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For decades, demand forecasting followed a relatively clear path. Forecasters relied on what customers expressed through purchases, search queries, and seasonal trends. These signals were observable, repeatable, and mostly predictable. Forecasting models based on historical data could produce reliable estimates. Supply chains could plan with a reasonable degree of confidence. That world is fading. Today, social media is reshaping how people discover products, interact with co

More and more businesses are using integrated AI and machine learning (ML) models within their ERP systems to forecast product demand but many struggle to explain these forecasts to management and colleagues. Why? Because AI and ML models are complex and operate in high-dimensional spaces, which are harder to understand than traditional models based on simpler, linear relationships. Even though these models have demonstrated that they can improve forecast accuracy, they are d

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

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. This second article focuses on the technical and data-related challenges that often prevent AI solutions from scaling effectively. Without high-quality, integrated, and accessible data and without the infrastructure to support real-t

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 focus on organizational and cultural barriers. AI has emerged as a transformative force in manufacturing and supply chain management, promising enhanced efficiency, predictive capabilities, and strategic agility. However, integrating AI into strategic decision-making processes is n

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 European Commission are investing millions of euros in research projects [1] that will lead the green and digital transitions towards a more sustainable, human-centric and resilient European industry. This article explains what is Industry 5.0 and how it is different to Industry 4.0, as well as outlines the potential impact on current operations and supply chain planning, and how it can be implemented. What is Industry 5.0? Industry 5.0 is a term used to describe the nex