Machine Learning in Supply Chain Management: A Systematic Literature Review

Document Type : GOL20

Authors

1 Research team AMIPS, The Mohammadia School of Engineers, Mohammed V University in Rabat, Morocco

2 Department of management sciences, University of Quebec at Rimouski, Levis, QC, Canada

Abstract

The supply chain ecosystem is currently benefiting from a great dynamic resulting from the digitalization of organizations and trades. For all the stakeholders in the area, this is a real breakthrough, and machine learning is at the core of this revolution. It has profoundly revolutionized companies in many aspects including the evolution of communication methods, the automation of many processes, the growing importance of information systems, etc. With shrinking margins and more demanding customers, supply chain management in increasingly becoming a source of competitive advantage. Its management and optimization requires a factual to Supply Chain decision making at strategique, tactical and operational levels. In this context and data rich environment, machine learning approaches and techniques find numerous useful applications for supply chain decision making. Today, companies have no choice but to apply Machine Learning solutions in almost every part of their processes. This fact seems even clearer in markets where competition is fierce. While Machine Learning does not redefine the enterprise, it is certainly a powerful asset for both marketing and process optimization purposes. It is so ingrained in the strategies of companies that now most of them rely heavily on it for all processes from creation, to product quality control, to public relations. In recent years, a series of practical applications of machine learning (ML) for supply chain decisions have been introduced.

Keywords

References

Arribas, V., and Alfaro, J. A. (2018). 3D technology in fashion: from concept to consumer. Journal of Fashion Marketing and Management: An International Journal, Vol. 22(2), pp. 240-251.
Ayers, J. B. (2006). Handbook of supply chain management. Auerbach publications.
Becker, T., Illigen, C., McKelvey, B., Hülsmann, M., and Windt, K. (2016). Using an agent-based neural-network computational model to improve product routing in a logistics facility. International Journal of Production Economics, Vol. 174, pp. 156-167.
Beutel, A. L., and Minner, S. (2012). Safety stock planning under causal demand forecasting. International Journal of Production Economics, Vol. 140(2), pp. 637-645.
Blackhurst, J., Rungtusanatham, M. J., Scheibe, K., and Ambulkar, S. (2018). Supply chain vulnerability assessment: A network based visualization and clustering analysis approach. Journal of Purchasing and Supply Management, Vol. 24(1), pp. 21-30.
Budak, A., Ustundag, A., and Guloglu, B. (2017). A forecasting approach for truckload spot market pricing. Transportation Research Part A: Policy and Practice, Vol.97, pp. 55-68.
Cadavid, J. P. U., Lamouri, S., and Grabot, B., (2018). Trends in Machine Learning Applied to Demand & Sales Forecasting: A Review, International Conference on Information Systems, Logistics and Supply Chain, July 2018, Lyon, France. Hal-01881362
Chen, I. J., and Paulraj, A. (2004). Towards a theory of supply chain management: the constructs and measurements. Journal of operations management, Vol. 22(2), pp. 119-150.
Chopra, S., and Meindl, P. (2007). Supply chain management. Strategy, planning & operation. In Das summa summarum des management (pp. 265-275). Gabler.
Ciupan, E. (2014). A study regarding the possibility of optimizing the supply batch using artificial neural networks. Procedia Engineering, Vol. 69, pp. 141-149.
Fan, X., Zhang, S., Wang, L., Yang, Y., and Hapeshi, K. (2013). An evaluation model of supply chain performances using 5DBSC and LMBP neural network algorithm. Journal of Bionic Engineering, Vol. 10(3), pp. 383-395.
Fan, Z. P., Che, Y. J., and Chen, Z. Y. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, Vol. 74, pp. 90-100.
Fasli, M., and Kovalchuk, Y. (2011). Learning approaches for developing successful seller strategies in dynamic supply chain management. Information Sciences, Vol.  181(16), pp. 3411-3426.
Ghorbani, M., Arabzad, S. M., and Bahrami, M. (2012). Applying a Neural Network algorithm to Distributor selection problem. Procedia-Social and Behavioral Sciences, Vol. 41, pp. 498-505.
Gupta, R., and Pathak, C. (2014). A machine learning framework for predicting purchase by online customers based on dynamic pricing. Procedia Computer Science, Vol. 36, pp. 599-605.
Huang, J. Y., and Tsai, P. C. (2011). Determination of order quantity for perishable products by using the support vector machine. Journal of the Chinese Institute of Industrial Engineers, Vol. 28(6), pp. 425-436.
J. Tian, M. GAO and S. Zhou (2009). The Research of Building Logistics Cost Forecast Based on Regression Support Vector Machine, 2009 International Conference on Computational Intelligence and Security, Beijing, , pp. 648-652.
Jaipuria, S., and Mahapatra, S. S. (2014). An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, Vol. 41(5), pp. 2395-2408.
Jordan, M.I.,and  Mitchell,  T.M. (2015). Machine learning:  Trends perspectives and prospects.  Science, Vol.349, pp. 255–260.
Kache, F. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International Journal of Operations and Production Management, Vol. 37(1), pp.10-36.
Kara, A., and Dogan, I. (2018). Reinforcement learning approaches for specifying ordering policies of perishable inventory systems. Expert Systems with Applications, Vol. 91, pp. 150-158.
Kartal, H. B., and Cebi, F. (2013). Support vector machines for multi-attribute ABC analysis. International Journal of Machine Learning and Computing, Vol. 3(1), pp. 154.
Kartal, H., Oztekin, A., Gunasekaran, A., and Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, Vol. 101, pp. 599-613.
Kocamaz, U. E., Taşkın, H., Uyaroğlu, Y., and Göksu, A. (2016). Control and synchronization of chaotic supply chains using intelligent approaches. Computers & Industrial Engineering, Vol. 102, pp. 476-487.
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., and Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, Vol.13, pp. 8-17.
Kuo, R. J., and Li, P. S. (2016). Taiwanese export trade forecasting using firefly algorithm based K-means algorithm and SVR with wavelet transform. Computers & Industrial Engineering, Vol.99, pp. 153-161.
Lambert, D. M., Stock, J. R., and Ellram, L. M. (1998). Fundamentals of logistics management. McGraw-Hill/Irwin.
Liu ,C. , Shu, T.,  Chen, S., Wang, S., Lai, KK. and Gan L.(2016). An improved grey neural network model for predicting transportation disruptions. Expert Systems with Applications, Vol. 45, pp. 331-340
Lolli, F., Ishizaka, A., Gamberini, R., Balugani, E., and Rimini, B. (2017). Decision trees for supervised multi-criteria inventory classification. Procedia Manufacturing, Vol.11, pp. 1871-1881.
Makkar, S.,Devi,G. N. R., and Solanki, V. K. (2019). Applications of Machine Learning Techniques in Supply Chain Optimization. ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management, pp. 861–869.
Martínez, A., Schmuck, C., Pereverzyev Jr, S., Pirker, C., and Haltmeier, M. (2018). A machine learning framework for customer purchase prediction in the non-contractual setting. European Journal of Operational Research, Vol. 281(3), pp.  588-596.
Min, H., 2009. Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics Research and Applications, Vol. 13(1), pp. 13–39. 
Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., and Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business logistics, Vol. 22(2), pp. 1-25.
Mercier, S., and Uysal, I. (2018). Neural network models for predicting perishable food temperatures along the supply chain. Biosystems engineering, Vol. 171, pp. 91-100.
Min, H., 2009. Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics Research and Applications, Vol. 13(1), pp. 13–39. 
Min, H., 2009. Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics Research and Applications, Vol. 13(1), pp. 13–39. 
Mokhtarinejad, M., Ahmadi, A., Karimi, B., and Rahmati, S. H. A. (2015). A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment. Applied Soft Computing, Vol. 34, pp. 274-285.
Mortazavi, A., Khamseh, A. A., and Azimi, P. (2015). Designing of an intelligent self-adaptive model for supply chain ordering management system. Engineering Applications of Artificial Intelligence, Vol. 37, pp. 207-220.
Qiu, X., Ren, Y., Suganthan, P. N., and Amaratunga, G. A. (2017). Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Applied Soft Computing, Vol. 54, pp. 246-255.
Rana, R., and Oliveira, F. S. (2015). Dynamic pricing policies for interdependent perishable products or services using reinforcement learning. Expert Systems with Applications, Vol. 42(1), pp. 426-436.
Rana, R., and Oliveira, F. S. (2015). Dynamic pricing policies for interdependent perishable products or services using reinforcement learning. Expert Systems with Applications, Vol. 42(1), pp. 426-436.
Raschka, S.,and Mirjalili, V.:Python Machine Learning, 2nd Ed. Packt Publishing, Birmingham, UK, 2 edition, 2017.
Samuel, A.L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, 3, 210-229.
Samuel, A.L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development, Vol. 3, pp. 210-229.
Seif, G., 2018. The 5 Clustering Algorithms Data Scientists Need to Know.
Sui, Z., Gosavi, A., and Lin, L. (2010). A reinforcement learning approach for inventory replenishment in vendor-managed inventory systems with consignment inventory. Engineering Management Journal, Vol. 22(4), pp. 44-53.
Tamagawa, D., Taniguchi, E., and Yamada, T. (2010). Evaluating city logistics measures using a multi-agent model. Procedia-Social and Behavioral Sciences, Vol. 2(3), pp. 6002-6012.
The MathWorks, I., 2016. Introduction to Machine Learning.
Vahdani, B., Mousavi, S. M., Tavakkoli-Moghaddam, R., and Hashemi, H. (2017). A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study. International Journal of Computational Intelligence Systems, Vol. 10(1), pp. 293-311.
Villegas, M. A., Pedregal, D. J., and Trapero, J. R. (2018). A support vector machine for model selection in demand forecasting applications. Computers & Industrial Engineering, Vol. 121, pp. 1-7.
Witten, H. I., Eibe, F. and Hall, A. M., 2011. Data Mining: Practical Machine Learning Tools and Techniques: Elsevier.
Yu, M. C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, Vol. 38(4), pp. 3416-3421.
Žácik, T., Mracka, I., Hajossy, R., and Hycko, M. (2018, May). Reinforcement Learning in Gas Transport Control. In PSIG Annual Meeting. Pipeline Simulation Interest Group.
Zhou,L.,Pan,S.,Wang,J.,and Vasilakos,  A.V.(2017). Machine learning on big data:  Opportunities and challenges. Neurocomputing, Vol. 237, pp. 350–361,
Okoli, Chitu and Schabram, Kira, A Guide to Conducting a Systematic Literature Review of Information Systems Research (May 5, 2010). Available at SSRN: https://ssrn.com/abstract=1954824 or http://dx.doi.org/10.2139/ssrn.1954824
Polyzotis, N, Roy, S, Whang, SE, and Zinkevich, M (2018). Data lifecycle challenges in production machine learning: a survey. ACM SIGMOD Record, Vol. 47(2), pp. 17-28.
Min, Q, Lu, Y, Liu, Z, Su, C, and Wang, B (2019). Machine learning based digital twin framework for production optimization in petrochemical industry. International Journal of Information Management, Vol. 49, pp. 502-519
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., and De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, Vol. 12(2), pp.492.
Stanula, P, Ziegenbein, A, and Metternich, J (2018). Machine learning algorithms in production: A guideline for efficient data source selection. Procedia CIRP, Vol. 78, pp. 261-266.
Mayr, A., Kißkalt, D., Meiners, M., Lutz, B., Schäfer, F., Seidel, R. and Franke, J.  (2019). Machine Learning in Production–Potentials, challenges and exemplary applications. Procedia CIRP, Vol. 86, pp. 49-54.
Tercan, H, Guajardo, A, and Meisen, T (2019). Industrial Transfer Learning: Boosting Machine Learning in Production. In 2019 IEEE 17th International Conference on Industrial Informatics (INDIN) (Vol. 1, pp. 274-279). IEEE.
Kang, Z, Catal, C, and Tekinerdogan, B (2020). Machine learning applications in production lines: A systematic literature review. Computers & Industrial Engineering, Vol. 149, 106773.
Krauß, J, Frye, M, Beck, GTD, and Schmitt, RH (2019). Selection and Application of Machine Learning-Algorithms in Production Quality. In Machine learning for cyber physical systems (pp. 46-57). Springer Vieweg, Berlin, Heidelberg
Krauß, J., Dorißen, J., Mende, H., Frye, M., and Schmitt, R. H. (2019). Machine learning and artificial intelligence in production: Application areas and publicly available data sets. In Production at the leading edge of technology (pp. 493-501). Springer Vieweg, Berlin, Heidelberg.
Sobottka, T, Kamhuber, F, Faezirad, M, and Sihn, W (2019). Potential for machine learning in optimized production planning with hybrid simulation. Procedia Manufacturing, Vol. 39, pp. 1844-1853.
Thiede, S., Turetskyy, A., Loellhoeffel, T., Kwade, A., Kara, S., and Herrmann, C. (2020). Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: A case of battery production. CIRP Annals, Vol. 69(1), pp. 21-24.
Guo, K, Yang, M, and Zhu, H (2020). Application research of improved genetic algorithm based on machine learning in production scheduling. Neural Computing and Applications, Vol. 32(7), pp. 1857-1868.
Garre, A, Ruiz, MC, and Hontoria, E (2020). Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty. Operations Research Perspectives, Vol. 7, 100147.
Li, Y., Carabelli, S., Fadda, E., Manerba, D., Tadei, R., & Terzo, O. (2020). Machine learning and optimization for production rescheduling in Industry 4.0. The International Journal of Advanced Manufacturing Technology, 110(9), 2445-2463.
International Journal of Supply and Operations Management
Volume 9, Issue 4
November 2022
Pages 398-416

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