A plain-English layer over supply-chain optimization. A planner asks a question; the system writes the optimization, runs the solver, and explains the trade-off back in words, without ever moving proprietary data.
Demo note. The interactive demo runs on synthetic data and the client is not named. This is a sanitized recreation of the delivered system, built to show the approach without exposing confidential details.
A working sketch of the real thing: a planner asks the scenario manager a what-if in plain English; the system rewrites the underlying mixed-integer optimization, re-solves it, and answers with the trade-off, recommended next moves, a projected trend, and the intermediate solver code for anyone who wants to audit the reasoning. The live network on the right re-routes to each new optimum. The production system did this with in-context learning, so proprietary data never left the enterprise.
Composite persona: a hub-operations planner whose job is to keep daily capacity safe across a 26-hub network, no overflows, no wasted volume.
Modern supply chains demand sophisticated optimization, but the output is a black box. A planner is handed a decision with no sense of why, and no safe way to ask "what if the supplier's lead time doubles?" The math is sound; the trust isn't. And the obvious fix, pipe everything into a general LLM, means leaking proprietary network data.
I built Supply Sense, a system that pairs a large language model with combinatorial optimization. It turns a plain-text question into a real answer about an optimization outcome, and lets a planner reshape the problem (add or drop a constraint, run a what-if) in conversation, while the proprietary data stays put.
It was proven on a live workload: server placement inside IBM's own cloud supply chain.
"A 90% accuracy rate for in-distribution queries, improving inventory turnover, reducing development time, and shortening order cycles."
A small set of agents runs the round trip. One translates the question into optimization code; the solver processes it; the result comes back as business outcomes in plain language. Underneath is a mixed-integer model maximizing net revenue across supplier cost, inventory and capacity, subject to constraints like supplier limits, lead times and safety stock.
I led a small team of operations-research scientists and ML engineers and worked directly with the business partners who'd use it. The system drew attention from executives across industries, the CTO of the world's largest freight railroad, the supply-chain VP of the world's biggest food manufacturer, and leaders at the largest U.S. drug distributor, and is now recognized as a reusable IBM asset.
For this work I received the IBM Technical Premier Award, the firm's most prestigious technical honor, reserved for the top 0.01% of global contributors across research, software and consulting.