance. Central to the
success of the training is the need to develop a
common and shared understanding across all
the trainees in order to generate the same
intended learning outcome(s). This is necessary to ensure consistent practices and in
particular, consistent operation of equipment,
control of materials and the adoption of
appropriate machine settings to maintain

quality and avoid excessive wear (Adebanjo

and Kehoe 2001).
(3) Maintenance. The ability to keep a manufacturing process efficient depends heavily upon
good work practices and effective maintenance.
This is particularly important in today’s just-intime production environment, where as a
consequence of reduced stock level minor
breakdowns are even more likely to stop or
inhibit production (Eti et al. 2006a) and reduce
overall equipment effectiveness (efficiency).
There are two approaches for achieving this.


Training to ensure changes to working
practices and operating procedures
are effectively taken-up. The
importance of training in motivating
the operators and promoting ‘team
work’ has been widely acknowledged
(Reik et al., 2006; Scholtes et al.,
2003).

Improve the speed and ease of exchange
of subassemblies (Boothroyd et al.,
2001).

Focuses on the machine operator as the
key component of maintenance.
Operator is tasked with performing the
routine maintenance tasks (Wilmott,
1997). The motto of TPM is ‘‘zero
error, zero work-related accident, and

zero loss’’. Hence it can be thought of
as ‘deterioration prevention’ and
‘maintenance reduction’, not purely
the ‘fixing’ of machines.

Training of the
operating staff

Design out
Maintenance or
Design for
Service

Total Productive
Maintenance
(TPM)

Operator-led

Maintenance

Five goals:
1. to maximize equipment effectiveness;
2. to develop a system of productive
maintenance for the life of the
equipment;
3. to involve all departments that plan,
design, use, or maintain equipment in
implementing TPM;
4. to involve all employees and

5. to promote TPM through motivational
management (Redman and Grieves,
2005).

(continued)

Machines and products are monitored/
measured by virtue of appropriate sensors
– vision, proximity etc, and where
undesirable measures are recorded the
production system is altered
automatically – both upstream to correct
and downstream to compensate.

Intelligent monitoring and control of
production system to provide near
real-time correction/adjustment
(Limanond et al.,1998).

In-process
monitoring and
control

Central to the success of training is the need
to develop a common and shared
understanding across all the trainees in
order to generate the same intended
learning outcome(s). This is necessary to
ensure consistent practices and in
particular, consistent operation of

equipment, control of materials and the
adoption of appropriate machine settings
to maintain quality and avoid excessive
wear (Adebanjo and Kehoe, 2001).
Depending on the relative likelihood of
failure of a particular component or
subassembly more effort into improving
maintainability (disassemblability and
assemblability) of this component is
justified.

The understanding necessary to explicitly
define set-up rules and parameters – right
first time/best compromise. These rules
need to be programmed into the machine
controller and their adjustment may
require a skilled operator and/or prior
knowledge of, in many cases, sophisticated
logic and machine sequencing.
Requires in-depth knowledge of the
relationship between product variation and
process variation - both upstream and
downstream - in order to alter machine
parameters and settings during run-up and
operation. Central to this is the need to
describe the acceptable variation in
product attributes during all stages of
production.
For effective training to be delivered the
trainer needs to possess an in-depth

understanding of the content (Davis and
Davis 1998), which in the case of
manufacturing improvement concerns both
the tools and methods and the production
system(s). Further, the content and
learning outcomes of the training have to
reflect good-practice or at least improved
practices, which must be determined in
advance.
While this design-led approach does not
directly impact upon the nature of the
production process the influence of the
procedures associated with disassembly
and assembly on process set-up must be
understood in order to reduce both
exchange time and minimise run-up.
Relies heavily on both the management and
the operators possessing an understanding
of: the function of the process, suitable
machine settings, the impact of wear on the
process, and the effect of operating
conditions (production rate and
environmental conditions).

Machine settings are pre-programmed into
a controller and associated with a
particular product.

Automation of the physical changes to
the manufacturing system necessary to

process a product variant.

Automation of
changeover and
machine set-up

Process control

Knowledge requirements

Principles

Description

The principles and underlying knowledge requirements of tools and methods for manufacturing systems improvement – Part a.

Approach

Table 1.

International Journal of Computer Integrated Manufacturing
589


Quality

Table 1.

A planning and communication method
(Cohen, 1993) that is widely used in

the development phase of equipment
and machinery for identifying the
customer requirements and
translating them into technical
characteristics.
Aimed at embedding awareness of
quality in all organisational processes
and ultimately strives to create
customer satisfaction at continually
lower real costs (Oakland, 2003).
A business management strategy,
originally developed by Motorola, to
identify and remove the causes of
defects and errors in manufacturing and
business processes (Adam et al., 2003).

Quality Function
Deployment
(QFD)

Total Quality
Management
(TQM)

Six Sigma

Relies on an understanding of the function of
the machine /production system and the
use of predictive techniques such as Failure
Mode Effects and Criticality Analysis

(FMECA) or Fault Tree Analysis (FTA)
implementation. (Hague and Johnston,
2001).

An engineering framework that enables the
definition of a complete maintenance
regime (Moubray, 1997). It regards
maintenance as the means to maintain
the functions a user may require of
machinery in a defined operating context.
As a discipline it allows manufacturers to
monitor, assess, predict and generally
understand the workings of the
equipment (Mitchell, 2002).
Developed as a ‘‘method to transform user
demands into design quality, to deploy
the functions forming quality, and to
deploy methods for achieving the design
quality into subsystems and component
parts, and ultimately to specific elements
of the manufacturing process’’ (Akao,
1996).
Include three activities:
1. quality of return for shareholders,
2. quality of products/services to endusers and
3. quality of life at work and home.
Uses a set of quality management methods,
including statistical methods, and requires
an infrastructure of people within the
organization who are experts in these

methods. Each Six Sigma project carried
out within an organization follows a
defined sequence of steps and has
quantified financial targets (cost reduction
or profit increase). One commonly used
statistic method is Control charts
(Wheeler, 2000) to assess the nature of
variation in a process and to facilitate
forecasting and quality management

Focuses on identifying and establishing
the operational, maintenance and
capital improvement policies that will
manage the risks of equipment failure
most effectively (Smith, 2005).

ReliabilityCentred
Maintenance
(RCM)

Core to this activity is the development of the
knowledge and understanding of the
process and product, and specifically the
areas where the product quality is
influenced by interaction with the process.
Thomas and Webb (2003) and Antony
(2007a; 2007b), shows that knowledge and
understanding are key factors for
successful Six Sigma implementation. This
understanding centres on the interaction

between the process and the product, and is
essential for directing the measurement,
analysis, improvement and control of
process and process inputs (materials and
staff).

A design focused activity (process and
products) that can be applied to a new or
existing product or service and requires an
understanding of function and its
relationship to quality (Govers, 2001).

Knowledge requirements

Principles

Description

Approach

(Continued).

590
B.J. Hicks and J. Matthews


Approach

Description

Principles

Tooling design

Product modification and new product
introduction

Tooling design
and
changeover

Goods manufacturers are often faced with the task of processing new or altered
products – such as new sizes, new materials and modified configurations (Matthews
et al., 2009). Central to achieving this, is the need to determine an appropriate set of
machine settings. This involves production trials and potentially time-consuming
trial and error testing and demands that a modified product or prototype be
obtained. An alternative approach is to perform a comparative assessment of new
products with existing products and their associated machine settings to derive an
initial set of new machine settings (Giess and Culley, 2003). In some cases a suitable
set-up may not be possible and the product cannot be processed. Here the
production team must determine how to modify the system and/or provide
recommendations for altering the properties or characteristics of the product.

Improve production performance and in
particular flexibility - without
compromising efficiency – through
improved design of tooling.

(continued)

To determine the functional requirements for
redesign it is necessary to understand the
limitations of the existing equipment. The
factors that limit the capability can be
inverted in order to define the rules which
are necessary for successful processing.
Further, the rules provide a series of
objective measures for the evaluation and
assessment of new equipment.
No matter whether it is the determination of
settings for a new product or the
improvement in process capability through
product modification, it is necessary to
understand the capability of the production
process and its relationship with the
properties and characteristics of the product
(Frey et al., 2000). The intrinsic
relationship between product design and
process capability is widely acknowledged
(Deleryd, 1998) and there are a variety of
methods for improving process capability
through product modification. These include
Design for Assembly (DfA), Design for
Manufacturability (DFM) and Design For
Manufacture Assembly (DFMA)
(Dewhurst and Abbatiello, 1996).

Central to the success of the SMED or DFC
activities is the need to be able to
understand and specify in advance the

machine settings (set-up point) and range
of variation (run-up adjustment)
necessary for the successful processing of
each product variant (Mileham et al.,
2003).

Central to the ability to improve tooling
design is the need to generate functional
design rules (design requirements) (Pahl
and Beitz, 1996) i.e. what needs to be
achieved by the process in terms of the
final product.

Knowledge requirements

The principles and underlying knowledge requirements of tools and methods for manufacturing systems improvement – Part b.

Key to achieving this is to determine the
most appropriate design or configuration
of tooling and, if appropriate, the most
efficient methods for changeover between
tooling configurations (i.e. minimising
changeovers and/or changeover time).
This includes both the physical geometry
(size, profile and number of) and control
of the tooling (kinematics - motion,
velocity and acceleration, timing and
clearances) (Hicks et al., 2001).
The methodologies guide the designer
Changeover

Improve changeover performance
through a step-by-step process from
through automation and/or
analysing changeover capabilities
techniques such as Single-Minute
through to the identification of
Exchange of Die (SMED) (Shingo,
improvement opportunities. The
1985) or Design for Changeover
approach builds understanding of the
(DFC) (McIntosh et al., 2001).
basic concepts and methods for the
identification of improvement ideas and
their potential benefits.
Equipment redesign, modification and Where existing equipment cannot meet increases in manufacturing capability it is
replacement
necessary to either modify or replace the equipment. In cases where the process and
the design principles which underlie the equipment are close to their limits then a
process and equipment redesign is necessary (Hicks et al., 2004). In either case –
modification, replacement or redesign – it is a prerequisite that both capability and
functional requirements are determined.

Table 2.

International Journal of Computer Integrated Manufacturing
591


592

Focuses on improving the efficiency and effectiveness of the overall business processes that
exist within and across an organization. Involves the fundamental assessment of mission
and goals and the reengineering of the organization’s business processes - the steps and
procedures that govern how resources are used to create products and services that meet
the needs of particular customers or markets.
Business Process
Reengineering/
Redesign
(BPR)

For the successful adoption of lean a
functional perspective of the production
system is required in order for value
streams to be identified and mapped, and
to ensure that value streams flow. In a
manufacturing context, function is the only
means to add value to the product.
Although not all functions may add value.
Achieved by establishing the processes and
assigning responsibility for those processes
to dedicated teams and, where appropriate,
systems (Hammer and Champy, 1993).In
order to maintain and improve processes
an understanding of the functions and
processes and the value of each function
must be elicited.
The core principles of lean thinking are:
1. Specify value
2. Identify value streams
3. Make value flow

4. Let the customer pull value
5. Pursue perfection
The term ‘lean’ was coined by Womack
et al. (1990) to describe the philosophy –
of reducing waste throughout a
company’s value stream. It is not just a
set of tools for the reduction of waste
(Bicheno, 2003), but a way of thinking
which puts the customer first.

Approach

Lean thinking
Other
manufacturing
philosophises

Table 2.

(Continued).

Description

Principles

Knowledge requirements

B.J. Hicks and J. Matthews
The first is preventive maintenance which aims
to reduce the probability of failure in the time

period after maintenance has been applied. The
second is corrective maintenance, which strives
to reduce the severity of equipment failures
once they occur (Loftsen 2000). As noted by
Waeyenbergh and Pintelon (2004) industrial
systems evolve rapidly so maintenance initiatives will also have to be reviewed periodically
in order to take into account the changing
systems and the changing environment. This
calls not only for a structured maintenance
concept, but also one that is flexible. There are
a variety of maintenance improvement methods
including Design for Service (DfS) (Dewhurst
and Abbatiello 1996), Total Productive Maintenance (TPM) (Willmott 1997) and Reliability
Centred Maintenance (RCM) (Smith 2005)
which arguably focus on the design, the
operator and the engineering function respectively. These various approaches depend on
both the management and the operators
possessing an understanding of: the function
of the process, the influence of machine settings
on process performance, the impact of wear on
the process, and the effect of operating conditions (production rate and environmental
conditions).
(4) Quality. In a similar manner to maintenance
there are a variety of methods and initiatives
that support quality control, improvement and
assurance. These include Quality Function
Deployment (QFD) (Govers 2001), Total
Quality Management (TQM) (Oakland 2003)
and aspects of Six Sigma (Adams et al. 2003).
These various approaches require an understanding of function and its relationship to

quality, and an understanding of the interaction between the process and product, which
are essential for directing the measurement,
analysis, improvement and control of process
and process inputs (materials and staff) (Thomas and Webb ( 2003) and Antony (2007a,
2007b)).
(5) Tooling design and changeover. The ultimate
aim of improving tooling design is to improve
production performance and in particular
flexibility, without compromising efficiency.
Key to achieving this is to determine the most
appropriate design or configuration of tooling
and, if appropriate, the most efficient methods
for changeover between tooling configurations
(i.e. minimising changeovers and/or changeover
time). This includes both the physical geometry
(size, profile and number of) and control of the


International Journal of Computer Integrated Manufacturing
tooling (kinematics – motion, velocity and
acceleration, timing and clearances) (Hicks
et al. 2001). Central to the success of the
Single-Minute Exchange of Die (SMED) (Shingo 1985) or Design for Changeover (DFC)
(McIntosh et al. 2001) activities is the need to
be able to understand and specify in advance
the machine settings (set-up point) and range of
variation (run-up adjustment) necessary for the
successful processing of each product variant.
(6) Equipment redesign, modification and replacement. Where an increase in manufacturing
capability is sought that exceeds the existing

equipment or process capability, it is necessary
to either modify or replace the equipment. In
cases where the process and the design principles which underlie the equipment are identified
to be close to their limits then a process and
equipment redesign may be necessary (Hicks
et al. 2002). In either case – modification,
replacement or redesign – it is a prerequisite
that both capability and functional requirements are determined. Central to determining
these requirements is the need to understand the
limitations of the existing equipment (Matthews
et al. 2007, Ding et al. 2009). The factors that
limit the capability can be inverted in order to
define the rules which are necessary for successful processing. This understanding is central to
realising redesigned or new equipment that
overcomes the limitations of existing equipment
and ultimately improves performance (quality,
efficiency and/or flexibility and capability). The
rules also provide a series of objective measures
for the evaluation and assessment of new
equipment (Matthews et al. 2008).
(7) Product modification and new product introduction. In today’s dynamic global markets,
goods manufacturers are frequently faced with
the task of processing new or altered products –
such as new sizes, new materials and modified
configurations (Matthews et al. 2009). Central
to achieving this, is the need to determine an
appropriate set of machine settings that enable
the product to be successfully processed. No
matter whether it is the determination of
settings for a new product or the improvement

in process capability through product modification, it is necessary to understand the
capability of the production process and its
relationship with the properties and characteristics of the product (Frey et al. 2000).
(8) Other manufacturing philosophises. In addition to these seven areas of manufacturing
improvement there exist a number of

593

philosophies to support improvements in manufacturing and management. These include
lean thinking and Business Process Reengineering. The term ‘lean’ was coined by Womack
et al. (1990) to describe the main aim of the
philosophy – the reduction of waste throughout
a company’s value stream. However, for some
lean promoters it is not just a set of tools for the
reduction of waste (Bicheno 2003), but a way of
thinking which puts the customer first. Once
this way of thinking is adopted, lean tools are
available to reduce waste and improve benefits
for the customer. For the successful adoption
of a lean approach a functional perspective of
the production system is required in order for
value streams to be identified and mapped, and
to ensure that value streams flow. In a
manufacturing context, function is the only
means to add value to the product. Although
not all functions may add value. In contrast to
lean, business process reengineering or business
process redesign (BPR) focuses on improving
the efficiency and effectiveness of the overall
business processes that exist within and across

an organisation. This is achieved by establishing the processes and assigning responsibility
for those processes to dedicated teams and,
where appropriate, systems (Hammer and
Champy 1993). In order to maintain and
improve processes an understanding of the
functions and processes and the value of each
function must be elicited.
The previous sections have discussed the various
manufacturing improvement paradigms and corresponding tools and methods with respect to their
underlying principles and the knowledge and understanding that underpin their use. Further examination
of the knowledge requirements reveals six fundamental
knowledge concepts relating to the improvement of
manufacturing systems. These include:
(1) An understanding of the relationship between
the properties and characteristics of the product, and the machine and process settings.
(2) An understanding of the relationship between
product variation and process variation, and
their influence on quality and efficiency.
(3) An understanding of the influence of
operator procedures on quality, efficiency, and
flexibility.
(4) An understanding of the impact of wear
and operating conditions (production rate
and environmental conditions) on quality and
efficiency.


594

B.J. Hicks and J. Matthews

(5) An understanding of the limitations of the
existing equipment (quality, efficiency, flexibility and capability).
(6) A functional perspective of the production
system that contextualises the process and its
operations with respect to the final product.
It is arguable that these six knowledge concepts are
critical for effective implementation of improvement
programmes and that they are hence a prerequisite for
realising sustainable improvement. In order to explore
this further the barriers and root causes of failed or
partially successful organisational improvement programmes are reviewed.
3.

Barriers to realising manufacturing improvement

While there exists a plethora of publications presenting
the successful implementation of different manufacturing improvement strategies (Brown et al. 1994, Sohal
et al. 1998, Bamber 1999, Henderson and Evans 2000,
Antony and Banuelas 2002, Apte and Goh 2004, Chan
et al. 2005) the experiences of the authors and those of
the practitioners we have worked with are that many
initiatives fail to meet expectations and can fail to
deliver any improvement at all. Furthermore, in
extreme cases these initiatives can have a detrimental
impact on capability or bring about new less well
understood problems. This can result in an indirect
cost to an organisation that represents a magnitude of

Figure 3.

cost and lost opportunity that far exceeds the level of
investment in the original improvement programme.
The existence of only partially successful and failed
initiatives is supported by past and contemporary
literature, an example of this being Redman and
Grieves (1999), who noted that between 70–90% of
TQM programmes implemented have failed.
In order to provide some insight into the common
causes of partially successful and failed initiatives –
and what can be thought of as the barriers to successful
implementation – literature from the fields of manufacturing, management and information systems are
critically reviewed. These fields are selected because of
the considerable bodies of work that deal with process
improvement, change management, information systems implementation and production systems. An
appraisal of the literature reveals six core areas: lack
of commitment, reactive organisations, layered initiatives, incomplete implementations, incorrect implementations and resistance to change. These six
dimensions are shown in Figure 3 and discussed in
the following sections.
3.1.

Lack of commitment from the organisation

One of the most common causes for organisational
improvement programmes to fail is the lack of
commitment from the organisation (Sterman et al.
1997, Olivia et al. 1998, Mellor et al. 2002, Tari and
Sabaner 2004). This can lead to inadequate support

Causes of failure and barriers to realising manufacturing improvement.

International Journal of Computer Integrated Manufacturing
infrastructure or training in improvement techniques,
thereby limiting the potential for successful implementation (Keating et al. 1999). Top-down organisational
commitment is imperative to successful improvement
programmes, although, McIntosh et al. ( 2001) argue
that the focus is often heavily concentrated on
organisational-led improvement and that the benefits
of product/ process design amendments are often
considerably under-exploited. If those responsible for
the allocation of resources are not well informed about
the pros and cons of the implementation programme, it
is highly likely they will underestimate the effort, in
terms of time and cost, needed for the successful
completion of the project (Wilkinson et al. 1998, Tari
and Sabanter 2004). In the field of business transformation and Enterprise Resource Planning (ERP) a
lack of commitment is also highlighted as a common
cause of failure. This includes both lip service from
senior staff and a lack of engagement from middle
management (Buckhout et al. 1999, Whittaker 1999).
3.2.

Reactive approaches

In the dynamic business environments of today where
resources are already stretched it is common for
organisations to adopt a reactive approach, always
‘fire fighting’ issues such as quality and efficiency.
Research by Olivia et al. (1998) showed that such a

reactive approach not only assisted the failure of
specific initiates but caused profound effects on other
functions in the organisation such as product development, pricing and human resources. Overzealous
application of quality tools has led to declining
effectiveness and a backlash that damages even the
effective programmes in many companies (Keating
et al. 1999). Eti et al. (2006b) show that chemical plants
employing reactive strategies of maintenance are
incurring maintenance cost of 5% per annum of the
asset-replacement cost, in lost productivity i.e. wastage
of $30,000 per $M of asset value, this in comparison to
companies employing proactive strategies who are
seeing 25% savings on these values. Furthermore, with
increased adoption of Total Quality Management
approaches and reduced stock level owing to just-intime work practices minor breakdowns are even more
likely to stop or inhibit production (Eti et al. 2006a).
Because of this, reactive maintenance approaches such
as run-to-fail or breakdown are becoming less common, and are only employed in areas that do not result
in increased expenditure (Mostafa 2004). It therefore
follows that initiatives, such as those involving quality
can rarely be implemented in isolation. Rather, they
need to be implemented as part of an overall
improvement programme, which in the aforementioned case of quality also includes reliability.

3.3.

595

Layered initiatives one on top of another

The reactive approach discussed in Section 3.2 can also
contribute to organisations implementing multiple
improvement initiatives concurrently. This makes the
lifecycle of the implementation difficult to identify
(Irani and Love 2001) and the tasks of planning,
implementation and monitoring difficult. Although
research has shown that quality and productivity
improvements need to occur together for organisations
to maintain or improve their competitive position
(Chapman and Hyland 1997), particular initiatives
need to be completed so that their effect can be
understood (Bessant et al. 2001), and concurrent
initiatives need to be carefully coordinated. In the
field of manufacturing, Wilkinson et al. (1998) identify
that a lack of understanding and structure when
implementing multiple quality improvements leads to
situations that are considered ‘indigestible’ for those
on the receiving end of management. In essence,
employees struggle to differentiate between improvement initiatives, so tend to have cursory ‘buy-in’ to the
process, or implement initiatives incorrectly.
3.4.

Incomplete implementations

A common cause of underperforming improvement
initiatives can be attributed to incomplete implementations. This includes partial implementation of an
initiative, implementations which have not been fully
implemented across the entire organisation and implementations which have not been integrated within
the business strategy and processes. The consequences
of this are that either little or no measurable

performance improvements can be identified and
organisations need to maintain their existing systems
and processes – effectively maintaining two parallel
processes (Hicks et al. 2006). These issues are further
frustrated by the fact that there is normally deterioration in performance measures when such programmes
are up-and-running (Carroll et al. 1998). This again
causes managers to lose faith in the programme and
withdraw to the existing working practices. Haley and
Cross (1993) noted how some managers saw their
implementation of quality improvement paradigms as
a ‘fashion statement’. Redman and Grieves (1999) also
reviewed multiple sources of TQM failure through the
1990s and identified that incomplete implementation
was the most common cause.
3.5. Resistance to change
Resistance to change has been widely reported as one
of the key barriers to successful implementation of
business process transformation and improvement


596

B.J. Hicks and J. Matthews

programmes (Hill 1991, Rees 1991, Marchington et al.
1992). While senior managers appear to be committed
to quality improvement strategies, it was the middle
and junior managers that were resistant to such
programmes. Middle management see the implementation of such programmes as both labour and resource
demanding (Wilkinson et al. 1992), whereas junior

management thought it would ‘reduce their discretion’
in the current job roles. From the shop floor viewpoint,
almost every book, and academic publication presents
the issues of operator ‘buy-in’. If the members of the
shop floor, who are to be the hands-on users of such
processes, do not understand them or the benefits to
themselves, the implementation is bound to falter
(Schaffer and Thompson 1992). In addition to this,
previous research highlights shop floor suspicion as a
barrier when using performance measures as indicators
of success of implementation (Ukko et al. 2007). The
perception being that the implementation of such
programmes only benefits management, and have little
impact on the welfare of the shop floor staff.
3.6.

Incorrect implementation

The most commonly reported reason for unsuccessful
implementation is that of incorrect implementation
(Miller and Congemi 1993, Regle et al. 1994, Taylor
1997, Redman and Grieves 1999, Nwabueze 2001). This
can include the inappropriate adoption of a particular
tool, method or process given the industry sector of the
organisation and its existing business processes (Beer
et al. 1990), and the incorrect tailoring of the tool,
method or process to the business; its processes, people,
procedures and products. For example, where ERP
systems are considered the alignment of fit to an
organisation is critical for success (Bingi et al. 1999,

Holland and Light 1999) this includes both the level of
business process reengineering necessary and the
amount of customisation (tailoring) of the system that
is necessary. Where quality programmes are considered,
Guptara (1994) highlighted how quality guru’s can raise
awareness of quality issues; however they rarely provide
the tailored mechanisms to integrate improvement
programmes within the organisation and this can
eventually lead to incorrect implementation.
3.7.

The root cause of failed implementations

When considering the causes and consequences of the
six areas detailed previously, it becomes apparent that
many arise as either a result of a lack of understanding,
an inability to communicate understanding, or an
inability to generate the necessary understanding.
Where this understanding relates to the system, its
intrinsic processes, external interactions, the wider

environment and the product of the process itself. For
example, in the case of resistance to change the
primary causes are a lack of understanding, a lack of
communication and lack of inclusion – which ultimately leads to lack of shared understanding. In the
case of layered initiatives, the consequences are an
inability to elicit the core understanding and difficulties
in performance measurement – which ultimately
influences understanding.
Given the aforementioned argumentation it follows

that in the context of manufacturing improvement, the
underlying root cause of failed and suboptimal
initiatives can be largely attributed to the level of
understanding of the relationship between the production system, its constituent processes, raw materials and
the product. As previously stated, it is this understanding that is necessary for effectively implementing
improvement initiatives and determining the optimum
mix of tools and methods to generate the maximum
benefit. The importance of understanding has been
recognised implicitly by researchers; however, addressing this deficiency has been largely overlooked. For
example, a weakness of Reliability-Centred Maintenance is that it is not always as analytically rigorous as
all reliability-based analysis and hence is not developed
upon a fundamental understanding but rather a
simplified or Bayesian approach (Sivia 1996). Where
quality initiatives are considered there is a tendency to
hire Total Quality Management (TQM) consultants to
visit for a half-day or so to start the process. This puts
incredible pressure on managers since they have little
ongoing access to the expert help they need to make this
work. Also, some activities that are part of TQM are
best carried out by ‘outsiders’ who bring a different
kind of objectivity to the process and may help in
developing the necessary understanding.
4.

Generating a fundamental understanding

In the previous sections it has been shown that the
majority of manufacturing improvement approaches
and tools require a fundamental understanding of the
production system – including its constituent processes, raw materials and the product – and that the

barriers to successful implementation can be considered to relate to either a lack of understanding, an
inability to generate understanding or an inability to
communicate understanding. Furthermore, in today’s
dynamic business environments where products, materials, processes and staff continually change, organisations must continually reinforce and extend their
understanding. The ability to increase and evolve
understanding depends heavily upon tools and methods which support the generation of understanding.
For these reasons, it can be argued that a prerequisite


International Journal of Computer Integrated Manufacturing
for realising sustainable process improvement is
fundamental understanding and in particular, an
ability to generate understanding.
In the context of manufacturing improvement there
exist a variety of tools and methods which can be considered to support the development of understanding.
These include methods such as Root Cause Analysis
(RCA) (Ammerman 1998) Fault Tree Analysis (FTA)
(Vesely et al. 1981), Failure Mode Effect and Critical
Analysis FMECA (Stamatis 1981) and Value Stream
Mapping (VSM) (Rother and Shook 1999). FMECA
and FTA are based on the investigation of errors and
their causes, and are employed in the product lifecycle’s
idea identification, development and manufacturing
phases (Pisano 1997). However, their scope is limited as
they are only generally applied to investigate observed
failure and its impact, not why it has been observed.
Although this is partially addressed by Root Cause
Analysis, where there is investigation into why the
failure happened, neither method adopts a functional
view that contextualises the failure with respect to the

intended function and the final product. In contrast to
these failure driven approaches, customer focused
techniques such as Value Stream Mapping do adopt a
more functional perspective and attempt to identify
what action adds value to the product (Rother and
Shook 1999). However, this is also limited as it does not
consider how to assure value levels and whether the
levels of value are maintained, only that it flows.
From a manufacturing organisation’s perspective it
is necessary to have an in-depth understanding of the
production system, its constituent processes, raw
materials and the product. This perspective must be
interdisciplinary (maintenance, operators, quality,
materials etc) not just a single perspective such as
engineering. Furthermore, the developed understanding needs to be contextualised with respect to the
overall production system, product and function. The
organisation needs to focus on observed failure
(reactive) and possible failure (proactive) this includes
the various dimensions of quality and efficiency and
their relationship to the production system, its
processes, materials and the product.
5. Interaction, the key to fundamental understanding
In the context of manufacturing systems the relationship between the various factors of machine, products,
process and materials is defined at the interface during
physical interactions (MMI) between the machine and
materials. These machine-material interactions occur
where a machine component physically interacts with,
or influences, the product and any of its constituent
elements. This includes the entire product lifecycle
from the processing of raw materials to the assembly of

597

the product, packaging operations and materials,
collation and product handling, and eventually disposal and recycling. One specific factor that is evident
from the review in Section 3 is that before an
organisation can begin to make targeted improvements, implement change or identify the limitations of
existing systems, it is first necessary to possess the
fundamental understanding of product, process and
their combined interaction.
This understanding will ultimately provide the
structure against which an organisation can reason
about a system and thus, implicitly constrains the scope
(potential) for realising improvements and for foreseeing and overcoming particular problems and conflicts.
More specifically, fundamental understanding is a
prerequisite for developing a complete description of
the system, its function(s) and performance, the
development of common terminology (definitions)
and a structured representation (diagram) of the
system, its internal relations, inputs and external
influences. These elements provide the basis for
communication and reasoning about the system and
also provide a framework against which tools and
methods can be aligned and targeted, and their effects
measured. The latter of which is essential for determining the appropriate (optimal) mix of tools and methods
which generate the maximum benefit for an organisation. It follows that there is a need to support the
investigation of MMIs as not only a means to introduce
a specific improvement but to provide support in the
generation of the fundamental understanding necessary
to best use the various tools and methods to bring

about successful improvement (change).
5.1. The requirements for a supportive methodology
The previous section outlines the need to create a
structured approach (method) that supports practitioners in investigating machine-material interaction
and contextualising the understanding generated with
respect to the production system, its constituent
processes, raw materials and the product. More
specifically, such an approach needs to:
. Support the generation of the understanding and
knowledge requirements that underpin common
improvement paradigms (section 2.0).
. Address the barriers to realising sustainable
improvement, and in particular the inability to
communicate understanding (section 3.0).
. Overcome the limitations of current techniques
for generating understanding and in particular
the lack of a proactive approach and the inability
to contextualise failure with respect to function
(section 4.0).


598

B.J. Hicks and J. Matthews

Through consideration of these areas eight core
requirements can be elaborated for a new supportive
methodology.
(1) To provide a scalable and extensible method
that provides the generation of a comprehensive and fundamental understanding of the

entire production system, its operations, functions and interactions.
(2) To support the development of common
terminology (definitions) for machinery, operations and functions that is agreed by representatives from production, engineering, quality
and operations and shared across an
organisation.
(3) To enable a formalisation of the understanding
and the unification of appropriate interdisciplinary knowledge including materials, machinery and environmental conditions. This would
provide an objective view of the process which
integrates materials and machinery knowledge
providing a means for different departments
and groups to undertake objective discussion
rather than adopting the cross-department
blame culture.
(4) To provide a more complete description of
process efficacy (efficiency and effectiveness)
including measures of performance, quality and
process failure (including observed and possible
modes of failure) across the entire production
system.
(5) To enable the identification of the factors
(including the properties, characteristics and
settings of machinery, product and packaging)
that affect process efficacy and to elicit the
important relationships.
(6) To provide a structured representation (standardised diagram) of the system, its internal
relations, inputs and external influences, which
can be used to communicate and ensure all
stakeholders have a common, shared
understanding.
(7) To enable the generation of qualitative and

quantitative rules that govern the efficacy of
functions (interactions) and define the properties and characteristics of the product, the
machine and settings necessary to achieve
desired levels of process efficacy. These rules
may include for example limiting values,
suitable ranges of settings and/or optimal
settings for given products and/or materials.
(8) To provide direction for the targeting of tools
and methods for manufacturing improvement
in order to deliver targets and sustainable
improvements and maximise benefits.

It is has been argued that these requirements and a
supportive methodology which meets these requirements would generate the understanding and knowledge necessary to effectively implement targeted
improvements in the areas of process control, training,
maintenance, quality, tooling design and changeover,
redesign and replacement of machinery and new
product introduction. To illustrate the importance
and potential of a new supportive method the relationships between various common improvement approaches and the requirements (1–7) are shown in
Figure 4. In particular, Figure 4 highlights the
importance of holistic understanding, adopting a
functional perspective, determining a complete description of process efficacy and identifying the factors
which affect it. It also highlights the importance of
‘rules’ for maintenance and design-led methods and
their benefit to quality based methods.
While the approach presented in this paper
concerns manufacturing systems, the requirements
and argumentation have been developed from a variety
of fields including manufacturing, management and
information systems, leading to a more generalised set

of issues. Similarly, the proposed requirements of a
supportive methodology are arguably of wider applicability than manufacturing systems alone. In particular, the interaction-centred approach could be applied
to any systems that can be decomposed into operations
and functions that interact or manipulate the product.
This could include, for example, manual tasks, data
processing and work flows. In fact, interaction
diagrams have been developed within the UML
framework to describe interactions among the different

Figure 4. The knowledge requirements
manufacturing improvement approaches.

of common


International Journal of Computer Integrated Manufacturing
elements of a model. This interactive behaviour is
represented in UML by two diagrams known as the
Sequence diagram and Collaboration diagram
(Abdurazik and Offutt 2000, Bauer et al. 2001). The
sequence diagram emphasises on time sequence of
messages and the collaboration diagram emphasises on
the structural organisation of the objects that send and
receive messages. While this form of diagram has been
applied predominantly to software systems there may
be opportunities for its application to production
systems.
6. Conclusions
This paper deals with the area of manufacturing
(production) systems improvement and considers the

issues surrounding the realisation of sustainable
process improvement within the context of today’s
dynamic business environments. In particular, the
motivations for manufacturing improvement have
been discussed and the important relationship between
quality, efficiency, flexibility and capability are
described within the context of equipment design/
redesign, improved maintenance, operator-led improvement, process-control, product modification
and new product introduction, quality improvement,
and tooling design and changeover improvement.
Within these seven areas of manufacturing improvement the principles and underlying knowledge requirements of a range of common improvement paradigms
are examined and six fundamental knowledge concepts
are elaborated that can be considered to represent the
understanding necessary to implement the various
tools and methods. In addition to examining the
knowledge requirements of improvement paradigms
the barriers to realising sustainable improvement are
also examined through consideration and review of the
literature from the fields of manufacturing, management and information systems. These fields are selected
because of the considerable bodies of work that deal
with process improvement, change management, information systems implementation and production
systems. This review reveals the importance of understanding and highlights the issues of a lack of
understanding, an inability to generate understanding
and an inability to communicate understanding as the
root causes of failed and partially successful implementations. The issue concerning understanding and
generating understanding are further examined
through consideration of existing techniques that
support the generation of understanding within the
context of manufacturing. The limitations of these
approaches and in particular, the lack of a ‘proactiveness’ and the inability to contextualise failure with

respect to function are highlighted.

599

In order to overcome these deficiencies, within the
context of manufacturing systems, the concept of
machine-material interaction is introduced and its
relationship to ‘function’ and fundamental understanding is discussed. Using the six fundamental
knowledge requirements of manufacturing improvement tools, the barriers to successful implementation
and the limitations of existing techniques for generating an understanding of manufacturing systems, a set
of eight requirements for a new supportive methodology are developed. These requirements include the
need for a functional perspective, an interdisciplinary
understanding, common terminology, a complete
understanding of process efficacy, identification of
key relationships, a structured representation and the
generation of qualitative and quantitative rules, and
the need to provide direction for targeting improvements. To illustrate the importance and potential of a
new supportive method that meets these requirements,
the relationship between the various improvement
paradigms and the individual requirements are
described.
Acknowledgements
The work reported in this paper has been supported by
Department for Environment Food and Rural Affairs
(DEFRA) and the Food Processing Faraday Knowledge
Transfer Network, involving a large number of industrial
collaborators. In particular, current research is being undertaken as part of the EPSRC Innovative Design and
Manufacturing Research Centre at the University of Bath
(reference GR/R67507/01).

References
Adams, C.W., Gupta, P., and Wilson, C.E., 2003. Six sigma
deployment. Burlington, MA: Butterworth-Heinemann.
ISBN 0750675233.
Abdurazik, A. and Offutt, J., 2000. Using UML collaboration diagrams for static checking and test generation. In:
Proceedings of the Third International Conference on the
Unified Modelling Language, York, UK, 383–395.
Adebanjo, D. and Kehoe, D., 2001. An evaluation of factors
influencing teamwork and customer satisfaction. Managing Service Quality, 1 (11), 49–56.
Ammerman, M., 1998. The root cause analysis handbook: A
simplified approach to identifying, correcting, and reporting workplace errors. Productivity Press. ISBN
0527763268.
Antony, J., 2000a. Ten key ingredients for making SPC
successful in organisations. Measuring Business Excellence, 4 (4), 7–10.
Antony, J., 2000b. Improving the manufacturing process and
capability using experimental design: a case study.
International Journal of Production Research, 38 (12),
2607–2618.
Antony, J. and Banuelas, B., 2002. Key ingredients for the
effective implementation of Six Sigma program. Measuring Business Excellence, 6 (4), 20–27. ISSN: 1368–3047.


600

B.J. Hicks and J. Matthews

Apte, U.M. and Goh, C., 2004. Applying lean manufacturing
principles to information-intensive services. International
Journal of Service Technology and Management Fall, 5
(5–6), 488–506.

Akao, Y., 1990. Quality function deployment. Cambridge,
MA: Productivity Press.
Bengtsson, J., 2001. Manufacturing flexibility and real
options: A review international. Journal of Production
Economics, 74, 213–224.
Bessant, J., Caffyn, S., and Gallagher, M., 2001. An
evolutionary model of continuous improvement behaviour. Technovation, 21, 67–77.
Bicheno, J., 2003. The new lean toolbox – towards fast,
flexible flow. Buckingham: PICSIE Books.
Buckhout, S., Frey, E., and Nemel, J., 1999. Making ERP
succeed: turning fear into promise. Strategy and Business,
Second Quarter, 15, 60–72.
Bauer, B., Muller, J.P., and Odell, J., 2001. Agent UML: A
formalism for specifying multiagent software systems.
International Journal of Software Engineering and Knowledge Engineering, 11 (3), 207–230.
Bingi, P., Sharma, M.K., and Golda, J.K., 1999. Critical
issues affecting an ERP implementation. Information
Systems Management, 16 (3), 7–14.
Boothroyd, G., Dewhurst, P., and Knight, Q.A., 2001.
Product design for manufacture and assembly. CRC Press.
ISBN 9780824705848.
Brown, M.G., Hitchcock, D.E., and Willard, M.L., 1994.
why tqm fails and what to do about it. Business One Irwin
publishing. ISBN-13: 9780786301409.
Brassard, M. and Ritter, D., 1994. The memory joggerII: A
guide of tools for continuous improvement and effects
planning. Methlen, MA: GOAL/QPC.
Bamber, C., Sharp, J., and Hides, M., 1999. Factors affecting
successful implementation of total productive maintenance: UK manufacturing case study. Journal of Quality
in Maintenance Engineering, 5 (3), 162–181.

Carroll, J., Sterman, J., and Marcus, A., 1998. Playing the
maintenance game: How mental models drive organizational decisions. In: J. Halpen and R. Stern, eds. Debating
rationality: Nonrational elements of organizational decision
making. Ithaca, NY: Cornell University Press, 99–121.
Chan, F.T.S., Lau, H.C.W., Ip, R.W.L., Chan, H.K., and
Kong, S., 2005. Implementation of total productive
maintenance: a case study. International. Journal Production. Economics, 95 (1), 71–94.
Chapman, R.L. and Hyman, P.W., 1997. Continuous
improvement strategies across selected manufacturing
sectors. Benchmarking for Quality Management and
Technology, 4 (3), 175–188.
Christopher, M. and Peck, H., 2003. Marketing logistics.
Oxford: Butterworth-Heinemann. ISBN 0750652241.
Cohen, L., 1995. Quality function deployment: How to make
QFD work for you. Cambridge, MA: Addison-Wesley
Publishing Company.
Ding, L., Matthews, J., McMahon, C.A., and Mullineux, G.,
2009. An information support approach for machine
design and build companies. Concurrent Engineering:
Research and Applications, 17 (2), 382–389.
Davis, J.R. and Davis, A.B., 1998. Effective training
strategies: A comprehensive guide to maximizing learning
in organizations. San Francisco: Berrett-Koehler Publishers. ISBN 1576 75037X.
Dewhurst, P. and Abbatiello, N., 1996. Design for service. In:
G.Q. Huang, ed. Design for X: concurrent engineering
imperatives. London: Chapman & Hall.

Deleryd, M., 1998. On the gap between theory and practice
of process capability studies. International Journal of
Quality Reliability Management, 15, 178–191.

Eti, M.C., Ogaji, S.O.T., and Probert, S.D., 2006a. Strategic
maintenance management in Nigerian industries. Applied
Energy, 83, 211–227.
Eti, M.C., Ogaji, S.O.T., and Probert, S.D., 2006b. Reducing
the cost of preventative maintenance (PM) through
adopting a proactive reliability-focused culture. Applied
Energy, 83, 1235–1248.
European Union Directive. European packaging and packaging waste directive. 2004/12/EC of 11 February 2004
amending 94/62/EC11, 2004.
European Union Directive. Machine Safety 98/37/EC of the
European Parliament and of the Council, 22 June 1998.
Frey, D.D., Otto, K.N., and Wysockij, A., 2000. Evaluating
process capability during the design of manufacturing
systems. Journal of Manufacturing Science and Engineering, 122 (3), 513–519.
Gelderman, C. and Semeijn, J., 2006. Managing the global
supply base through purchasing portfolio management.
Journal of Purchasing and Supply Management, 12, 209–
217.
Giess, M.D. and Culley, S.J., 2003. Investigating manufacturing data for use within design. In: Proceedings of 14th
International Conference on Engineering Design, Stockholm, Sweden, 18–22 (10 pages on CD).
Govers, C.P.M., 2001. QFD not just a tool but a way of
quality management. International Journal of Production
Economics, 69 (2), 151–159.
Guptara, P., 1994. Lesson of experience- learning from
others mistakes. In: D. Lock, ed. Gower handbook of
quality management. 2nd ed. Gover, Brookfield VT.
ISBN: 978-0-566-07451-6.
Haley, J. and Cross, P., 1993. Discussion in Niven, D., When
times get tough, what happens to TQM? Harvard
Business Review, 14 (1), 45–61.

Hammer, M. and Champy, J., 1993. Reengineering the corporation: A manifesto for business revolution. Harper Business.
Hansen, R.C., 2005. Overall equipment effectiveness (OEE).
Industrial Press, ISBN-13 978-0-8311-3237-8.
Hauge, B.S. and Johnston, D.C., 2001. Reliability centred
maintenance and risk assessment. In: Proceedings of the
IEEE Reliability and Maintainability Symposium, New
York, USA, 36–40. ISBN: 0-7803-5848-1.
Henderson, K. and Evans, J., 2000. Successful implementation of six sigma: benchmarking General Electric
Company. Benchmarking and International Journal, 7
(4), 260–281.
Hicks, B.J., Bowler, C., Medland, A.J., and Mullineux, G.,
2002. A redesign methodology for analysing and
improving the performance capability of packaging
machinery. International Journal of Industrial Engineering, 9 (4), 389–398.
Hicks, B.J., Medland, A.J., and Mullineux, G., 2001. A
constraint based approach to the modelling and analysis
of packaging machinery. Packaging Technology and
Science, 14 (5), 209–225.
Hicks, B.J., Culley, S.J., and McMahon, C.A., 2006. A
study of issues relating to Information Management
across engineering SMEs. International Journal of
Information Management, 26 (4), 267–289. ISSN 02684012.
Hill, S., 1991. How do you manage a flexible firm? The total
quality model, Work. Employment and Society, 5 (3),
397–415.


International Journal of Computer Integrated Manufacturing
Holland, C.P. and Light, B., 1999. A critical success
factors model for ERP implementation. IEEE Software,

30–36.
Hou, T., Liu, W.-L., and Lin, L., 2003. Intelligent remote
monitoring and diagnosis of manufacturing processes
using an integrated approach of neural networks and
rough sets. Journal of Intelligent Manufacturing, 14, 239–
253.
Irani, Z. and Love, P.E.D., 2001. Information systems
evaluation: past, present and future. European Journal
of Information Systems, 10, 183–188.
Jones, R.B., 1997. Risk-based management. Jerico Publishing,
Mumbai India.
Jiao, J. and Tseng, M.M., 1999. An information modeling
framework for product families to support mass customization manufacturing. CIRP Annals, 48 (1), 93–98.
Juran, J.M., 1992. Quality by design. The Free Press. ISBN 002-916683-7.
Keating, E.K., Olivia, R., Repenning, N.P., Rockart, S.,
and Sterman, J.D., 1999. Overcoming the improvement
paradox. European Management Journal, 17 (2), 120–
134.
Limanond, S., Si, J., and Tsakalis, K., 1998. Monitoring and
control of semiconductor manufacturing process. Control
Systems Magazine IEEE, 18 (6), 46–58.
Liang, S.Y., Hecker, R.L., and Landers, G.R., 2004.
Machining process monitoring and control: the state of
the art. Journal of Manufacturing Science Engineering,
128 (2), 297–310.
Lofsten, H., 2000. Measuring maintenance performance: in
search for a maintenance productivity index. International Journal Production Economics, 63, 47–58.
Manarro-Viseras, E., Baines, T., and Sweeney, M., 2005. Key
success factors when implementing strategic manufacturing initiatives. International Journal of Operations and
Production Management, 25 (2), 151–179.

Marchington, M., Goodman, J., Wilkinson, A., and Ackers,
P., 1992. New developments in emloyee involvement.
Employment Department Research Series No. 2. London,
HMSO.
Maskell, B.H., 1991. Performance measurement for world
class manufacturing: A model for american companies.
New York: Productivity Press. ISBN 0915299992.
Matthews, J., Singh, B., Mullineux, G., and Medland, A.J.,
2006. A constraint–based approach to investigate
the ‘process flexibility’ of food processing equipment.
Computers and Industrial Engineering, 51 (4), 809–820.
Matthews, J., Singh, B., Mullineux, G., and Medland, A.J.,
2007.
A
constraint-based
limits
modelling
approach to investigate manufacturing machine design
capability. Journal of Mechanical Engineering, 53 (7–8),
462–477.
Matthews, J., Singh, B., Mullineux, G., Ding, L., and
Medland, A.J., 2008. Modelling to reduce the configuration phase time of machine design. Advanced Materials
Research, 44–46. DOI: 10.4028/www.scientific.net/
AMR.44-46.659
Matthews, J., Singh, B., Deslandes, A., Mullineux, G., and
Medland, A.J., 2009. Modelling approach for dedicated
machinery to support the effects of mass customization.
International Journal of Computer Integrated Manufacture, 22 (11), 1000–1011.
McIntosh, R.I., Culley, S.J., Mileham, A.R., and Owen,
G.W., 2001. Improving changeover performance: a strategy for becoming a lean, responsive manufacturer. Butterworth-Heinemann.

601

Mellor, R., Hyland, P., and Karayan, J., 2001. Continuous
Improvement tools and support mechanisms in Australian manufacturing. Journal of Interdisciplinary Studies,
14, 189–201.
Mileham, A.R., Culley, S.J., Owen, G.W., Newnes, L.B.,
Giess, M.D., and Bramley, A.N., 2004. The impact of
run-up in ensuring Rapid Changeover. Annals of CIRP,
53 (1), 407–410.
Miller, R.L. and Cangemi, J.P., 1993. Why Total quality
management fails: perspective of top management.
Journal of Management Development, 12 (7), 40–50.
Mitchell, L., 2002. Preventative maintenance and RCM II.
Manufacturing Engineer, August, 153–155.
Mostafa, S., 2004. Implementation of proactive maintenance
in an Egyptian glass company. Journal of Quality
Maintenance Engineering, 10 (2), 107–122.
Moubray, J., 1997. Reliability-cantered maintenance. New
York: Industrial Press. ISBN 0-8311-3146-2.
Murdock, J.L and Hayes-Roth, B., 1991. Intelligent monitoring and control of semiconductor manufacturing.
IEEE Expert, 6, 19–31.
Nwabueze, U., 2001. An industry betrayed: the case of total
quality management in manufacturing. TQM Magazine,
13 (6), 400–408.
Oakland, J.S., 2003. Total quality management: Text with
cases.
Oxford:
Butterworth-Heinemann.
ISBN

0750657405.
Olivia, R., Rockart, S., and Sterman, J., 1998. Managing
multiple improvement efforts: lesson from a semiconductor manufacturing site. In: D. Fedor and S. Gosh, eds.
Advances in management of organisational quality. Vol. 3.
Greenwich, CT: JAI press, 1–55.
Pahl, G. and Beltz, W., 1996. In: Ken Wallace, ed.
Engineering design: a systematic approach. 2nd ed.
London: Springer-Verlag.
Pisano, G.P., 1997. The development factory: Unlocking the
potential of process innovation. Harvard Business Press,
USA. ISBN 0875846505.
Redman, T. and Grieves, J., 1999. Managing strategic
change through TQM: learning from failure. New
technology. Work and Employment, 1999, 14 (1), 44–61.
Reger, R.K., Gustafson, L.T., Demarie, S.M., and Mullane,
J.V., 1994. Reframing the organisation: why implementing total quality is easier said than done. Academy of
Management Review, 19 (3), 565–584.
Reik, M.P., McIntosh, R.I., Owen, G.W., Culley, S.J., and
Mileham, A.R., 2006. A formal design for changeover
(DFC) methodology. Part 1: theory and background.
Proceedings of the IMechE, Part B: Journal of Engineering Manufacture, 22 (B8), 1225–1235.
Roth, A.V. and Miller, J.G., 1992. Success factors
in manufacturing. Business Horizons, Elsevier, 35 (4),
73–81.
Rother, M. and Shook, J., 1999. Learning to see: Value-stream
mapping to create value and eliminate muda. Lean
Enterprises Institute, Incorporated. US. ISBN 0966784308.
Scholtes, P.R., Joiner, B.L., and Streibel, B., 2003. The Team
Handbook. ISBN:1884731260. Madison, WI: Oriel Inc.
Sivia, D., 1996. Data analysis: A Bayesian tutorial. Oxford:

Clarendon Press, 7–8. ISBN 0-19-851889-7.
Smith, A.M., 2005. RCM: gateway to world class maintenance. Madison, WI: Oriel Inc. Elsevier ButterworthHeinemann. ISBN: 9780750674614.
Schaffer, R. and Thomson, H., 1992. Successful change
programs begin with results. Harvard Business Review,
80–89.


602

B.J. Hicks and J. Matthews

Shingo, S., 1985. A Revolution in manufacturing: The SMED
system. Cambridge, MA: Productivity Press.
Slack, N.D.C., 2005. The flexibility of manufacturing
systems. International Journal of Operations and Production Management, 25, 1190–1200.
Sommer, R.A., 2003. Business process flexibility: a driver for
outsourcing. Industrial Management and Data Systems,
103 (3), 177–182.
Sohal, A.S., Samson, D., and Ramsay, L., 1998. Requirements for successful implementation of total quality
management. International Journal of Technology Management, 16 (4–6), 505–519.
Stamatis, D.H., 2003. Failure mode and effect analysis:
FMEA from theory to execution. American Society for
Quality. ISBN 0873895983.
Swink, M., Narasimhan, R., and Kim, S.W., 2005. Manufacturing practices and strategy integration: Effects on
cost efficiency, flexibility, and market-based performance.
Decision Sciences, 36 (3), 427–457.
Sterman, J., Repenning, N., and Kofman, A., 1997.
Unanticipated side effects of successful quality programs:
Exploring a paradox of organizational improvement.
Management Science, 43 (4), 503–521.

Tari, J.J. and Sabater, V., 2004. Quality tools and techniques:
Are they necessary for quality? International Journal
Production Economics, 92, 67–280.
Taylor, W.A., 1997. Leadership challenges for smaller
organisations: self perceptions of TQM implementation.
Omega, 25 (5), 567–579.
Thomas, A.J. and Webb, D., 2003. Quality systems
implementation in Welsh small- to medium-sized enterprises: a global comparison and a model for change.
Proceedings of IMECHE, Journal of Engineering Manufacture, 217 (4), 573–579.

Ukko, J., Tenhunen, J., and Rantanen, H., 2007. Performance measurement impacts on management and leadership: Perspectives of management and employees.
International Journal of Production Economics, 110, 39–
51.
Uraikul, V., Chan, W.C., and Tontiwachwuthikul, P., 2000.
Development of an expert system for optimizing natural
gas pipeline operations. Expert System with Applications,
18, 271–282.
Vesely, W.E., Goldberg, F.F., Roberts, N.H., and Haasl,
D.F., 1981. Fault tree handbook. NUREG-0492, U.S.
Nuclear Regulatory Commission, Jan.
Waeyenbergh, G. and Pintelon, L., 2004. Maintenance
concept development: A case study. International Journal
of Production Economics, 89, 395–405.
Willmott, P., 1997. Total productive maintenance: the western
way. Butterworth-Heinemann. ISBN 0 7506 1925 2.
Woodcock, J.A.D., 1972. Cost reduction through operator
training and retraining. Kogan Page (Associates). ISBN:
0850380111.
Womack, J.P., Jones, D.T., and Ross, D., 1990. The machine
that change the world. New York: Rawson Associates.

Wheeler, D.J., 2000. Normality and the process-behaviour
chart. SPC Press. ISBN 0-945320-56-6.
Wilkinson, A., Marchington, M., Goodmann, J., and
Ackers, P., 1992. Total quality management and employee involvement. Human Resource Management Journal, 2
(4), 1–20.
Wilkinson, A., Redman, T., Snape, E., and Marchington,
M., 1998. Managing through total quality management.
Macmillan Press. London.
Whittaker, B., 1999. What went wrong? Unsuccessful
Information Technology projects. Information Management and Computer Security, 7 (1), 23–29.


International Journal of Computer Integrated Manufacturing
Vol. 23, No. 7, July 2010, 603–618

A new approach for conceptual design of product and maintenance
Zaifang Zhang and Xuening Chu*
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 PR China
(Received 1 July 2009; final version received 20 February 2010)
Services such as maintenance are increasingly important for a manufactured product, which can improve customer
satisfaction and promote sustainable consumption. A trend has appeared that manufacturers change their attentions
from providing only a product to offering both product and its services as a whole. However, preliminary literature
review shows that few studies focus on integrated design of product and service. In the paper, a design approach is
proposed for supporting conceptual design of product and maintenance (P&M). In the layout process, the approach
uses an improved quality function deployment (QFD) tool to translate customer requirements into concept
specifications. An information exchange mechanism is developed to exploit the interrelationships between P&M. In
the mechanism, a failure mode and effects analysis (FMEA) tool is used to identify and analyse failure modes and
their effects on the product concept. Then maintenance concepts are generated based on the results of QFD and
FMEA. The proposed approach is applied in a conceptual design case of a horizontal directional drilling machine
with its maintenance. Furthermore, the paper also addresses the management and improvement of P&M concepts.

Keywords: product-service system; maintenance; conceptual design; quality function deployment; failure mode and
effects analysis

1.

Introduction

With the increasing need of sustainable production and
consumption, service activities, as a source of core
value, are becoming more and more important for a
manufacturing enterprise (Aurich et al. 2006). Customer requirements (CRs) are shifting from purchasing
just a physical product to acquiring a result or a
function supported by the product combined with
related services (Mont 2002, Maussang et al. 2005,
Baines et al. 2007). Enterprises are then shifting their
business focus from designing physical products only,
to designing the offering of product and related
services which are jointly capable of fulfilling specific
CRs (Manzini and Vezzoli 2003). A product-service
system (PSS) is proposed and implemented to deal with
this issue. With the assistance of PSS, appropriate
integrated concepts can be generated and sustainable
production and consumption can be promoted.
The PSS was defined as ‘a marketable set of
products and services capable of jointly fulfilling a
user’s need. The product and service ratio in this set
can vary, either in terms of function fulfilment or
economic value’ (Goedkoop et al. 1999). Three main
categories of PSS have been identified: productoriented, use-oriented and result-oriented PSS. Each
category has different emphasis to deliver the required

function, i.e. the reliance emphasis changes from on

*Corresponding author. Email:
ISSN 0951-192X print/ISSN 1362-3052 online
Ó 2010 Taylor & Francis
DOI: 10.1080/09511921003736766


the product to on the service (Tukker 2004). The
potential benefits of PSS can be summarised according
to the studies of Mont (2002) and Aurich et al. (2006):
PSS can improve product core competences, meet
individual CRs, change traditional stakeholder relationships, and reduce environmental loads.
Although product design and service design focus
on different aspects, both product and service should
be considered to satisfy CRs. Maintenance is the most
efficient way to keep the function available during the
product lifecycle (Takata et al. 2004), which is selected
as the representative of services. An integrated framework of product & maintenance (P&M) development
is proposed which can be considered as an initiate
work for PSS development (future work will enlarge to
all related services but not only maintenance). Generally, conceptual design is one of the most important
stages because most lifecycle cost and critical performance of a product or service is determined in this
stage. Pahl and Beitz (1996) also emphasised the
importance of conceptual design because it is very
difficult to correct the fundamental shortcomings in the
later embodiment and detail design phases. Therefore,
this study will address the conceptual design problems
of P&M, which begin with CRs and end with a set of
feasible P&M concepts.

Since information and activities in P&M conceptual design are mutually dependent, development of an


604

Z. Zhang and X. Chu

integrated environment to model the relations between
product design and maintenance design and to achieve
the optimal P&M concept considering both the product quality and the maintenance quality is required.
Presently, engineering design methodologies for P&M
conceptual design have not been discussed sufficiently.
An integrated approach based on quality function
deployment (QFD) and failure mode and effects
analysis (FMEA) is proposed for the issue. Translating
CRs is the first important step during conceptual
design (Dean et al. 2009). QFD has been developed
into a proven successful methodology which can
facilitate the understanding and response to CRs in
the product/service development (Hauser and Clausing
1988, Akao, 1990, Pun et al. 2000, Chan and Wu 2002,
Fung et al. 2003, Bu¨yu¨ko¨zkan et al. 2007, Kahraman
et al. 2006, Luo et al. 2008, Zhang and Chu 2009a).
An improved QFD is adopted to translate CRs into
Engineering Characteristics (ECs) of P&M, and then
product module characteristics and maintenance strategies. FMEA is a systematic assessment tool for
product safety and reliability analysis in design or
other engineering fields, with the aim of preventing
potential failures (Stamatis 1995, Xu et al. 2002, Teoh
and Case 2005). An information exchange mechanism

based on FMEA is proposed to tackle with the
interrelationships between product concept and maintenance concept. Furthermore, fuzzy set theory is
incorporated with the integrated approach to capture
the vagueness and uncertainty of the designers’
judgments in P&M conceptual design stage.
The paper is organised as follows. The related work
is reviewed in the next section. The third section
describes the framework of P&M concept design.
The fourth section proposes the integrated approach
for P&M conceptual design. In the fifth section the
proposed approach is applied in a real-world case of
horizontal directional drilling (HDD) machine. Conclusions are then presented in the final section.
2.
2.1.

Related work

concomitant systemic resource optimisation. Recently,
engineering methods and tools have been developed
to support PSS development. Aurich et al. (2004 and
2006) argued that technical services can influence the
economic and ecologic performance of product and
provide new user benefits. Based on process modularisation, a systematic design method was proposed for
lifecycle oriented technical PSS. Morelli (2006) considered the capability of PSS should belong to the
design domain and proposed the tool of IDEF0
(integration definition for function modelling, level 0)
to represent and blueprint a PSS. In order to generate
optimal PSS concept, Sakao and Shimomura (2007)
and Sakao et al. (2009) introduced a new design
discipline, Service Engineering (SE), which can allow

designing services in parallel with products. The
researchers also developed a computer-aided design
tool called Service Explorer to implement SE development. In SE, a design model is composed of four
sub-models: flow sub-model, scope sub-model, view
sub-model, and scenario sub-model. The model was
proved to be effective through service redesign of an
existing hotel in Italy and business cases such as selling
washing machines, providing pay-per-wash service and
cleaning washing machines. Extending the traditional
QFD from product into product and service, An et al.
(2008) built a concrete integrated roadmap structure
and provided a supporting approach for efficient roadmapping. The approach divides the traditional House
of Quality (HoQ) into two main parts for product and
service. Motivated by PSS, manufacturers have greater
accessibility to product lifecycle data (e.g. in-service
information and knowledge) which can improve the
companies’ core design and engineering capabilities
(Ward and Graves 2006). Yang et al. (2009) proposed
a service enabler, an information management system
that can receive product lifecycle data and manage
them for the realisation of product-oriented and useoriented PSS. Goh and McMahon (2009) reported
their experiences and suggested two methods (i.e.
statistical analysis and data mining) to improve reusing
of this in-service information capture and feedback.

PSS

Most of the early studies on PSS development were
primarily conducted from the viewpoint of marketing
and management. Mont (2002) built a theoretical PSS

framework to improve core competitiveness and to
provide new business benefits. Three main uncertainties are analysed regarding the applicability and
feasibility of PSS: the readiness of companies to adopt
them, the readiness of consumers to accept them, and
their environmental implications. Manzini and Vezzoli
(2003) described PSS as a framework of the new types
of stakeholder relationships which can produce new
convergence of economic interests and a potential

2.2. QFD and FMEA in maintenance
Takata et al. (2004) analysed technical role change of
maintenance in product lifecycle and then presented a
maintenance framework that shows management
cycles of maintenance activities, including maintenance
planning, maintenance task execution and lifecycle
maintenance management. Waeyenbergh and Pintelon
(2002, 2004) proposed a design framework to generate
the customised maintenance concept (i.e. maintenance
strategy) which had been applied in a company
producing cigars and cigarillos. Within the framework,


International Journal of Computer Integrated Manufacturing
the optimum maintenance concept can be selected
through identification of the most important systems
and most critical components and analysis of maintenance strategy decision tree (maintenance strategies
include failure based maintenance, design-out maintenance, detective based maintenance, condition based
maintenance, and use based maintenance).
QFD is a general tool for maintenance design.
Pramod et al. (2006 and 2008) proposed a maintenance

QFD model based on QFD and total productive
maintenance for enhancing maintenance quality of
product, and then checked the implementing feasibility
of the proposed method through an automobile service
station. Lazreg and Gien (2009) proposed an integrated model linking two popular approaches (i.e. six
Sigma and maintenance excellence) to improve the
effectiveness of maintenance. Coupled with QFD,
the model is used to deploy the design parameters in
order to reduce variations and time and eliminate the
occurrence errors in the maintenance process.
It is important to perform appropriate maintenance
activities in maintenance management. Kimura et al.
(2002) developed a virtual maintenance system
using FMEA to perform appropriate maintenance
operations for manufacturing facility management.
A computer-aided FMEA tool was developed for
maintenance planning considering the time-consuming
and tedious characteristics in the traditional FMEA.
Park et al. (2009) also developed a maintenance system based on FMEA. The system implemented the
diagnosis item and cycle update for maintenance
of mail sorting machine. The researchers described
FMEA deployment step, result and statistics analysis
in detail. Echavarria et al. (2007) developed a design
methodology to increase the availability of a product/
system by reconfiguring the system or subsystems. The
methodology uses FMEA to identify connectivity and
criticality of components and then adopts functional
redundancies to keep the system reliability. Bae et al.
(2009) developed a web-based Reliability Centered
Maintenance (RCM) system for the Korean automated guideway transit train. The system uses FMEA

to identify and prioritise possible failure modes of the
train. The results can be updated through collecting
historical failure data and the reliability indexes.
QFD and FMEA can also be ‘applied as the dual
role tools or within an engineering process framework’ (Al-Mashari 2005). Ginn et al. (1998) proposed
a methodology for interactions between QFD and
FMEA and then analysed their common value
throughout the product development. An example of
Ford Motor Company was cited to illustrate the
advantages of the tools. Chin et al. (2005) developed an
integrated system framework for product design
optimisation in terms of cost, quality and reliability

605

considerations by using QFD, value engineering and
FMEA. Almannai et al. (2008) proposed an integrated
decision tool based on both QFD and FMEA in
addressing technology, organisation and people at
the earliest stages of manufacturing decision-making.
QFD and FMEA are adopted to identify the most
suitable manufacturing automation alternative and
the associated risk in the manufacturing system design
and implementation phases, respectively. Korayem
and Iravani (2008) applied FMEA and QFD during
the robot design in order to improve its reliability and
quality. In their study, FMEA and QFD are used to
identify failure modes and key quality characteristics
of the robot, respectively. And then corrective actions
are implemented for critical items. In order to respond

to CRs effectively and efficiently, Wang and Chang
(2007) developed an integrated approach for supporting product conceptual design. QFD coupled with
theory of inventive problem solving (TRIZ) assists the
designers to find the suitable engineering parameters
for the product, and FMEA fulfils reliable analysis of
the product.
Based on the review, QFD and FMEA were used
effectively for product or maintenance development.
However, how to carry out concurrent design of P&M
should be discussed adequately. QFD and FMEA
should be improved to translate CRs into ECs of
P&M, and then concept characteristics in an integrated
manner. Information exchange should be implemented
for P&M concepts. Meanwhile, subjective uncertainty
is needed to tackle within the conceptual design
process.
3.

The framework of P&M conceptual design

Product combining with its related maintenance, as the
core of PSS, is the main part for providing and
ensuring the expected function of customers. Conceptual design is to generate the optimum P&M
concept considering both product quality and maintenance quality. According to the study of Aurich et al.
(2006), a new model for PSS concept is proposed
in Figure 1(a). Product concept is surrounded by
related maintenance concept, which is constructed by a
hierarchy product structure tree. Each cell of the tree
represents a module concept. Maintenance concept
is an attachment for a particular product or module

concept, which can be described by maintenance
strategies and maintenance actions. Maintenance strategies generally include failure based maintenance,
condition based maintenance, use-based maintenance,
and so on. The parameters of maintenance strategies
also should be determined, e.g. maintenance frequency
or time interval. Action module expresses a process
which consisted of the related core activities for


606

Figure 1.

Z. Zhang and X. Chu

(a) The concept of P&M; (b) maintenance action model; (c) information exchange mechanism.

implementing the service as shown in Figure 1(b). For
example, action module of part polishing may include
several activities: order assigning, part disassembling,
part polishing, part assembling and debugging.
Determination of interrelationships between P&M
is a key activity in the P&M conceptual design. As
shown in Figure 1(c), product risk information and
maintenance information can be seen as the key
interrelationships between product concept and maintenance concept. A FMEA tool is used to identify the
potential failure models, analyse their effects, determine their priority and then implement appropriate
maintenance actions. Each maintenance concept may
have different maintenance information which should
be returned to its corresponding product or module

concept. According to the information, corresponding
improvements should be taken on the product concept.
Although the mutual collaboration, the aggregating
mechanism of P&M can be established.
The methodologies for product design have been
well developed and some of these methodologies can
be extended to support P&M concept development.
Based on the domain theory in Axiomatic Design (Suh
1990), a three-domain framework of P&M conceptual
design is given at the top of Figure 2. It includes
requirement domain, function domain and concept
domain. The requirement domain represents the total
CRs of target customers. CRs can be described as a set
of features, {CRk}, where k is the total number of CRs.
The function domain represents the function structure
and its ECs. For a better translation, ECs are divided
into product-related-ECs and maintenance-relatedECs {P–ECp1, M–ECp2}, where p1 and p2 are the
total numbers of ECs of product and maintenance,
respectively. The concept domain represents product
concept and maintenance concept. The product concept is similar to the traditional definition, which
includes a product structure tree with module concepts. Considering the complexity of product, modulelevel maintenance concept should be developed for
each module concept. The holistic maintenance concept is generated through the combinations of these
module-level maintenance concepts.

4. The proposed approach
The three-domain framework is transformed into a
tangible quantitative developing process by incorporating the improved QFD and FMEA tools as shown at
the bottom of Figure 2. The process can be divided
into planning level and operation level.
In the planning level, the QFD tool creates two

interlinked mappings. The first mapping starts with
CRs (inputs) and then translates these requirements
into P-ECs and M-ECs, respectively. The second
mapping follows through these ECs and translates
them into the product module characteristics and
maintenance strategies (outputs). Compared with the
traditional QFD, several significant modifications
are done in the proposed approach. First, similar to
the study of An et al. (2008), two separate sets of
P-ECs and M-ECs are linked to CRs in the proposed
HoQs. This can be convenient for systematic analysis.
The correlation matrix in the first HoQ is divided into
four matrices: two self-correlation matrices of P-ECs
and M-ECs, and two correlation matrices between
P-ECs and M-ECs. The correlation matrices may be
asymmetric because the correlation value of ECi to ECj
may be not equal to that of ECj to ECi (Moskowitz
and Kim 1997, Reich and Levy 2004). Similarly, the
correlation matrix in the second HoQ is divided into
three matrices: two self-correlation matrices of product
and maintenance, and one attaching matrix between
each module with its maintenance. The attaching
matrix indicates the affiliations of the maintenance
with their modules. In the second HoQ, the relationship matrix between M-ECs and maintenance strategies is used to express the evaluation values of each
maintenance strategy with each M-EC. Based on
multiplying them with the weights of M-ECs, maintenance strategies are easily evaluated. Generally, only
one appropriate maintenance strategy is adopted for
a given product or module concept. Considering the
complexity, the product can be divided into several
modules. The total evaluation score for each maintenance strategy of the product concept can be

determined through summing the evaluation score
of each module. And then the optimal one for the


International Journal of Computer Integrated Manufacturing

Figure 2.

607

P&M concept development using QFD and FMEA.

product concept can be identified. The outputs of the
second HoQ are also used to guide P&M conceptual
design and then feasible P&M concepts are generated
by designers.
In the operation level, the main task is to determine
the maintenance actions and make the P&M concepts
match with each other. An information exchange
mechanism is fulfilled by using the FMEA tool. The
tool is to identify failure modes, their causes and effects
of the product concepts. A module-level analysis

procedure is proposed for FMEA. For a specific
module concept, the designers use the FMEA tool to
identify and analyse the potential failure modes. For
these failure modes, a judgment should be used to
estimate whether they can be eliminated. If yes, the
designers will give some suggestions for modifying the
module concept. If not, other actions should be added

to prevent or monitor the failure modes. For example,
if a failure mode has high risk but cannot be easily
eliminated, the action may be ‘‘sample inspecting


The first HoQ.

CR2

(0.161,0.185,
0.192,0.242)

MEC1
0
0

0
0
0

0

PEC8
0
(0.047,0.151,
0.173,0.277)
0
0
(0.047,0.151,
0.173,0.277)

0

MEC2

MEC3
MEC4
MEC5

MEC6

0

0
0

0

PEC 7

PEC8
MEC1

MEC2

PEC 6

PEC 3
PEC 4
PEC 5

PEC1
PEC 2

0

0

PEC4
PEC5
PEC6
PEC7
PEC8
MEC1

0

0
0

0

0

0
0
0

(0.011,0.115,
0.136,0.241)
0

0
0
0
0
(0.047,0.151,
0.173,0.277)
(0.047,0.151,
0.173,0.277)
0
0
0

(0.047,0.151,
0.173,0.277)
0
0
0
0
0
0

PEC3

0
0

0
0

PEC2

(0.033,0.049,
0.054,0.080)

CR3

PEC1
PEC2

PEC1

(b) Correlation matrix

(0.120,0.155,
0.163,0.202)

CR1

(a) Relative weights of CRs

Table 1.

0

0
0

0

0

0
0
0

MEC2
0
0

0

0
0
0

0

0
0
0
0
0
0

0
(0.011,0.115,
0.136,0.241)
0

PEC3

(0.100,0.138,
0.146,0.174)

CR4

0

(0.047,0.151,
0.173,0.277)
0
0

0

0
0
0

MEC3
0
0

0

0
0
0

0

0
0
0
0
0
0

0

0
0

PEC4

(0.031,0.048,
0.054,0.076)

CR5

0
(0.170,0.275,
0.313,0.418)
0

(0.170,0.275,
0.313,0.418)
0

0

0
0

MEC4
0
0

0

0
0
0

0

0
0
0
0
0
0

0

0
0

PEC5

(0.059,0.084,

0.091,0.27)

CR6

(0.047,0.151,
0.173,0.277)

0
0

0

0

0
0
0

0
0
(0.011,0.115,
0.136,0.241)
(0.011,0.115,
0.136,0.241)
MEC5
0
0

0

(0.360,0.476,
0.522,0.628)
0
0
0
0
0
0

0
0

PEC6

(0.115,0.152,
0.160,0.193)

CR7

(continued)

0

0
0

0

0

0
0
0

MEC6
0
0

0

0
0
0

0

0
0
0
0
0
0

0

0
0

PEC7

(0.134,0.1632,
0.171,0.216)

CR8

608
Z. Zhang and X. Chu