Benchmarking Automotive After-sales Service Companies with Dependent Criteria-Application of Data Envelopment ‎Analysis

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, ‎I‎ran.

2 Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, ‎Ir‎an‎.

3 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, ‎I‎ran‎.

4 Department of Mathematics, Nour Branch, Islamic Azad University, Nour, ‎Mazandaran, Iran

Abstract

Benchmarking is a tool for evaluating organizational performance with a learning approach from others. The importance of benchmarking in every industry is clear for anyone. In the automotive industry, the performance of after-sales service agencies in Iran is evaluated every year by Iran Standard and Quality Inspection company. One of the ways to continuously improve in after-sales service agencies is benchmarking of successful and efficient examples in the network. In this paper, a benchmarking model is developed considering that the repair index and customer satisfaction are interdependent. To improve the accuracy and operationality of benchmarking, some constraints have been added to the model with the opinion of experts. Considering the dependent parameters, a data envelopment analysis model has been proposed and this model has been implemented to benchmark 20 after-sales service agencies of a car company. By solving the model and comparing it with the results of the original model, it was observed that the considered conditions changed the benchmarking and increased the accuracy. This paper discusses the concept of the impact and importance of dependent parameters in benchmarking, and with this concept, a benchmarking model for automotive after-sales service agencies is presented.

Keywords


 [1] G. Bykzkan, Y. Karabulut, Sustainability performance evaluation: Literature review and future directions, Journal of Environmental Management 217 (2018) 253-267.
[2] M.J. Farrell, The Measurement of Productive Efficiency,
Journal of the Royal Statistical Society. Series A (General) 8 (1982) 1-7.
[3] A. Charnes, W.W. Cooper, E. Rhodes, Measuring the efficiency of decision making units,
European Journal of Operational Research 2 (1978) 429-444.
[4] M.-C. Lai, H.-C. Huang, W.-K. Wang, Designing a knowledge-based system for benchmarking: A DEA approach,
KnowledgeBased Systems 24(5) (2011) 662-671.
[5] N. Donthu, E.K. Hershberger, T. Osmonbekov, Benchmarking marketing productivity using data envelopment analysis,
Journal of Business Research 58(11) (2005) 1474-1482.
[6] S. Jong Joo, H. Min, Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis,
Supply Chain Management: An International Journal 11(3) (2006) 259-265.
[7] J.C. Martn, C. Romn, A Benchmarking Analysis of Spanish Commercial Airports, A Comparison Between SMOP and DEA Ranking Methods,
Networks and Spatial Economics 6(2) (2006) 111-134.
[8] H.D. Sherman, J. Zhu, Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S.bank application,
Annals of Operations Research 145(1) (2006) 301-319.
[9] H. Seol, et al., A framework for benchmarking service process using data envelopment analysis and decision tree,
Expert Systems with Applications 32(2) (2007) 432-440.
[10] I.M. Horta, et al., The impact of internationalization and diversification on construction industry performance,
International Journal of Strategic Property Management 20 (2016) 172-183.
[11] C.E. Bogan, M.J. English, Benchmarking for Best Practices: Winning Through Innovative Adaptation, McGraw-Hill,
New York, USA, (1994).
[12] R.C. Camp, Benchmarking : the search for industry best practices that lead to superior performance, ASQ Quality Press,
Milwaukee, USA, (1989).
[13] H.L. Chen, Benchmarking and quality im (2002) 757-773.
[14] M. Yasin Mahmoud, The theory and practice of benchmarking: then and now,
Benchmarking: An International Journal 9 (2002) 217-243.
[15] S.-M. Hong, Improved benchmarking comparability for energy consumption in schools,
Building Research Information 42(1) (2014) 47-61
[16] A. Brah Shaukat, A. Lin Ong, B. Madhu Rao, Understanding the benchmarking process in Singapore,
International Journal of Quality Reliability Management 17(3) (2000) 259-275.
[17] R. Rostamzadeh, et al., Application of DEA in benchmarking: a systematic literature review from 2003-2020,
Technological and Economic Development of Economy 27(1) (2021) 175-222.
[18] P. Fernandez, T. RakotobeJoel, I.P. McCarthy, An evolutionary approach to benchmarking,
Benchmarking: An International Journal 8(4) (2001) 281-305.
[19] W. Peng Wong, K. Yew Wong, A review on benchmarking of supply chain performance measures,
Benchmarking: An  International Journal 15(1) (2008) 25-51.
[20] A. Charnes, et al., Two Phase Data Envelopment Analysis Approach to Policy Evaluation and Management of Army Recruiting Activities: Tradeoffs between Joint Services and Army Advertising,
Research Report CCS (1986).
[21] J.L. Ruiz, I. Sirvent, Performance evaluation through DEA benchmarking adjusted to goals, Omega 87 (2019) 150-157.
[22] R.D. Banker, A. Charnes, W.W. Cooper, Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis,
Management Science 30(9) (1984) 1078-1092.
[23] S. Kaffash, et al., A survey of data envelopment analysis applications in the insurance industry 1993-2018,
European Journal of Operational Research 284(3) (2020) 801-813.
[24] M. Toloo, E.K. Mensah, Robust optimization with nonnegative decision variables: A DEA approach,
Computers Industrial Engineering 127 (2019) 313-325.
[25] Z. Zhou, et al., China’s urban air quality evaluation with streaming data: A DEA window analysis,
Science of The Total Environment 727 (2020) 138213.
[26] N. Zhu, C. Zhu, A. Emrouznejad, A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies,
Journal of Management Science and Engineering (2020).
[27] A. Ghaffari-Hadigheh, W. Lio, Network data envelopment analysis in uncertain environment,
Computers Industrial Engineering 148 (2020) 106657.
[28] M. Boa, M. Dlouh, E. Zimkov, Modeling a shared hierarchical structure in data envelopment analysis: An application to bank branches,
Expert Systems with Applications 162 (2020) 113700.
[29] P. Peykani, E. Mohammadi, A. Emrouznejad, An adjustable fuzzy chance-constrained network DEA approach with application to ranking investment firms,
Expert Systems with Applications 166 (2021) 113938.
[30] M. Khoveyni, R. Eslami, Efficiency stability region for two-stage production processes with intermediate products,
Computers Industrial Engineering 151 (2021) 106950.
[31] H.-S. Lee, Efficiency decomposition of the network DEA in variable returns to scale:
An additive dissection in losses, Omega 100 (2021) 102212.
[32] S. Siti Fatimah, U. Mahmudah, Two-Stage Data Envelopment Analysis (DEA) for Measuring the Efficiency of Elementary Schools in Indonesia,
International Journal of Environmental and Science Education 12 (2017) 1971-1987.
[33] D. Niu, et al., Analysis of wind turbine micrositing efficiency: An application of two-subprocess data envelopment analysis method,
Journal of Cleaner Production 170 (2018) 193-204.
[34] A. Najahi, et al., Relationship between Structure and Performance in the Banking Industry of Iran,
Journal of Money and Economy 11(4) (2016) 443-466.
[35] A. Ji, et al., Data envelopment analysis with interactive variables,
Management Decision 53 (2015) 2390-2406.
[36] V.V. Podinovski, T. Bouzdine-Chameeva, On single-stage DEA models with weight restrictions,
European Journal of Operational Research 248(3) (2016) 1044-1050.
[37] V.V. Podinovski, Optimal weights in DEA models with weight restrictions,
European
[38] S. Gner, Ground-level aircraft operations as a measure of sustainable airport efficiency: A weight-restricted DEA approach, Case Studies on Transport Policy 9(2) (2021) 939-949.
[39] G.O.S. Medeiros, et al., Efficiency analysis for performance evaluation of electric distribution companies, International Journal of Electrical Power Energy Systems 134 (2022) 107430.