Proceedings of
INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN ENGINEERING RESEARCH (ICITER-2016)
International Journal of Innovations in Engineering, Research and Technology,IJIERT-ICITER-16,ISSN:2394-3696
26th June,2016
AN APPROACH TO MEASURE MANUFACTURING SYSTEM FLEXIBILITY
[PAPER ID ICITER-D203]
VYANKATESH S. KULKARNI (Research Scholar)
ASST. PROF.JSPM-BIT,BARSHI
DR. BRAJESH TRIPATH
UNIVERSITY COLLEGE OF ENGG.TECH.UNI.
KOTA RAJASTHAN
ABSTRACT:
Flexibility plays a vital role in manufacturing
system. The impact of fluctuations is to be minimized
in order to achieve an excellent flexibility in order to
handle product over wide range of variety.
Flexibility is dependent on the decision making of
operator for desire operational measurements.
Flexibility of the manufacturing system is also
depends upon the various types, situations and
various working conditions.
To
take
flexibility
into
adequate
consideration in decision making, an operational
measurement is needed. Yet, difficulties exist since
flexibility has various dimensions, various types
and is usually situation specific. This paper presents
the approach to measure flexibility. The flexibility
metric was developed from the transfer function
namely the reciprocal of demand fulfillment time
delay.
KEYWORDS: Flexibility, Flexibility characterization:
types and dimensions, Flexibility classification
different types, Flexibility dimension: range and
response, Flexibility measurement
INTRODUCTION:
Originally, flexibility means the ability to be
bent, in a mechanics context. With more attention being
paid to system dynamics, flexibility is used to indicate
the system’s ability to adapt to different circumstance.
Particularly in manufacturing, since two decades ago,
flexibility has becomes a key consideration ranging from
long term decision making like production system design
to short term operation planning.
As it is said by Viswanadham & Narahari (1992),
flexibility is “an adapting organism capable of surviving
in uncertain and changing markets”. It has been defined
to be:
1. The adaptability to change (Nof et al. 1979, Buzacott
1982, Brown et al. 1984),
2. Ability to change, (Gupta 2004)
3.
Sensitivity to change, (Chryssolouris 1992,1998
Bateman 1999)
4.
The ability to cope with or adaptability to
uncertainty effectively and efficiently. (Gerwin 1987,
Radcliffe 1996, Mandelbaum 1978, Nof et al 1979,
Buzacott 1982)
From those similar statements, distinction can
be observed that there’re mainly two schools of
viewpoints on flexibility. From the first viewpoint,
flexibility is an intrinsic attribute of manufacturing
system. It’s a property of the system itself, independent
of its operating context. The second one views flexibility
as a relative concept. It can be related to demand,
economic environment and performance criteria etc.
Both viewpoints have their good reasons, while the latter
one hinders an absolute measurement.
Fig: 1. Basics of flexible manufacturing system
Eighty papers have been classified according to
the approach illustrated above. A summary showing the
distribution of articles is given in figure 1.
241 | P a g e
Proceedings of
INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN ENGINEERING RESEARCH (ICITER-2016)
International Journal of Innovations in Engineering, Research and Technology,IJIERT-ICITER-16,ISSN:2394-3696
26th June,2016
Figure 2. A categorization of published papers (1990–
1995) on the basis of sector and scope.
LITERATURE SURVEY:
The philosophy of SD was invented in 1956 at
the Massachusetts Institute of Technology by Forrester
(1961). He introduced new ideas in Industrial Dynamics
as well as launched his thoughts as a ‘major
breakthrough for decision makers’ (Forrester 1958). The
use of the method spread into the social sciences area
(Forrester 1975), and as a consequence Forrester
re‐name the performance ‘system dynamics’. He
considered SD to re act a universal applicability to any
situation which can be modeled as a ‘system’, which
combines people and/or machines (Towill 1993). Since
this early conception, many contributions have been
made to Wolstenholme.
The SD led, as discussed later in this paper. The
basic values of SD are briefly summarized by
Wolstenholme considers SD to exist in both a qualitative
or quantitative form. Qualitative SD includes figure
which symbolize the system under study by means of
various ‘resources’. The resource may be either physical
(e.g. the flow of money, material, people, etc.) or
non‐physical (e.g. knowledge or motivation). The state is
denned as any accretion of assets which is related to the
concern. Otherwise the states are clearly identifiable, e.g.
record of manufacturing system, cash balance. They are
the assessable quantities of much reserve in a system at
any point in any time, and the measurement is the unit of
resource. States all time to survive even if all the system
activity ceases. The resources stream through the system
give result of a driving activity which helps change the
resource from one state to another. Such a driving
activity shows in SD by rate variables, which are control
variables as well as which increase or deplete resource
levels directly i.e. they control flows into and out of store.
As an example, in a developed system, the stock of a
product is affected by the flow rates of sales and
production. These flow charge changes constantly and
must be represented in such a way as to capture this
variation.
System dynamics can be considered to be a
method of system enquiry, as such occupies a position
among the sciences of operations research (OR) as well
as ‘systems thinking’). In considering how SD could be
related to these ‘soft’ and ‘hard’ sciences, Keys (1988)
concluded that the exact position of SD remained in
doubt, but try to maintained that it is possible for
scientists in both fields to relate to it. SD may also be
considered in the sense of cybernetics and
servomechanisms
(organizational/human
systems
structuring for problem solving. To will (1993) believes
that advocates of either viewpoint would benefit from an
accepting of both the ‘soft’ and ‘hard’ perspectives.
System dynamics met with some criticism in its
early days, most notably from Slevin and Ansoff (1968),
who queried the validity of the theory base applied by
Forrester. To his credit, Forrester reiterated the theory
which is published in Industrial Dynamics (1961) and
pointed out that his models were not absolutely
complete. Slevin’s and Ansoff article is particularly
relevant as it represents one of the earliest examples of
an independent evaluate of SD. The review focus the
rapid growth of SD at that time, for Forrester pointed out
in his response to Slevin and Ansoff (Forrester 1975)
that much work had been carried out which was yet to
be published, and this research had not been available to
Slevin and Ansoff when writing their critique of
Forrester’s work.
Pidd (1992) search two main problems
associated with SD in the early years: the mathematical
equations were too complex to be understood by
managers. On the other hand, Forrester foresaw in his
early work that the development of the computer would
help the use of his technique. Now the equations may be
solved rapidly whilst being hidden behind a
user‐friendly graphical interface.
In terms of manufacturing applications of SD, a
conceptualized model of a manufacturing system is
outlined by Parnaby in (1979). Similarly, by Roberts and
Byrne (1994) who use SD to evaluate manufacturing
performance using kanban‐based system. To an
advocate of the value of Forrester’s work and much of
Towill’s research activity has been involved with
developing the Forrester supply chain models (Towill
and del Vecchio 1994). Edghill and To will (1989a,b)
have, e.g. developed a generic library of control
242 | P a g e
Proceedings of
INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN ENGINEERING RESEARCH (ICITER-2016)
International Journal of Innovations in Engineering, Research and Technology,IJIERT-ICITER-16,ISSN:2394-3696
26th June,2016
theory‐based models of manufacturing systems. They
argue that these models fulfill the criteria of being
meaningful and comprehensible, as these three
components give the holistic view that a manufacturing
manager requires. Similarly, Baines (1994) considers the
Respective merits of DES and SD for evaluating the effect
of proposed changes to a manufacturing system. He
argues that although DES seems to give more credible
models due to the level of detail that can be included in
such models, SD model building times are considerably
less. Baines argues that when considering strategic
issues within a manufacturing company, then SD has
some distinct advantages over DES.
2.2 FLEXIBILITY CHARACTERIZATION: TYPES AND
DIMENSIONS:
Flexibility can be defined generally as the
adaptability to changes [Nof et al. 1979, Buzacott 1982,
Brown et al. 1984]. However, it means different things to
different people [Upton 1995], To a firm which desires a
number of broad product lines and/or numerous
variations within a line, flexibility is the ability to handle
wide range of products with fast setups [Gerwin
1993]. To a manager whose focus is on cost control
under demand fluctuations, flexibility means the ability
to profitably produce at different output levels. In a mass
customization context, while company’s competitive
edge comes from its ability to offer broad spectrum of
products, make easily switch between products, deal
with various demand levels.
Various flexibilities
deal with various
uncertainties; various flexibilities serve various strategic
objectives. In addition, one type of flexibility has two
layers of meaning, each of which has different
implications. In the following sections of this chapter, we
will discuss about flexibility classifications and
dimension
CHARACTERIZATION IN DETAILS:
.
Figure 3 Flexibility type hierarchy modified from Sethi
and Sethi 1990
Flexibility has different types, another way to
put it. It is mirrored into different images from different
perspectives. Individual resource flexibility, such as
machines and labor, process alternatives, and material
handling mechanism comprise the base. The base
constitute and support system level flexibility through
the organizational structure, enable system to handle
multiple products, mix flexibility, to profitably produce
at different output levels, volume flexibility, to produce
new parts/handle design changes economically and
quickly, new product/modification flexibility, and to
produce a part by alternate routes, routing flexibility.
In this work, system level flexibility is our focus. New
product and modification flexibility is more long term
product life cycle related. Routing flexibility is to
coping with machine breakdowns. We would like to
discuss in detail the rest two, mix and volume
flexibility, which mitigates the negative impact of
demand uncertainty on regular production operation.
Although researchers had used different terms
to describe flexibilities and there’re some confusion
caused by that, we’re not going to list and clarify
those terms here, but rather quote well accepted terms
and their definition. For who are interested in term
comparison, please refer to Sethi and Ssethi 1990 and
Gupta 2004.
Mix flexibility derives from the machines flexibility,
operation flexibility of qproducts,
and
material
handling system, it enables the system to produce a set
of product without major setup( Sethi & Sethi 1990) with
fast setups (Gerwin 1993), with presumed low
changeover cost (Berry and Cooper 1999), or to change
the mix within a given time period (Slack 1988).
Volume flexibility is the ability to profitably
produce at different aggregated output level (Browne
1984, Sethi & Sethi 1990), is the extent of change in
aggregated output level without incurring high
transitional penalties, is the ability to overcome the
changes in aggregated volume (Gupta 2004)
Oke(2003) did research on drivers of volume flexibility
requirement and found volume flexibility is increasingly
required as demand variability and uncertainty increase,
and customer’s influence in lead-time determination
increase. Holweg and Pil (2004) once quote a folk saying
on volume flexibility, “I’ve got my head in the freezer and
my feet in the oven, but on average the temperature is
quite pleasant here.” Feeling pleasant implies high ability
of capacity expansion, contraction, switching, in short,
high volume flexibility.
2.2.2 FLEXIBILITY DIMENSION: RANGE AND
RESPONSE:
Adaptability to changes, flexibility’s definition
implies two layers of meaning: can or cannot adapt to,
243 | P a g e
Proceedings of
INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN ENGINEERING RESEARCH (ICITER-2016)
International Journal of Innovations in Engineering, Research and Technology,IJIERT-ICITER-16,ISSN:2394-3696
26th June,2016
and the ease of adaptation. The first layer is about “the
quality or state of being able” that is range. The second
layer is about “the competence in doing”, that is
response. (Merriam-Webster Dictionary). Since Slack
(1983) did a survey on ten manufacturing firms, based
on which he suggest range, and response as dimensions
of flexibility, more than two decades of flexibility
literatures, more of them follow this idea [Gerwin (1987
1993), Crowe (1992), Bateman (1999), Gupta A. (2004)].
Some others trying to enrich this decomposition by
adding heterogeneity ( Koste 2004), mobility, uniformity
(Upton 1994 ) etc. There is another stream of
researchers who follow Mandelbaum (1978) with action
and state flexibility. Action flexibility is the ability
created by managerial action in response ot new
circumstances and state flexibility is the ability inherent
in the system.
We’ll
adopt
more
widely
accepted
characterization, range and response, with range
referring to the number of different options or the size of
the option universe, and response referring to the ease
of movement between these options.
Generally speaking range is more strategic
related, such like a wider range of parts offering for
larger market size, excessive capacity banking for peak
season’s customer service level and market expansion.
Response factor, on the other hand, is more operational.
According to two previous examples, shorter switching
time from one part type to another, less effort to shifting
capacity would make above option possibility attractive.
2.3 FLEXIBILITY MEASUREMENT:
2.3.1
MEASURING
PERSPECTIVE
AND
METHODOLOGIES:
1. ABSOLUTE INTRINSIC MEASURE VS. RELATIVE
MEASURE:
When it comes to the problem of designing
the right metrics for flexibility, we noticed there’re
two types of measures due to researcher’s distinctive
views about flexibility. Gupta and Buzacott (1996)
argued the measure should relate to a specific situation
or change. Kumar (1987) considered flexibility as an
intrinsic attribute of the system, thus measures the
potential of the system to handle changes. Both
viewpoints have merit (Chryssolouris 2006), and he
argued any flexibility calculation which omits external
demand run the risks of omitting relevance and thus,
estimation beats omission. Meanwhile Kahyaoglu and
Kayaligil (2002) said many flexibility measurement in
literature requires specifying the relevant probability
distribution associate with uncertainty explicitly.
However, the need of a flexibility measure is greatest
when probability cannot be assigned, and when it
doesn’t vary with situations. Here, we are standing on
“Intrinsic” side, holding the preference for the second
view so that our flexibility can be reliable to assist
decision making. And we believe system demonstrated
its flexibility during its regular performance.
2. MEASURING METHODOLOGIES:
As to measurement approaches, “Intrinsic” side
are loyal supporters of measuring-by-type approach,
using physical characteristics of the system to measure
each flexibilities. Financial analysis methods are
common tools for “Relative” side. Other approaches are
used by both side in different context, such as survey,
performance benchmarking, decision theory approach
(Mandelbaum 1978; Mandelbaum and Buzacott 1990),
Petri nets (Kochikar and Narendran 1992), fuzzy logic
(Tsourveloudis and phillis 1998, Beskese et al. 2004),
information theory approach, i.e. entropy measure.(Yao
1985, Kumar 1987, Rao and Gu 1994 Shuiabi et al. 2005).
2.3.2 FLEXIBILITY QUANTIFICATION: MEASURE BY
TYPE VS. HOLISTIC MEASURE:
2.3.2.1 F L E X I B I L I T Y MEASUREMENT: MEASURE
BY TYPE:
1. VOLUME FLEXIBILITY:
Descriptive measure of volume flexibility can
be the lower and upper bound of capacity for range
dimension and time/cost of capacity adjustment for
response perspective. But economic metrics are most
commonly used in literature, since many researchers
define volume flexibility as the ability to profitably
produce at different levels. Stigler (1939) considered a
plant to be flexible if it has a relatively flat average cost
curve. Falkner (1986) suggest the stability of
manufacturing costs over widely varying levels of total
production volume to be the measure of volume
flexibility. Sethi and Sethi (1990) developed volume
flexibility metric to be the curvature of the average cost
curve.
There’re also metrics that consider the interaction with
demand market. Marschak and Nelson (1962) argued
the value of volume flexibility increases as the variation
in market price increases and as the ability to predict
market price before making an output decision
increases. Gerwin (1987) measures it by the ratio of
average volume fluctuations over a given period of time
to the production capacity limit.
2. MIX FLEXIBILITY:
There exists a lot of measures on mix flexibility
range, the number of product types a system can
produce. (Ettlie 1988, Jaikumar 1986), the size of the
universe of products the system is capable of producing
(e.g. Chatterjee et al. 1984), or the range of products
characteristics handled (e.g. Gerwin 1987) These metrics
are measuring the potential of the system, while the
others, The number of products handled by the system,
244 | P a g e
Proceedings of
INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN ENGINEERING RESEARCH (ICITER-2016)
International Journal of Innovations in Engineering, Research and Technology,IJIERT-ICITER-16,ISSN:2394-3696
26th June,2016
or the ratio of the number of products handled by the
system to the total number of products which the
manufacturer is interest in, are relative measures.
Buzacott (1982) About mix flexibility response, people
either care about setup duration or setup cost, or both.
Relative measures estimate the percentage of
product demand and possibility of sequence and using
expected setup duration, which probability from demand
estimation) (Bateman 1999) as the flexibility response
measure.
2.3.2.2 FLEXIBILITY MEASUREMENT: MEASURE BY
PERFORMANCE:
Holistic measure is a unification or
integration of flexibility measures of various types.
These measures typically cover two or three
flexibility types. A variety of ways
are
applied
to
holistically
evaluate
flexibility,
such
like
qualitative measurement, measurement based on
economic
consequences,
multi-variant
analysis
approach,
performance
benchmarking,
and
information theory. A special metrics developed by
Chryssolouris (1998) is based on system transfer
function.
Gerwin (1987) used domain definition to
investigate the impact on manufacturing flexibility of
computerized processes for body framing. But
qualitative measure usually runs the risk of perceptual
bias.
Typical multi-variant analysis approaches,
principle component analysis and factor analysis, are
applied in the flexibility measurement to investigate
whether flexibility is a unitary or multi-dimensional
variable under specific situations.
As to performance criteria, Buzacott(1982)
evaluated system flexibility by determining
the
expected value for a particular performance criterion
over
all feasible job characteristics. Brill and
Mandelbaum (1990) formulated it by weighted average
performance relative to a reference task set. Pflughoeft
(1999) measured flexibility by the relative performance
improvement due to flexibility difference.
Monetary terms can be easily integrated. That
might be the reason why literature applies economic
consequence to evaluate flexibility by manufacturing
cost saving and machine utilization increase etc. (Son
and Park 1987) (Gupta 1993). Some of the researchers
calculate the value of flexibility using option price
formula (Andreou 1988, Bengtsson 2002). Besides being
applied in routing and loading flexibility measured,
entropy is also used to study operational flexibility, mix
and volume by Shuiabi et al (2005).
An interesting and special measurement comes
from a “spring damper analogy” by Chryssolouris
(1998). He drew an analogy between a mechanical
system, a mass spring oscillator, and a manufacturing
system. Based on the analogy, he argued that damping
factor, which measure the ability of this mechanical
system to keep stable under time varying input force,
can be a good metric for manufacturing system
flexibility, defined as sensitivity to change. Flexible
system will have uniform performance when demand
fluctuates. Inflexible system cannot. Order expected
processing time is the input, like an external force.
Actual time order spends in the system is the output, like
the displacement/response. Manufacturing system
“damping” is defined as system’s flexibility and
calculated from system transfer function. This metric is
easy to apply and mathematically elegant. However,
there’re limitations. First of all, flexibility is widely
accepted as the adaptability to change, which doesn’t
directly lead to stability. Secondly, assumptions such as
Poisson arrival and normal demand quantity are made.
Reality may not conform well to the assumption. Thirdly,
the analogy from a spring damper to manufacturing
system is hard for readers to build up, since there’s no
fewer similarity, e.g. how the manufacturing product
can be force when it’s the input and changed into
displacement when it output. Finally, this analogy is built
on a strong assumption of LSI while the author doesn’t
reason clearly.
CONCLUSION:
The proposed method covers broad range of
flexibility reference appeared in literature. We
categorized and critically reviewed existing literatures
on flexibility definition, characterization, and most
importantly different flexibility measurement in terms
of methodology, and perspectives. From literature,
flexibility measure that treats flexibility as internal
property, yet encompass all interested system flexibility
types is still lacking. Theoretically systematical and
meanwhile practically useful flexibility metrics is still to
be developed. Through a broad range literature review,
flexibility definition, characterization, and most
importantly different flexibility measurement in terms of
methodology, and perspectives are categorized and
summarized. From literature, flexibility measure that
treats flexibility as internal property, yet industryindependent is still lacking. Theoretically systematical
and meanwhile practically useful flexibility metrics is
still to be developed.
We approach manufacturing system flexibility
measurement problem from system dynamics point of
view and consider flexibility as one of the “identity code”
which can be revealed from its system transfer function.
Flexibility index is derived from system transfer
245 | P a g e
Proceedings of
INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN ENGINEERING RESEARCH (ICITER-2016)
International Journal of Innovations in Engineering, Research and Technology,IJIERT-ICITER-16,ISSN:2394-3696
26th June,2016
function, which can be estimated from production
systems regular input demand and production output
data.
REFERENCES:
i.
Baines, T. S. and D. K. Harrison (1999). "An
opportunity for system dynamics in manufacturing
system modeling." Production planning and
control 10(6):
ii.
Bateman, N., D. J. Stockton, et al. (1999).
"Measuring the mix response flexibility of
manufacturing systems." International journal of
production research 37(4): 871-880.
iii.
Beach, R. and A. P. Muhlemann (2000). "A review
of manufacturing flexibility." European journal of
operation research 122: 41-57.
iv.
Bengtsson, J. (2002). "The impact of the product
mix on the value of flexibility." Omega 30: 265273.
v.
Abdel-Malek L. and Wolf C., “Evaluating Flexibility
of Alternative FMS Designs—A Comparative
Approach,” International Journal of Production
Economics, Vol. 23, pp. 3–10 (1991).
vi.
AnthonyR.H., “Planning and Control Systems: A
Framework for Analysis,” Graduate School of
Business, Harvard University, Cambridge, MA
(1965).
vii.
Adler,
P.S.,
“Managing
Flexible
Automation,” California Management Review, pp.
34–56 (Spring 1988).
viii.
BerradaM. and SteckeK.E., “A Branch and Bound
Approach for Machine Load Balancing in
FMS,” Management Science, Vol. 32, No. 10, pp.
1316–1335 (October 1986).
ix.
BrillP.H. and MandelbaumM., “On Measures of
Flexibility
in
Manufacturing
Systems,” International Journal of Production
Research, Vol. 27, No. 5, pp. 747–756 (May 1989).
x.
BrowneJ., DuboisD., RathmillK., SethiS.P., and
SteckeK.E.,
“Classification
of
Flexible
Manufacturing Systems,” FMS Magazine, Vol. 2,
No. 2, pp. 114–117 (April 1984).
xi.
BuzacottJ.A., “The Fundamental Principles of
Flexibility in Manufacturing Systems,” Proceedings
of the 1st International Conference on FMS, IFS
Publications Ltd., Bedford, UK, pp. 13–22
(September 1982).
xii.
ChatterjeeA., CohenM.A., Maxwell W. C., and Miller
L. W., “Manufacturing Flexibility: Models and
Measurement,” in Proceedings of the First
ORSA/TIMS Special Interest Conference on FMS,
Ann Arbor, MI, K.E. Stecke and R.Suri (Eds.),
xiii.
xiv.
xv.
xvi.
xvii.
xviii.
xix.
xx.
xxi.
xxii.
xxiii.
xxiv.
xxv.
246 | P a g e
Elsevier Science Publishers B.V., Amsterdam, pp.
49–64 (August 1984).
GuptaY.P. and GoyalS., “Flexibility of Manufacturing
Systems: Concepts and Measurements,” European
Journal of Operational Research, Vol. 43, pp. 119–
135 (1989).
GustavsonS., “Flexibility and Productivity in
Complex
Production
Processes,” International
Journal of Production Research, Vol. 22, No. 5, pp.
801–808 (May 1984).
HutchinsonG.K. and SinhaD., “Quantification of the
Value of Flexibility,” Journal of Manufacturing
Systems, Vol. 8, No. 1, pp. 47–56 (1989).
JaikumarR.,“Postindustrialmanufacturing,” Harvar
d Business Review, Vol. 64, pp.
69–76
(November/ December 1986).
Krasa A. and Llerena P., “L' Evaluation de l'
automatisation flexible dans les PMI,” Project
Report, BETA, Université Louis Pasteur,
Strasbourg, France (1987).
LimS.H., “Flexible Manufacturing Systems and
Manufacturing Flexibility in the UK,” International
Journal
of
Operations
and
Production
Management, Vol. 7, No. 6, pp. 44–54 (1987).
MandelbaumM. and BuzacottJ., “Flexibility and its
Use: A Formal Decision Process,” Proceedings of the
Second ORSA/TIMS Special Conference on FMS,
Ann Arbor, MI, K.E. Stecke and R. Suri (Eds.),
Elsevier Science Publishers B.V., Amsterdam, pp.
119–130 (August 1986).
ProthJ.M., “La Flexibility des systemes de
production,” Revue francaise de gestion, Vol. 35,
pp. 12–20 (1982).
SethiA.K.
and
SethiS.P.,
“Flexibility
in
Manufacturing: A Survey,” International Journal of
Flexible Manufacturing Systems, Vol. 2, No. 4, pp.
289–328 (June 1990).
SlackN., “The Flexibility of Manufacturing Systems,”
International Journal of Operations and
Production Management, Vol. 7, No. 4, pp. 35–45
(1987).
SonY.K. and ParkC.S., “Economic Measure of
Productivity, Quality and Flexibility in Advanced
Manufacturing Systems,” Journal of Manufacturing
Systems, Vol. 6, No. 3, pp. 193–207 (1987).
Taymaz E., “Types of Flexibility in a Single Machine
Production Systems,” International Journal of
Production Research, Vol. 27, No. 9, pp. 1891–
2000 (1989).
ZelenovicD.M., “Flexibility—A Condition for
Effective
Production
Systems,” International
Journal of Production Research, Vol. 20, No. 3, pp.
319–337 (1982).
Proceedings of
INTERNATIONAL CONFERENCE ON INNOVATIVE TRENDS IN ENGINEERING RESEARCH (ICITER-2016)
International Journal of Innovations in Engineering, Research and Technology,IJIERT-ICITER-16,ISSN:2394-3696
26th June,2016
xxvi.
xxvii.
xxviii.
xxix.
xxx.
xxxi.
xxxii.
xxxiii.
xxxiv.
xxxv.
Faust, M. A., Robison, O. W., and Tees, M. W.,
1993, integrated systems analysis of sow
replacement rates in a hierarchical swine
breeding structure. Journal of Animal Science,
71, 2885–2990.
Forrester, J. W., 1958, Industrial dynamics—a
major breakthrough for decision makers.
Harvard Bus. Rev., 36, 37–66.
Forrester, J. W., 1961, Industrial Dynamics
(Cambridge, USA: MIT Press).
Forrester, J.W., 1975, Collected Papers of J ay W.
Forrester (MA, USA: Wright‐Allen).
Gaynor, A. K., Morrow, J., and Georgiou, S. N.,
1991, Aging, contraction, and cohesion in a
religious order: a policy analysis. System
Dynamics Review, 7, 1–19.
Henderson, S.M., Gavine, A.W., Wolstenholme, E.
F., and Watts, K., 1991, A system dynamics
approach to assessing the impact of management
information systems. In M. C. Jackson, G. J.
Mansell, R. L. Flood, R. B. Blackham and S. V.
E. Probert (eds) Systems Thinking in Europe.
Proceeding s of the United Kingdom Systems
Society Conference (NY,USA: Plenum), pp. 243–
249.
Institute
of
Management
International
Databases Plus, 1998, Bowker‐Saur. Keys, P.,
1988, System dynamics: a methodological
perspective. Trans. Inst. MC, 10, 218–224.
Kumar, N., and Vrat, P., 1994, A simulation study
of unit exchange spares management of diesel
locomotives in the Indian Railways. International
Journal of Production Economics, 33, 225–236.
Law, A.M., and Kelton,W. D., 1991, Simulation
Modeling and Analysis (Singapore: McGraw‐Hill).
247 | P a g e