Productivity and comparative analysis in service and manufacturing operations

Productivity assessment has received increased attention over the past several years. At the same time, the focus has moved from single-factor productivity measures, or attempts to characterize performance in terms of simple ratios, to a multi-factor construct. In this dissertation, I propose a new framework of productivity assessment—using notations of relative, absolute, and comparative performance evaluation—via Data Envelopment Analysis (DEA)-based Malmquist indexes. Performance evaluation of member units of a set, whether companies or other organizational units, with respect to a technology that is determined by the entire set may be termed relative performance evaluation. Alternatively, performance evaluation of units of such a set against a different technology may be termed absolute performance evaluation. Traditional DEA evaluates the relative efficiency of such a set of units with respect to the frontier determined by the entire set, and can be used to determine relative performance change. Absolute performance evaluation can be in the context of a different technology, whether determined by a different industry or technology or determined by a different period of time. The latter context occurs in Malmquist productivity index calculations. The former context has not been considered in the open literature. This context defines what is termed comparative performance evaluation, namely, evaluating the performance of a set of units or observed behaviors with respect to the frontier (or best practice) of another set of units, with a different technology, held to exemplary or held to comprise a benchmark set. To operationalize absolute and comparative performance evaluation, and to allow calculation of DEA-based Malmquist productivity indexes, an extension to DEA models is developed in this dissertation. The extension, namely the Benchmark DEA model and score, is applied, in illustrative empirical studies of the productivity of service and manufacturing industries. The aggregated nature of the constituent calculations of the Malmquist index obscure sources and patterns of productivity change. We introduce a process for the analysis of the components of the Malmquist index, which reveals such patterns and presents a new interpretation along with the managerial implication of each component. The approach that is developed can identify strategy shifts of individual companies in a particular time period. Furthermore, we are able to make judgments on whether or not such strategy shifts are favorable and promising. The developed productivity measurement approach is applied to address productivity trends in the computer and automobile industries using Global Fortune 500 data for the period 1991–1997