Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol65.Issue1.Apr2000:
Int. J. Production Economics 65 (2000) 43}54
Can support systems adversely a!ect cell performance?
A. Dickens!,*, C. Baber"
!Tompkins Associates International Limited, Innovation Centre, Warwick Technology Park, Gallows Hill, Warwick,
Warwickshire, CV34 6UW, UK
"Room E7, School of Mechanical and Manufacturing Engineering, The University of Birmingham, Edgbaston,
Birmingham, B15 2TT, UK
Abstract
This paper aims to determine the e!ect that support systems have upon the productivity of manufacturing systems is
demonstrated in this paper. Two case studies were undertaken in two separate factories. The method encompassed:
Determining the support system con"guration, identifying signi"cant production problems, problem-solving to identify
root cause and the tracking of process routes. Links between con"guration, production problems and process routes are
emphasised. The results show that the design of the support system adversely a!ects the manufacturing system by causing
quality problems and an inability to meet production deadlines. ( 2000 Elsevier Science B.V. All rights reserved.
Keywords: Support systems; Manufacturing cells
1. Introduction
In order to remain competitive, many companies
seek to implement more e$cient and #exible manufacturing systems. Previous research has centred
upon the development of these systems, that is,
followed a technocentric approach, while very little
work has been undertaken concerning the e!ect of
holistic organisational design upon the success of
a factory. This paper attempts to address this issue
by heeding Small's [1] recommendations that
knowing what the whole plant and its support
functions will eventually look like is vital in the
generation of a master plan for manufacturing. The
following paragraph discusses the systems that constitute a manufacturing organisation. It is proposed
that a manufacturing organisation consists of two
* Corresponding author.
E-mail address: adickens@tompkinsinc.com (A. Dickens)
distinct, but interacting systems: (1) the manufacturing system; and (2) the support system [2]. This
research focuses upon the support system and its
e!ect upon the manufacturing system. A support
system is de"ned as the &indirect' element of the
manufacturing organisation, and is an essential
contributor to the success of the overall process [3].
This means that, although support systems are not
directly involved in manufacturing products, they
have an essential supportive role within the factory
[4]. A support system is composed of up to 11
support functions; (1) information technology; (2)
maintenance; (3) engineering; (4) quality; (5) design
and development; (6) human resource management;
(7) procurement; (8) sales; (9) marketing; (10) production planning; and (11) "nance [5]. A company
may operate using either all or some of the support
functions. The following section describes how information pertaining to the support system is used
in this research.
0925-5273/00/$ - see front matter ( 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 9 9 ) 0 0 0 8 9 - 4
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A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
2. Aims
The aim of this research is to discover whether
the way in which support functions work a!ects the
performance of the manufacturing system either
positively or negatively. Examining the way in
which support functions work entails determining
their structure, their location within the factory,
whether they are team based or functionally
divided, and "nally, the processes they use to
achieve their goals. This information is gathered in
two case study companies, a method which allows
the investigation of support functions in situ. The
research is standardised by selecting two companies which use manufacturing cells. The following section summarises the literature pertaining to
this research.
3. Background
This section is structured around three questions:
(1) do manufacturing cells attain the projected bene"ts?; (2) if they do not, in which areas are they
failing to achieve their projected bene"ts?; and (3)
are these areas linked to the support system?
The literature indicates a high level of performance in terms of cell engineering, with many companies professing to attain bene"ts in terms of
reducing "xtures, equipment used, space and unit
cost, throughput time, quality rejects, WIP, complex product #ow and material handling [6}11].
However, the more removed the research focus is
from the immediate manufacturing cell, the greater
the identi"ed problems, indicating a technocentric
approach within factories. This is supported by the
fact that many companies are failing to achieve
bene"ts in terms of the people operating the system.
Bratton [10], Wemmerlov and Hyer [7], Kumar
and Hadjinicola [12], Delbridge et al. [13], Nichols
and Beynon [14], Waterson et al. [6] and Loo [8]
"nd that in terms of people, many companies su!er
low levels of operator training other (with the exception of companies described as world-class),
very little operator accountability beyond quality
and a negligible degree of cell self-management.
Again, as the focus moves even further from the
manufacturing cell and more towards the support
system, goal attainment becomes worse. Appleby
and Prabhu [15] found that even world class companies perform poorly in terms of production innovation and design cycle time, one reason for this
being that 60% of companies do not use any form
of concurrent engineering, which is inherently support-orientated [6]. Similarly, many companies experience delays with customer orders within the
support system, unresponsive supply chains, assembly drawings arriving after the product has been
manufactured and incorrect components at the
point of assembly, all of which are the responsibility
of support functions [16,17]. Due to the lack of
published research in the area, it is not clear why
this is the case, but one possibility is that companies
might be retaining in#exible, Tayloristic structures
beyond the manufacturing cell. There is some evidence to support this. Firstly, it appears that many
companies still have support workers divided by
function rather than multifunctionally [18}20].
Muhlemann et al. [21], Adair and Murray [22] and
Schubert and Couchman [23] all claim that the
functional division of labour leads to complex, sequential product #ow, in#exibility and a lack of
traceability. Considering this, it is not surprising
that systems such as MRP/II are still used by
between 82 and 90% of companies [7,8] respectively, even though they are acknowledged to be
in#exible [18]. Similarly, although many authors
advocate the use of cell-based rather than centralised support functions [8,24,25], in reality there is
scant evidence that this is happening in industry.
The literature in this section does hint at a link
between the support system and problems in the
manufacturing cells, although published research is
not comprehensive enough to draw solid conclusions [26]. If there is a link however, it is likely
to be magni"ed in the future. Wemmerlov and
Hyer [7] and Wemmerlov and Johnson [27] claim
that currently support workers constitute 49.5 and
44.34% of the workforce, respectively, and this
number is likely to increase in the future, resulting
in a situation where direct workers account for only
5}10% of the total workforce [4]. In light of this, it
is important to understand the way in which support workers work in order that factories can maintain competitiveness. In order to be able to draw
solid conclusions in this area, additional systematic
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
research needs to be undertaken. The following
sections present the methodology used to obtain
data from two case study companies.
4. The case study companies
To standardise the data obtained, it is proposed
that the case study companies need to have 2 years
operational experience of manufacturing cells, centrally based and functionally divided support. The
two companies selected are described below.
4.1. Company A
Company A is a manufacturer of gas- and oil"red heating products, employing 800 workers,
with a turnover of C60 million. The company has
"ve process and six product cells, producing subassemblies and "nished products, respectively. The
products are all manufactured on site, except for
heat exchanger castings which are supplied by a sister company.
4.2. Company B
Company B employs 450 people, has a turnover
of C40 million and is a manufacturer of diesel engines and steam turbines varying from 50 to 80 tons
in size. Although based on one site, the factory is
divided into three autonomous "nancial units, of
which two are relevant to this research: (1) &Company B Steam Turbines' who procure customer
orders and design steam turbines to speci"cation;
and (2) &Company B Manufacturing', who are paid
by Company B Steam Turbines to build engines for
their customers. The company has "ve process cells
and six product cells.
The methodology used to collect data from the
case study companies is described below.
5. Case study methodology
There are three steps in obtaining data from the
case study companies which are described below.
45
5.1. Determining the support system conxguration
The "rst stage in data collection involves outlining the support system con"guration for the 11
support functions. This involves examining: (1) the
degree of association between support functions;
and (2) the division of support functions. In terms of
the degree of association between support functions
and the manufacturing cells, previous research [5]
has shown four possible categories into which the
11 support functions can be placed. These degrees
of association are (1) cell based and responsible for
supporting one cell, i.e. the support function is
based within the one cell it supports; (2) centrally
based but dedicated to a few speci"c cells, i.e. the
support function is based in central o$ces, with
responsibility for supporting only a few cells; (3)
centrally based and directly supporting all cells, i.e.
the support function is based in central o$ces with
responsibility for supporting all factory cells; (4)
centrally based with little direct cell contact, i.e. the
support function is centrally based, but perform
their tasks in isolation. In terms of the division of
support functions, they can be either functionally
divided or based in multifunctional teams.
5.2. Identixcation of signixcant production problems
After determining the support system con"guration, a representative manufacturing cell within the
factory is selected for stage 2 of the analysis. The
next stage is the identi"cation of a signi"cant production problem related to that cell. Common signi"cant problems are line stoppages, late delivery
of products and recurring poor quality.
Once the signi"cant production problem has
been identi"ed, it is veri"ed and quanti"ed through
data collection. For example, if a product is often
delivered late, the type of product and the frequency of its late delivery is recorded in addition to
other pertinent data. The next stage involves identifying the root cause of the production problem, and
a problem solving technique called &5 whys' [28] is
applied. This enables the identi"cation of any links
between the production problem and the support
system. If a link is identi"ed, stage 3 (described
below) can commence. If a link is not identi"ed, the
methodology is discontinued.
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A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
5.3. Tracking process routes
Problem solving identi"es the cause of a production problem, but does not determine why that
cause exists. A #ow process chart [29] is used in
order to elaborate upon the role of the support
function in causing production problems. The #ow
process chart has a "xed start (either the forecaset
of demand or customer places order, depending
upon the logistics system within the factory) and
end (components delivered to cell) in order to standardise inter-company data. The level of detail in
#ow process charts give a good indication of support system e$ciency.
In terms interpreting the chart, each box represents a single stage in the process and each arrow
indicates the direction in which the sequence proceeds. Unmarked arrows show that the process
stage moves ahead unhindered while arrows labelled ¬ OK' show the path of the process if a
problem occurs. In addition, the #ow process chart
is adapted slightly for the bene"t of this research.
The adaptations are: (1) a box with a grey background indicates a link to another system, for
example an MRPII system; and (2) bold type in the
boxes represents departments where the tasks are
carried out.
6. Company A results
6.1. Conxguration
The con"guration shows that virtually no
support functions are based in the cell. Indeed
the skew is very much towards centrally based
workers, with four functions rarely contacting the
cell (Table 1).
In terms of the division of support functions, all
are functionally divided.
6.2. Identixcation of signixcant production problems
The representative cell selected for analysis is
a product cell containing 15 full-time direct operatives and a cell manager. It manufactures four main
products which have 29 variations. The output goal
is 35 boilers/day. An interview with the cell manager pinpointed that main problem su!ered in the
cell is downtime which averages 395/labour hours
per week. The manager claimed that the downtime
is caused by one supplier either not delivering control boxes on time or delivering non-conforming
control boxes. The fact that outsourced control
boxes are non-conforming and delivered late is
veri"ed through data obtained from the company
COPICS system, shown in Table 2.
The data in Table 2 relate to the eight months
preceding the case study (03/95 to 01/96). Once the
problems have been veri"ed, they are subjected to
the &5 whys' problem-solving exercise. In this instance the information was obtained from the supplier of the control boxes, referred to as Company
A supplier. It can be seen from Table 3 that the root
cause of the production problems is #uctuating
orders from Company A.
The above table shows that there is a direct link
between the Company A supplier not being able to
deliver on time and the purchasing function of
Company A. A #ow chart is now produced in order
to determine whether Company A's support system
a!ects this process.
Table 1
Degree of association between support functions and cells
Cell based and responsible for
supporting one cell
Centrally based but dedicated
to a few speci"c cells
Centrally based and directly
supporting all cells
Centrally based with little direct
cell contact
Production planning
Production engineers
Purchasing material control
Quality
Maintenance
Information
Technology
Personnel
Stores
Finance
Marketing
Sales (& forecast)
Design
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
6.3. Company A yow process chart
Fig. 1 shows the process used from forecast of
demand to delivery of parts in the cell.
A #ow chart analysis of the non-value adding
stages is shown in Table 4. An additional category
of rework is added in this instance because it represents non-value adding work but this is not clear
using the existing categories.
Table 4 makes the system appear e$cient, with
11 operations out of a total of 15 which are generally classi"ed as value-adding. However, upon
closer examination, most of the operations in the
Table 2
Data relating to outsourced control boxes
Boiler number
% Non-conforming
% On-time delivery
21/19695
21/19806
21/19655
21/19654
21/19694
21/19679
21/19711
21/19978
21/19712
21/19678
21/19721
Average
13.79
0.77
2.06
2.90
1.25
0.16
0.00
2.40
29.93
17.78
0.00
6.46
0.00
0.00
3.03
1.52
9.09
0.00
4.55
8.33
0.00
0.00
9.09
3.24
47
#ow chart are simply not needed. The only essential
operations are forecast parts, order parts and receive delivery of the parts to the cell. Instead, six
departments are involved and the total lead time of
the process is over a month. In addition, the table
does not take into account the rework loops, of
which there are three, each meaning that a segment
of the process must be repeated.
The following section shows the data obtained
from Company B.
7. Company B results
7.1. Conxguration
Table 5 depicts the degree of association between
support functions and manufacturing cells within
the organisation.
Company B again shows skew towards being
centrally based with very little cell contact. Similarly,
the support workers are all functionally divided.
7.2. Identixcation of signixcant production problems
The representative cell is a steam turbine assembly cell which has been operational for over 2 years.
All steam turbines manufactured by Company
B must pass through the cell, making the cell part of
Table 3
&Five whys' problem-solving exercise for downtime
Problem
Non-conforming control boxes
Late delivery of control boxes
First Why
The control box is non-conforming because one of
the PCB components is defective
A defective part is used because it is not detected when
it arrives from the supplier
Control boxes are delivered late because they do not
have time to build control boxes
Company A supplier does not have time to build
control boxes because they have to carry out a world
search for companies who can quickly supply the
necessary components
Company A supplier uses a world wide search
because they have to "nd another means by which
to obtain components
They have to "nd another way to obtain these
components because normal delivery lead times from
regular suppliers can be as long as 16}26 weeks
They cannot use components with long lead times
because Company A changes its order with very
little notice
Second Why
Third Why
It is not detected because inspection at Company A
supplier is not rigorous enough to "nd all faults
Fourth Why
Inspection is not rigorous enough because Company
A supplier does not have the time to test all control
boxes
It does not have the time to test all control boxes
because Company A's orders constantly #uctuate
Fifth Why
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A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
Fig. 1. Flow process chart from forecast to delivery of parts to the cell.
Table 4
Classi"cation of process #ow chart stages
Classi"cation
No. of stages
Rework
2
Storage
0
the critical path. An interview with the manager of
the steam turbine assembly cell, highlighted that
the most consequential problem is the inability to
meet build-plan dates. This claim was veri"ed by an
analysis of process lead times for critical components involved in the production of one steam turbine (see Table 6).
The above table con"rms that build plan dates
are not being met due to component shortages.
Analysis continues with the problem-solving exercise which seeks to determine the cause of the
components shortages detailed above. Table 7
shows the results of the problem solving exercise.
Table 7 shows that the root cause of production
problems is speci"cation changes in steam turbines
Transport
1
Operation
11
Inspection
1
Total
15
which are issued to the manufacturing cells by the
design department. In addition, the cell manager
claimed that steam turbine engines were delivered
to the customer up to 1 year late. Due to the fact
that the problem-solving exercise and late delivery
claims were made by one person, a further veri"cation exercise was undertaken, in which the number
of engineering changes made by the design department were monitored. The results are shown in
Table 8. The total number of drawings produced as
part of the manufacturing process was 2896, and
the period covers 1 year.
Table 8 con"rms the results of the problemsolving exercise; the design function do cause delays through engineering changes, with 431 recalls
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
49
Table 5
Support system con"guration for Company B
Cell based and responsible for
supporting one cell
Centrally based but dedicated
to a few speci"c cells
Centrally based and directly
supporting all cells
Centrally based with little direct
cell contact
Tool services
Production planning
Stores control
Maintenance
Purchasing
Tool design
Design
Finance
Marketing
Sales
Scheduling
Personnel
IT
Average length of time each
component is overdue (days),
in terms of meeting product
delivery date
54.01
Average length of time since the
last operation on that
component (days)
Table 6
Data relating to turbine component shortages
Number of components or
sub-assemblies required before
steam turbine can be completed
Average number of operations
the components require before
they are ready for assembly
72
5
Table 7
&Five whys' problem-solving exercise for inability to meet build
plan dates
Problem
Unable to meet build-plan deadlines
First Why
Product priority is changed and another
product is more urgent
The new priority product is more urgent
because the deadline is more overdue
The new priority product is overdue because it did not have all components
needed in the original schedule
The components were not available because they were awaiting design drawings
Changes to the steam turbine speci"cation
meant that the design drawings had to be
re-draughted and were therefore late
Second Why
Third Why
Fourth Why
Fifth Why
to approved designs within the space of 1 year as
opposed to an ideal of zero.
7.3. Tracking process routes
The results encompass two "nancial units: Company B Steam Turbines and Company B Manufacturing. Each unit has a separate chart for clarity
110.479
Table 8
Engineering change analysis 1997
Reason for change
Number of
changes
Percentage of
changes
Additional information
Change of standard
Customer change
Error within operations
Design standard change
Change for cost saving
Workshop error
Total changes
149
80
22
73
35
66
6
431
5.14
2.76
0.76
2.52
1.21
2.28
0.21
14.88
and the two charts are sequential, that is, Fig. 3
continues from Fig. 2.
Two observations should be noted about the
above "gure. The "rst is that in 35% of cases, the
customer changes the product speci"cation following approval. The second observation is that in
14% of instances, Company B rejects the drawings.
Both observations show percentages which are
considerably higher than the "gures quoted in the
engineering change analysis (see Table 8). This
could be due either to inaccuracies in the data
50
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
supplied by the Senior Design Engineer, or to incorrect data being presented in the o$cial report of
engineering changes which is supplied to senior
management.
Fig. 3 is a continuation of Fig. 2, and encompasses Company B Manufacturing. It should be
noted that neither Company B manufacturing, nor
Company B Steam Turbines have any knowledge
of the others process routes.
An #ow chart analysis of the storage, operation,
transport and inspection is shown in Table 9.
Table 9 shows 26 stages in the combined process
routes. Of the 26, 15 (storage#transport#inspection) are non-value added. Similarly, the operations
exist within a non-optimised system and may therefore, be unnecessary. The results of the #ow process
charts show that Company B's support system is
extremely complex with a high number of nonvalue-adding stages.
8. Discussion
The results show that there is a relationship
between the support system and poor production
performance. The discussion addresses both case
studies individually.
8.1. Company A discussion
Initially, downtime problems within the cell seem
to be caused by the supplier of control boxes, although the problem-solving exercise and #ow process chart disproved this. The results show that the
sales' forecast of demand to delivery of parts to the
cell process has become part of the critical path in
terms of manufacturing lead time. This means that
the overall time taken to complete a product can be
either lengthened or shortened by the sales' forecast
of demand to delivery of parts to the cell process,
Fig. 2. Flow chart showing process route for Company B steam turbines.
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
51
Fig. 3. Flow chart showing process route for Company B manufacturing.
Table 9
Classi"cation of process #ow chart stages
Classi"cation
No. of stages
Storage
2
Transport
9
and the manufacturing cell has no control over this.
The reason that the process is part of the critical
path is the long lead time, which frequently exceeds
a month. Even worse, if the sales' forecast #uctuates, which it often does, the whole process has to
be repeated. Repeating the whole process does not
leave enough time for suppliers to deliver the correct quantity of control boxes on time, and thus the
manufacturing cell su!ers component shortages
and downtime. The following paragraphs try to
explain these process problems in Company A.
The "rst problem is that support functions are
remotely based in relation to the cell and have very
little contact. This means that #uctuations in fore-
Operation
11
Inspection
4
Total
26
cast for demand are not communicated to the cell,
who schedule a build plan not realising that; (1)
demand has changed; and (2) it is unlikely that they
will receive the necessary components from the
suppliers.
The second problem is functionally based support workers. This leads to: (1) the evolution of
a sequential approach to work where each function
performs their own task before passing it on to
another function. This means that the process takes
longer, hence contributing to long lead times; (2)
individual support functions having little understanding about the tasks performed by other functions. This manifests itself negatively in that
52
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
individual functions become self-focused rather
team-focused. Self-focus is exacerbated by the reaction of management to poor support system performance. Interviewed support workers believe that
the sequential nature of the system means they are
trapped in a rigid process. However, they are
blamed when parts are late, even though it is the
process rather than the individual causing the delay. For example, when the manufacturing cells do
not receive their parts on time, energy is spent
apportioning blame rather than solving the fundamental problem of a poor support system structure.
In addition, sta! also feel that their suggestions to
improve the process are ignored by management.
The complexity of the sales' forecast of demand
to delivery of parts in the cell process is demonstrated by the number of stages involved. When
examining the process there are 15 stages and six
departments in total. Interviews with managers
con"rmed that the process evolved as the company
expanded and this lack of formal design is borne
out by the complex, rigid network of links and
loops. System re-design could eliminate up to 70%
of these stages as non-value-adding. The following
section discusses the results of the case study in
Company B.
8.2. Company B discussion
This inability to meet production deadlines
seems to be inextricably linked to the design support function in particular. Indeed, analysis showed
that this was the case, with 14.88% of products
being subject to design changes after they have
entered Company B Manufacturing, leading to
large #uctuations in build-plan schedules. But is
this a support system or a support function problem? The results tend to suggest that the problem
lies with the latter. If the design support function
was more stringent in its cut-o! deadlines regarding engineering changes, then the build-plans
would not require alteration. This problem is due
to poor management and planning rather than an
inherent support system problem. This does not
support the hypothesis (stated in Section 1 of this
paper); it is the support function rather than the
support system which causes manufacturing delays.
If however, the support system did operate in an
e$cient manner, the ine$ciencies of the design
department may not form part of the critical path,
that is, the poor design of the support system serves
only to highlight problems within the design department. So why isn't the support system e$cient?
The answer is that the support system su!ers a lack
of #exibility.
The lack of support system #exibility is caused
by three factors. The "rst is the division of the
factory into three autonomous business units. This
means that there are three separate support systems, when, for e$cient production, they should be
intertwined. The second factor is the functional
division of support (people performing the same or
similar jobs are based in the same o$ce). Functional division leads to a sequential approach to
work; when one function completes their task, it is
passed to a subsequent function for completion of
another task, and so on. The time taken in a sequential approach to work is high in comparison to
that taken by multi-skilled teams. The third and
"nal factor contributing toward a lack of #exibility
is remotely based support. This means that the
support functions are based in o$ces away from
the shop#oor. The three factors combined lead to
the complex process routes shown in Figs. 2 and
3 and long process lead times (approximately one
month from start to "nish). Long process lead times
mean that changes in product design are obviously
problematic. For each of the 431 design changes in
1997 (see Table 8), the support process route had to
be repeated, taking on average 2 months for each
change to reach the shop#oor (many of these
changes pass through the system concurrently).
Whilst the product design is being changed, production has to stop, resulting in the rescheduling of
the build-plan, and subsequently an inability to
meet deadlines. The problems highlighted in the
paragraph above show that the support system is
fundamental in causing productivity problems,
thus supporting the hypothesis.
As a "nal note for those who think that factory
improvement comes about by optimising the
manufacturing system; many of the 52 steam turbines on order for 1998 have contractual penalty
clauses for late delivery equating to C10,000 per
week (each). Despite having excess capacity in the
manufacturing cells, each turbine is delivered on
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
average, a year late due to the delays in processing
design changes.
[6]
9. Conclusions
Data show that both companies are su!ering
from the same fundamental problems: (1) remotely
based support functions; and (2) functional division
of support workers. This leads to a sequential support system process, a high number of non-value
adding stages, long lead times and in#exibility.
These inherent problems mean that the manufacturing cells su!er in terms of not receiving parts or
drawings on time. More seriously, the customer
ultimately su!ers because they do not receive their
order on time. In conclusion, the #ow chart results
show that the #ow of information and components
is overly complex and not conducive to a typical
cellular manufacturing pull system, resulting in the
need for re-design.
[7]
[8]
[9]
[10]
[11]
[12]
Acknowledgements
This research was funded by the School of Mechanical and Manufacturing Engineering, The University of Birmingham. Thanks go to the
companies involved in this research.
References
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Can support systems adversely a!ect cell performance?
A. Dickens!,*, C. Baber"
!Tompkins Associates International Limited, Innovation Centre, Warwick Technology Park, Gallows Hill, Warwick,
Warwickshire, CV34 6UW, UK
"Room E7, School of Mechanical and Manufacturing Engineering, The University of Birmingham, Edgbaston,
Birmingham, B15 2TT, UK
Abstract
This paper aims to determine the e!ect that support systems have upon the productivity of manufacturing systems is
demonstrated in this paper. Two case studies were undertaken in two separate factories. The method encompassed:
Determining the support system con"guration, identifying signi"cant production problems, problem-solving to identify
root cause and the tracking of process routes. Links between con"guration, production problems and process routes are
emphasised. The results show that the design of the support system adversely a!ects the manufacturing system by causing
quality problems and an inability to meet production deadlines. ( 2000 Elsevier Science B.V. All rights reserved.
Keywords: Support systems; Manufacturing cells
1. Introduction
In order to remain competitive, many companies
seek to implement more e$cient and #exible manufacturing systems. Previous research has centred
upon the development of these systems, that is,
followed a technocentric approach, while very little
work has been undertaken concerning the e!ect of
holistic organisational design upon the success of
a factory. This paper attempts to address this issue
by heeding Small's [1] recommendations that
knowing what the whole plant and its support
functions will eventually look like is vital in the
generation of a master plan for manufacturing. The
following paragraph discusses the systems that constitute a manufacturing organisation. It is proposed
that a manufacturing organisation consists of two
* Corresponding author.
E-mail address: adickens@tompkinsinc.com (A. Dickens)
distinct, but interacting systems: (1) the manufacturing system; and (2) the support system [2]. This
research focuses upon the support system and its
e!ect upon the manufacturing system. A support
system is de"ned as the &indirect' element of the
manufacturing organisation, and is an essential
contributor to the success of the overall process [3].
This means that, although support systems are not
directly involved in manufacturing products, they
have an essential supportive role within the factory
[4]. A support system is composed of up to 11
support functions; (1) information technology; (2)
maintenance; (3) engineering; (4) quality; (5) design
and development; (6) human resource management;
(7) procurement; (8) sales; (9) marketing; (10) production planning; and (11) "nance [5]. A company
may operate using either all or some of the support
functions. The following section describes how information pertaining to the support system is used
in this research.
0925-5273/00/$ - see front matter ( 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 9 9 ) 0 0 0 8 9 - 4
44
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
2. Aims
The aim of this research is to discover whether
the way in which support functions work a!ects the
performance of the manufacturing system either
positively or negatively. Examining the way in
which support functions work entails determining
their structure, their location within the factory,
whether they are team based or functionally
divided, and "nally, the processes they use to
achieve their goals. This information is gathered in
two case study companies, a method which allows
the investigation of support functions in situ. The
research is standardised by selecting two companies which use manufacturing cells. The following section summarises the literature pertaining to
this research.
3. Background
This section is structured around three questions:
(1) do manufacturing cells attain the projected bene"ts?; (2) if they do not, in which areas are they
failing to achieve their projected bene"ts?; and (3)
are these areas linked to the support system?
The literature indicates a high level of performance in terms of cell engineering, with many companies professing to attain bene"ts in terms of
reducing "xtures, equipment used, space and unit
cost, throughput time, quality rejects, WIP, complex product #ow and material handling [6}11].
However, the more removed the research focus is
from the immediate manufacturing cell, the greater
the identi"ed problems, indicating a technocentric
approach within factories. This is supported by the
fact that many companies are failing to achieve
bene"ts in terms of the people operating the system.
Bratton [10], Wemmerlov and Hyer [7], Kumar
and Hadjinicola [12], Delbridge et al. [13], Nichols
and Beynon [14], Waterson et al. [6] and Loo [8]
"nd that in terms of people, many companies su!er
low levels of operator training other (with the exception of companies described as world-class),
very little operator accountability beyond quality
and a negligible degree of cell self-management.
Again, as the focus moves even further from the
manufacturing cell and more towards the support
system, goal attainment becomes worse. Appleby
and Prabhu [15] found that even world class companies perform poorly in terms of production innovation and design cycle time, one reason for this
being that 60% of companies do not use any form
of concurrent engineering, which is inherently support-orientated [6]. Similarly, many companies experience delays with customer orders within the
support system, unresponsive supply chains, assembly drawings arriving after the product has been
manufactured and incorrect components at the
point of assembly, all of which are the responsibility
of support functions [16,17]. Due to the lack of
published research in the area, it is not clear why
this is the case, but one possibility is that companies
might be retaining in#exible, Tayloristic structures
beyond the manufacturing cell. There is some evidence to support this. Firstly, it appears that many
companies still have support workers divided by
function rather than multifunctionally [18}20].
Muhlemann et al. [21], Adair and Murray [22] and
Schubert and Couchman [23] all claim that the
functional division of labour leads to complex, sequential product #ow, in#exibility and a lack of
traceability. Considering this, it is not surprising
that systems such as MRP/II are still used by
between 82 and 90% of companies [7,8] respectively, even though they are acknowledged to be
in#exible [18]. Similarly, although many authors
advocate the use of cell-based rather than centralised support functions [8,24,25], in reality there is
scant evidence that this is happening in industry.
The literature in this section does hint at a link
between the support system and problems in the
manufacturing cells, although published research is
not comprehensive enough to draw solid conclusions [26]. If there is a link however, it is likely
to be magni"ed in the future. Wemmerlov and
Hyer [7] and Wemmerlov and Johnson [27] claim
that currently support workers constitute 49.5 and
44.34% of the workforce, respectively, and this
number is likely to increase in the future, resulting
in a situation where direct workers account for only
5}10% of the total workforce [4]. In light of this, it
is important to understand the way in which support workers work in order that factories can maintain competitiveness. In order to be able to draw
solid conclusions in this area, additional systematic
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
research needs to be undertaken. The following
sections present the methodology used to obtain
data from two case study companies.
4. The case study companies
To standardise the data obtained, it is proposed
that the case study companies need to have 2 years
operational experience of manufacturing cells, centrally based and functionally divided support. The
two companies selected are described below.
4.1. Company A
Company A is a manufacturer of gas- and oil"red heating products, employing 800 workers,
with a turnover of C60 million. The company has
"ve process and six product cells, producing subassemblies and "nished products, respectively. The
products are all manufactured on site, except for
heat exchanger castings which are supplied by a sister company.
4.2. Company B
Company B employs 450 people, has a turnover
of C40 million and is a manufacturer of diesel engines and steam turbines varying from 50 to 80 tons
in size. Although based on one site, the factory is
divided into three autonomous "nancial units, of
which two are relevant to this research: (1) &Company B Steam Turbines' who procure customer
orders and design steam turbines to speci"cation;
and (2) &Company B Manufacturing', who are paid
by Company B Steam Turbines to build engines for
their customers. The company has "ve process cells
and six product cells.
The methodology used to collect data from the
case study companies is described below.
5. Case study methodology
There are three steps in obtaining data from the
case study companies which are described below.
45
5.1. Determining the support system conxguration
The "rst stage in data collection involves outlining the support system con"guration for the 11
support functions. This involves examining: (1) the
degree of association between support functions;
and (2) the division of support functions. In terms of
the degree of association between support functions
and the manufacturing cells, previous research [5]
has shown four possible categories into which the
11 support functions can be placed. These degrees
of association are (1) cell based and responsible for
supporting one cell, i.e. the support function is
based within the one cell it supports; (2) centrally
based but dedicated to a few speci"c cells, i.e. the
support function is based in central o$ces, with
responsibility for supporting only a few cells; (3)
centrally based and directly supporting all cells, i.e.
the support function is based in central o$ces with
responsibility for supporting all factory cells; (4)
centrally based with little direct cell contact, i.e. the
support function is centrally based, but perform
their tasks in isolation. In terms of the division of
support functions, they can be either functionally
divided or based in multifunctional teams.
5.2. Identixcation of signixcant production problems
After determining the support system con"guration, a representative manufacturing cell within the
factory is selected for stage 2 of the analysis. The
next stage is the identi"cation of a signi"cant production problem related to that cell. Common signi"cant problems are line stoppages, late delivery
of products and recurring poor quality.
Once the signi"cant production problem has
been identi"ed, it is veri"ed and quanti"ed through
data collection. For example, if a product is often
delivered late, the type of product and the frequency of its late delivery is recorded in addition to
other pertinent data. The next stage involves identifying the root cause of the production problem, and
a problem solving technique called &5 whys' [28] is
applied. This enables the identi"cation of any links
between the production problem and the support
system. If a link is identi"ed, stage 3 (described
below) can commence. If a link is not identi"ed, the
methodology is discontinued.
46
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
5.3. Tracking process routes
Problem solving identi"es the cause of a production problem, but does not determine why that
cause exists. A #ow process chart [29] is used in
order to elaborate upon the role of the support
function in causing production problems. The #ow
process chart has a "xed start (either the forecaset
of demand or customer places order, depending
upon the logistics system within the factory) and
end (components delivered to cell) in order to standardise inter-company data. The level of detail in
#ow process charts give a good indication of support system e$ciency.
In terms interpreting the chart, each box represents a single stage in the process and each arrow
indicates the direction in which the sequence proceeds. Unmarked arrows show that the process
stage moves ahead unhindered while arrows labelled ¬ OK' show the path of the process if a
problem occurs. In addition, the #ow process chart
is adapted slightly for the bene"t of this research.
The adaptations are: (1) a box with a grey background indicates a link to another system, for
example an MRPII system; and (2) bold type in the
boxes represents departments where the tasks are
carried out.
6. Company A results
6.1. Conxguration
The con"guration shows that virtually no
support functions are based in the cell. Indeed
the skew is very much towards centrally based
workers, with four functions rarely contacting the
cell (Table 1).
In terms of the division of support functions, all
are functionally divided.
6.2. Identixcation of signixcant production problems
The representative cell selected for analysis is
a product cell containing 15 full-time direct operatives and a cell manager. It manufactures four main
products which have 29 variations. The output goal
is 35 boilers/day. An interview with the cell manager pinpointed that main problem su!ered in the
cell is downtime which averages 395/labour hours
per week. The manager claimed that the downtime
is caused by one supplier either not delivering control boxes on time or delivering non-conforming
control boxes. The fact that outsourced control
boxes are non-conforming and delivered late is
veri"ed through data obtained from the company
COPICS system, shown in Table 2.
The data in Table 2 relate to the eight months
preceding the case study (03/95 to 01/96). Once the
problems have been veri"ed, they are subjected to
the &5 whys' problem-solving exercise. In this instance the information was obtained from the supplier of the control boxes, referred to as Company
A supplier. It can be seen from Table 3 that the root
cause of the production problems is #uctuating
orders from Company A.
The above table shows that there is a direct link
between the Company A supplier not being able to
deliver on time and the purchasing function of
Company A. A #ow chart is now produced in order
to determine whether Company A's support system
a!ects this process.
Table 1
Degree of association between support functions and cells
Cell based and responsible for
supporting one cell
Centrally based but dedicated
to a few speci"c cells
Centrally based and directly
supporting all cells
Centrally based with little direct
cell contact
Production planning
Production engineers
Purchasing material control
Quality
Maintenance
Information
Technology
Personnel
Stores
Finance
Marketing
Sales (& forecast)
Design
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
6.3. Company A yow process chart
Fig. 1 shows the process used from forecast of
demand to delivery of parts in the cell.
A #ow chart analysis of the non-value adding
stages is shown in Table 4. An additional category
of rework is added in this instance because it represents non-value adding work but this is not clear
using the existing categories.
Table 4 makes the system appear e$cient, with
11 operations out of a total of 15 which are generally classi"ed as value-adding. However, upon
closer examination, most of the operations in the
Table 2
Data relating to outsourced control boxes
Boiler number
% Non-conforming
% On-time delivery
21/19695
21/19806
21/19655
21/19654
21/19694
21/19679
21/19711
21/19978
21/19712
21/19678
21/19721
Average
13.79
0.77
2.06
2.90
1.25
0.16
0.00
2.40
29.93
17.78
0.00
6.46
0.00
0.00
3.03
1.52
9.09
0.00
4.55
8.33
0.00
0.00
9.09
3.24
47
#ow chart are simply not needed. The only essential
operations are forecast parts, order parts and receive delivery of the parts to the cell. Instead, six
departments are involved and the total lead time of
the process is over a month. In addition, the table
does not take into account the rework loops, of
which there are three, each meaning that a segment
of the process must be repeated.
The following section shows the data obtained
from Company B.
7. Company B results
7.1. Conxguration
Table 5 depicts the degree of association between
support functions and manufacturing cells within
the organisation.
Company B again shows skew towards being
centrally based with very little cell contact. Similarly,
the support workers are all functionally divided.
7.2. Identixcation of signixcant production problems
The representative cell is a steam turbine assembly cell which has been operational for over 2 years.
All steam turbines manufactured by Company
B must pass through the cell, making the cell part of
Table 3
&Five whys' problem-solving exercise for downtime
Problem
Non-conforming control boxes
Late delivery of control boxes
First Why
The control box is non-conforming because one of
the PCB components is defective
A defective part is used because it is not detected when
it arrives from the supplier
Control boxes are delivered late because they do not
have time to build control boxes
Company A supplier does not have time to build
control boxes because they have to carry out a world
search for companies who can quickly supply the
necessary components
Company A supplier uses a world wide search
because they have to "nd another means by which
to obtain components
They have to "nd another way to obtain these
components because normal delivery lead times from
regular suppliers can be as long as 16}26 weeks
They cannot use components with long lead times
because Company A changes its order with very
little notice
Second Why
Third Why
It is not detected because inspection at Company A
supplier is not rigorous enough to "nd all faults
Fourth Why
Inspection is not rigorous enough because Company
A supplier does not have the time to test all control
boxes
It does not have the time to test all control boxes
because Company A's orders constantly #uctuate
Fifth Why
48
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
Fig. 1. Flow process chart from forecast to delivery of parts to the cell.
Table 4
Classi"cation of process #ow chart stages
Classi"cation
No. of stages
Rework
2
Storage
0
the critical path. An interview with the manager of
the steam turbine assembly cell, highlighted that
the most consequential problem is the inability to
meet build-plan dates. This claim was veri"ed by an
analysis of process lead times for critical components involved in the production of one steam turbine (see Table 6).
The above table con"rms that build plan dates
are not being met due to component shortages.
Analysis continues with the problem-solving exercise which seeks to determine the cause of the
components shortages detailed above. Table 7
shows the results of the problem solving exercise.
Table 7 shows that the root cause of production
problems is speci"cation changes in steam turbines
Transport
1
Operation
11
Inspection
1
Total
15
which are issued to the manufacturing cells by the
design department. In addition, the cell manager
claimed that steam turbine engines were delivered
to the customer up to 1 year late. Due to the fact
that the problem-solving exercise and late delivery
claims were made by one person, a further veri"cation exercise was undertaken, in which the number
of engineering changes made by the design department were monitored. The results are shown in
Table 8. The total number of drawings produced as
part of the manufacturing process was 2896, and
the period covers 1 year.
Table 8 con"rms the results of the problemsolving exercise; the design function do cause delays through engineering changes, with 431 recalls
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
49
Table 5
Support system con"guration for Company B
Cell based and responsible for
supporting one cell
Centrally based but dedicated
to a few speci"c cells
Centrally based and directly
supporting all cells
Centrally based with little direct
cell contact
Tool services
Production planning
Stores control
Maintenance
Purchasing
Tool design
Design
Finance
Marketing
Sales
Scheduling
Personnel
IT
Average length of time each
component is overdue (days),
in terms of meeting product
delivery date
54.01
Average length of time since the
last operation on that
component (days)
Table 6
Data relating to turbine component shortages
Number of components or
sub-assemblies required before
steam turbine can be completed
Average number of operations
the components require before
they are ready for assembly
72
5
Table 7
&Five whys' problem-solving exercise for inability to meet build
plan dates
Problem
Unable to meet build-plan deadlines
First Why
Product priority is changed and another
product is more urgent
The new priority product is more urgent
because the deadline is more overdue
The new priority product is overdue because it did not have all components
needed in the original schedule
The components were not available because they were awaiting design drawings
Changes to the steam turbine speci"cation
meant that the design drawings had to be
re-draughted and were therefore late
Second Why
Third Why
Fourth Why
Fifth Why
to approved designs within the space of 1 year as
opposed to an ideal of zero.
7.3. Tracking process routes
The results encompass two "nancial units: Company B Steam Turbines and Company B Manufacturing. Each unit has a separate chart for clarity
110.479
Table 8
Engineering change analysis 1997
Reason for change
Number of
changes
Percentage of
changes
Additional information
Change of standard
Customer change
Error within operations
Design standard change
Change for cost saving
Workshop error
Total changes
149
80
22
73
35
66
6
431
5.14
2.76
0.76
2.52
1.21
2.28
0.21
14.88
and the two charts are sequential, that is, Fig. 3
continues from Fig. 2.
Two observations should be noted about the
above "gure. The "rst is that in 35% of cases, the
customer changes the product speci"cation following approval. The second observation is that in
14% of instances, Company B rejects the drawings.
Both observations show percentages which are
considerably higher than the "gures quoted in the
engineering change analysis (see Table 8). This
could be due either to inaccuracies in the data
50
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
supplied by the Senior Design Engineer, or to incorrect data being presented in the o$cial report of
engineering changes which is supplied to senior
management.
Fig. 3 is a continuation of Fig. 2, and encompasses Company B Manufacturing. It should be
noted that neither Company B manufacturing, nor
Company B Steam Turbines have any knowledge
of the others process routes.
An #ow chart analysis of the storage, operation,
transport and inspection is shown in Table 9.
Table 9 shows 26 stages in the combined process
routes. Of the 26, 15 (storage#transport#inspection) are non-value added. Similarly, the operations
exist within a non-optimised system and may therefore, be unnecessary. The results of the #ow process
charts show that Company B's support system is
extremely complex with a high number of nonvalue-adding stages.
8. Discussion
The results show that there is a relationship
between the support system and poor production
performance. The discussion addresses both case
studies individually.
8.1. Company A discussion
Initially, downtime problems within the cell seem
to be caused by the supplier of control boxes, although the problem-solving exercise and #ow process chart disproved this. The results show that the
sales' forecast of demand to delivery of parts to the
cell process has become part of the critical path in
terms of manufacturing lead time. This means that
the overall time taken to complete a product can be
either lengthened or shortened by the sales' forecast
of demand to delivery of parts to the cell process,
Fig. 2. Flow chart showing process route for Company B steam turbines.
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
51
Fig. 3. Flow chart showing process route for Company B manufacturing.
Table 9
Classi"cation of process #ow chart stages
Classi"cation
No. of stages
Storage
2
Transport
9
and the manufacturing cell has no control over this.
The reason that the process is part of the critical
path is the long lead time, which frequently exceeds
a month. Even worse, if the sales' forecast #uctuates, which it often does, the whole process has to
be repeated. Repeating the whole process does not
leave enough time for suppliers to deliver the correct quantity of control boxes on time, and thus the
manufacturing cell su!ers component shortages
and downtime. The following paragraphs try to
explain these process problems in Company A.
The "rst problem is that support functions are
remotely based in relation to the cell and have very
little contact. This means that #uctuations in fore-
Operation
11
Inspection
4
Total
26
cast for demand are not communicated to the cell,
who schedule a build plan not realising that; (1)
demand has changed; and (2) it is unlikely that they
will receive the necessary components from the
suppliers.
The second problem is functionally based support workers. This leads to: (1) the evolution of
a sequential approach to work where each function
performs their own task before passing it on to
another function. This means that the process takes
longer, hence contributing to long lead times; (2)
individual support functions having little understanding about the tasks performed by other functions. This manifests itself negatively in that
52
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
individual functions become self-focused rather
team-focused. Self-focus is exacerbated by the reaction of management to poor support system performance. Interviewed support workers believe that
the sequential nature of the system means they are
trapped in a rigid process. However, they are
blamed when parts are late, even though it is the
process rather than the individual causing the delay. For example, when the manufacturing cells do
not receive their parts on time, energy is spent
apportioning blame rather than solving the fundamental problem of a poor support system structure.
In addition, sta! also feel that their suggestions to
improve the process are ignored by management.
The complexity of the sales' forecast of demand
to delivery of parts in the cell process is demonstrated by the number of stages involved. When
examining the process there are 15 stages and six
departments in total. Interviews with managers
con"rmed that the process evolved as the company
expanded and this lack of formal design is borne
out by the complex, rigid network of links and
loops. System re-design could eliminate up to 70%
of these stages as non-value-adding. The following
section discusses the results of the case study in
Company B.
8.2. Company B discussion
This inability to meet production deadlines
seems to be inextricably linked to the design support function in particular. Indeed, analysis showed
that this was the case, with 14.88% of products
being subject to design changes after they have
entered Company B Manufacturing, leading to
large #uctuations in build-plan schedules. But is
this a support system or a support function problem? The results tend to suggest that the problem
lies with the latter. If the design support function
was more stringent in its cut-o! deadlines regarding engineering changes, then the build-plans
would not require alteration. This problem is due
to poor management and planning rather than an
inherent support system problem. This does not
support the hypothesis (stated in Section 1 of this
paper); it is the support function rather than the
support system which causes manufacturing delays.
If however, the support system did operate in an
e$cient manner, the ine$ciencies of the design
department may not form part of the critical path,
that is, the poor design of the support system serves
only to highlight problems within the design department. So why isn't the support system e$cient?
The answer is that the support system su!ers a lack
of #exibility.
The lack of support system #exibility is caused
by three factors. The "rst is the division of the
factory into three autonomous business units. This
means that there are three separate support systems, when, for e$cient production, they should be
intertwined. The second factor is the functional
division of support (people performing the same or
similar jobs are based in the same o$ce). Functional division leads to a sequential approach to
work; when one function completes their task, it is
passed to a subsequent function for completion of
another task, and so on. The time taken in a sequential approach to work is high in comparison to
that taken by multi-skilled teams. The third and
"nal factor contributing toward a lack of #exibility
is remotely based support. This means that the
support functions are based in o$ces away from
the shop#oor. The three factors combined lead to
the complex process routes shown in Figs. 2 and
3 and long process lead times (approximately one
month from start to "nish). Long process lead times
mean that changes in product design are obviously
problematic. For each of the 431 design changes in
1997 (see Table 8), the support process route had to
be repeated, taking on average 2 months for each
change to reach the shop#oor (many of these
changes pass through the system concurrently).
Whilst the product design is being changed, production has to stop, resulting in the rescheduling of
the build-plan, and subsequently an inability to
meet deadlines. The problems highlighted in the
paragraph above show that the support system is
fundamental in causing productivity problems,
thus supporting the hypothesis.
As a "nal note for those who think that factory
improvement comes about by optimising the
manufacturing system; many of the 52 steam turbines on order for 1998 have contractual penalty
clauses for late delivery equating to C10,000 per
week (each). Despite having excess capacity in the
manufacturing cells, each turbine is delivered on
A. Dickens, C. Baber / Int. J. Production Economics 65 (2000) 43}54
average, a year late due to the delays in processing
design changes.
[6]
9. Conclusions
Data show that both companies are su!ering
from the same fundamental problems: (1) remotely
based support functions; and (2) functional division
of support workers. This leads to a sequential support system process, a high number of non-value
adding stages, long lead times and in#exibility.
These inherent problems mean that the manufacturing cells su!er in terms of not receiving parts or
drawings on time. More seriously, the customer
ultimately su!ers because they do not receive their
order on time. In conclusion, the #ow chart results
show that the #ow of information and components
is overly complex and not conducive to a typical
cellular manufacturing pull system, resulting in the
need for re-design.
[7]
[8]
[9]
[10]
[11]
[12]
Acknowledgements
This research was funded by the School of Mechanical and Manufacturing Engineering, The University of Birmingham. Thanks go to the
companies involved in this research.
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