Prescriptive analytics for operations teams
Turn complexity into clear actions. Move past dashboards that report what happened to systems that recommend what to make, order or stock next — and show the reasoning behind each recommendation.
The dashboard problem
Many operations teams have invested in reporting and are disappointed with the result. The dashboard is accurate, it is current, and almost nobody opens it. The reason is simple: it describes a situation, and the person looking at it still has to work out what to do.
That translation step is where the time goes. A planner sees that coverage on an item is low, then checks the open purchase orders, then the supplier's recent performance, then whether a customer order is already committed against it, and finally makes a call. They do this dozens of times a week, and the quality of the answer depends on how much time they had.
Prescriptive analytics moves that reasoning into the system. The output is not a chart — it is a ranked list of actions, each with the evidence that produced it.
Where prescriptive sits
Four kinds of analytics, each answering a different question. Most operations have the first two and stop there.
| Kind | Question it answers | Typical output |
|---|---|---|
| Descriptive | What happened? | A report or dashboard |
| Diagnostic | Why did it happen? | A root-cause analysis |
| Predictive | What is likely next? | A forecast with uncertainty |
| Prescriptive | What should we do? | A ranked, explained action list |
What we deliver
Recommendations in the workflow, with the reasoning attached.
- A ranked action list for the decision in scope, refreshed on your planning cycle.
- The reasoning behind each recommendation — inputs, binding constraint, and what would change the answer.
- Explicit, editable constraints: minimum order quantities, tooling changeovers, capacity, supplier terms, commitments.
- Override tracking, so the decisions your team makes differently become evidence for improving the model.
- A comparison against the simple rule it replaces, so the added complexity has to earn its place.
How it works
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Pick one decision
Not a platform. One repeating decision that costs real money when it is made badly, and that someone makes at least weekly. Scope discipline here is what makes the difference between a tool that ships and one that does not.
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Write down how it is made today
Sitting with the person who makes it. The rules that are written down, and the ones that are not. The exceptions matter more than the general case.
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Make the constraints explicit
Lead times, minimum order quantities, capacity, tooling changeovers, committed orders. Most of these live in people's heads, and writing them down is often valuable on its own.
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Build the recommendation, and the explanation with it
The explanation is not documentation added afterwards. It is part of the output, because a recommendation nobody can interrogate is one nobody will follow.
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Run in parallel before switching
The model recommends, the planner decides, and we compare. Once the recommendations are consistently sound, the default flips. Trust is earned on real decisions.
What you need to get started
A decision worth improving, and the data that feeds it.
- Current stock, open orders and demand history for the items in scope.
- The constraints that apply — supplier terms, capacity, tooling and changeover rules, customer commitments.
- Access to the person who makes the decision today. This is the single most important input.
Typical outcomes
- Routine decisions made consistently, regardless of who is covering.
- Less time spent assembling context before a decision can be made.
- Constraints applied every time, including the ones that are easy to forget under pressure.
- A record of what was decided and why, which makes the next review possible.
Common questions
What makes analytics prescriptive rather than predictive?
A predictive model estimates what will happen — demand next month, the chance a supplier is late. A prescriptive one takes that estimate, applies your constraints and costs, and recommends the action: order this quantity, run this product first, hold this much here. Prediction is an input; prescription is an answer.
Does this replace our planners?
No, and we would be sceptical of anyone selling it that way. It removes the routine decisions — the eighty percent where the right answer is calculable — so planners spend their time on the twenty percent that needs negotiation, judgement or a phone call. Every recommendation can be overridden, and we log overrides because they are the most useful signal about what the model is missing.
How do we know why it recommended something?
Every recommendation shows its reasoning: the inputs, the binding constraint, and what would have to change for the answer to be different. A recommendation a planner cannot interrogate is one they will stop trusting the first time it looks wrong.
What kinds of decisions can be prescribed?
The repeating, constrained ones. What to order and when. Which production order to run first. Where to move stock between locations. Which customer order to allocate scarce material to. Which purchase orders to expedite this week. The common thread is a decision made regularly, against known constraints, where consistency beats improvisation.
Is this an optimisation model? Those can be fragile.
Sometimes, and you are right to be cautious. A brittle optimisation that produces a mathematically perfect plan nobody can execute is a classic failure. We bias towards methods that degrade gracefully, keep constraints explicit and reviewable, and always compare against the simpler rule-based alternative before recommending anything complex.
Find out what your data can already tell you
A free 30-minute data readiness assessment. We look at what you have, tell you what it can support today, and name the first thing worth building. No preparation needed.