Predictive intelligence
You found out on Friday.
You needed to know on Monday.
Most operational problems are visible in the data days before anyone reacts to them. Predictive work is only worth doing where earlier warning would genuinely change what somebody does.
If a report or a rule would solve it more cheaply, we will tell you that instead.
Sound familiar?
Nobody asks for a prediction.
They say they keep finding out too late, and that the same firefight happens every month.
The backlog is obvious once service is already slipping
By the time the warehouse feels it, the overtime is already committed.
Late orders get noticed with hours left, not days
Everyone can see the problem. Nobody has time left to solve it.
Excess stock shows up after the cash has gone
The report is accurate. It is also six weeks after the useful moment.
A supplier is quietly getting worse
It becomes visible when it disrupts production, not while it is drifting.
The experienced manager just knows
Which works well until they are on leave, or they retire.
How RAPID PI works
Not another dashboard. A decision engine.
Most analytics projects stop at visibility. RAPID PI is designed to continue from insight to decision to measurable outcome.
- Observe: Bring together the operational history already being generated by your business systems.
- Predict: Identify patterns associated with future risk, cost or opportunity.
- Explain: Show the factors driving the prediction and the confidence behind it.
- Recommend: Identify practical actions and alternative scenarios.
- Quantify: Estimate the financial or operational value of taking action.
- Approve: Keep people responsible for important business decisions.
- Measure: Compare predicted benefit against the outcome actually achieved.
Customer experience
Customers describing what we are like to work with.
The strongest evidence of how we work comes from the organisations we work with.
80%
of our business comes from existing customers and referrals.
The best I’ve ever experienced in over 20 years of collaboration.
Programme Manager, Hill & Smith Plc
See what it looks like
Three warehouse decisions, shown honestly.
These are demonstrations built on synthetic data. They show the operating experience and the decision each one supports. They are not customer results.
Warehouse Slotting Advisor
Are products stored for the way customers order today?
Movement intensity by zone, with the current layout and a pattern worth investigating.
Optimisation model · warehouse layout
Zone movement map
Movement intensity is an unitless index derived from historical pick and put-away activity. It describes today's demand pattern rather than forecasting future demand.
Zone A1 · front pick face
High movement
Highest movement, closest to dispatch
Zone A2 · front reserve
Moderate movement
Mixed velocity, some slow movers held forward
Zone B1 · mid aisle
Low movement
Capacity available for faster items
Zone B2 · mid reserve
Low movement
Stable, mostly replenishment stock
Zone C1 · rear aisle
High movement
Frequently picked items sitting furthest from dispatch
Zone C2 · rear reserve
Low movement
Low movement, appropriate for slow lines
The recommended view redistributes movement towards the zones with the shortest travel to dispatch. It is a prompt to investigate, not an instruction to relayout the warehouse.
Labour and Backlog Forecast
Where is workload likely to exceed capacity?
Expected workload against available capacity by shift window, with the pressure points named.
Forecasting model · capacity
Expected workload against available capacity
Forecast horizon: next six shift windows. Values are an illustrative workload index, not hours, lines or labour units.
Mon AM
State: Normal
Expected workload62Capacity80- Illustrative range
- 55 to 70
- Contributing signals
- Order intake close to the usual pattern
- Time still available to act
- No action expected
Expected workload within available capacity
Mon PM
State: Normal
Expected workload74Capacity80- Illustrative range
- 64 to 85
- Contributing signals
- Carry-over from the morning wave
- Time still available to act
- Around 6 hours before the window opens
Expected workload within available capacity
Tue AM
State: Watch
Expected workload81Capacity80- Illustrative range
- 70 to 94
- Contributing signals
- Higher order intake. Two replenishment runs due
- Time still available to act
- Around 18 hours before the window opens
Expected workload close to available capacity
Tue PM
State: Risk
Expected workload96Capacity80- Illustrative range
- 82 to 111
- Contributing signals
- Promotional order lines. Backlog carried from the morning
- Time still available to act
- Around 24 hours before the window opens
Expected workload above the illustrative risk boundary
Wed AM
State: Risk
Expected workload104Capacity84- Illustrative range
- 88 to 122
- Contributing signals
- Peak intake day. Backlog accumulating across zones
- Time still available to act
- Around 42 hours before the window opens
Expected workload above the illustrative risk boundary
Wed PM
State: Watch
Expected workload88Capacity84- Illustrative range
- 73 to 104
- Contributing signals
- Residual backlog. Cut-off pressure on next-day orders
- Time still available to act
- Around 48 hours before the window opens
Expected workload close to available capacity
The range shown is illustrative. A deployed forecast should express uncertainty in a way the underlying method genuinely supports, rather than presenting a single confident number.
Watch and Risk boundaries here are illustrative. In production they are agreed with the operation against the cost of a false alarm, the cost of a missed problem, the service requirement and the capacity available to intervene.
The forecast highlights where pressure may build. Decisions about resourcing, priority and workload remain with warehouse leadership.
Pick Wave SLA Risk
Which work is most likely to miss its service window?
A ranked worklist with contributing factors and the time still left to act.
Risk prediction model · fulfilment
Work most likely to need attention
Wave 4182 · next-day cut-off
High risk · illustrative score 72/100
- Time remaining
- 1h 40m to cut-off
- Progress
- 38% complete
- Evidence behind the score
- Data basis: wave, workload and completion history for this zone
Contributing signals
- Backlog increasing in the zone
- Wave larger than the usual profile
- Current throughput below the expected level
Recommended attention. Review resource or priority allocation for this wave.
Wave 4176 · standard service
Medium risk · illustrative score 48/100
- Time remaining
- 3h 05m to cut-off
- Progress
- 55% complete
- Evidence behind the score
- Data basis: open exceptions and pick path context
Contributing signals
- Two open exceptions on lines
- Pick path crosses a congested aisle
Recommended attention. Check the open exceptions before they hold the wave.
Wave 4169 · standard service
Low risk · illustrative score 17/100
- Time remaining
- 5h 20m to cut-off
- Progress
- 74% complete
- Evidence behind the score
- Data basis: progress against the historical completion pattern
Contributing signals
- Progress ahead of the historical completion pattern
Recommended attention. No action expected. Monitor only.
A risk score ranks attention. It should not be read as a probability unless a deployed model has specifically been calibrated and presented that way.
Contributing signals show what the model associated with the output. They are evidence for investigation rather than proof of cause.
Two minutes
Is prediction the right answer for you?
Answer a few questions and we will suggest a likely next step. Sometimes that step is reporting or automation rather than a model.
Question 1 of 7
0 of 7 answered
Before you call
The questions leaders ask us first.
A conversation, not a demo
Tell us what you keep finding out too late.
Describe the decision, when you currently discover the problem, and what you could still do with a few more days. We will give you an honest view on whether a model is justified.