International Journal of Management and Applied Science (IJMAS)
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Statistics report
Nov. 2018
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 54
Paper Published : 3316
No. of Authors : 6857
  Journal Paper




Paper Title :
Advantage Of Make-To-Stock Strategy Based On Linear Mixed-Effect Model

Author :Yu-Pin Liao, Shin-Kuan Chiu

Article Citation :Yu-Pin Liao ,Shin-Kuan Chiu , (2015 ) " Advantage Of Make-To-Stock Strategy Based On Linear Mixed-Effect Model " , International Journal of Management and Applied Science (IJMAS) , pp. 254-265, Volume-1,Issue-9, Special Issue-2, Oct

Abstract : In the past few decades, demand forecasting becomes relatively difficult because of the rapid changes of world economic environment. In this research, the make-to-stock (MTS) production strategy is applied as an illustration to explain that forecasting plays an essential role in business management. We also suggest that linear mixed-effect (LME) model could be used as a tool for prediction and against environment complexity. Data analysis is based on a real data of order quantity demand from an international display company operating in the industry field, and the company needs accurate demand forecasting before adopting MTS strategy. The forecasting result from LME model is compared to the common used approaches, times series model, exponential smoothing and linear model. The LME model has the smallest average prediction errors. Furthermore, multiple items in the data are regarded as a random effect in the LME model, so that the demands of items can be predicted simultaneously by using one LME model. However, the other approaches need to split the data into different item categories, and predict the item demand by establishing model for each item. This feature also demonstrates the practicability of the LME model in real business operation. Index Terms- forecasting, linear mixed-effect model, make-to-stock, order demand, production strategy

Type : Research paper

Indexed : Google Scholar


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