Supply Chain

TL;DR Most “multi‑objective” solutions collapse trade‑offs into a single number. MORSE keeps the trade‑offs alive: it evolves a Pareto front of policies—not just solutions—so operators can switch policies in real time as priorities shift (profit ↔ emissions ↔ lead time). Add a CVaR knob and the system becomes tail‑risk aware, reducing catastrophic outcomes without babysitting. Why this matters (for operators & P&L owners) Supply chains live in tension: service levels vs working capital, speed vs emissions, resilience vs cost. Traditional methods either: ...

Modern retail planning isn’t a spreadsheet; it’s a loop. A new supply‑chain agent framework—deployed at JD.com’s scale—treats planning as a closed‑loop system: gather data → generate plans → execute → diagnose → correct → repeat. That shift from “one‑and‑done forecasting” to continuous replanning is the core idea worth copying. What’s actually new here Agentic decomposition around business intents. Instead of dumping a vague prompt into a model, the system classifies the operator’s request into three intent families: (1) inventory turnover & diagnostics, (2) in‑stock monitoring, (3) sales/inventory/procurement recommendations. Each intent triggers a structured task list rather than ad‑hoc code. Atomic analytics, not monoliths. The execution agent generates workflows as chains of four primitives—Filter → Transform → Groupby → Sort—and stitches them with function calls to vetted business logic. This keeps code inspectable, traceable, and reusable. Dynamic reconfiguration. After every sub‑task, observations feed back into the planner, which prunes, reorders, or adds steps. The output isn’t a static report; it’s a plan that learns while it runs. Why it matters for operators (not just researchers) Traditional MIP‑heavy or rule‑based planning works well when the world is stationary and well‑specified. Retail isn’t. Promotions, seasonality, logistics bottlenecks, supplier constraints—these create moving objective functions. The agentic design here bakes in: ...

TL;DR Pair a classic mixed‑integer inventory redistribution model with an LLM-driven context layer and you get explainable optimization: the math still finds near‑optimal transfers, while the LLM translates them into role‑aware narratives, KPIs, and visuals. The result is faster buy‑in, fewer “why this plan?” debates, and tighter execution. Why this paper matters for operators Most planners don’t read constraint matrices. They read stockout risks, truck rolls, and WOS. The study demonstrates a working system where: ...

Agentic LLMs are graduating from chat to control rooms—taking actions, maintaining memory, and optimizing business processes. Inventory is a natural proving ground: a clean cocktail of uncertainty, economics, and coordination. AIM-Bench arrives precisely here, testing LLM agents across newsvendor, multi-period replenishment, the Beer Game, two-level warehouses, and a small supply network—each with explicit uncertainty sources (stochastic demand, variable lead times, and partner behavior). ...

Imagine asking, “Why wasn’t Order A scheduled for production yesterday?” and getting not just an answer, but a causal breakdown, an alternative plan, and a visual comparison — all without involving your operations research (OR) consultant. That’s exactly what SMARTAPS delivers. Built by Huawei researchers, SMARTAPS is a tool-augmented LLM interface for interacting with Advanced Planning Systems (APS) using natural language. It doesn’t try to replace optimization solvers — it simply makes them accessible. In doing so, it redefines how planners interact with complex decision-making models. ...