Showing papers by "William W. Cooper published in 1992"

Measuring Complexity in High-Technology Manufacturing: Indexes for Evaluation

Abstract: Making strategic decisions based on performance evaluation across wafer fabrication plants in semiconductor manufacturing companies or across companies within the semiconductor industry is challenging because relevant information regarding competitors is not publicly available. One common practice is to analyze the performance of the plant longitudinally. However, as product and process technologies and product mixes change rapidly, the same plant cannot be compared across time periods. In a collaborative research project with a semiconductor manufacturing company, we conceptualized complexity and operationalized it in the form of indexes. We then applied these indexes to the operations of a wafer fabrication plant to assess the changes in its operational complexity on a monthly basis. The indexes proved very useful as inputs to models for evaluating the performance of the plant relative to competition, for formulating cost strategies, and for supporting production planning decisions.

Chance Constrained Programming Approach to Empirical Analyses of Mutual Fund Investment Strategies

Abstract: Chance constrained programming concepts are used to formalize risk and return relations which are then modeled for use in an empirical study of mutual fund behavior during the period 1984 through 1988. The publicly announced strategies of individual funds are used to form ex ante risk classifications which are employed in examining ex post performance. Negative relations between risk and return held in every year of the period studied. The bearing of these negative risk-return findings for the Bowman paradox, as studied in the strategic management literature, are thus extended from the industrial firms studied by Bowman (and others) and shown to be present even in these investment oriented mutual funds in each of the years of the great bull market from 1984 through 1988. Finally, our use of chance constrained programming enables us to separate risk from return behavior and evaluate their relative strengths as sources of these negative relations, which are found to be more in the returns than the risks.

Data disclosure and data sharing in scientific research.

Abstract: Data sharing is examined for its bearing on (i) quality assurance and (ii) extensions of results in scientific research as well as (iii) part of a tradition of openness in science. It is suggested that sharing can be accomplished in a simple manner that is also sufficiently flexible to fit varying individual situations by asking authors of data dependent articles and grant proposals to footnote (a) whether they are willing to make their data available to others and, if so, (b) how the data may be accessed. Appendices report results from a survey of current policies and practices in professional societies and in Federal government fund granting agencies. Emphasis is on the social and management sciences.