Publication Date
Executive Summary

MIT’s Center for Transportation and Logistics (CTL) hosted a virtual roundtable for its Supply Chain Exchange partners in which leading companies discussed predictive analytics. The event combined presentations from academia and industry with sharing by all attendees of their experiences, challenges, and ideas. To encourage candor, no statements in this report have been attributed to any specific company.

A short presentation summarized key concepts and the main algorithmic methods (see Appendix) for doing predictive analytics, including decision trees, random forests, k-nearest neighbors, support vector machines, artificial neural networks, regression, and time series.

During the roundtable, participants introduced themselves and described their firms’ uses of predictive analytics; this initial discussion showed the diversity of use cases for predictive analytics in supply chains. Companies listed various applications in demand forecasting, predicting the timing of events (e.g., driver availability, container unloading, and shipment events), and anomaly or risk prediction (e.g., manufacturing scrap rates, anomalous orders, and service failures). Applications for forecasting predominated in 70% of the companies, a pre-roundtable CTL survey found.

One company, a maker of technology products, presented its approach to predictive analytics, which included processes for identifying target projects, prioritizing them, developing minimum viable products in order to get feedback, and then iterating to create tools that address business users’ needs. The company centralized its data, analytics, and optimization efforts to provide enterprise-level management of data strategy and application development. The company’s team for analytics included both technical staff and “data translators” who bridge the gap between technology and business.

Discussions often focused on the challenges of predictive analytics in companies. Prevalent obstacles included data availability (e.g., quantity of samples, the right variables, and quality), organization maturity, and alignment of data science projects to organizational needs.

Ultimately, predictive analytics is a journey with a beginning but no end. Companies can always find new sources of data and new applications for using that data to reduce costs, improve reliability, and add value.

Public Attachment