Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method
DOI:
https://doi.org/10.31181/dmame8120251115Keywords:
Artificial Intelligence, Supplier Selection, Strategic Sourcing, Supply Chain Safety, Knowledge EngineeringAbstract
The "Make or Buy” decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.
Downloads
References
Courtney, H., Lovallo, D., & Clarke, C. (2013). Deciding How to Decide. Harvard Business Review.
Li, J., Sun, M., Han, D., Wu, X., Yang, B., Mao, X., & Zhou, Q. (2018). Semantic multi-agent system to assist business integration: An application on supplier selection for shipbuilding yards. Computers in Industry, 96, 10-26. https://doi.org/10.1016/j.compind.2018.01.001
Ferreira, L., & Borenstein, D. (2012). A fuzzy-Bayesian model for supplier selection. Expert Systems with Applications, 39(9), 7834-7844. https://doi.org/10.1016/j.eswa.2012.01.068
Wang, X., Persson, G., & Huemer, L. (2016). Logistics Service Providers and Value Creation Through Collaboration: A case study. Long range planning, 49(1), 117–128. https://doi.org/10.1016/j.lrp.2014.09.004
Maltz, A., & Ellram, L. (1999). Outsourcing Supply Management. Journal of Supply Chain Management, 35(1), 4–17. https://doi.org/10.1111/j.1745-493X.1999.tb00232.x
Ekici, A. (2012). An improved model for supplier selection under capacity constraint and multiple criteria. Internation Journal of Production Economics, 141(2), 574-581. https://doi.org/10.1016/j.ijpe.2012.09.013
Aguezzoul, A. (2014). Third-party logistics selection problem: A literature review on criteria and methods. Omega, 49, 69–78. https://doi.org/10.1016/j.omega.2014.05.009
Tsai, M. C., Lai, K. H., Lloyd, A. E., & Lin, H. J. (2012). The dark side of logistics outsourcing-Unraveling the potential risks leading to failed relationships. Transportation Research Part E, 48(1), 178-189. https://doi.org/10.1016/j.tre.2011.07.003
Cisneros-Cabrera, S., Pishchulov, G., Sampaio, P., Mehandjiev, N., Liu, Z., & Kununka, S. (2021). An approach and decision support tool for forming Industry 4.0 supply chain collaborations. Computers in Industry, 125, 103391. https://doi.org/10.1016/j.compind.2020.103391
Barak, S., & Javanmard, S. (2020). Outsourcing modelling using a novel interval-valued fuzzy quantitative strategic planning matrix (QSPM) and multiple criteria decision-making (MCDMs). International Journal of Production Economics, 222, 107494. https://doi.org/10.1016/j.ijpe.2019.09.015
Chai, J., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885. https://doi.org/10.1016/j.eswa.2012.12.040
Tu, L., Lv, Y., Zhang, Y., & Cao, X. (2021).Logistics service provider selection decision making for healthcare industry based on a novel weighted density-based hierarchical clustering. Advanced Engineering Informatics, 48, 101301. https://doi.org/10.1016/j.aei.2021.101301
Erdem, S. A., & Göçen, E. (2012). Development of a decision support system for supplier evaluation and order allocation. Expert Systems with Applications, 39, 4927–4937. https://doi.org/10.1016/j.eswa.2011.10.024
Alvarez-Rodríguez, M. J., Labra-Gayo, J. E., & de Pablos, P. O. (2014). New trends on e-Procurement applying semantic technologies: Current status and future challenges. Computers in Industry, 65(5), 800-820. https://doi.org/10.1016/j.compind.2014.04.005
Power, D. J., & Sharda, R. (2005). Model-driven decision support systems: Concepts and research directions. Decision Support Systems, 43(3), 1044-1061. https://doi.org/10.1016/j.dss.2005.05.030
Scott, J., Ho, W., Dey, P. K., & Talluri, S. (2015). A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. International Journal of Production Economics, 166, 226–237. https://doi.org/10.1016/j.ijpe.2014.11.008
Korpela, J., & Tuominen, M. (1996). production economics A decision support system for strategic issues management of logistics. International Journal of Production Economics, 46–47, 605–620. https://doi.org/10.1016/0925-5273(95)00178-6
Falsini, D., Fondi, F., & Schiraldi, M. M. (2012). A logistics provider evaluation and selection methodology based on AHP, DEA and linear programming integration. International Journal of Production Research, 50(17), 4822–4829. https://doi.org/10.1080/00207543.2012.657969
Chen, W., Goh, M., & Zou, Y. (2018). Logistics provider selection for omni-channel environment with fuzzy axiomatic design and extended regret theory. Applied Soft Computing, 71, 353–363. https://doi.org/10.1016/j.asoc.2018.07.019
Mavi, R. K., Goh, M., & Mavi, N. K. (2016). Supplier Selection with Shannon Entropy and Fuzzy TOPSIS in the Context of Supply Chain Risk Management. Procedia - Social and Behavioral Sciences, 235, 216–225. https://doi.org/10.1016/j.sbspro.2016.11.017
Tajik, G., Azadnia, A. H., Ma’aram, A. B., & Hassan, S. A. H. S. (2014). A Hybrid Fuzzy MCDM Approach for Sustainable Third-Party Reverse Logistics Provider Selection. Advanced Materials Research, 845, 521–526. https://doi.org/10.4028/www.scientific.net/AMR.845.521
Alkhatib, F. S., Darlington, R., Yang, Z., & Thanh Nguyen, T. (2015). A novel technique for evaluating and selecting logistics service providers based on the logistics resource view. Expert Systems with Applications, 42(20), 6976–6989. https://doi.org/10.1016/j.eswa.2015.05.010
Li, Y., & Liao, X. (2004). Decision support for risk analysis on dynamic alliance. Decision Support Systems, 42(4), 2043–2059. https://doi.org/10.1016/j.dss.2004.11.008
Khan, N., Ma, Z., Ullah, A., & Polat, K. (2022). Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive survey. Expert Systems With Applications, 206, 117737. https://doi.org/10.1016/j.eswa.2022.117737
Sarabi, E. P., & Darestani, S. A. (2021). Developing a decision support system for logistics service provider selection employing fuzzy MULTIMOORA & BWM in mining equipment manufacturing. Applied Soft Computing Journal, 98, 106849. https://doi.org/10.1016/j.asoc.2020.106849
Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge Engineering: Principles and methods. Data & Knowledge Engineering, 25(1-2), 161–197. https://doi.org/10.1016/S0169-023X(97)00056-6
Lai, K., Edwin Cheng, T. C., & Yeung, A. C. L. (2004). An Empirical Taxonomy for Logistics Service Providers. Maritime Economics & Logistics, 6, 199–219. https://doi.org/10.1057/palgrave.mel.9100109
Friedrich, A., Lange, A., & Elbert, R. (2022). How additive manufacturing drives business model change: The perspective of logistics service providers. International Journal of Production Economics, 249, 108521. https://doi.org/10.1016/j.ijpe.2022.108521
Ho, W., Dey, P. K., & Bhattacharya, A. (2015). Strategic supplier selection using multi-stakeholder and multi-perspective approaches. International Journal of Production Economics, 166, 152–154. https://doi.org/10.1016/j.ijpe.2015.03.028
Maltz, A. B. (1994). The Relative Importance of Cost and Quality in the Outsourcing of Warehousing. Journal of Business Logistics, 15(2), 45.
Deutsche AG Post. (2022). We keep delivering. https://www.dpdhl.com/content/dam/dpdhl/en/media-center/investors/documents/annual-reports/DPDHL-2022-Annual-Report.pdf
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. Pearson education.
Müller-Bloch, C., & Kranz, J. (2015). A Framework for Rigorously Identifying Research Gaps in Qualitative Literature Reviews. Thirty Sixth International Conference on Information Systems, Fort Worth. https://www.researchgate.net/publication/283271278
Baracskai, Z., Velencei, J. & Dörfler, V. (2005). Reductive Reasoning. Montenegrin Journal of Economics, 1(1), 59–66. https://www.researchgate.net/publication/229665382
Choy, K. L., Lee, W. B., & Lo, V. (2003). Design of a case based intelligent supplier relationship management system-the integration of supplier rating system and product coding system. Expert Systems with Applications, 25(1), 87–100. https://doi.org/10.1016/S0957-4174(03)00009-5
Ayachi, R., Guillon, D., Aldanondo, M., Vareilles, E., Coudert, T., Beauregard, Y., & Geneste, L. (2022). Risk knowledge modeling for offer definition in customer-supplier relationships in Engineer-To-Order situations. Computers in Industry, 138, 103608. https://doi.org/10.1016/j.compind.2022.103608
Yan, J., Chaudhry, P. E., & Chaudhry, S. S. (2003). A model of a decision support system based on case-based reasoning for third-party logistics evaluation. Expert Systems, 20(4), 196–207. https://doi.org/10.1111/1468-0394.00244
Chen, Z. S., Zhang, X., Govindan, K., Wang, X. J., & Chin, K. S. (2021). Third-party reverse logistics provider selection: A computational semantic analysis-based multi-perspective multi-attribute decision-making approach. Expert Systems With Applications, 166, 114051. https://doi.org/10.1016/j.eswa.2020.114051
Yin, R. K. (2009). Case Study Research: Design and Methods. SAGE Publications.
Polanyi, M. (1962). Personal knowledge : towards a post-critical philosophy.
Shaw, M. L. G., & Woodward, B. (1990). Modeling Expert Knowledge. Knowledge Acquisition, 2(3), 179–206. https://doi.org/10.1016/S1042-8143(05)80015-9
Basden, A., & Klein, H. K. (2008). New research directions for data and knowledge engineering: A philosophy of language approach. Data & Knowledge Engineering, 67(2), 260–285. https://doi.org/10.1016/j.datak.2008.05.005
Boegl, K., Adlassnig, K. P., Hayashi, Y., Rothenfluh, T. E., & Leitich, H. (2004). Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system. Artificial Intelligence in Medicine, 30(1), 1–26. https://doi.org/10.1016/S0933-3657(02)00073-8
Szoboszlai, V., Velencei, J., & Baracskai, Z. (2014). Post-Experiential Education: from Knowledge to “Knowing.” Acta Polytechnica Hungarica, 11(10), 235-247. https://doi.org/10.12700/APH.11.10.2014.10.14
Hameed, A., Sleeman, D., & Preece, A. (2002). Detecting mismatches among experts’ ontologies acquired through knowledge elicitation. Knowledge-Based Systems, 15(5-6), 265–273. https://doi.org/10.1016/S0950-7051(01)00162-9
Khan, S. A., Naim, I., Kusi-Sarpong, S., Gupta, H., & Idrisi, A. R. (2021). A knowledge-based experts’ system for evaluation of digital supply chain readiness. Knowledge-Based Systems, 228, 107262. https://doi.org/10.1016/j.knosys.2021.107262
Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 1, 81–106. https://doi.org/10.1023/A:102264320487 7
Richter, M. M. (2008). The search for knowledge, contexts, and Case-Based Reasoning. Engineering Applications of Artificial Intelligence, 22 (1), 3–9. https://doi.org/10.1016/j.engappai.2008.04.021
Zaraté, P., & Liu, S. (2016). A new trend for knowledge-based decision support systems design. International Journal of Information and Decision Sciences, 8(3), 305–324. https://doi.org/10.1504/IJIDS.2016.078586
Petrovic, D., & Petrovic, R. (1992). SPARTA II: Further development in an expert system for advising on stocks of spare parts. International Journal of Production Economics, 24(3), 291–300. https://doi.org/10.1016/0925-5273(92)90141-S
Hatzilygeroudis, I., & Prentzas, J. (2004). Integrating (rules, neural networks) and cases for knowledge representation and reasoning in expert systems. Expert Systems with Applications, 27(1), 63–75. https://doi.org/10.1016/j.eswa.2003.12.004
Chua, C. E. H., Storey, V. C., & Chiang, R. H. (2012). Deriving knowledge representation guidelines by analyzing knowledge engineer behavior. Decision Support Systems, 54(1), 304–315. https://doi.org/10.1016/j.dss.2012.05.038
Oly Ndubisi, N., Jantan, M., Cha Hing, L., & Salleh Ayub, M. (2005). Supplier selection and management strategies and manufacturing flexibility. The Journal of Enterprise Information Management, 18(3), 330–349. https://doi.org/10.1108/17410390510592003
Von Delft, S., Kortmann, S., Gelhard, C., & Pisani, N. (2019). Leveraging global sources of knowledge for business model innovation. Long Range Planning, 52(5), 101848. https://doi.org/10.1016/j.lrp.2018.08.003
Berrais, A. (1997). Knowledge-based expert systems: user interface implications. Advances in Engineering Software, 28(1), 31–41. https://doi.org/10.1016/S0965-9978(96)00030-0
Toth-Haasz, G. (2017). F-then scenarios: Smart decisions at smes. Economic and Social Development: Book of Proceedings, 641-648.
Anderson, E. J., Coltman, T., Devinney, T. M., & Keating, B. (2011). What drives the choice of a third party logistics provider? Journal of Supply Chain Management, 47(2), 97–115. https://doi.org/10.1111/J.1745-493X.2011.03223.X
Ren, W., Wu, K., Gu, Q., & Hu, Y. (2020). Intelligent decision making for service providers selection in maintenance service network: An adaptive fuzzy-neuro approach ✩. Knowledge-Based Systems, 190, 105263. https://doi.org/10.1016/j.knosys.2019.105263
Nimmy, F. S., Hussain, O. K., Chakrabortty, R. K., Hussain, F. K., & Saberi, M. (2022). Explainability in supply chain operational risk management: A systematic literature review. Knowledge-Based Systems, 235, 107587. https://doi.org/10.1016/j.knosys.2021.107587
Gyenge, B., Kasza, L. & Vasa, L. (2021). Introducing the EPP house (topological space) method to solve MRP problems. Plos one, 16(6), e0253330 https://doi.org/10.1371/journal.pone.0253330
Tomchuk, O., Lepetan, I., Zdyrko, N., & Vasa, L. (2018). Environmental activities of agricultural enterprises: accounting and analytical support. Economic Annals-XXI, 169(1-2), 77-83. https://doi.org/10.21003/ea.V169-15
Malone, T. W. (2018). Superminds: The Surprising Power of People and Computers Thinking Together. Little, Brown and Company Hachette Book Group.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Decision Making: Applications in Management and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.