David Burke

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…

Demand can change in an instant. A flash promotion takes off faster than expected. An influencer post sends a niche SKU into the stratosphere. A heat wave flips seasonal demand on its head. And suddenly, your forecast—carefully crafted weeks ago—feels like yesterday’s weather report. This is where demand signal management earns its keep. Instead of reacting too late or overreacting too early, it helps supply chain teams interpret what’s really happening and respond with confidence. For retailers and consumer brands navigating constant demand volatility, it’s the difference between chasing noise and acting on insight.   The problem: demand forecasts lag reality Traditional forecasting has a fundamental challenge: timing. Forecasts are built on historical patterns and planning cadences that move at a measured pace, but demand levels today won’t wait for the next planning cycle. Modern demand shocks often arrive without warning: a promotional discount goes viral, a competitor runs out of stock, weather drives sudden regional swings, or a trend outpaces merchandising plans. In these moments, planners face a familiar tension. React too slowly, and shelves go empty. React too aggressively, and you’re stuck with excess inventory once the spike fades.   What demand signals are (and what they aren’t)…