Theses and Dissertations

Issuing Body

Mississippi State University


Greenwood, Allen G.

Committee Member

Walden, Clayton T.

Committee Member

Jin, Mingzhou

Committee Member

Bullington, Stanley F.

Date of Degree


Document Type

Dissertation - Open Access


Industrial Engineering

Degree Name

Doctor of Philosophy (Ph.D)


James Worth Bagley College of Engineering


Department of Industrial and Systems Engineering


To effectively analyze and design a flexible supply chain (FSC), a variety of variables need to be considered. This research presents a framework, an extension of Chan et al. (2009) that identifies a more extensive yet salient set of variables for designing FSCs. This framework provides a basis for using simulation to better understand, and to better design, FSCs. Conceptual simulation models are developed to represent general flexible supply chains in terms of using design and system variables. The proposed conceptual model incorporates many elements from the framework considering a wide variety of variables to demonstrate the approach for building a FSC model. This research provides a general FSC simulation model, built in FlexSim, that implements many variables from the framework and aspects of the conceptual framework. Variability plays an important role in FSC model. Two key supply-chain performance measures are lead time and variability in lead time. One way that has been proposed to improve both measures is to increase supplier flexibility. Through simulation this research provides a means to assess the effects of various manufacturing and logistics flexibility-related variables on lead time and its variability. This research includes effect of several experiments that consider the effect of supplier flexibility level, proportion of process time that is production and transportation time, and level of variability in process time on lead time. The triangular distribution is used often in simulation when process data are not available. Thus, the triangular distribution is used in the FSC simulation model. This research provides a means to effectively consider alternative values of the parameters of the triangular distribution during experimentation. The method facilitates specification of both moment and location parameters.