Summary

It is difficult to evaluate an organization's performance when there are multiple inputs and multiple outputs to the system. The difficulties are further enhanced when the relationships between the inputs and the outputs are complex and involve unknown tradeoffs. This work introduces DEA as a multiple-measure performance evaluation and benchmarking tool. The focus of performance evaluation and benchmarking is shifted from characterizing performance in terms of single measures to evaluating performance as a multidimensional systems perspective. New DEA models and approaches are presented to deal with performance evaluation problems in a variety of contexts. A context-dependent DEA measures the relative attractiveness of similar operations/processes/products. Sensitivity analysis techniques can be easily applied and used to identify critical performance measures. Value chain efficiency models and DEA benchmarking models can be utilized to study the impact of information technology (IT) investments. These models can help organizations better understand the real impact of their IT investments and integrate technology more efficiently and effectively for the future.