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5 Discussion
In this chapter, we demonstrated successful application of fuzzy multicriteria
decision making technique based on TOPSIS for supplier quality evaluation. The
results were validated against fuzzy SAW and found to be consistent. However, it is
dif
cult to generalize the results since the results of proposed technique is highly
dependent on the data used i.e. number of experts and their rankings which is again
dependent on their familiarity and experience with the subject. Therefore, interested
readers are advised to interpret these results from methodological perspective and
make a careful selection of the model parameters (number of experts, rankings of
criteria and alternatives) for their respective works.
Secondly, the criteria proposed for supplier quality evaluation although con-
sidered from multiple perspectives are generic in nature to be able to be applied for
majority of industries. However, for speci
c industries such as pharmaceuticals,
defense etc. adding more/adapting the proposed criteria is recommended depending
on the context under consideration.
Thirdly, fuzzy triangular numbers were used for modeling uncertainties in our
study. There is also possibility of investigating other types of fuzzy numbers such as
trapezoidal, intuitionistic and various defuzzi
cation techniques to see their impact
on
final results.
Lastly, the sensitivity analysis showed that for the speci
c numerical application
under study, no changes in the best supplier ranking was observed with variation in
weights. The sensitivity analysis can be further extended by incorporating more
experiments and/or linking with simulated data sets. There is also possibility of
coupling uncertainty analysis with the same to test model robustness.
6 Conclusions and Future Works
This chapter presents a multi-criteria decision making approach based on fuzzy
TOPSIS for evaluating supplier quality from multiple perspectives under partial or
lack of quantitative information. Four perspectives namely product quality, process
quality, organizational quality and service quality are adopted to retain 17 criteria
for supplier quality evaluation. These criteria are Quality of service, Reliability,
Quality certi
cations, Employee training, Management commitment, Flexibility,
Quality of product, Technical Capability, Statistical process control, Environmental
considerations, Nonconformities/defects generated during production, Process
Capability Index, Product features, Documentation, Handling of returned material
and warranties, Quality in Design of products, and Conformance to Quality stan-
dards. Fuzzy TOPSIS is used to aggregate the expert ratings and generate an overall
performance score for measuring the quality performance of each alternative
(supplier). The alternative with the highest score is
finally chosen and is recom-
mended for procurement. Sensitivity analysis to determine the in
fl
uence of criteria
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