| This research focuses on developing a model that can be used to assess the performance of Small to Medium-Sized Manufacturing Enterprises (SMEs) and then utilizing it to identify the top SMEs in order to provide a set of performance benchmarks for the SMEs. The model will result from the integration of a decision tool called the Analytical Hierarchy Process (AHP) and a data analysis model called Data Envelopment Analysis (DEA).; The approach was applied on QuickView, an existing benchmarking model that has been used on 3,500 SMEs in the past eight years to modify and update the model in order to allow the model to be applied outside of the United States. By using the measure of efficiency that AHP and DEA provide, this research links both qualitative and quantitative factors of multiple manufacturing input/output in which previous productivity analyses were not been able to capture. In particular, the relation between operational practice and manufacturing efficiency was analyzed and interpreted. In addition, a comparison of the performance of the SMEs across the country, including differences in performance by business type and geographic location was presented.; This research demonstrates that by eliminating flaws and taking advantage of each methodology's specific characteristics in identifying and solving problems, the new integrated AHP/DEA model appears to be a logical and sensible solution in multi-criteria decision-making problem. Even though this study focuses on an efficiency evaluation of SMEs, its applicability can be extended to the non-SMEs problem as well. |
