Edith Heroux

AI Use Cases in CPG should be assessed with the same rigor used to approve a formulation change, a price-pack move, or a major capacity investment. An attractive demonstration is not evidence that a capability will improve a weekly demand plan, a customer promotion, or a plant deployment decision. Large branded manufacturers operate through linked commercial, innovation, supply, quality, and retailer workflows. A weak assumption in one area can surface elsewhere as excess inventory, lost distribution, margin leakage, or an avoidable service failure. The following checklist provides a practical way to qualify AI Use Cases in CPG before committing to scale. It is intended for category and portfolio leaders, demand-planning teams, RGM and sales functions, brand and innovation teams, supply planners, quality professionals, and data owners. The questions deliberately extend beyond model accuracy because CPG value is realized only when an insight changes an executable decision at the right ...

After fifteen years managing inventory operations across regional distribution centers, I thought I had seen every challenge the supply chain could throw at me. Seasonal demand spikes, supplier delays, the constant balancing act between overstock and stockouts—these were the daily realities I navigated with spreadsheets, experience, and more than a little intuition. Then our leadership team decided to invest in artificial intelligence for our inventory operations, and I discovered just how much I didn't know about what was possible. What followed were three years of implementation, failures, breakthroughs, and lessons that fundamentally changed how I think about inventory forecasting, stock replenishment, and the future of retail operations. The journey into AI Inventory Management began with ambitious goals and a healthy dose of skepticism from our warehouse teams. We were managing approximately 45,000 SKUs across six distribution centers, serving 200+ retail locations, and our i...

Five years ago, I watched our regional distribution center scramble to handle a sudden surge in demand for a product category we had drastically underestimated. Pallets were rerouted from three states away, expedited freight costs ate into margins, and our Logistics Performance Index took a hit that quarter. The root cause wasn't a black-swan event or supply chain disruption—it was our reliance on outdated, spreadsheet-driven demand planning. That experience became the catalyst for our journey into intelligent, data-driven forecasting, and the lessons learned along the way have fundamentally reshaped how we approach inventory allocation and replenishment. The transformation didn't happen overnight, but integrating Intelligent Demand Forecasting into our operations proved to be the turning point between reactive firefighting and proactive inventory optimization. What started as a pilot project in two product categories eventually scaled across our entire SKU portfolio, reducing...