Three ways that AI is revolutionising supply chain management

Two academics have identified three ways in which AI eliminates inefficiencies, redundancies, and environmental damage…...

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Two academics have identified three ways in which AI eliminates inefficiencies, redundancies, and environmental damage in the supply chain, and revolutionise cost reduction efforts.

Robert Boute, Professor of Operations & Supply Chain Management at Vlerick Business School, and Joren Gijsbrechts, a PhD student at the Research Centre for Operations Management at KU Leuven, highlight:

Collaborative shipping

Vlerick Business School and KU Leuven have developed an algorithm that harnesses GPS shipping data to log the drop-off points of shipping companies. It enables the system to remain constantly aware of all shipping, stocks, transport methods, and expenses, sharing some details of a firm’s supply chain with other companies to improve shipping efficiency.

It can, for instance, record the amount of stock on a given truck, where it’s heading, and what the transportation costs are. Partially empty trucks can thereby be filled to capacity by other firms using the algorithm to transport their stocks to the same destination, achieving impressive cost reductions and decreasing pollution.


An AI-enabled system that allows companies to combine a range of different transport methods, synchromodality matches them with differing delivery urgency levels. Its real-time data capabilities allow transportation methods to be adapted while a shipment is in transit: its AI algorithm can ‘decide’ to switch it to the most environmentally clean and cost-effective supply chain at any feasible point. A continuous operation, it selects the most efficient, least expensive, and least environmentally damaging opportunities throughout.

Deep reinforcement learning

This involves ‘training’ a machine-learning algorithm to select the best possible options, beginning with a trial-and-error process that correctively feeds the best outcomes back into the algorithm while dispensing with the least optimal ones. The robot learns to restrict its random actions and repeat only those that have optimal company outcomes.

Organisations can use deep reinforcement learning to efficiently harness the collaborative shipping methods and synchromodality systems mentioned above, allowing them, as Boute and Gijsbrechts put it, “to create the most sustainable and efficient supply chain possible for the organisation.”


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