Can AI Make Your Supply Chain More Responsive?

Most inventory policies are built for the plan. Responsiveness is won or lost after the plan, when real demand and real supply show up.

AI makes a supply chain more responsive by showing the people who run execution where the odds of a stockout, excess inventory, or a missed commitment have shifted, as conditions change, while there is still time to act. Inventory policies set during sales and operations planning get materials flowing. The execution horizon, the days and weeks when actual orders and actual supply arrive, is where responsiveness is decided, and it is the horizon most planning technology has served least.

Over the last twenty years I have looked inside the planning processes of a great many companies. The pattern is remarkably consistent.

The inventory policies are usually good. They are aimed at the mid and long term, and they do their job: materials flow from suppliers, through production, and out to customers. Those targets anchor the sales and operations planning process, and the S&OP horizon is where most planning investment has gone.

Then real demand and real supply appear, and a different problem begins.

What does a responsive supply chain actually require?

A responsive supply chain requires two things that are usually built separately: policies that position inventory well for the plan, and fast, informed decisions when reality departs from that plan.

The first is a mature discipline. Safety stock targets, reorder points, service levels, and multi-echelon positioning all live in the planning horizon, and the tools for them have improved for decades.

The second is where most companies are exposed. In the execution horizon, a customer pulls an order forward, a supplier ships short, a line goes down for a shift. The policy says what inventory should look like on average. It says nothing about which of today's two hundred exceptions deserves attention first.

Resilience is what you call responsiveness that holds up under pressure. A supply chain that reacts quickly to small surprises is the one that absorbs large ones.

The plan gets materials flowing. The execution horizon decides whether they arrive where the customer needs them.

Who makes execution decisions in most supply chains today?

In most of the companies I have worked with, execution decisions are made by experienced production and logistics people acting on what they believe is best.

Those people are often very good. They know which supplier runs late in December and which customer revises its forecast every quarter. But their judgment does not scale, it walks out the door when they retire, and it cannot weigh hundreds of competing exceptions at once. They triage the loudest problem, which is not always the most expensive one.

Some companies have added software that replans to guide these decisions. In my experience it is often too little, too late. A replan takes a snapshot, solves, and publishes, and by the time the new plan reaches the floor, the conditions it solved for have moved again. I described that treadmill in The Replan Trap.

The cost of slow response hides across the income statement: expedited freight, weekend overtime, buffer stock held just in case, and the order a customer quietly gives to someone else.

Why didn't demand sensing solve the problem?

Demand sensing solved half of the problem: it updated the demand picture to current conditions, while supply was left to fend for itself.

At SmartOps, we developed demand sensing technology that adjusted near-term demand to the latest signals. It worked. Planners got a truer picture of what customers were about to order, sooner than the monthly forecast could give it to them.

What it did not address was what was happening with supply. A sharper demand signal meets the same late inbound shipment, the same short production run, the same carrier delay. Responsiveness depends on both sides at once, because a stockout is the collision of a demand outcome and a supply outcome. Seeing one side clearly still leaves you guessing about the collision.

That lesson stayed with me. Execution needs a view that treats demand and supply as uncertain together, and updates both as events land.

How does AI make the execution horizon more responsive?

AI makes the execution horizon more responsive by simulating many possible near-term futures across demand and supply together, then telling planners the current odds on each decision and which action moves those odds most.

Instead of one plan and a wall of alerts, the planner sees a ranked list. This order has a 40 percent chance of shipping late unless the transfer moves today. That location is carrying stock with a high chance of sitting idle for a quarter. Exceptions get ranked by the cash and service at risk, not by which one arrived last.

The math behind this is decades old. What changed is that the compute to run it frequently became affordable, a story I told in the step change supply chain planning has been waiting for. That post also explains why the long and mid horizon still benefit from optimization. This one is about the horizon optimization has served least well.

The experienced people stay in charge. They now see what their instincts could only estimate, and they spend their judgment on the decisions that matter most. That matches what leaders say they want: in a January 2026 survey of 514 retail, manufacturing, and wholesale supply chain leaders by RELEX, a planning software vendor, 54 percent preferred AI to make recommendations while people finalize decisions, and only 10 percent would trust AI to decide alone.¹ The same survey found 47 percent already using or planning AI-driven inventory and supply optimization.¹

What results has this approach produced?

In a six-week pilot at a Fortune 500 consumer packaged goods company, River by Pillar AI reduced inventory outliers by 15 percent and produced cash savings of 2 to 4 percent of inventory value, while holding service levels equal or better.²

Pillar AI also reports a win-to-loss ratio above four to one across all segments in that pilot.² I find that number more useful than any single savings figure. No system is right every time. When every recommendation carries its odds, the wins systematically outweigh the losses.

A disclosure, because you should hear it from me: I advise Pillar AI as its fractional chief revenue officer. The pilot predates my work with the company. I chose to help commercialize River because it runs probabilistic simulation as its core method, and because it reaches planners without a new screen to learn.

Is this only for large enterprises?

No. The pilot was at a Fortune 500 company, but the execution gap exists at every size, and midsize companies often feel it more sharply.

A midsize planning team covers more items per person, rarely has a data science group, and often runs execution in spreadsheets. It is not alone in that. In inFlow's 2026 survey of 400 inventory and operations professionals, 85 percent used spreadsheets as a primary inventory tool, including 53 percent of companies with 500 or more employees.³ The same survey found 81 percent wanted to implement AI and only 11 percent used any AI tool day to day.³

That gap between interest and use is the opportunity. A midsize company does not need a planning transformation to close it. Three things make the difference at that scale.

First, start narrow. One product family or one distribution region is enough to prove the approach against your own baseline.

Second, add no new screen. The intelligence should arrive inside the tools your planners already use, the principle I laid out in where AI should sit relative to your existing systems.

Third, judge on win-to-loss. Count how often the recommended action beat what would have happened anyway. That is a measure a lean team can track without a data science group.

How should a planning team start?

A planning team should start the execution horizon with a probabilistic approach that looks at both demand and supply conditions, giving early recognition of when the odds of inventory excess or shortage are high.

Pick the product family where your experienced people are making the most expensive calls on instinct. Measure today's outliers and the cash tied up in them for that product family. Run the new approach side by side with current practice for a few weeks. Keep the deterministic policies that already get materials flowing, and add odds to the horizon where real demand and real supply meet.

For manufacturers in this region, that execution horizon is one of the four supports I described in where a western Pennsylvania manufacturer should start with AI. And if budget is the question, what it typically costs a midsize company to get started lays out the range.

Proxigee Services, based in Pittsburgh, partners with technology companies to commercialize planning innovations like River, guides implementation firms through planning technology choices, and helps small and midsize businesses put AI to work on their hardest operational problems.

Your policies already chart the course. The next gain is in steering through what actually happens.