A survey of soft computing applications for decision making in supply chain management

It is widely recognized that effective supply chain management (SCM) is imperative in order for organizations to compete and have strategic competitive advantage. In order to maintain profit margins, organizations are working extensively on reducing operational costs and improving customer service....

Full description

Saved in:
Bibliographic Details
Published in2017 IEEE 3rd International Conference on Engineering Technologies and Social Sciences (ICETSS) pp. 1 - 6
Main Authors Burney, S. M. Aqil, Ali, Syed Mubashir, Burney, Shamaila
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2017
Subjects
Artificial neural networks
Customer relationship management
Decision making
Forecasting
Fuzzy logic
Genetic algorithms
Soft Computing. Decision Support Systems
Supply chain management
Online AccessGet full text

Cover

More Information
Summary:It is widely recognized that effective supply chain management (SCM) is imperative in order for organizations to compete and have strategic competitive advantage. In order to maintain profit margins, organizations are working extensively on reducing operational costs and improving customer service. A number of processes within SCM involve complex decision making (DM). Therefore a lot of academicians have developed research interest in improving and/or optimizing SCM performance and decision making capability. Numerous soft computing(SC) techniques including but not limited to fuzzy logic and fuzzy sets, artificial neural networks, genetic algorithm, Bayesian network, rough set theory etc has been applied for decision making and analysis within a number of supply chain management processes. This paper aims to review the existing research articles that deal with the applications of SC techniques for DM in SCM and provides future research directions.
DOI:10.1109/ICETSS.2017.8324158