Planning takes three hours, lives with one expert, and cannot see past the next shift. We deploy an AI planning engine on top of the ERP, MES and spreadsheets you already run - so the plan is generated in minutes, judged against finite capacity, and executed without retyping.
Pull the order book out of the ERP. Check what the line actually produced yesterday. Open the spreadsheet where the changeover matrix lives. Rebuild the sequence by hand.
That takes two to three hours on a normal day. When a machine goes down, an order moves, or a delivery date shifts, it takes longer - and the whole plan is rebuilt from scratch by one person who knows how.
None of this is a training problem. It is a capacity problem: the number of combinations that produce a genuinely better plan is far beyond what a person can evaluate in a shift. And because the plan lives in a spreadsheet, the organisation never learns the plan-versus-actual number - so nobody can prove whether the current schedule is good, or simply familiar.
Advanced planning and scheduling (APS) is the layer that decides what to make, in what order, on which resource, and when - taking orders, material availability, machine capacity, changeover times and due dates into account simultaneously, and producing an executable sequence for the shop floor. For the long version, including the four complexity tiers and the seven questions that tell you whether you need APS at all, read the full guide to advanced planning and scheduling.
An ERP records and transacts; it does not search. A typical ERP planning module schedules in the sequence you enter, applies infinite capacity, and produces one answer. APS inverts that: it treats scheduling as an optimisation problem, evaluates thousands of feasible sequences against your constraints and your economics, and returns ranked alternatives for a human to approve.
That is the difference between production planning software that records your decisions and one that improves them.
The plan respects what the line can physically produce - not what the order book wishes it could.
The real cost of switching between products is built into the decision, not discovered afterwards.
You can see the cost of the plan you did not choose - and defend the one you did.
Planning drops from roughly three hours to five minutes.
Better deadline adherence and measurably better customer satisfaction.
Load is optimised across people, lines and equipment instead of assumed.
The engine picks the optimal option in seconds, not the first plausible one.
A new planner works independently within weeks, not months.
ERP, MES and Excel operate on one plan - no parallel truth.
Most plants already have a planning module. The gap is not the tool - it is that the tool does not search.
The sequence is rebuilt by hand every morning by the one person who knows how
The plan assumes infinite capacity, so it looks fine until the line can't keep up
Changeover cost is estimated, not modelled - and only discovered after the run
When a machine goes down, everything downstream is recalculated from scratch
Nobody can say whether last week's schedule was good or just familiar
Ranked plans are waiting before the shift starts - the planner chooses, not calculates
Finite capacity is enforced: the plan respects what the line can actually produce
Changeover time is a modelled input, so the sequence is optimised around it
Replanning after a disruption takes minutes, not most of a shift
Every plan is scored against plan-versus-actual, so management gets a number it never had
Most planning tools do one of these. The value comes from doing all three against the same data, so the plan you approve and the forecast behind it never disagree.
The engine generates several feasible planning alternatives and picks the most effective, rather than returning whatever you typed.
Forecasting that knows about weekday patterns, holidays, payday timing and promotions - instead of repeating last week's numbers.
The approved plan is pushed into the systems your operators already use, so there is no retyping and no verbal translation.
No rip-and-replace. The engine runs alongside what you have, and your existing spreadsheets stay valid inputs while you adopt it.
Orders and the changeover matrix are loaded, and the system automatically checks format and content. Routings, capacities and historical output are reconciled into one planning model.
A genetic algorithm analyses roughly 13,000 scenarios and computes the most efficient plan against the objective you set - on-time delivery, changeover cost, recovered material, utilisation, or a weighted mix.
Three optimised plans are generated with the trade-offs made visible. The planner chooses, and manual adjustments remain available where judgement matters.
The approved plan is integrated with your ERP, MES and WMS, and tasks can be initiated directly in those systems - without retyping or verbal translation.
A producer working to strictly enforced customer schedules - planning previously run in Excel, with around 15% of output arriving late
| Before | After | |
|---|---|---|
| Delays | 15% | 5% OTIF 95% |
| Planning time | 3 hours | 5 min. |
| Planner workload | 100% | 25% |
Figures are engagement-specific results for one deployment. Your starting point and outcome will differ - the planning assessment is where we say honestly what is realistic for your plant. We deployed the same engine in wood products: AI production planning in hardwood flooring, where planning the cutting sequence recovered 2.4 points of lumber recovery.
The planning engine is ours - we build it, we tune it, we run it in your plant. The people who configure your constraints are the people who wrote the algorithm.
The scheduling core - a genetic algorithm and related optimisation methods - is selected and tuned to fit your problem. We only call it AI where the system searches, evaluates and improves rather than executing fixed rules.
Data security is not a feature we add later. We operate under ISO/IEC 27001 with full GDPR compliance, and integrations use industry-standard authentication.
Our AI systems run in factories, hospitals and financial institutions. Planning deployments draw on the same engineering bench as our computer vision and machine learning work.
An ERP module applies infinite capacity, schedules in the order you enter, and returns one plan. APS searches: it respects finite capacity and sequence-dependent changeovers, evaluates thousands of alternatives, and shows you the trade-offs between them. Most ERPs were never built to solve that problem, and their planning modules reflect it.
No - that is the point. The engine sits on top of your ERP, MES and WMS and pushes the approved plan back into them. Most deployments start alongside the existing process rather than replacing it, and your spreadsheets stay valid inputs while you adopt.
The scheduling core is an optimisation method - a genetic algorithm and related techniques - selected to fit the problem. We would only call it AI where the system searches, evaluates and improves rather than executing fixed rules.
That is usually the first thing the assessment addresses. Data reconciliation is step one of every deployment, and in practice the existing spreadsheets stay valid as inputs during adoption rather than being replaced upfront. Poor master data changes the timeline, not the feasibility.
Both depend entirely on your scope - the number of resources and lines, the state of your master data, and how many systems need integrating. Rather than quote a figure that would not fit your plant, we scope it in the planning assessment: one conversation, your current setup, and a phased timeline you can judge for yourself. That is also where we will tell you if planning is not your bottleneck.
They move from calculating to deciding. In the food deployment, planner workload fell to about a quarter of its previous level - the time went into exception handling and process work rather than being removed. The judgement stays human; the arithmetic does not.