Performance Evaluation of Production Chain using Two-Stage DEA Method (Case Study: Iranian Poultry Industry)

Document Type : Research Paper

Authors

1 Associate Professor, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin branch, Islamic Azad University, Qazvin, Iran.

2 Master of Science Student, Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Abstract

Abstract
Today, the poultry industry is one of the most important food industries in any country in providing food to the communities. Knowledge of how the production chain works in this industry is essential. The purpose of this paper is to evaluate the performance of 28 active production chains in the Iranian poultry industry and to calculate their relative efficiency and also determines the supply chain pattern for other chains. In order to achieve this goal, non-parametric data envelopment analysis (DEA) method has been used. The relative efficiency of each unit is first calculated with the network DEA approach and then the traditional DEA and the results of the two methods are compared. Finally, the cross-efficiency method is used to rank the efficient units. The results showed that in the network and traditional model, out of 28 production chains under study, 3 and 7 efficient chains, respectively. In models, the minimum efficiency is 63% and 67%, respectively. Given the number of efficient units in the traditional DEA model, it is obvious that the decision to determine the pattern production chain for other production chains is difficult. This study proposes a network DEA model to evaluate the performance of Iranian poultry industry production chains.

Keywords: Efficiency, Supply chain, Two-stage DEA, Poultry Industry.
JEL Classification: C67, H0, L80

Keywords

References

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new economy and trad
Volume 16, Issue 3
March 2022
Pages 105-130

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History

  • Receive Date: 19 June 2021
  • Revise Date: 09 September 2021
  • Accept Date: 25 November 2021

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