Federico Gomez

Inventory audits may be one of retail’s most overlooked revenue opportunities. Every large retailer has invested in software that predicts demand, triggers replenishment, and manages shelf space with precision. Almost none of that software questions whether the inventory number it’s reading is actually correct. A recent study in the Journal of Business Logistics suggests that blind spot is expensive, and that fixing it is one of the more overlooked levers a retailer has for growing sales. The researchers, working with a major UK grocery chain, examined roughly 24,000 products across 11 stores to understand why inventory records drift from what’s physically on the shelf, a problem known in the industry as inventory record inaccuracy. They then ran a field experiment to measure what happens to sales when a store corrects those records through a full audit. The results reframe a task most retailers treat as a nuisance into something closer to a growth strategy. Start with why the records go wrong in the first place. The study found that products carrying more inventory and products replenished more often were significantly more prone to inaccuracy. That makes intuitive sense: every case broken down, every unit moved from backroom to shelf, is…

Special Guest Blog Post written by Matt McKinney, Co-Founder and CEO of Loop   I spend most of my time with supply chain and innovation leaders at major enterprises who are sitting on significant AI budgets but struggling to show measurable business impact in an increasingly complex and volatile supply chain environment.   These conversations have evolved dramatically. A year ago, executives were asking basic questions about AI feasibility. Today, the questions have shifted to strategic implementation at enterprise scale.   Based on hundreds of these discussions, three questions consistently emerge that separate companies making transformational progress from those stuck in pilot purgatory.   How do we move from AI experiments to enterprise-scale impact?   Most organizations have yet to see organization-wide, bottom-line impact from AI use. This is the strategic challenge keeping C-suite leaders awake at night.   The problem isn’t the technology. It’s the application of the technology. Too many enterprises are trying to treat AI like a magic wand they can bolt onto existing systems. But garbage in, garbage out. If your underlying data is fragmented and inconsistent, AI won’t solve your problems; in fact they’ll get worse.   At its core, anything automated is powered by…