Multi-factor performance measure model with an application to Fortune 500 companies

The paper develops tools for reconciling diverse measures which characterize the financial performance of the Fortune 500 companies. The technology of data envelopment analysis (DEA) is employed to determine a multi-factor financial performance model which inherently recognizes tradeoffs among various financial measures. This study offers an alternative perspective and characterization on the performance of the Fortune 500 companies. It is shown that the top-ranked companies by revenue do not necessarily have top-ranked performance viewed as being multidimensional. Only about 3% companies were operating on the best-practice frontier. Substantial technical and scale inefficiencies are found. Decreasing returns to scale (DRS) are uncovered among the relatively large (revenue-top-ranked) companies. The study of congestion shows that a reduction in current levels of employees, assets and equity may actually increase revenue and profit levels. Factor-specific measures, within the framework of multidimensional measure, are developed to further study the performance of companies and industries. The performance of best-practice frontier companies is analyzed by constructing reference-share measures which indicate the role each frontier company plays in evaluating non-frontier companies. Finally, the reliability of the best-practice frontier is examined.