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This article is part of a 3-part series exploring the key barriers preventing successful AI adoption in manufacturing and supply chains, based on recent research and industry insights. In  Part 1 , I examined organizational and cultural barriers.  Part 2  addressed the technical and data challenges. This final article focuses on the ethical and governance issues that shape responsible and sustainable AI adoption. In   Part 1   of this series, I examined how organizational and

You forecast demand for 1,000 SKUs, and half are slow movers with lots of zeros. Your MAPE looks great, but your warehouse is still full, cash is tied up in inventory, and planners are losing trust in the numbers. Sound familiar? Intermittent demand is one of the most persistent and costly challenges in supply chain forecasting. It shows up in spare parts, seasonal or niche SKUs, luxury and slow-moving goods, and increasingly, in long-tail assortments in retail and e-commerce

The Game-Changer You’re Not Using... Yet Imagine identifying early signs of customer dissatisfaction before they decide to leave, how investors feel before they act, or what your employees truly think before they leave. Sentiment analysis is the key to unlocking these insights, yet many businesses still underutilize its potential. Sentiment analysis, also known as opinion mining, uses AI and machine learning to analyze text data and determine whether the sentiment is positive