Production starting time control for compensating forecast error and customer loss in waiting

Pao Yue-kong Library Electronic Theses Database

Production starting time control for compensating forecast error and customer loss in waiting

 

Author: Qian, Chen
Title: Production starting time control for compensating forecast error and customer loss in waiting
Degree: Ph.D.
Year: 2014
Subject: Hong Kong Polytechnic University -- Dissertations
Production management.
Production management -- Data processing.
Department: Dept. of Industrial and Systems Engineering
Pages: viii, 146 leaves : illustrations (some color) ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2757528
URI: http://theses.lib.polyu.edu.hk/handle/200/7524
Abstract: Production systems play a key role in modern society, and significant improvements have been achieved through the years. However, due to the diversity of human behavior, customer demand uncertainty does exist in practice. Consequently, production solutions that are capable of coping with such diverse behavior are necessary. To have a better competitive position, forecasting is an important element in production management. Thus, there is always research for improving forecasting accuracy and the development of new methods is on-going. Apart from working on new forecast methods or improving existing models, this research focused on working with the expected forecast error in a most economical way. To achieve this goal, the aim was to achieve a balance between the effect of forecast error with time and the customer loss in the waiting period. In this research, a production approach named Make-to-Balance (MTB) is introduced. To verify the concept and the operating result of the proposed model, a simulation process was built with STELLA, and a software program was also coded in C# language. The SETLLA results and program results match well in different situations (one general case and four extreme cases) and identify the correctness of the MTB model. The program eases the calculations and it was found in this research that the optimal solution could be obtained from MTB and Smart-MTB version programs. Indeed, the contributions of this research are not only in its inspirations but also in that it extends the view on how to run a production system effectively by taking uncertainty and customer behavior into account, and it also shows that customer loyalty helps to reduce the effect on forecast error.

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