The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	On the design for flexibility of manufacturing systems : a stochiastic approach
Author:	Areeratchakul, Nathapol
View Online:	njit-etd2005-125 (xiii, 106 pages ~ 4.7 MB pdf)
Department:	Department of Industrial and Manufacturing Engineering
Degree:	Doctor of Philosophy
Program:	Industrial Engineering
Document Type:	Dissertation
Advisory Committee:	Abdel-Malek, Layek (Committee chair) Bladikas, Athanassios K. (Committee member) Wolf, Carl (Committee member) Caudill, Reggie J. (Committee member) Cordero, Rene (Committee member)
Date:	2005-08
Keywords:	Manufacturing flexibility Flexibility attributes Newsboy Real options Uncertainty Optimal design
Availability:	Unrestricted
Abstract:	Flexibility has emerged as one of the most strategic imperatives for company viability in today's fast paced economy. This realization has stimulated extensive research efforts in this area most of which have focused mainly on defining flexibility and its attributes, the need for flexibility and how to measure it. Nevertheless, despite the considerable amount of publications regarding flexibility and its related subjects, insufficient attention has been given to the optimality of the design for flexibility and the inherent needs to meet uncertainty. Bridging this gap is the intent of this work. In this dissertation, developed analytical models are for the optimum design of flexible systems. The models introduced are based on extensions of the single period stochastic inventory model and real option theory to determine the optimum level of the various flexibility attributes that are required to meet the needs of a concern in an uncertain environment. Our premise stems from the fact that flexibility does not come at "no cost." That is, when designing a system, the more flexibility built in it, the more the cost that will be incurred to maintain it. On the other hand, if the system is designed with low levels of flexibility, it may not be able to meet the uncertain demand, therefore causing loss of future revenue. The developed models, then, are applied to examples where data are obtained from machine tool manufacturers to show how to strike a balance between the two conflicting scenarios of over and under-flexible designs.