A study on behaviour of bullwhip effect in (R, S) inventory control system considering DWT-MGGP demand forecasting model. (8th February 2019)

• Record Type: Journal Article
• Authors: Jaipuria, Sanjita; Mahapatra, Siba Sankar
• Is Part Of: Journal of modelling in management. Volume 14:Number 2(2019)
• Page Start: 385
• Page End: 407
• Publication Date: 2019-02-08
• Subjects: Artificial intelligence -- Forecasting -- Supply chain management -- Inventory control; Industrial management -- Mathematical models -- Periodicals; Industrial management -- Computer simulation -- Periodicals; Business -- Mathematical models -- Periodicals; Business -- Computer simulation -- Periodicals; 658.4033
• DOI: 10.1108/JM2-04-2018-0053
• Languages: English
• ISSNs: 1746-5664

Abstract

Abstract : Purpose: The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013) . Design/methodology/approach: A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies. Findings: A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R, βS) (R, γO) and (R, γO, βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ). Research limitations/implications: This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy. Practical implications: The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R, βS) (R, γO) and (R, γO, βS)inventory control policies are followed for replenishment. Originality/value: This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R, βS) (R, γO) and (R, γO, βS) inventory control policies.