Window Analysis and MPI for Efficiency and Productivity Assessment Under Fuzzy Data

Abstract

This research develops a procedure for DEA window analysis and MPI evaluation of a manufacturing process with fuzzy inputs and outputs. A real case study was provided to illustrate relative efficiency and MPI assessment of a blowing machine over a period of one a year. The proposed approach was implemented to measure the technical, pure technical, and scale efficiency scores for decision making unit. The results showed that the blowing process was technically inefficient due to scale inefficiency. Therefore, management should optimize the size of operations and better utilize resources. Then, the lower and upper MPI values and their corresponding technology change and efficiency change were calculated. The MPI results revealed the reasons behind MPI progress or regress in current period measured with respect to next period. This procedure provides great assistance to process engineering in obtaining reliable feedback on process performance and guide them to take proper actions.

A Proposed DEA Window Analysis for Assessing Efficiency from Asymmetry Dynamic Data

“…Let θ k be the efficiency of DMU k (k ∈ j). Then, the objective of the optimization model aims to maximize θ k [2,9]. Generally,…”

“…The proposed approach was implemented on a real data set to evaluate the performance of hospitals in the USA. Al-Refaie [2] proposed a DEA window analysis and Malmquist productivity index under fuzzy data. The proposed window analysis was based on providing crisp efficiency scores by solving a single model.…”

“…qk , denote the low, middle, and high optimal PTE efficiencies, respectively. For illustration, the optimal PTE, δ * L qk , at the low data level DMU k, is estimated using an input-oriented BCC model without slacks [2]. Mathematically,…”

“…Industries across the globe rely on the efficiency evaluation of manufacturing processes to create quality products and save on expensive manufacturing and quality costs [1,2]. In practice, information asymmetries arise in the only collected data due to subjective judgments based on experience and historical data or variations, which may significantly affect the accuracy of the efficiency assessment results and the effectiveness of the improvement decisions [3].…”

“…Nevertheless, for DEA models to avoid producing multiple efficient DMUs, the number of DMUs should be at least two times the sum of the number of inputs and outputs [6]. Fortunately, DEA window analysis was introduced to improve discriminating power by increasing the number of DMUs when using a limited number of DMUs [2]. DEA window analysis regards the same DMU in distinct periods, which are then treated as entirely different DMUs.…”