How Machine Learning Will Transform Supply Chain Management

It does a better job of using data and forecasts to make decisions.

by

and

Vinayak Deshpande

by

and

Vinayak Deshpande

From the Magazine (March–April 2024) · Long read

Didier Engels

Summary.

Businesses need better planning to make their supply chains more agile and resilient. After explaining the shortcomings of traditional planning systems, the authors describe their new approach, optimal machine learning (OML), which has proved effective in a range of industries. A central feature is its decision-support engine that can process a vast amount of historical and current supply-and-demand data, take into account a company’s priorities, and rapidly produce recommendations for ideal production quantities, shipping arrangements, and so on. The authors explain the underpinnings of OML and provide concrete examples of how two large companies implemented it and improved their supply chains’ performance.

Idea in Brief

The Problem

Flawed planning methods make it extremely difficult for companies to protect themselves against supply chain disruptions.

A Remedy

A new approach, called optimal machine learning (OML), can enable better decisions, without the mystery surrounding the planning recommendations produced by current machine-learning models.

The Elements

OML relies on a decision-support engine that connects input data directly to supply chain decisions and takes into account a firm’s performance priorities. Other features are a “digital twin” representation of the entire supply chain and a data storage system that integrates information throughout the supply chain and allows for quick data access and updating.

The Covid-19 pandemic, the Russia-Ukraine conflict, trade wars, and other events in recent years have disrupted supply chains and highlighted the critical need for businesses to improve planning in order to be more agile and resilient. Yet companies struggle with this challenge. One major cause is flawed forecasting, which results in delivery delays, inventory levels that are woefully out of sync with demand, and disappointing financial performance. Those consequences are hardly surprising. After all, how can inventory and production decisions be made effectively when demand forecasts are widely off?

A version of this article appeared in the March–April 2024 issue of Harvard Business Review.

Narendra Agrawal is the Benjamin and Mae Swig Professor of Information Systems and Analytics at Santa Clara University’s Leavey School of Business.
Morris A. Cohen is the Panasonic Professor Emeritus of Manufacturing & Logistics at the University of Pennsylvania’s Wharton School. He is also the founder of AD3 Analytics, a start-up that developed the OML methodology for supply chain management.
Rohan Deshpande is a machine learning scientist at Cerebras Systems and a former chief technology officer at AD3 Analytics.
Vinayak Deshpande is the Mann Family Distinguished Professor of Operations at the University of North Carolina’s Kenan-Flagler Business School.

How Machine Learning Will Transform Supply Chain Management

Idea in Brief

The Problem

A Remedy

The Elements

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