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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

Can social media predict product demand?  Many supply chain professionals still rely on traditional forecasting methods, but what if social media could give us real-time demand signals before sales even happen? In my recent research, we found that analyzing social media sentiment and engagement can improve demand forecasting accuracy by 42%, especially for new products with no historical data. Let’s break it down. Image generated by AI (DALL·E) The Challenge: Forecasting New

The digital supply chain is a transformative shift in the way supply chains are managed, leveraging the power of digital technologies to drive greater efficiency, agility, and customer satisfaction. It encompasses the end-to-end flow of goods, services, and information, from the sourcing of raw materials to the delivery of finished products to customers. Digitalisation has led to changes in how key performance indicators (KPIs) are measured and monitored, with new KPIs foc