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DEA window analysis for assessing efficiency of blistering process in a pharmaceutical industry

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Abstract

This research evaluates the efficiency of blistering lines over a 2-year period starting January 2013 till December 2014 using data envelopment analysis models. The planned production quantity in units, defect quantity in units, and idle time in units are selected as inputs. The actual produced quantity in units is the output measure. The data are then normalized using the min–max normalization. Six windows are formed, and then the technical, pure technical, and scale efficiency are calculated for three identical blistering machines lines, BL1, BL2, and BL3, in each year. Results showed significant reductions in technical (TIE), pure technical (PTIE) inefficiency, and scale inefficiency (SIE) scores in year 2014. For BL1, the average TIE, PTIE, and SIE are reduced from 0.1152 to 0.0477, 0.0751 to 0.0176, and 0.0429 to 0.0304, respectively. For BL2, the average TIE, PTIE, SIE are reduced from 0.0968 to 0.0282, 0.0514 to 0.0133, and 0.0486 to 0.0149, respectively. Finally, for BL3, the average TIE, PTIE, SIE are reduced from 0.0936 to 0.0527, 0.0396 to 0.0154, and 0.0556 to 0.0380, respectively. In practice, the sources of TIE are mainly failure to operate at most productive scale size (SIE) and/or the poor input utilization (PTIE). In conclusion, the research results provide valuable feedback on how to improve efficiency, utilize resources, and effectively manage production lines.

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Authors and Affiliations

  1. Department of Industrial Engineering, University of Jordan, Amman, 11942, Jordan

    Abbas Al-Refaie & Moaath Sawalheh

  2. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, 30013, Taiwan

    Chien-Wei Wu

Authors
  1. Abbas Al-Refaie
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  2. Chien-Wei Wu
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  3. Moaath Sawalheh
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Corresponding author

Correspondence to Chien-Wei Wu.

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Al-Refaie, A., Wu, CW. & Sawalheh, M. DEA window analysis for assessing efficiency of blistering process in a pharmaceutical industry. Neural Comput & Applic 31, 3703–3717 (2019). https://doi.org/10.1007/s00521-017-3303-2

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