International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
Evaluation of Operational Quality of a Supply
Chain Using Genetic Algorithm”
“
Purvendu Sharma1, Dr.Nagendra Sohani2
Department of Mechanical Engineering, Devi Ahliya Vishwavidyalaya, Institute of Engineering and Technology,
Indore, M.P, India.
2. Department of Mechanical Engineering, Devi Ahliya Vishwavidyalaya, Institute of Engineering and Technology,
Indore, M.P, India
Abstract- The paper seeks to evaluate the impact of
1. Introduction: Managers and supervisors directing
various Operational parameters (For ex. Design
Quality, Supplier’s Quality etc ) practices within a
Supply chain. A Performance Matrix (in the form of
graphical user interface (GUI)) is then prepared and
marking are inserted for each and every parameters so
judged. Based upon the marking so obtained various
parameters are scrutinized and evaluated using Genetic
Algorithm .This not only helps managers in evaluating
SCM performance in an optimized manner but also
helps in various decision making process by means of a
reference so created by the Performance Matrix. Also
with the help of Performance matrix various domains
of SCM are scanned and thus they are also ranked
accordingly. All those parameters which were less
performing or which were not confirming up to the
desired level (which hampers the performance and
affects adversely a Supply Chain ) are dropped down
or they are subjected to further improvement. Thus one
is able to figure out an Optimized solution, helps in
better decision making process, helps in improving the
performance of a Supply Chain, find out less
performing domains, weaker areas which are not
confirming up to the desired level of satisfaction or
hinders the performance of a Supply Chain, ranking
based upon the marking ( being inserted in the
performance matrix either manually or survey
based).The concept of Genetic Algorithm helps in
optimization by evaluating the parameters step by step
thus the job gets eased.
the efforts of an organization or a group have a
responsibility to know how, when, and where to institute a
wide range of changes. These changes cannot be sensibly
implemented without knowledge of the appropriate
information upon which they are based. There is currently
no standardized approach to developing and implementing
performance measurement systems[1], it is not simply
concerned with collecting data associated with a predefined
performance [4] goal or standard. Performance
measurement[7] is better thought of as an overall
management system involving prevention and detection
aimed at achieving conformance of the work product or
service to our customer's requirements. Additionally, it is
concerned with process optimization through increased
efficiency and effectiveness of the process or product.
These actions occur in a continuous cycle, allowing options
for expansion and improvement of the work process or
product as better techniques are discovered and
implemented. It is primarily managing outcome, and one of
its main purposes is to reduce or eliminate overall variation
in the work product or process. The goal is to arrive at
sound decisions about actions affecting the product or
process and its output. The main objectives comprises of:
Keywords- Supply Chain Management (SCM), Genetic
Algorithm (GA),
Parameters.
Performance
ISSN: 2231-5381
Matrix,
Operational
 It points out the importance of keyfactors[12] in
the performance measurement[4] of SCM, and the
nature of roles they need to play.
 It focuses on critical factors that are likely to
contribute for the successful performance
measurement [7] of SCM.
 It examines the impact of various operational
quality[12] management within a supply chain.
 It studies various aspects such as Customer Input,
Service Quality, and Design Quality[12] in
performance evaluation.
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
1.1 Introduction To Genetic Algorithm: In contrast
to traditional optimization techniques, Gas[8] work with
coding of parameters[12], rather than the parameters
themselves To evolve good solutions and to implement
natural selection, we need a measure for distinguishing
good solutions from bad solutions or less performing
parameters[3],[4]. Genetic Algorithms [9] are search
algorithms based on natural selection and natural genetics.
They combine survival of fittest among structures with
structured yet randomized information exchange to form a
search algorithm. Genetic algorithms are theoretically and
empirically proved to provide robust search in complex
spaces. Its validity in – Function Optimization and Control
Applications is well established. Since GA[8] only requires
a way to evaluate the performance of its solution guesses
without any prior information, they can be applied
generally to nearly any optimization problem. GA does not
guarantee convergence that the optimal solution will be
found, but does provide, on average, a “good” solution. GA
is usually extensively modified to suit a particular
application. As a result, it is hard to classify a “generic” or
“traditional” GA, since there are so many variants.
However, by studying the original ideas involved with the
early GA and studying other variants, one can isolate the
main operations and compose a “traditional” GA.
1.2 Graphical User Interface: A graphical user
interface (GUI) is a human-computer interface (i.e., a way
for humans to interact with computers) that uses
windows, icons and menus and which can be manipulated
by a mouse (and often to a limited extent by a keyboard as
well). A performance matrix is prepared in JAVA which
depicts various domains of Supply Chain in a tabular
format along with that a proper spacing to insert the scores
in various parameters. A major advantage of GUIs is that
they make computer operation more intuitive, and thus
easier to learn and use. For example, it is much easier for a
new user to move a file from one directory to another by
dragging its icon with the mouse than by having to
remember and type seemingly arcane commands to
accomplish the same task.
2. Design Methodology : Concept of GA[9] is used
in carrying out the optimization process, for that purpose a
Performance Matrix[3] is prepares which is in the form of a
GUI ( here JAVA programming is used in NETBEANS to
prepare a matrix chart showing all the components of a
Supply Chain in a tabular form). GA is used for identifying
the relationship between the various domains and hence
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optimizing (which will depend upon various industries and
also it will change according to the requirement for that
particular industry too) the whole process based upon the
marking so obtained or the differential score. The
Performance Matrix[4] is prepared analyzing different
domains of the Operational Quality [12],[2] of supply chain
and inserting the markings by conducting a survey
accordingly such as :
Primary data collection (that is the score based upon the
respondents from different demographics) using Sample
Surveying[12] can be used. The respondents may be asked
to insert scores in the matrix so prepared and on the basis
of that score further optimization, ranking, and
performance[3] will be evaluated either cumulatively the
scores can be calculated or mathematically the scores can
give us the idea about the same.
2.1 Performance Matrix: The performance Matrix is
prepared based on the above collected and evaluated
operational parameters as a GUI in Java as shown which is
followed by the optimization using Java. The matrix shows
and briefs all the relevant data and the information in a
more synchronized and orderly manner for the managers to
take the decision as if which parameters are performing and
all the non-conforming parameters accordingly. Following
bifurcations are provided in the various SCM parameters
along with which following points were evaluated and
checked for further evaluation and optimization under
following categories.
CUSTOMER’S INPUT : Ease of cutomer’s assistance
,Resolving Customer’s Complains ,Understanding how
customers use our products ,Routine Follow-up and
Determining Key factors ,Customer’s satisfaction
measurment system.
SUPPLIER’S QUALITY : Documenting Quality system
,Wheather Quality policy is understood or not,Wheather
Quality activities comply with planned activities or not,If
they have comprehensive quality auditing system or not,If
crucial processes are carried out in a controlled action or
not,To control and verify design of a product, investigating
causes of non-conformance and taking corrective
measures.
DESIGN QUALITY : Standard component parts,
Manufacturability and assembly product design,Early
supplier involvement,Modular design,design quality,
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
Quality function deployment, Using standard component
parts , Concurrent engineering.
JIT QUALTY : Reducing lot size ,Reducing inventory to
expose manufacturing , Reducing step up time, reducing
supplier’s base.
2.2 Surveying : The primary data is collected using the
surveying which is conducted by inserting the markings in
the various parameters of the Performance Matrix as a
result it becomes easier to study the various less
performing parameters accordingly. The bifurcation into
various categories helps in further optimization using
Genetic Algorithm. The snapshots of the various outcomes
are being illustrated and shown in the figures.
2.3 Genetic Algorithm: The algorithm basically works
on the concept of the iterative loop method that is all the
parameters are grouped and bifurcated into the various
categories the Performance matrix is prepared using GUI in
Java window which is shown in the snapshot. The next step
is followed by the iterative looping of Customer’s Input,
Supplier’s Quality based on the markings being inserted by
the respondents and hence optimized results are being
obtained due to the possible iterative looping. The
Algorithm basically follows the concept of the survival of
the fittest concept that is the one which gets passed in all
the iterations finally becomes the optimized solution to the
problem domain, also based upon the results of all the
iterations ranking is also done for the same.
I. SNAPSHOTS SHOWING RESULTS AFTER EVERY ITERATION
OF GENETIC ALGORITHM
3. Results: The performance matrix so prepared allows a
holistic study of the various operational parameters of the
supply chain. Depending upon the various markings being
inserted in various domains a generalized result is obtained
and a proper ranking is obtained for that. The rankings are
subjected to further editing depending upon the various
industry requirements because depending upon different
factors the focus may be given to different factors
accordingly. As per the data so obtained is C secures rank
first then B followed by E then A and finally D.
4. Conclusions: The findings and the research allows
the managers and also the customers to give a light on the
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International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue5- May 2013
various aspects of a product and the industry which
otherwise may be neglected. It helps in making better
decision making process and choice and product selection.
Performance Matrix provides a better and simplified
picture of various domains in a tabular format by means of
GUI which makes it easier to read and understand. The
same Performance Matrix can be used across a wide
variety of industries which may result in better
optimization of the available resources. Future research is
recommended in order to determine whether the proposed
perspectives and measures are a necessary and sufficient
set. Nevertheless, the framework does represent a strategic
SCM evaluation tool that can be used to monitor and guide
specific projects and general performance improvement
efforts.
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Business Performance Management, 8(2/3),110– 131.
[8].K.A. De Jong, W.M. Spears and D.F. Gordon (1995)
Using Markov chains to analyze GAFOs. In D. Whitley
and M. Vose (eds.), Foundations of Genetic Algorithms 3,
Morgan Kaufmann, San Mateo, CA, 115–137.
[9]. C.C. Peck and A.P. Dhawan (1995) Genetic
algorithms as global random search methods: An
alternative perspective. Evolutionary Computation, 3, 39–
80.
[10]. C.R. Reeves (1999) Predictive measures for problem
difficulty. In: Proceedings of 1999 Congress on
Evolutionary Computation, IEEE Press, pp. 736–743.
[11].Chopra,Meindl & Kalra Supply Chain Management
(Strategy Planning & Operation ) fourth edition.
[12] Vijay R.Kannan,The impact of operational Quality :a
supply chain view.
Acknowledgement: The contribution of the work was
a result of hard work and efforts of the faculty members of
the institute in addition to the contributions to the works of
those mentioned in the references. The work is would not
have been possible without the sincere efforts and help of
my mentors and guides , lastly I would like to thank the
contributions of every single entity which helped me in
completing
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