Prediction of Production Capability for Subcontractors in Automotive Rubber Part Supply Chain Using Neuro-Fuzzy System


  • Suthep Butdee

    Department of Industrial Engineering, Faculty of Engineering, Rajamangala University of Technology Krungthep, Bangkok, 10120, Thailand

  • Pichai Janmanee

    Research and Development Institute, Rajamangala University of Technology Krungthep, Bangkok, 10120, Thailand



The paper proposes prediction production capability for automotive rubber part supply chain subcontractors in order to remain competitive in the global market. Rubber parts are used widely in automotive, motorcycles, trucks and other types of vehicles which are mostly small sizes but huge quantities to support original equipment manufacturer (OEM) brands with specific parts. The rubber part manufacturing process is complex and uncertain with compression molding and rubber curing conditions. Therefore, good conditions can predict to obtain high production capability for customer commissioning and delivery on schedule. The Neuro-fuzzy system is adopted and developed to deal with the uncertain capability under multi-criteria decision making. The methodology development can be used in the actual situation of the rubber part manufacturing supply chain environment and can predict uncertain problems that might occur in the subcontractor factories. The prediction of the production capability of the rubber part supply chain might be more effective on the real-time monitoring control system and can be tracked location part progressive for further planning both successful or has to be rescheduled. The platform was applied to audit and test in the actual industrial supply chain in Thailand. The methodology development was originally created for the particular supply chain in rubber automotive parts that can replace the existing manual approach to obtain a more efficient process of monthly performance evaluation.


Prediction of production capability, Supply chain management (SCM) automotive rubber parts, Neuro fuzzy system


[1] Vafaeenezhad, T., Tavakkoli-Moghaddam, R., Cheikhrouhou, N., 2019. Multi-objective mathematical modeling for sustainable supply chain management in the paper industry. Computers & Industrial Engineering. 135, 1092-1102. DOI:

[2] Awudu, I., Zhang, J., 2012. Uncertainties and sustainability concepts in biofuel supply chain management: A review. Renewable and Sustainable Energy Reviews. 16(2), 1359-1368. DOI:

[3] Brandenburg, M., Govindan, K., Sarkis, J., et al., 2014. Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research. 233(2), 299-312. DOI:

[4] Sharifi, E., Fang, L., Amin, S.H., 2023. A novel two-stage multi-objective optimization modelfor sustainable soybean supply chain design under uncertainty. Sustainable Production and Consumption. 40, 297-317. DOI:

[5] Osiro, L., Lima-Junior, F.R., Carpinetti, L.C.R., 2014. A fuzzy logic approach to supplier evaluation for development. International Journal of Production Economics. 153, 95-112. DOI:

[6] Jung, J.Y., Blau, G., Pekny, J.F., et al., 2004. A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering. 28(10), 2087-2106. DOI: 2004.06.006

[7] Chen, W., Zou, Y., 2017. An integrated method for supplier selection from the perspective of risk aversion. Applied Soft Computing. 54, 449-455. DOI:

[8] Park, J., Shin, K., Chang, T.W., et al., 2010. An integrative framework for supplier relationship management. Industrial Management & Data Systems. 110(4), 495-515. DOI:

[9] Sarkar, A., Mohapatra, P.K., 2009. Determining the optimal size of supply base with the consideration of risks of supply disruptions. International Journal of Production Economics. 119(1), 122-135. DOI:

[10] Jadidi, O.M.I.D., Zolfaghari, S., Cavalieri, S., 2014. A new normalized goal programming model for multi-objective problems: A case of supplier selection and order allocation. International Journal of Production Economics. 148, 158-165. DOI:

[11] Kádárová, J., Trebuňa, P., Lachvajderová, L., 2021. Model for optimizing the ratios of the company suppliers in Slovak automotive industry. Sustainability. 13(21), 11597. DOI:

[12] Aghezzaf, E.H., Sitompul, C., Najid, N.M., 2010. Models for robust tactical planning in multi-stage production systems with uncertain demands. Computers & Operations Research. 37(5), 880-889. DOI:

[13] Pan, A.C., Ramasesh, R.V., Hayya, J.C., et al., 1991. Multiple sourcing: The determination of lead times. Operations Research Letters. 10(1), 1-7. DOI:

[14] Dong, Y., Xu, K., Xu, Y., et al., 2016. Quality management in multi-level supply chains with outsourced manufacturing. Production and Operations Management. 25(2), 290-305. DOI:

[15] Lee, H.H., Li, C., 2018. Supplier quality management: Investment, inspection, and incentives. Production and Operations Management. 27(2), 304-322. DOI:

[16] Lascelles, D.M., Dale, B.G., 1989. The buyer-supplier relationship in total quality management. Journal of Purchasing and Materials Management. 25(2), 10-19. DOI: tb00477.x

[17] Duong, N.H., Ha, Q.A., 2021. The links between supply chain risk management practices, supply chain integration and supply chain performance in Southern Vietnam: A moderation effect of supply chain social sustainability. Cogent Business & Management. 8(1), 1999556. DOI:

[18] Arzu Akyuz, G., Erman Erkan, T., 2010. Supply chain performance measurement: A literature review. International Journal of Production Research. 48(17), 5137-5155. DOI:

[19] Fatorachian, H., Kazemi, H., 2021. Impact of Industry 4.0 on supply chain performance. Production Planning & Control. 32(1), 63-81. DOI:

[20] Delipinar, G.E., Kocaoglu, B., 2016. Using SCOR model to gain competitive advantage: A literature review. Procedia-Social and Behavioral Sciences. 229, 398-406. DOI:

[21] Butdee, S., Phuangsalee, P., 2019. Uncertain risk assessment modelling for bus body manufacturing supply chain using AHP and fuzzy AHP. Procedia Manufacturing. 30, 663-670. DOI:

[22] Sellitto, M.A., Pereira, G.M., Borchardt, M., et al., 2015. A SCOR-based model for supply chain performance measurement: Application in the footwear industry. International Journal of Production Research. 53(16), 4917-4926. DOI:

[23] Bhagwat, R., Sharma, M.K., 2007. Performance measurement of supply chain management using the analytical hierarchy process. Production Planning and Control. 18(8), 666-680. DOI:

[24] Bhagwat, R., Sharma, M.K., 2007. Performance measurement of supply chain management: A balanced scorecard approach. Computers & Industrial Engineering. 53(1), 43-62. DOI:

[25] Ganga, G.M.D., Carpinetti, L.C.R., 2011. A fuzzy logic approach to supply chain performance management. International Journal of Production Economics. 134(1), 177-187. DOI:

[26] Shen, L., Olfat, L., Govindan, K., et al., 2013. A fuzzy multi criteria approach for evaluating green supplier’s performance in green supply chain with linguistic preferences. Resources, Conservation and Recycling. 74, 170-179. DOI:

[27] Kumar, D., Singh, J., Singh, O.P., 2013. A fuzzy logic based decision support system for evaluation of suppliers in supply chain management practices. Mathematical and Computer Modelling. 58(11-12), 1679-1695. DOI:

[28] Chen, S.G., 2012. Fuzzy-scorecard based logistics management in robust SCM. Computers & Industrial Engineering. 62(3), 740-745. DOI:

[29] Kocamaz, U.E., Taşkın, H., Uyaroğlu, Y., et al., 2016. Control and synchronization of chaotic supply chains using intelligent approaches. Computers & Industrial Engineering. 102, 476-487. DOI:

[30] Xu, X., Kim, H.S., You, S.S., et al., 2022. Active management strategy for supply chain system using nonlinear control synthesis. International Journal of Dynamics and Control. 10(6), 1981-1995. DOI:

[31] Butdee, S., Nitnara, C., 2019. A fuzzy logic combined with LP model for performance evaluation to distribute purchase orders in cluster manufacturing. Procedia Manufacturing. 30, 19-25. DOI:

[32] Ho, J.W., Fang, C.C., 2013. Production capacity planning for multiple products under uncertain demand conditions. International Journal of Production Economics. 141(2), 593-604. DOI:

[33] Gerchak, Y., Hassini, E., Ray, S., 2002. Capacity selection under uncertainty with ratio objectives. European Journal of Operational Research. 143(1), 138-147. DOI:

[34] Song, Z., Tang, W., Zhao, R., et al., 2021. Inventory strategy of the risk averse supplier and overconfident manufacturer with uncertain demand. International Journal of Production Economics. 234, 108066. DOI:

[35] Naraharisetti, P.K., Karimi, I.A., Srinivasan, R., 2006. Capacity management in the chemical supply chain. IFAC Proceedings Volumes. 39(2), 253-258. DOI:


How to Cite

Butdee, S., & Janmanee, P. (2023). Prediction of Production Capability for Subcontractors in Automotive Rubber Part Supply Chain Using Neuro-Fuzzy System. Journal of Management Science & Engineering Research, 7(1), 1–12.