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Original Articles

Artificial intelligence in operations management and supply chain management: an exploratory case study

ORCID Icon &
Pages 1573-1590 | Received 30 Nov 2019, Accepted 26 Aug 2020, Published online: 01 Apr 2021

References

  • Amirkolaii, K. N., A. Baboli, M. K. Shahzad, and R. Tonadre. 2017. “Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by Using Artificial Intelligence (AI).” IFAC-PapersOnLine 50 (1): 15221–15226. doi:10.1016/j.ifacol.2017.08.2371.
  • Baryannis, G., S. Dani, and G. Antoniou. 2019. “Predicting Supply Chain Risks Using Machine Learning: The Trade-off between Performance and Interpretability.” Future Generation Computer Systems 101: 993–1004. doi:10.1016/j.future.2019.07.059.
  • Baryannis, G., S. Validi, S. Dani, and G. Antoniou. 2019. “Supply Chain Risk Management and Artificial Intelligence: state of the Art and Future Research Directions.” International Journal of Production Research 57 (7): 2179–2202. doi:10.1080/00207543.2018.1530476.
  • Brynjolfsson, E., and A. McAfee. 2017. “Artificial Intelligence, for Real.” Harvard Business Review. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
  • Brynjolfsson, E., D. Rock, and C. Syverson. 2018. “Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics.” In The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press.
  • Çaliş, B., and S. Bulkan. 2015. “A Research Survey: Review of AI Solution Strategies of Job Shop Scheduling Problem.” Journal of Intelligent Manufacturing 26 (5): 961–973. doi:10.1007/s10845-013-0837-8.
  • Caniato, F., M. Caridi, L. Crippa, and A. Moretto. 2012. “Environmental Sustainability in Fashion Supply Chains: An Exploratory Case Based Research.” International Journal of Production Economics 135 (2): 659–670. doi:10.1016/j.ijpe.2011.06.001.
  • Cavalcante, I. M., E. M. Frazzon, F. A. Forcellini, and D. Ivanov. 2019. “A Supervised Machine Learning Approach to Data-Driven Simulation of Resilient Supplier Selection in Digital Manufacturing.” International Journal of Information Management 49: 86–97. doi:10.1016/j.ijinfomgt.2019.03.004.
  • Childe, S. J. 2011. “Editorial Case Studies in Operations Management.” Production Planning & Control 22 (2): 107–107. doi:10.1080/09537287.2011.554736.
  • Choi, T. M., T. C. E. Cheng, and X. Zhao. 2016. “Multi‐Methodological Research in Operations Management.” Production and Operations Management 25 (3): 379–389. doi:10.1111/poms.12534.
  • Choi, T. M., S. W. Wallace, and Y. Wang. 2018. “Big Data Analytics in Operations Management.” Production and Operations Management 27 (10): 1868–1883. doi:10.1111/poms.12838.
  • Chui, M., and S. Malhotra. 2018. AI Adoption Advances, but Foundational Barriers Remain. New York: Mckinsey & Company.
  • Davenport, T. H. 2018. “From Analytics to Artificial Intelligence.” Journal of Business Analytics 1 (2): 73–78. doi:10.1080/2573234X.2018.1543535.
  • Davenport, T. H., and R. Ronanki. 2018. “Artificial Intelligence for the Real World.” Harvard Business Review 96 (1): 108–116.
  • Duan, Y., J. S. Edwards, and Y. K. Dwivedi. 2019. “Artificial Intelligence for Decision Making in the Era of Big Data–Evolution, Challenges and Research Agenda.” International Journal of Information Management 48: 63–71. doi:10.1016/j.ijinfomgt.2019.01.021.
  • Dubey, R., A. Gunasekaran, S. J. Childe, D. J. Bryde, M. Giannakis, C. Foropon, and B. T. Hazen. 2019. “Big Data Analytics and Artificial Intelligence Pathway to Operational Performance under the Effects of Entrepreneurial Orientation and Environmental Dynamism: A Study of Manufacturing Organisations.” International Journal of Production Economics 226: 107599.
  • Dwivedi, Y. K., L. Hughes, E. Ismagilova, G. Aarts, C. Coombs, T. Crick Galanos. et al. 2019. “Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy.” International Journal of Information Management : 101994.
  • Erhan, D., C. Szegedy, A. Toshev, and D. Anguelov. 2014. “Scalable Object Detection using Deep Neural Networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2147–2154.
  • Fatorachian, H., and H. Kazemi. 2020. “Impact of Industry 4.0 on Supply Chain Performance.” Production Planning & Control 32(1): 1–19.
  • Fu, T., and B. Sun. 2017. “Application of Speech Recognition Technology in Logistics Selection System.” International Conference on Human Centered Computing, 654–659. Cham: Springer.
  • Goli, A., H. K. Zare, R. Tavakkoli-Moghaddam, and A. Sadeghieh. 2019. “Hybrid Artificial Intelligence and Robust Optimization for a Multi-Objective Product Portfolio Problem Case Study: The Dairy Products Industry.” Computers & Industrial Engineering 137: 106090. doi:10.1016/j.cie.2019.106090.
  • Gupta, S., and E. C. Jones. 2014. “Optimizing Supply Chain Distribution Using Cloud Based Autonomous Information.” International Journal of Supply Chain Management 3 (4): 79–90.
  • Haas, A. 2020. “Logistics and Supply Chain Intelligence.” In Integration of Information Flow for Greening Supply Chain Management, edited by A. Kolinski, D. Dujak, and P. Golinska-Dawson, 111–129. Cham: Springer.
  • Hellingrath, B., and S. Lechtenberg. 2019. “Applications of Artificial Intelligence in Supply Chain Management and Logistics: focusing onto Recognition for Supply Chain Execution.” In K. Bergener, M. Räckers, and A. Stein, edited by The Art of Structuring, 283–296. Cham: Springer.
  • Hengstler, M., E. Enkel, and S. Duelli. 2016. “Applied Artificial Intelligence and Trust – The Case of Autonomous Vehicles and Medical Assistance Devices.” Technological Forecasting and Social Change 105: 105–120. doi:10.1016/j.techfore.2015.12.014.
  • Jarrahi, M. H. 2018. “Artificial Intelligence and the Future of Work: Human-AI Symbiosis in Organizational Decision Making.” Business Horizons 61 (4): 577–586. doi:10.1016/j.bushor.2018.03.007.
  • Jung, Y., C. Hur, and M. Kim. 2018. “Sustainable Situation-Aware Recommendation Services with Collective Intelligence.” Sustainability 10 (5): 1632. doi:10.3390/su10051632.
  • Knoll, D., D. Neumeier, M. Prüglmeier, and G. Reinhart. 2019. “An Automated Packaging Planning Approach Using Machine Learning.” Procedia CIRP 81: 576–581. doi:10.1016/j.procir.2019.03.158.
  • Kochak, A., and S. Sharma. 2015. “Demand Forecasting Using Neural Network for Supply Chain Management.” International Journal of Mechanical Engineering and Robotics Research 4 (1): 96–104.
  • Kolinski, A., A. Horzela, M. Cudzilo, and R. Domanski. 2020. “Reference Model of Information Flow in Business Relations with 4pl Operator.” In Integration of Information Flow for Greening Supply Chain Management, edited by A. Kolinski, D. Dujak, and P. Golinska-Dawson, 19–45. Cham: Springer.
  • Korolov, M. 2018. “AI in the Supply Chain: Logistics Gets Smart.” CIO. Supply Chain Management. Available at: https://www.cio.com/article/3269513/ai-in-the-supply-chain-logistics-get-smart.html
  • Kousiouris, George, Stylianos Tsarsitalidis, Evangelos Psomakelis, Stavros Koloniaris, Cleopatra Bardaki, Konstantinos Tserpes, Mara Nikolaidou, and Dimosthenis Anagnostopoulos. 2019. “A Microservice-Based Framework for Integrating IoT Management Platforms, Semantic and AI Services for Supply Chain Management.” ICT Express 5 (2): 141–145. doi:10.1016/j.icte.2019.04.002.
  • Lamba, K., and S. P. Singh. 2017. “Big Data in Operations and Supply Chain Management: Current Trends and Future Perspectives.” Production Planning & Control 28 (11-12): 877–890. doi:10.1080/09537287.2017.1336787.
  • Lee, W. J., H. Wu, H. Yun, H. Kim, M. B. Jun, and J. W. Sutherland. 2019. “Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data.” Procedia CIRP 80: 506–511. doi:10.1016/j.procir.2018.12.019.
  • Levy, F. 2018. “Computers and Populism: Artificial Intelligence, Jobs, and Politics in the near Term.” Oxford Review of Economic Policy 34 (3): 393–417. doi:10.1093/oxrep/gry004.
  • Li, Q., and A. Liu. 2019. “Big Data Driven Supply Chain Management.” Procedia CIRP 81: 1089–1094. doi:10.1016/j.procir.2019.03.258.
  • Lyutov, A., Y. Uygun, and M. T. Hütt. 2019. “Managing Workflow of Customer Requirements Using Machine Learning.” Computers in Industry 109: 215–225. doi:10.1016/j.compind.2019.04.010.
  • Mahroof, K. 2019. “A Human-Centric Perspective Exploring the Readiness towards Smart Warehousing: The Case of a Large Retail Distribution Warehouse.” International Journal of Information Management 45: 176–190. doi:10.1016/j.ijinfomgt.2018.11.008.
  • Manzini, R., M. Gamberi, and A. Regattieri. 2005. “Design and Control of a Flexible Order-Picking System (FOPS) a New Integrated Approach to the Implementation of an Expert System.” Journal of Manufacturing Technology Management 16 (1): 18–35. doi:10.1108/17410380510574068.
  • Miller, A. 2004. “Order Picking for the 21st Century Voice vs Scanning Technology.” White Paper, Tompkins Associates, http://www.slideshare.net/rmfuhr/voice-vs-scan-white-paper.
  • Min, H. 2010. “Artificial Intelligence in Supply Chain Management: theory and Applications.” International Journal of Logistics Research and Applications 13 (1): 13–39. doi:10.1080/13675560902736537.
  • Min, Q., Y. Lu, Z. Liu, C. Su, and B. Wang. 2019. “Machine Learning Based Digital Twin Framework for Production Optimization in Petrochemical Industry.” International Journal of Information Management 49: 502–519. doi:10.1016/j.ijinfomgt.2019.05.020.
  • Mortazavi, A., A. A. Khamseh, and P. Azimi. 2015. “Designing of an Intelligent Self-Adaptive Model for Supply Chain Ordering Management System.” Engineering Applications of Artificial Intelligence 37: 207–220. doi:10.1016/j.engappai.2014.09.004.
  • Nemati, H. R., D. M. Steiger, L. S. Iyer, and R. T. Herschel. 2002. “Knowledge Warehouse: An Architectural Integration of Knowledge Management, Decision Support, Artificial Intelligence and Data Warehousing.” Decision Support Systems 33 (2): 143–161. doi:10.1016/S0167-9236(01)00141-5.
  • Nilsson, N. J. 1980. Principles of Artificial Intelligence. San Francisco, CA: Morgan Kaufmann.
  • Panetta, K. 2018. “Gartner Predicts 2019 for Supply Chain Operations.” Smarter with Gartner. Available at: https://www.gartner.com/smarterwithgartner/gartner-predicts-2019-for-supply-chain-operations/
  • Paul, S. K., A. Azeem, and A. K. Ghosh. 2015. “Application of Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network in Inventory Level Forecasting.” International Journal of Business Information Systems 18 (3): 268–284. doi:10.1504/IJBIS.2015.068164.
  • Ramanathan, R., E. Philpott, Y. Duan, and G. Cao. 2017. “Adoption of Business Analytics and Impact on Performance: A Qualitative Study in Retail.” Production Planning & Control 28 (11–12): 985–998. doi:10.1080/09537287.2017.1336800.
  • Ransbotham, S., P. Gerbert, M. Reeves, D. Kiron, and M. Spira. 2018. “Artificial Intelligence in Business Gets Real.” MIT Sloan Management Review. https://sloanreview.mit.edu/projects/artificial-intelligence-in-business-gets-real/
  • Roden, S., A. Nucciarelli, F. Li, and G. Graham. 2017. “Big Data and the Transformation of Operations Models: A Framework and a New Research Agenda.” Production Planning & Control 28 (11–12): 929–944. doi:10.1080/09537287.2017.1336792.
  • Russell, S. J., and P. Norvig. 2016. Artificial Intelligence: A Modern Approach. Malaysia: Pearson Education Limited.
  • Schiavone, F., and S. Sprenger. 2017. “Operations Management and Digital Technologies.” Production Planning & Control 28 (16): 1281–1283. doi:10.1080/09537287.2017.1375151.
  • Schoemaker, P. J., and P. E. Tetlock. 2017. “Building a More Intelligent Enterprise.” MIT Sloan Management Review 58 (3): 28.
  • Seyedghorban, Z., H. Tahernejad, R. Meriton, and G. Graham. 2020. “Supply Chain Digitalization: Past, Present and Future.” Production Planning & Control 31 (2-3): 96–114. doi:10.1080/09537287.2019.1631461.
  • Shaharudin, M. R., K. Govindan, S. Zailani, and K. C. Tan. 2015. “Managing Product Returns to Achieve Supply Chain Sustainability: An Exploratory Study and Research Propositions.” Journal of Cleaner Production 101: 1–15. doi:10.1016/j.jclepro.2015.03.074.
  • Singh, L. P., and R. T. Challa. 2016. “Integrated Forecasting Using the Discrete Wavelet Theory and Artificial Intelligence Techniques to Reduce the Bullwhip Effect in a Supply Chain.” Global Journal of Flexible Systems Management 17 (2): 157–169. doi:10.1007/s40171-015-0115-z.
  • Singh, S. K., S. Rathore, and J. H. Park. 2020. “Block IoT Intelligence: A Blockchain-Enabled Intelligent IoT Architecture with Artificial Intelligence.” Future Generation Computer Systems 110: 721–743. doi:10.1016/j.future.2019.09.002.
  • Skender, H. P., and P. A. Zaninović. 2020. “Perspectives of Blockchain Technology for Sustainable Supply Chains.” In Integration of Information Flow for Greening Supply Chain Management, edited by A. Kolinski, D. Dujak, and P. Golinska-Dawson, 77–92. Cham: Springer.
  • Soleimani, S. 2018. “A Perfect Triangle with: artificial Intelligence, Supply Chain Management, and Financial Technology.” Archives of Business Research 6 (11): 5681. doi:10.14738/abr.611.5681.
  • Solomonoff, R. J. 1985. “The time scale of artificial intelligence: Reflections on social effects.” Human Systems Management 5 (2): 149–153.
  • Stefanovic, N., and D. Stefanovic. 2009. “Supply Chain Business Intelligence: technologies, Issues and Trends.” In Artificial Intelligence: An International Perspective, 217–245. Berlin, Heidelberg: Springer.
  • Stake, R. E. 2005. Qualitative case studies.
  • Tammela, I., A. G. Canen, and P. Helo. 2008. “Time-Based Competition and Multiculturalism: A Comparative Approach to the Brazilian, Danish and Finnish Furniture Industries.” Management Decision 46 (3): 349–364. doi:10.1108/00251740810863834.
  • Tellaeche, A., and R. Arana. 2013. “Machine Learning Algorithms for Quality Control in Plastic Molding Industry.” Emerging Technologies & Factory Automation (ETFA), 2013 IEEE 18th Conference on, 1–4.
  • Umeda, Y., H. Muto, M. Tomita, K. Kondoh, T. Kominami, and Y. Hidaka. 2017. “Warehouse Product Inspection System Achieves Work Efficiency and Quality Improvements.” NEC Technical Journal 12 (1): 40–44.
  • Wu, C., and D. Barnes. 2014. “Partner Selection in Agile Supply Chains: A Fuzzy Intelligent Approach.” Production Planning & Control 25 (10): 821–839. doi:10.1080/09537287.2013.766037.
  • Xu, L. D., E. L. Xu, and L. Li. 2018. “Industry 4.0: State of the Art and Future Trends.” International Journal of Production Research 56 (8): 2941–2962. doi:10.1080/00207543.2018.1444806.
  • Yin, R. K. 2003. Case Study Research—Design and Methods. 3rd ed. Thousand Oaks, CA: Sage.
  • Zhu, Y., L. Zhou, C. Xie, G. J. Wang, and T. V. Nguyen. 2019. “Forecasting SMEs' Credit Risk in Supply Chain Finance with an Enhanced Hybrid Ensemble Machine Learning Approach.” International Journal of Production Economics 211: 22–33. doi:10.1016/j.ijpe.2019.01.032.
  • Zijm, H., and M. Klumpp. 2016. “Logistics and Supply Chain Management: Developments and Trends.” In Logistics and Supply Chain Innovation, edited by H. Zijm, M. Klumpp, and U. Clausen, 1–20. Cham: Springer.