Tony Thrasher

Special Guest Blog Post written by Chris Cunnane with InterSystems   In a world defined by rapid market shifts, volatile supply chains, and unpredictable customer behavior, traditional forecasting methods often fall short. Relying primarily on historical data is no longer enough. To stay competitive, organizations are increasingly turning to demand sensing and forecasting, an approach that blends real-time data, advanced analytics, and AI to anticipate demand more accurately and respond faster to change. This shift is not limited to retail or manufacturing. Demand sensing is transforming how organizations across industries plan operations, allocate resources, and improve service levels. Below, we explore key industry use cases where demand sensing is delivering measurable value, and why businesses should care.   Why Demand Sensing Matters Demand sensing moves beyond static historical trends. It incorporates current, high-velocity data signals such as sales transactions, weather patterns, logistics feeds, economic indicators, and even social sentiment to generate short-term demand forecasts that reflect real-world conditions. The benefit is clear. Organizations gain better visibility and responsiveness across procurement, production, inventory, and distribution. Instead of reacting to outdated forecasts, they can make timely decisions that reduce costs, prevent stockouts, and improve customer satisfaction. FMCG, CPG, Retail, & E-Commerce Fast-moving…

Special Guest Blog Post written by Deborah Dull   Imagine it’s launch season in your supply chain, and a team of AI agents is hard at work: coordinating shipments with suppliers, allocating inventory, and handling a missed delivery. It’s hard to go a day without hearing about the latest AI headlines: new LLMs, new applications, new use cases. These discussions are part of my every day. Business leaders and IT teams are eager to explore – and they have a lot of questions… and apprehension. In these discussions, we have found one framework that lands consistently: consider AI agents like new hires, not new software.   Step 1: Hire Like You Mean It Just like hiring humans, this step is about defining what you need. The first decision is the purpose of the role: what problems are you trying to solve? Where are your people overwhelmed? Where is your business growing in coming months? From here, the next decision is to build the business case just like you would for a new headcount. For example, consider the ROI of having a digital employee who can process supplier performance data 24/7. Now, decide on the type of agent that will join…