Data-driven Strategies for Reducing Shipper Fallout in Changing Trade Environments
DOI:
https://doi.org/10.36676/j.sust.sol.v1.i4.49Keywords:
Data-driven Strategies, Shipper Fallout, Trade EnvironmentsAbstract
Global trade environments are complex and constantly changing, creating significant challenges for the actual shippers and leading to what has been called “shipper fallout”, which means that the shippers experience disruptions, inefficiencies or fail to meet what is required in trading. This review paper discusses data-driven approaches as the way to solve the problem of shippers’ churn, stressing on flexibility of such solution in the conditions of shifts in trade environments. Thus, by adopting big data analytics to analyze historical data, and developing optimized predictive models, as well as monitoring executive systems, Christopher, M., & Peck, H. (2004) companies can increase the effectiveness of their decisions, supply chain robustness, and lower the likelihood of fallout. This paper also measures the primary obstacles, that are the data integration, data security and the legal demands, as well as future works on this subject.
References
• Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the Current Status as Well as Future Prospects on Logistics. Computers in Industry, 89, 23–34. https://doi.org/10.1016/j.compind.2017.04.002
• Maersk. (2022). Using IoT to Revolutionize Supply Chain Visibility. Retrieved from https://www.maersk.com
• World Economic Forum. (2022). Trade Disruptions and the Role of Digital Solutions. Retrieved from https://www.weforum.org
• Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275 DOI: https://doi.org/10.1108/09574090410700275
• Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/00207543.2018.1488086 DOI: https://doi.org/10.1080/00207543.2018.1488086
• Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34. https://doi.org/10.1016/j.compind.2017.04.002 DOI: https://doi.org/10.1016/j.compind.2017.04.002
• Kilpatrick, J., & Barter, L. (2020). COVID-19: Managing supply chain risk and disruption. Deloitte Insights. Retrieved from https://www2.deloitte.com
• World Economic Forum. (2021). Trade disruptions: Building resilience with digital solutions. Retrieved from https://www.weforum.org
• Maersk. (2022). Driving supply chain visibility with IoT solutions. Retrieved from https://www.maersk.com
• PwC. (2021). Digital twins in supply chain resilience. Retrieved from https://www.pwc.com
• Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488. https://doi.org/10.1016/j.ijpe.2005.12.006 DOI: https://doi.org/10.1016/j.ijpe.2005.12.006
• Sheffi, Y. (2015). The power of resilience: How the best companies manage the unexpected. MIT Press. DOI: https://doi.org/10.7551/mitpress/9780262029797.001.0001
• Christopher, M. (2016). Logistics and supply chain management (5th ed.). Pearson Education.
• Rodrigue, J.-P., & Notteboom, T. (2020). The geography of transport systems (5th ed.). Routledge. DOI: https://doi.org/10.4324/9780429346323
• McKinsey & Company. (2021). Supply chain resilience: A blueprint for the new era. Retrieved from https://www.mckinsey.com
• Deloitte. (2020). Blockchain and the future of trade compliance. Retrieved from https://www2.deloitte.com
• Porter, M. E., & Heppelmann, J. E. (2015). How smart, connected products are transforming companies. Harvard Business Review, 93(10), 96–114.
• IBM. (2021). Leveraging AI for predictive supply chain analytics. Retrieved from https://www.ibm.com
• UPS. (2020). Optimizing logistics through big data. Retrieved from https://www.ups.com
• Bhattacharya, A., & Michaelides, R. (2021). Digital twins and their impact on logistics efficiency. Computers & Industrial Engineering, 158, 107403. https://doi.org/10.1016/j.cie.2021.107403 DOI: https://doi.org/10.1016/j.cie.2021.107403
• Ghosh, S., & Shah, J. (2020). Artificial intelligence in transportation and logistics. AI in Logistics Review, 12(3), 20–35.
• Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 37(1), 10–36. https://doi.org/10.1108/IJOPM-02-2015-0078 DOI: https://doi.org/10.1108/IJOPM-02-2015-0078
• UNCTAD. (2021). Enhancing trade facilitation with digital technologies. Retrieved from https://unctad.org
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