Efficiency and Performance Stability Analysis of Nominated Suppliers in the Automotive Industry Using a Time-Series Data Envelopment Analysis Approach
Keywords:
Automotive Industry, Data Envelopment Analysis, BCC Model, nominated suppliers, technical efficiencyAbstract
Supplier management in the automotive industry often faces the dilemma of nominated suppliers, where suppliers are directly appointed by the Original Equipment Manufacturer (OEM), thereby limiting the company's bargaining power in ensuring operational efficiency. This study aims to analyze the technical efficiency and performance stability of 25 nominated suppliers at PT XYZ throughout 2025. The methodology employed is Data Envelopment Analysis (DEA) with the BCC model, input-oriented, and a monthly time-series approach, resulting in a total of 300 Decision Making Unit (DMU) observations. Input variables include Turnaround Time (TAT) and Price Index, while output variables consist of Delivery Rate, Quality Rate, and Response Rate. The results indicate significant fluctuations in technical efficiency scores across all suppliers, with no single supplier capable of maintaining a perfect efficiency level (1.00) consistently throughout the observation period. Slack analysis reveals that the dominant source of inefficiency is caused by TAT durations that exceed peer group reference benchmarks. These findings provide strategic implications for company management to implement a quantitative data-driven early warning system to mitigate late delivery risks and optimize coordination with OEMs.
References
[1] M. Christopher, Logistics and supply chain management: logistics & supply chain management. Pearson UK, 2016.
[2] S. Chopra and P. Meindl, “Supply Chain Management. Strategy, Planning & Operation,” in Das Summa Summarum des Management: Die 25 wichtigsten Werke für Strategie, Führung und Veränderung, C. Boersch and R. Elschen, Eds., Wiesbaden: Gabler, 2007, pp. 265–275. doi: 10.1007/978-3-8349-9320-5_22.
[3] M. M. Abushaega, O. Y. Moshebah, A. Hamzi, and S. Y. Alghamdi, “Enhancing supply chain resilience with data envelopment analysis and temporal convolutional networks for supplier efficiency and late delivery risk prediction,” Alexandria Engineering Journal, vol. 128, pp. 231–246, Sep. 2025, doi: 10.1016/j.aej.2025.05.062.
[4] R. S. Kaplan and D. P. Norton, The balanced scorecard: measures that drive performance, vol. 70. Harvard Business Review Boston, MA, USA, 2005.
[5] A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision making units,” Eur. J. Oper. Res., vol. 2, no. 6, pp. 429–444, 1978.
[6] P. Dutta, B. Jaikumar, and M. S. Arora, “Applications of data envelopment analysis in supplier selection between 2000 and 2020: a literature review,” Ann. Oper. Res., vol. 315, no. 2, pp. 1399–1454, Aug. 2022, doi: 10.1007/s10479-021-03931-6.
[7] K. Fotova Čiković, I. Martinčević, and J. Lozić, “Application of Data Envelopment Analysis (DEA) in the Selection of Sustainable Suppliers: A Review and Bibliometric Analysis,” Sustainability, vol. 14, no. 11, p. 6672, May 2022, doi: 10.3390/su14116672.
[8] S. Chul Park and J. H. Lee, “Supplier selection and stepwise benchmarking: a new hybrid model using DEA and AHP based on cluster analysis,” Journal of the Operational Research Society, vol. 69, no. 3, pp. 449–466, Mar. 2018, doi: 10.1057/s41274-017-0203-x.
[9] R. Restrepo and J. G. Villegas, “Supplier evaluation and classification in a Colombian motorcycle assembly company using data envelopment analysis,” Academia Revista Latinoamericana de Administración, vol. 32, no. 2, pp. 159–180, Aug. 2019, doi: 10.1108/ARLA-04-2017-0107.
[10] M. Brandenburg and G. J. Hahn, “Financial performance and firm efficiency of automotive manufacturers and their suppliers: A longitudinal data envelopment analysis,” Logistics Research, vol. 14, no. 1, pp. 1–26, 2021.
[11] H. Zareian Beinabadi, V. Baradaran, and A. Rashidi Komijan, “Sustainable supply chain decision-making in the automotive industry: A data-driven approach,” Socioecon. Plann. Sci., vol. 95, p. 101908, Oct. 2024, doi: 10.1016/j.seps.2024.101908.
[12] S. Lim and Y. Luo, “A SCOR-Based Two-Stage Network Range-Adjusted Measure Data Envelopment Analysis Approach for Evaluating Sustainable Supply Chain Efficiency: Evidence from the Korean Automotive Parts Industry,” Sustainability, vol. 17, no. 19, p. 8607, Sep. 2025, doi: 10.3390/su17198607.
[13] B. O. Babatunde, A. R. Tella, and T. T. Onewo, “Efficacy Of Suppliers Performance Evaluation In A Dynamic Business Environment Using Data Envelopment Analysis (Dea),” Zbornik radova-Journal of Economy and Business, p. 164, 2021.
[14] M. Goswami, Y. Daultani, F. T. S. Chan, and S. Pratap, “Assessing the impact of supplier benchmarking in manufacturing value chains: an Intelligent decision support system for original equipment manufacturers,” Int. J. Prod. Res., vol. 60, no. 24, pp. 7411–7435, Dec. 2022, doi: 10.1080/00207543.2022.2075811.
[15] M. Dotoli, N. Epicoco, M. Falagario, and F. Sciancalepore, “A stochastic cross‐efficiency data envelopment analysis approach for supplier selection under uncertainty,” International Transactions in Operational Research, vol. 23, no. 4, pp. 725–748, Jul. 2016, doi: 10.1111/itor.12155.
[16] M. Hashemi Tabatabaei and A. Bazrkar, “Providing a Model for Ranking Suppliers in the Sustainable Supply Chain Using Cross Efficiency Method in Data Envelopment Analysis,” Brazilian Journal of Operations & Production Management, vol. 16, no. 1, pp. 43–52, Mar. 2019, doi: 10.14488/BJOPM.2019.v16.n1.a4.
[17] M. Taleb, R. Khalid, M. Attallah, R. Ramli, and M. K. Mohd Nawawi, “Evaluating efficiency and ranking of suppliers using non-radial super-efficiency data envelopment analysis with uncontrollable factors,” International Journal of Computer Mathematics: Computer Systems Theory, vol. 8, no. 2, pp. 108–127, Apr. 2023, doi: 10.1080/23799927.2023.2193177.
[18] M. Izadikhah, R. Farzipoor Saen, R. Zare, M. Shamsi, and M. Khanmohammadi Hezaveh, “Assessing the stability of suppliers using a multi-objective fuzzy voting data envelopment analysis model,” Environ. Dev. Sustain., vol. 27, no. 9, pp. 22005–22047, May 2022, doi: 10.1007/s10668-022-02376-6.
[19] A. Torres-Ruiz and A. R. Ravindran, “Use of interval data envelopment analysis, goal programming and dynamic eco-efficiency assessment for sustainable supplier management,” Comput. Ind. Eng., vol. 131, pp. 211–226, 2019.
[20] M. J. Ramezankhani, S. A. Torabi, and F. Vahidi, “Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach,” Comput. Ind. Eng., vol. 126, pp. 531–548, 2018.
[21] R. D. Banker, A. Charnes, and W. W. Cooper, “Some models for estimating technical and scale inefficiencies in data envelopment analysis,” Manage. Sci., vol. 30, no. 9, pp. 1078–1092, 1984.
[22] W. W. Cooper, L. M. Seiford, and K. Tone, Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, vol. 2. Springer, 2007.
[23] A. Emrouznejad and G. Yang, “A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016,” Socioecon. Plann. Sci., vol. 61, pp. 4–8, 2018.
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