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روش پیش بینی تقاضا بهبود به منظور کاهش اثر شلاق چرمی در زنجیره تامین

An improved demand forecasting method to reduce bullwhip effect in

supply chains

]چکیده:

Accurate forecasting of demand under uncertain environment is one of the vital tasks for improving supply

chain activities because order amplification or bullwhip effect (BWE) and net stock amplification

(NSAmp) are directly related to the way the demand is forecasted. Improper demand forecasting results

in increase in total supply chain cost including shortage cost and backorder cost. However, these issues

can be resolved to some extent through a proper demand forecasting mechanism. In this study, an integrated

approach of Discrete wavelet transforms (DWT) analysis and artificial neural network (ANN)

denoted as DWT-ANN is proposed for demand forecasting. Initially, the proposed model is tested and validated

by conducting a comparative study between Autoregressive Integrated Moving Average (ARIMA)

and proposed DWT-ANN model using a data set from open literature. Further, the model is tested with

demand data collected from three different manufacturing firms. The analysis indicates that the mean

square error (MSE) of DWT-ANN is comparatively less than that of the ARIMA model. A better forecasting

model generally results in reduction of BWE. Therefore, BWE and NSAmp values are estimated using a

base-stock inventory control policy for both DWT-ANN and ARIMA models. It is observed that these

parameters are comparatively less in case of DWT-ANN model

👇محصولات تصادفی👇

دادگاه جنايي بين المللي معرفی VOIP نقشه هاي سازه اي ساختمان 5 طبقه متر طراحی شده در سالیدورک و کتیا تولید و مونتاژ تابلوهای برق فشار قوی و فشار ضعیف