Aaron Freed

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…

Companies are rapidly integrating generative AI into procurement and sourcing decisions. The promise is obvious. AI can read thousands of pages of supplier proposals faster than any human team, summarize technical requirements in seconds, and create the appearance of consistency and objectivity in evaluation. But there is an important distinction managers are starting to overlook. The same AI system that performs extremely well at identifying whether a supplier meets minimum requirements may perform much less reliably when judging which supplier is truly better. Recent research published in the Journal of Business Logistics examined this issue by comparing how large language models evaluated supplier bids against evaluations completed by experienced procurement professionals. It analyzed 123 supplier proposals tied to 31 public procurement projects conducted by the State of Ohio between 2023 and 2024. The projects involved complex IT services contracts, many containing large, text-heavy bid packages requiring evaluative judgment rather than simple arithmetic comparisons. The researchers tested three reasoning-oriented AI models: OpenAI o3, Grok-3-Mini, and DeepSeek R1. They then compared their evaluations against human procurement scores. The findings revealed a surprisingly clear pattern. AI performed well when evaluating compliance signals. These are signals tied to baseline qualifications and technical requirements. Does…