A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management
Abstract
:1. Introduction
- In which supply chain areas has ML already been considered for implementation in SCRM—both in literature and practice?
- Which are primary risks considered in the use-cases?
- How might ML shape and improve SCRM?
2. Methodology and Findings
2.1. Systematic Literature Review
- What examples of ML application in SCRM have already been described in literature?
- Which risks could be influenced by the integration of ML into SCRM?
2.2. Current Status of ML Application in SCRM in Scientific Literature
2.3. Analysis of Identified Practical Use Cases
3. Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Christopher, M.; Peck, H. Building the Resilient Supply Chain. Int. J. Logist. Manag. 2004, 15, 1–13. [Google Scholar] [CrossRef] [Green Version]
- Giunipero, L.C.; Aly Eltantawy, R. Securing the upstream supply chain: A risk management approach. Int. J. Phys. Distrib. Logist. Manag. 2004, 34, 698–713. [Google Scholar] [CrossRef] [Green Version]
- Jüttner, U.; Peck, H.; Christopher, M. Supply chain risk management: Outlining an agenda for future research. Int. J. Logist. Res. Appl. 2003, 6, 197–210. [Google Scholar] [CrossRef] [Green Version]
- Manuj, I.; Mentzer, J.T. Global Supply Chain Risk Management. J. Bus. Logist. 2008, 29, 133–155. [Google Scholar] [CrossRef]
- Ivanov, D.; Dolgui, A.; Sokolov, B. The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. Int. J. Prod. Res. 2019, 57, 829–846. [Google Scholar] [CrossRef]
- Schlüter, F.; Henke, M. Smart supply chain risk management—A conceptual framework. In Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment; Kersten, W., Blecker, T., Ringle, C.M., Eds.; epubli GmbH: Berlin, Germany, 2017; pp. 361–380. [Google Scholar]
- Ivanov, D. Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E Logist. Transp. Rev. 2020, 136, 101922. [Google Scholar] [CrossRef] [PubMed]
- Arto, I.; Andreoni, V.; Rueda Cantuche, J.M. Global Impacts of the Automotive Supply Chain Disruption Following the Japanese Earthquake of 2011. Econ. Syst. Res. 2015, 27, 306–323. [Google Scholar] [CrossRef]
- Natarajarathinam, M.; Capar, I.; Narayanan, A. Managing supply chains in times of crisis: A review of literature and insights. Int. J. Phys. Distrib. Logist. Manag. 2009, 39, 535–573. [Google Scholar] [CrossRef] [Green Version]
- Norrman, A.; Jansson, U. Ericsson’s proactive supply chain risk management approach after a serious sub-supplier accident. Int. J. Phys. Distrib. Logist. Manag. 2004, 34, 434–456. [Google Scholar] [CrossRef]
- Kersten, W.; Hohrath, P.; Boeger, M.; Singer, C. A Supply Chain Risk Management process. Int. J. Logist. Syst. Manag. 2011, 8, 152–166. [Google Scholar] [CrossRef]
- Tang, C.S. Robust strategies for mitigating supply chain disruptions. Int. J. Logist. Res. Appl. 2006, 9, 33–45. [Google Scholar] [CrossRef]
- Sodhi, M.S.; Son, B.-G.; Tang, C.S. Researchers’ Perspectives on Supply Chain Risk Management. Prod. Oper. Manag. 2012, 21, 1–13. [Google Scholar] [CrossRef]
- Finch, P. Supply chain risk management. Supply Chain Manag. 2004, 9, 183–196. [Google Scholar] [CrossRef]
- Dani, S. Predicting and Managing Supply Chain Risks. In Supply Chain Risk: A Handbook of Assessment, Management, and Performance; Zsidisin, G.A., Ed.; Springer: New York, NY, USA, 2009; pp. 53–66. [Google Scholar]
- Zimon, D.; Madzík, P. Standardized management systems and risk management in the supply chain. Int. J. Qual. Reliab. Manag. 2020, 37, 305–327. [Google Scholar] [CrossRef]
- Riley, J.M.; Klein, R.; Miller, J.; Sridharan, V. How internal integration, information sharing, and training affect supply chain risk management capabilities. Int. J. Phys. Distrib. Logist. Manag. 2016, 46, 953–980. [Google Scholar] [CrossRef]
- Baryannis, G.; Dani, S.; Antoniou, G. Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Gener. Comput. Syst. 2019, 101, 993–1004. [Google Scholar] [CrossRef]
- Hassan, A.P. Enhancing Supply Chain Risk Management by Applying Machine Learning to Identify Risks. Lect. Notes Bus. Inf. Process. 2019, 354, 191–205. [Google Scholar] [CrossRef]
- Layouni, M.; Tahar, S.; Hamdi, M.S. A survey on the application of Neural Networks in the safety assessment of oil and gas pipelines. In Proceedings of the IEEE Symposium on Computational Intelligence for Engineering Solutions, Orlando, FL, USA, 9–12 December 2014. [Google Scholar] [CrossRef]
- Scholz, R.W.; Bartelsman, E.J.; Diefenbach, S.; Franke, L.; Grunwald, A.; Helbing, D.; Hill, R.; Hilty, L.; Höjer, M.; Klauser, S.; et al. Unintended side effects of the digital transition: European scientists’ messages from a proposition-based expert round table. Sustainability 2018, 10, 2001. [Google Scholar] [CrossRef] [Green Version]
- Rodriguez-Aguilar, R.; Marmolejo-Saucedo, J.A. Structural Dynamics and disruption events in supply chains using fat tail distributions. IFAC-Pap. 2019, 52. [Google Scholar] [CrossRef]
- Yong, B.; Shen, J.; Liu, X.; Li, F.; Chen, H.; Zhou, Q. An intelligent blockchain-based system for safe vaccine supply and supervision. Int. J. Inf. Manag. 2020, 52. [Google Scholar] [CrossRef]
- Beam, A.L.; Kohane, I.S. Big data and machine learning in health care. JAMA J. Am. Med Assoc. 2018, 319, 1317–1318. [Google Scholar] [CrossRef]
- Roh, Y.; Heo, G.; Whang, S.E. A survey on data collection for machine learning: A big data-ai integration perspective. IEEE Trans. Knowl. Data Eng. 2019, 33, 1328–1347. [Google Scholar] [CrossRef] [Green Version]
- Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakaantan, A.; Shyam, P.; Sastry, G.; Amodei, D.; et al. Language models are few-shot learners. arXiv 2020, arXiv:2005.14165. [Google Scholar]
- Ilie-Zudor, E.; Ekárt, A.; Kemeny, Z.; Buckingham, C.; Welch, P.; Monostori, L. Advanced predictive-analysis-based decision support for collaborative logistics networks. Supply Chain Manag. Int. J. 2015, 20, 369–388. [Google Scholar] [CrossRef]
- Fan, Y.; Heilig, L.; Voß, S. Supply Chain Risk Management in the Era of Big Data. In Design, User Experience, and Usability: 4th International Conference, Proceedings of the DUXU 2015, Held as a Part of HCI International, 2–7 August 2015; Marcus, A., Ed.; Springer: Cham, Switzerland, 2015; pp. 283–294. [Google Scholar]
- Amodei, D.; Hernandez, D. AI and Compute. Available online: https://openai.com/blog/ai-and-compute (accessed on 27 May 2021).
- Ioffe, S.; Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv 2015, arXiv:1502.03167. [Google Scholar]
- Hinton, G.E.; Srivastava, N.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R.R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv 2012, arXiv:1207.0580. [Google Scholar]
- Simeone, O. A very brief introduction to machine learning with applications to communication systems. IEEE Trans. Cogn. Commun. Netw. 2018, 4, 648–664. [Google Scholar] [CrossRef] [Green Version]
- Singh, D.; Reddy, C.K. A survey on platforms for big data analytics. J. Big Data 2015, 2, 293–319. [Google Scholar] [CrossRef] [Green Version]
- Li, H.; Ota, K.; Dong, M. Learning IoT in edge: Deep learning for the Internet of Things with edge computing. IEEE Netw. 2018, 32, 96–101. [Google Scholar] [CrossRef] [Green Version]
- Ben-Daya, M.; Hassini, E.; Bahroun, Z. Internet of things and supply chain management: A literature review. Int. J. Prod. Res. 2019, 57, 4719–4742. [Google Scholar] [CrossRef] [Green Version]
- Rogetzer, P.; Nowak, T.; Jammernegg, W.; Wakolbinger, T. Impact of Digitalization on Sustainable Supply Chains. In Chancen und Grenzen der Nachhaltigkeitstransformation Ökonomische und Soziologische Perspektiven; Luks, F., Ed.; Springer: Wiesbaden, Germaney, 2019; pp. 131–144. [Google Scholar]
- Carbonneau, R.; Laframboise, K.; Vahidov, R. Application of machine learning techniques for supply chain demand forecasting. Eur. J. Oper. Res. 2008, 184, 1140–1154. [Google Scholar] [CrossRef]
- Tranfield, D.; Denyer, D.; Smart, P. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. Br. J. Manag. 2003, 14, 207–222. [Google Scholar] [CrossRef]
- Ghadge, A.; Dani, S.; Kalawsky, R. Supply chain risk management: Present and future scope. Int. J. Logist. Manag. 2012, 23, 313–339. [Google Scholar] [CrossRef] [Green Version]
- Colicchia, C.; Strozzi, F. Supply chain risk management: A new methodology for a systematic literature review. Supply Chain Manag. 2012, 17, 403–418. [Google Scholar] [CrossRef]
- Sharma, R.; Kamble, S.S.; Gunasekaran, A.; Kumar, V.; Kumar, A. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 2020, 119. [Google Scholar] [CrossRef]
- Baryannis, G.; Validi, S.; Dani, S.; Antoniou, G. Supply chain risk management and artificial intelligence: State of the art and future research directions. Int. J. Prod. Res. 2019, 57, 2179–2202. [Google Scholar] [CrossRef]
- Ogawa, R.T.; Malen, B. Towards rigor in reviews of multivocal literatures: Applying the exploratory case study method. Rev. Educ. Res. 1991, 61, 265–286. [Google Scholar] [CrossRef]
- Patton, M.Q. Towards utility in reviews of multivocal literatures. Rev. Educ. Res. 1991, 61, 287–292. [Google Scholar] [CrossRef]
- Butijn, B.-J.; Tamburri, D.A.; van den Heuvel, W.-J. Blockchains: A Systematic Multivocal Literature Review. ACM Comput. Surv. (CSUR) 2020, 53, 1–37. [Google Scholar] [CrossRef]
- Taibi, D.; El Ioini, N.; Pahl, C.; Niederkofler, J.R.S. Serverless Cloud Computing (Function-as-a-Service) Patterns: A Multivocal Literature Review. In Proceedings of the 10th international conference on Cloud Computing and Service Science (CLOSER2020), Prague, Czech Republic, 7–9 May 2020; pp. 181–192. [Google Scholar]
- Iqbal, A.; Colomo-Palacios, R. Key Opportunities and Challenges of Data Migration in Cloud: Results from a Multivocal Literature Review. Procedia Comput. Sci. 2019, 164, 48–55. [Google Scholar] [CrossRef]
- Fogarty, A.; Edgeworth, A.; Smith, O.; Dowling, M.; Yilmaz, M.; MacMahon, S.T.; Clarke, P. Agile Software Development–Do We Really Calculate the Costs? A Multivocal Literature Review. In European Conference on Software Process Improvement; Springer: Cham, Switzerland, 2020; pp. 203–219. [Google Scholar]
- Neto, G.T.G.; Santos, W.B.; Endo, P.T.; Fagundes, A.R. Multivocal literature reviews in software engineering: Preliminary findings from a tertiary study. In ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM); IEEE: Porto de Galinhas, Brazil, 2019; pp. 1–6. [Google Scholar]
- Elmore, R.F. Comment on “towards rigor in reviews of multivocal literatures: Applying the exploratory case study method”. Rev. Educ. Res. 1991, 61, 293–297. [Google Scholar] [CrossRef]
- Garousi, V.; Felderer, M.; Mäntylä, M.V. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. Inf. Softw. Technol. 2019, 106, 101–121. [Google Scholar] [CrossRef] [Green Version]
- Lwakatare, L.E.; Kuvaja, P.; Oivo, M. Relationship of DevOps to Agile, Lean and Continuous Deployment. In Proceedings of the Product-Focused Software Process Improvement 17th International Conference, Trondheim, Norway, 22–24 November 2016; Abrahamsson, P., Jedlitschka, A., Nguyen Duc, A., Felderer, M., Amasaki, S., Mikkonen, T., Eds.; Springer: Cham, Switzerland, 2016; pp. 399–415. [Google Scholar]
- Kumar, S.K.; Tiwari, M.K.; Babiceanu, R.F. Minimisation of supply chain cost with embedded risk using computational intelligence approaches. Int. J. Prod. Res. 2010, 48, 3717–3739. [Google Scholar] [CrossRef]
- Umar, A.; Ivanovski, I. Computer aided strategic planning for egovernment agility a global instrument for developing countries. In Artificial Intelligence for Business Agility—Papers from the AAAI Spring Symposium; Technical Report; The AAAI Press: Menlo Park, CA, USA, 2011; pp. 67–70. [Google Scholar]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 2012, 1097–1105. [Google Scholar] [CrossRef]
- Alfian, G.; Syafrudin, M.; Farooq, U.; Ma’arif, M.R.; Syaekhoni, M.A.; Fitriyani, N.L.; Lee, J.; Rhee, J. Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model. Food Control 2020, 110, 107916. [Google Scholar] [CrossRef]
- Brintrup, A.; Pak, J.; Ratiney, D.; Pearce, T.; Wichmann, P.; Woodall, P.; McFarlane, D. Supply chain data analytics for predicting supplier disruptions: A case study in complex asset manufacturing. Int. J. Prod. Res. 2020, 58, 3330–3341. [Google Scholar] [CrossRef]
- Cavalcante, I.M.; Frazzon, E.M.; Forcellini, F.A.; Ivanov, D. A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. Int. J. Inf. Manag. 2019, 49, 86–97. [Google Scholar] [CrossRef]
- Wichmann, P.; Brintrup, A.; Baker, S.; Woodall, P.; McFarlane, D. Extracting supply chain maps from news articles using deep neural networks. Int. J. Prod. Res. 2020, 58, 5320–5336. [Google Scholar] [CrossRef]
- Alfian, G.; Syafrudin, M.; Fitriyani, N.L.; Rhee, J.; Ma’arif, M.R.; Riadi, I. Traceability system using IoT and forecasting model for food supply chain. In Proceedings of the 2020 International Conference on Decision Aid Sciences and Application DASA, Sakheer, Bahrain, 8–9 November 2020. [Google Scholar] [CrossRef]
- Benjaoran, V.; Dawood, N. An application of artificial intelligence planner for bespoke precast concrete production planning: A case study. In Proceedings of the 13th Annual Conference of the International Group for Lean Construction, Sydney, Australia, 19–21 July 2005; pp. 493–499. [Google Scholar]
- Blackburn, R.; Lurz, K.; Priese, B.; Göb, R.; Darkow, I.-L. A predictive analytics approach for demand forecasting in the process industry. Int. Trans. Oper. Res. 2015, 22, 407–428. [Google Scholar] [CrossRef]
- Bouzembrak, Y.; Marvin, H. Impact of drivers of change, including climatic factors, on the occurrence of chemical food safety hazards in fruits and vegetables: A Bayesian Network approach. Food Control 2019, 97, 67–76. [Google Scholar] [CrossRef]
- Constante-Nicolalde, F.-V.; Guerra-Terán, P.; Pérez-Medina, J.-L. Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques. Commun. Comput. Inf. Sci. 2020, 1194, 145–159. [Google Scholar] [CrossRef]
- Fu, W.; Chien, C.-F. UNISON data-driven intermittent demand forecast framework to empower supply chain resilience and an empirical study in electronics distribution. Comput. Ind. Eng. 2019, 135, 940–949. [Google Scholar] [CrossRef]
- Lau, H.; Ning, A.; Pun, K.F.; Chin, K.S.; Ip, W.H. A knowledge-based system to support procurement decision. J. Knowl. Manag. 2005, 9, 87–100. [Google Scholar] [CrossRef] [Green Version]
- Pereira, M.M.; de Oliveira, D.L.; Portela Santos, P.P.; Frazzon, E.M. Predictive and Adaptive Management Approach for Omnichannel Retailing Supply Chains. IFAC-PapersOnLine 2018, 51, 1707–1713. [Google Scholar] [CrossRef]
- Hamdi, F.; Ghorbel, A.; Masmoudi, F.; Dupont, L. Optimization of a supply portfolio in the context of supply chain risk management: Literature review. J. Intell. Manuf. 2018, 29, 763–788. [Google Scholar] [CrossRef]
- Nychas, G.; Panagou, E.Z.; Mohareb, F. Novel approaches for food safety management and communication. Curr. Opin. Food Sci. 2016, 12, 13–20. [Google Scholar] [CrossRef] [Green Version]
- Smith, M.J. Getting value from artificial intelligence in agriculture. Anim. Prod. Sci. 2019, 60, 46–54. [Google Scholar] [CrossRef]
- Paul, S.K.; Riaz, S.; Das, S. Organizational Adoption of Artificial Intelligence in Supply Chain Risk Management. IFIP Adv. Inf. Commun. Technol. 2020, 617, 10–15. [Google Scholar] [CrossRef]
- Choi, T.-M.; Chan, H.K.; Yue, X. Recent Development in Big Data Analytics for Business Operations and Risk Management. IEEE Trans. Cybern. 2017, 47, 81–92. [Google Scholar] [CrossRef]
- Allgurin, A. Exploring Machine Learning for Supplier Selection—A Case Study at Bufab Sweden AB; Linnaeus University: Linnaeus, Sweden, 2018. [Google Scholar]
- Rao, S.; Havewala, A.M. Smart Legal Contract Migration using Machine Learning. In First International Conference on Digital Data Processing London, UK, 15–17 November 2019; Robles, R.S., Ed.; IEEE Computer Society, Conference Publishing Services: Los Alamitos, CA, USA, 2019; pp. 65–69. [Google Scholar]
- Qu, T.; Zhang, J.H.; Chan, F.T.; Srivastava, R.S.; Tiwari, M.K.; Park, W.-Y. Demand prediction and price optimization for semi-luxury supermarket segment. Comput. Ind. Eng. 2017, 113, 91–102. [Google Scholar] [CrossRef]
- Huang, J. How to drive a holistic end-to-end supply chain risk management. J. Supply Chain Manag. Logist. Procure. 2020, 2, 294–306. [Google Scholar]
- Woyke, E. How UPS Uses AI to Deliver Holiday Gifts in the Worst Storms. Available online: https://www.technologyreview.com/2018/11/21/139000/how-ups-uses-ai-to-outsmart-bad-weather (accessed on 19 May 2021).
- DHL. DHL Supply Watch Uses Machine Learning to Mitigate Supplier Risks: Supply Watch Analyzes Millions of Online Sources in Real-Time to Detect Early Indicators of Potential Supplier Distresses before They Occur. Available online: https://www.sdcexec.com/software-technology/press-release/12337269/dhl-dhl-supply-watch-uses-machine-learning-to-mitigate-supplier-risks (accessed on 27 May 2021).
- Johnston, L. How is Walmart Express Delivery Nailing that 2-Hour Window? Machine Learning 2020. Available online: https://risnews.com/how-walmart-express-delivery-nailing-2-hour-window-machine-learning (accessed on 28 May 2021).
- Weber, F.; Schütte, R. A Domain-Oriented Analysis of the Impact of Machine Learning—The Case of Retailing. Big Data Cogn. Comput. 2019, 3, 11. [Google Scholar] [CrossRef] [Green Version]
- Barrett, B. McDonald’s Bites on Big Data with $300 Million Acquisition: The Fast-Food Giant’s Largest Acquisition in 20 Years is Bringing Machine Learning to the Drive-Thru. Available online: https://www.wired.com/story/mcdonalds-big-data-dynamic-yield-acquisition/ (accessed on 21 May 2021).
- Route4Me. How Machine Learning Is Transforming Supply Chain Management. Available online: https://www.globaltrademag.com/how-machine-learning-is-transforming-supply-chain-management/ (accessed on 27 May 2021).
- Titze, F. Industrial Future by ROI: Wie Machine Learning Trennt, was Wichtig und Unwichtig Ist. Available online: https://www.produktion.de/industrial_future_roi/wie-machine-learning-trennt-was-wichtig-und-unwichtig-ist-256.html (accessed on 27 May 2021).
- Härle, P.; Havas, A.; Samandari, H. The Future of Bank Risk Management. Available online: https://www.mckinsey.com/business-functions/risk/our-insights/the-future-of-bank-risk-management (accessed on 27 May 2021).
- Canizo, M.; Onieva, E.; Conde, A.; Charramendieta, S.; Trujillo, S. Real-time predictive maintenance for wind turbines using Big Data frameworks. In Proceedings of the 2017 IEEE International Conference, Dallas, TX, USA, 19–21 June 2017; pp. 70–77. [Google Scholar]
- Handfield, R.; Jeong, S.; Choi, T. Emerging procurement technology: Data analytics and cognitive analytics. Int. J. Phys. Distrib. Logist. Manag. 2019, 49, 972–1002. [Google Scholar] [CrossRef]
- Bates, D.W.; Saria, S.; Ohno-Machado, L.; Shah, A.; Escobar, G. Big Data in Health Care: Using Analytics to Identify and Manage High-Risk and High-Cost Patients. Health Aff. 2014, 33, 1123–1131. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Grossman, R.; Siegel, K. Organizational models for big data and analytics. J. Organ. Des. 2014, 3, 20–25. [Google Scholar] [CrossRef] [Green Version]
- Waller, M.A.; Fawcett, S.E. Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. J. Bus. Logist. 2013, 34, 77–84. [Google Scholar] [CrossRef]
- Fisher, B.; Coops, A.; Klous, S.; op het Veld, M.; Raisbeck, M.; Zahawi, N. Guardians of Trust: Who is Responsible for Trusted Analytics in the Digital Age? Available online: https://assets.kpmg/content/dam/kpmg/xx/pdf/2018/02/guardians-of-trust.pdf (accessed on 27 May 2021).
- Kuner, C.; Svantesson, D.J.B.; Cate, F.H.; Lynskey, O.; Millard, C. Machine learning with personal data: Is data protection law smart enough to meet the challenge? Int. Data Priv. Law 2017, 7, 1–2. [Google Scholar] [CrossRef] [Green Version]
- Schroeder, M. Changes in Risk Management via Big Data. Research Blog on Supply Chain Risk Management. ISSN 2748-775X. Available online: https://scrm.hypotheses.org/352 (accessed on 29 June 2021).
- Bonabeau, E. Don’t Trust Your Gut: Decision Making. Available online: https://hbr.org/2003/05/dont-trust-your-gut (accessed on 27 May 2021).
- Rai, A. Explainable AI: From black box to glass box. J. Acad. Mark. Sci. 2020, 48, 137–141. [Google Scholar] [CrossRef] [Green Version]
- Kemper, J.; Kolkman, D. Transparent to whom? No algorithmic accountability without a critical audience. Inf. Commun. Soc. 2019, 22, 2081–2096. [Google Scholar] [CrossRef] [Green Version]
- Xu, Y.; Sun, Y.; Wan, J.; Liu, X.; Song, Z. Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications. IEEE Access 2017, 5, 17368–17380. [Google Scholar] [CrossRef]
- Chen, J.; Tao, Y.; Wang, H.; Chen, T. Big data based fraud risk management at Alibaba. J. Financ. Data Sci. 2015, 1, 1–10. [Google Scholar] [CrossRef] [Green Version]
- DuHadway, S.; Carnovale, S.; Kannan, V.R. Organizational communication and individual behavior: Implications for supply chain risk management. J. Supply Chain. Manag. 2018, 54, 3–19. [Google Scholar] [CrossRef]
- Pournader, M.; Kach, A.; Talluri, S. A Review of the Existing and Emerging Topics in the Supply Chain Risk Management Literature. Decis. Sci. 2020, 5, 867–919. [Google Scholar] [CrossRef] [PubMed]
- Boell, S.K.; Cecez-Kecmanovic, D. On being “systematic” in literature reviews in IS. J. Inf. Technol. 2015, 30, 161–173. [Google Scholar] [CrossRef]
Authors | Study Type | Scope of Application | Considered Risks | SCRM Focus |
---|---|---|---|---|
Alfian et al. [56] | Conceptual work | transport | transport risks | indirect |
Alfian et al. [60] | Conceptual work | transport | transport risks | indirect |
Baryannis, Dani, Antoniou [18] | Conceptual work | production | supplier risks | direct |
Benjaoran and Dawood [61] | Case Study | production | production risks | indirect |
Blackburn et al. [62] | Conceptual work & use case | production | production risks | indirect |
Bouzembrak and Marvin [63] | Conceptual work | transport | food quality/transport risks | indirect |
Brintrup et al. [57] | Case study | transport | supplier risks | direct |
Cavalcante et al. [58] | Conceptual work | transport | supplier risks | direct |
Constante-Nicolalde et al. [64] | Conceptual work | supply chain | quality risks | indirect |
Fu and Chien [65] | Conceptual work | production | supply risks | indirect |
Hassan [19] | Conceptual work | supply chain | supply risks | direct |
Lau et al. [66] | Conceptual work | procurement | information risks | indirect |
Layouni et al. [20] | Survey | production | transport risks | indirect |
Pereira et al. [67] | Conceptual work | supply chain | sales risks | indirect |
Rodriguez-Aguilar et al. [22] | Conceptual work | supply chain | supply risks | direct |
Wichmann et al. [59] | Conceptual work | supply chain | supplier risks | direct |
Yong et al. [23] | Conceptual work | supply chain | supply risks | direct |
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Schroeder, M.; Lodemann, S. A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management. Logistics 2021, 5, 62. https://doi.org/10.3390/logistics5030062
Schroeder M, Lodemann S. A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management. Logistics. 2021; 5(3):62. https://doi.org/10.3390/logistics5030062
Chicago/Turabian StyleSchroeder, Meike, and Sebastian Lodemann. 2021. "A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management" Logistics 5, no. 3: 62. https://doi.org/10.3390/logistics5030062