Demand Planning with OE and Palantir

From Chaos to Clarity: How AI-Legible Ontologies Are Transforming Demand Planning

For many manufacturers, demand planning is still a broken process—fractured across teams, shaped by competing assumptions, and often disconnected from what’s really happening in the market.

Sales builds a forecast based on pipeline deals and quarterly targets. Operations relies on historical run rates. Supply chain teams model demand based on lead times and delivery contracts. The result? Misalignment. Overproduction. Bloated warehouses. And when the market shifts, no one sees it in time.

Breaking Down the Silos Isn’t Enough

Best-in-class manufacturers know the first step is breaking down the walls between sales, operations, and supply chain. That’s the promise of S&OP (Sales and Operations Planning): to unify forecasting around shared data, tighter feedback loops, and a more coordinated response to shifting demand.

But here’s the problem—most S&OP platforms still rely on stitched-together reports. Data flows in one direction. And when assumptions change? Everyone scrambles.

The Next Step: Making the Enterprise AI-Legible

At Object Edge, our work with Palantir goes beyond just connecting the silos. We build ontologies—semantic data layers that translate fragmented, legacy data into AI-readable structures. This turns your messy real-world data into something machine-intelligent systems can reason over.

In other words: we don’t just integrate data—we give it meaning.

Why does this matter?

Because the minute your demand signals, inventory systems, and distributor inputs become AI-legible, you unlock real-time, predictive, and prescriptive power:

• More accurate forecasting, driven by market signals—not just internal guesses
• Faster responses to demand shifts, with decisions that adapt in real-time
• Two-way automation, where the digital twin doesn’t just observe but writes back into ERPs, CRMs, and WMS platforms

Real-World Impact: Smarter, Leaner, More Profitable

We’ve seen this firsthand with our clients. Take the case of a lab equipment manufacturer who struggled with unpredictable distributor orders. Once we helped them ingest actual end-user data and structure it via ontologies, they didn’t just improve forecast accuracy—they started managing inventory for their distributors, improving fill rates and inventory turns across the board.

Or another case where a manufacturer’s warehouse had earned the nickname “the accordion” due to constant fluctuations in output. Post-implementation of a unified, ontology-driven S&OP model, they didn’t just smooth demand—they increased profitability, even as sales volumes stayed flat.

The Bigger Picture

This is the evolution of planning:

1. Disconnected Forecasting: Everyone guesses differently
2. Siloed S&OP: Aligned, but slow and often reactive
3. AI-Native S&OP: Ontology-driven, real-time, market-connected, and system-actionable

The future of demand planning isn’t just about listening better. It’s about building a system that can understand, learn, and act—with humans still in the loop, but no longer buried under spreadsheets.