E2open Acquires Global Multi-Carrier E-Commerce Shipping Software Platform, Logistyx Technologies. Read More
BLOG

Logistyx President Ken Fleming Examines Machine Learning Adoption for Supply & Demand Chain Executive

In mid-2020, Logistyx President Ken Fleming spoke with Supply & Demand Chain Executive (SDCE) on the “emerging technologies that claim to help companies in the supply chain.” During this conversation, Ken mentioned the evolving role of artificial intelligence (AI) and machine learning (ML):

“In the near future, supply chain AI will begin to migrate to machine learning. Currently, supply chain AI consists of developers programming business rules, telling computers what to look for and what action to take when it encounters those situations, but as AI migrates to machine learning, it will begin to think for itself. As machine learning becomes more advanced, technologies will increasingly be able to make note of repetitive situations and past experiences to start learning and making recommendations on its own. Technology like this has already deployed on a wide scale in other industries, and it has the potential to rapidly automate and improve a wide range of supply chain processes.”

A year and a half later, Ken revisited this prediction and checked on the state of machine learning adoption in the supply chain in a follow-up article for SDCE, “Machine Learning in the 2022 Supply Chain.” Ken reported that while tech adoption is on the rise overall, machine learning lags. He offers a potential explanation of companies’ reticence but explains they’re leaving themselves vulnerable to the competition:

“As noted by Harvard Business Review, ‘those that adopt AI in the coming months and years will achieve significant competitive differentiation.’ As AI becomes ML, it can optimize supply chain operations in ways that manual processes simply cannot. By taking in large volumes of data, ML can rapidly spot trends and make recommendations for immediate improvement, whether the global supply chain is still bogged down by bottlenecks or not. It can learn, optimize, inform, simplify, verify, and scale, all in a much more time and cost-efficient manner.”

Read Ken’s full article for full details on how machine learning can rapidly unlock supply chain optimizations from the voluminous data generated by companies’ supply chain processes.