The Forecast Was Right. The Decision Was Nine Days Late.

Supply chain forecasting has improved a great deal over the past decade. Machine learning models catch demand shifts that would have slipped past a planner using only a spreadsheet, and most manufacturers trust their numbers more than they did five years ago.

So why are the same companies still missing orders, paying for rush components and tearing up production schedules they locked down a few days earlier?

The answer usually has nothing to do with the forecast. It has to do with what happens after the forecast changes, and how long it takes for that change to reach every team that has to act on it.

Picture a manufacturer with three plants and two ERP systems. On a Tuesday night, the forecast run flags a jump in demand for a high-margin product family. The model is right.

Demand planning sees it Wednesday morning, updates the plan and sends a note. Production scheduling reads the note, but the week’s schedule was locked in on Monday, so the change waits for the next cycle. Inventory adjusts safety stock at its Thursday review, at two of the three sites. Supplier management doesn’t see the higher component requirement until the following Monday, and by then the only option is a rush order at a premium. On Wednesday the parts still haven’t arrived, so the scheduler reshuffles the line, and bumps orders for a second product family to make room.

Nine days passed between an accurate signal and a coordinated response. Nobody made a bad call along the way. Every team acted sensibly on what it could see, when it could see it.

That’s the problem. Every team runs on its own clock.

Most supply chain organizations are built around functions, and each function has its own rhythm. Forecasts might refresh daily. Schedules lock weekly. Supplier reviews happen when they happen. Each team also answers to its own metric, whether that’s inventory turns, schedule adherence or purchase price variance, and those metrics don’t always point the same way.

Layer in the systems. A midmarket manufacturer often runs more than one ERP, a planning tool or two, and a set of spreadsheets that quietly hold the whole thing together. Information moves between those systems the way it always has: through exports, emails and meetings.

The result is a gap between teams that no forecast, however accurate, can close. Call it the coordination gap; it’s where a large share of avoidable supply chain cost actually sits.

To be fair, the industry hasn’t ignored this. There are three common responses, and each one solves part of the problem.

The first is consolidation: Move planning, inventory and supply decisions onto a single platform with one data model. Once a company gets there, visibility improves a lot. Getting there is the hard part. It means migrating data and processes, usually over several years, while the business keeps running. Systems that stay outside the platform remain disconnected.

The second is automation. If people are slow, take them out of the loop. That works well for repeatable decisions with stable rules. It works less well when conditions change, because the rules don’t know what the planner knows. Planners start overriding the system quietly, and the reasons for those overrides never get recorded anywhere a system can learn from them.

The third, and the newest, is deploying AI agents for specific tasks: one to tune reorder points, another to flag supplier risk, another to suggest schedule changes. Each one can show value quickly. But an agent that optimizes inventory has no reason to tell the scheduler why it did what it did. Ten smart tools that don’t talk to each other are still ten silos.

All three approaches shorten the time to insight. None of them, on its own, shortens the time to action across teams that don’t share a calendar.

Where the time actually goes

If you trace a late decision back through an organization, the delay almost never sits inside one team. It sits in the handoffs.

• Between the forecast and the schedule, what gets lost is timing.
• Between the schedule and inventory, it’s context. Inventory sees the new numbers but not the reason behind them.
• Between inventory and supplier management, it’s urgency. A requirement shows up as a quantity, not a priority.
• And between almost any two teams, what gets lost is judgment.

That last one deserves more attention than it gets. When an experienced planner overrides a recommendation because she knows a key supplier always runs late in the fourth quarter, that knowledge is some of the most valuable information in the building. Today it lives in her head, or in an email thread nobody will find again.

Closing the gap requires a layer above the existing systems that treats a demand change as a cross-functional event rather than a message to be passed along.

In practice, that means every affected team works from the same current picture of demand, capacity, inventory and supply. When the inventory answer and the scheduling answer conflict, the tradeoff is surfaced right away so a person can decide, instead of it being discovered next week. People stay in charge of approvals, and the reasoning behind their overrides is captured and fed back into future recommendations. And the decisions go back into the ERP and planning systems where the work actually happens, so nobody rekeys anything.

Governance comes with this almost for free. When every recommendation carries its data, its approver and its reasoning, you can trace a decision months later without having to track down whoever happened to be in the room.

The payoff can be significant. Taco Comfort Solutions, an industrial manufacturer, cut master production scheduling from 18 hours to 15 minutes once the decisions feeding the schedule were coordinated instead of handed off. The bigger change wasn’t the hours saved. A schedule that takes 18 hours to build gets rebuilt rarely, so everyone else plans around a stale version of it. A schedule that takes 15 minutes can keep up with the business.

You don’t need new software to measure this. Start by asking your team one question: When the forecast changes, how long until the production schedule does?

If the honest answer is measured in days, look a little further. Count how many manual handoffs a single demand change touches. Look at how often planners override system recommendations, and whether anyone knows why. Pull last quarter’s rush supplier orders and trace each one back to the decision that came too late.

Those numbers rarely show up on a dashboard, but they tell you more about supply chain performance than forecast accuracy does.

Start with one decision

Large transformation programs promise a lot and take a long time to deliver. A more practical path is to pick a single costly decision that your teams make often, one that crosses at least two functions, and that hurts when it’s late. Map every handoff it touches. Measure how long it takes today. Then coordinate that one decision, keep people approving it, and record every override and the reason behind it.

Once that works, the next decision is easier, because it usually shares data or people with the first.

Forecasts will keep getting better. That’s good news, but it won’t fix this problem on its own. The companies that pull ahead over the next few years will be the ones whose teams act on the same signal at the same time, with their best people’s judgment built into the process.

The data already arrives on time. The decision should, too.

Mike Romeri is CEO and founder of A2go.