Edith Heroux

E-commerce operations generate data at unprecedented scale across every customer touchpoint—from initial product discovery through post-purchase support interactions. Yet most retailers struggle to transform this data wealth into coordinated action across the interconnected functions that determine competitive outcomes: inventory planning, customer segmentation, order fulfillment, and demand forecasting. The disconnect between data availability and operational activation creates persistent inefficiencies: stockouts of high-demand items coexisting with overstock of slow movers, generic promotional campaigns that ignore customer purchase patterns, and fulfillment workflows that fail to prioritize orders by customer lifetime value. These operational gaps represent not just lost revenue but structural disadvantages against competitors who have mastered the integration of analytics into real-time decision processes. The emergence of Autonomous Retail Analytics addresses this integration ch...

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 The root cause wasn't a black-swan event or supply chain disruption—it was our reliance on outdated, spreadsheet-driven demand planning.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...