Information distortion in a supply chain and its mitigation using soft computing approach

The information transferred in the form of orders between the nodes of a supply chain tends to be distorted when it moves from downstream to upstream. This phenomenon is called as bullwhip effect and this research is aimed to analyze this effect deeply in a single input single output (SISO) model. A...

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Bibliographic Details
Published in:Omega (Oxford) 2009-04, Vol.37 (2), p.282-299
Main Authors: BALAN, S, VRAT, Prem, KUMAR, Pradeep
Format: Article
Language:English
Subjects:
Applied sciences
Exact sciences and technology
Fuzzy logic
Inference from stochastic processes
time series analysis
Logistics
Mathematical models
Mathematics
Operational research and scientific management
Operational research. Management science
Probability and statistics
Sciences and techniques of general use
Sensitivity analysis
Statistics
Studies
Supply chain management Bullwhip effect Neuro-fuzzy SISO model Soft computing
Supply chains
Time series
Online Access:Get full text
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Summary:The information transferred in the form of orders between the nodes of a supply chain tends to be distorted when it moves from downstream to upstream. This phenomenon is called as bullwhip effect and this research is aimed to analyze this effect deeply in a single input single output (SISO) model. A discrete time series SISO model is developed for the analysis and it proves to be very useful in revealing the dynamics characteristics of the system. The bullwhip effect is measured from the transfer function model and the effect can be reduced by applying soft computing approach. A detailed sensitivity analysis is carried out to investigate the behavior of the model under various conditions. The applied fuzzy logic theory controls the errors and change in errors associated with forecasted demand between the nodes of a supply chain and it allows a smooth information flow in the chain. Tuning of fuzzy logic controller has been performed using adaptive neuro-fuzzy inference system (ANFIS). The method is illustrated with a numerical example. The application of soft computing approach addresses the real situation of human judgment with fuzziness helps the managers to forecast the demand with less distortion and to improve the supply chain effectiveness.
ISSN:0305-0483
1873-5274
DOI:10.1016/j.omega.2007.01.004