Term of Award

Spring 2019

Degree Name

Doctor of Philosophy in Logistics and Supply Chain Management (Ph.D.)

Document Type and Release Option

Dissertation (restricted to Georgia Southern)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Logistics and Supply Chain Management

Committee Chair

Alan Mackelprang

Committee Member 1

Marc Scott

Committee Member 2

Matthew Jenkins

Abstract

It is common view that optimality refers to a state where no further improvement is possible. This idea of optimality is commonly referred to as Pareto optimality in microeconomics. We define operational optimality as the state of operations wherein any change in the operational performance parameters reduces operational performance. Firms that have optimal operations should exhibit better performance than sub-optimal firms. Using panel data for manufacturing firms, the relationship between operational optimality and firms’ performance is explored. The dissertation consists of two interconnected essays that explore different aspects of this relationship. The first study described in chapter two explores whether operational optimality has a positive relationship with manufacturing firms’ financial and market performance. Operational optimality is measured as the individual elasticity of six factors of production/ operational parameters (Capital Expenditure, Research & Development (R&D), Labor, Inventory Intensity, Capacity Utilization, and Production Variability) with respect to operating performance. Using these six, a combined measure of operational elasticity was also generated. The relationship between each of the seven elasticities and firm financial performance (measured as Return On Sales (ROS) and Return On Assets (ROA)) and market performance (measured as revenue growth) was analyzed. The results suggest that operational optimality does not result in better financial and market performance in manufacturing firms. In the second study described in chapter three, this counter-intuitive result is further explored. Using a split sample consisting of market dominant and bankrupt firms, the probability of market dominance as a function of operational optimality is further studied. For this study, the operational parameters were limited to inventory intensity, capacity utilization, and production variability. The study found that successful manufacturing firms tend to operate in regions of operational sub-optimality with regards to inventory levels and production variability over time. Additionally, the region of sub-optimality that they operate in is related to the level of environmental uncertainty and changes over time.

OCLC Number

1101903596

Research Data and Supplementary Material

No

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