| Data envelopment analysis (DEA) is a system-based approach to performance assessment that considers multiple inputs and outputs simultaneously. Despite the strength and a history of 25 years of theoretical foundation, most practitioners still utilize the output-input ratio (partial productivity) for assessment. The major hurdle in making this switch is the required paradigm shift—theoretical understanding of DEA requires economics and mathematical programming background, and the results are less intuitive than traditional partial productivity measures. To fuse the two approaches and simplify their joint use for practitioners, the relationship between efficiency scores provided by DEA, which corresponds to the economic concept of technical efficiency (TE), and conventional partial efficiency (PE) must be explained.; The main objective of this dissertation is to accomplish the fusion by answering the question “What is the relationship between the efficiency scores provided by DEA and the partial productivity metrics?”. There is little prior research on the relationship between TE and PE, and most of the published work presents empirical comparisons only.; We first connect TE and PE assuming a priori cost/price information. We show PE to be a special case of bilateral comparison of the performance of two organizations. Integrating the early works about the relationship between partial productivity and total factor productivity (TFP) and from TFP to TE, the bilateral comparison, thus, can be further decomposed into detail comparisons based on eleven metrics.; We also build the connection between TE and PE directly. We show that both PE and TE can be computed using similar LP formulations. Therefore, a sequence of LP models, which starts from TE and ends at PE, collectively, forms a bridge between PE and TE. This bridge has several “spans”, each corresponding to a particular effect. Therefore, a particular PE can be decomposed into seven multiplicative factors including TE. This theoretical linkage provides aids for output-input ratio benchmarking performance gap analysis and leads to some practical guidelines to select a PE to approximate system-based efficiency when DEA is not a possible solution.; At the end, we examine the benchmarking models for warehousing operations and also introduce the web-based benchmarking tool for warehouses, iDEAs-W. Input-output data for 147 warehouses are used for statistical analysis of the consistency between PE and TE. The results show the positive support for the suggested guidelines. |
