Predictive maintenance
Use sensor and service records to estimate when equipment is likely to need attention, before it fails.
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FORECASTING & PREDICTIVE ML · POSSIBILITIES
What your records
could predict.
Sales history, bookings, job sheets and sensor logs hold patterns that are hard to see by eye. Predictive machine learning uses them to forecast what is coming, spot what is unusual and estimate what is likely. Here are some of the ways it can help.
01 / IN PRACTICE
Five ways to put
your data to work.
Each example below is a common starting point. Real projects are shaped around the data you already keep and the decisions you want to support.
01 / DEMAND FORECASTING
Order the right amount, at the right time.
Forecasts built from your sales history, seasons, promotions and local events estimate what you are likely to sell in the weeks ahead — with a realistic range rather than a single guess. Stock, ordering and production can then be planned around it.
- Stock & reorder planning
- Seasonal & promotional peaks
- Production & ingredient planning
Less money tied up in stock, and fewer empty shelves.
Demand forecastingILLUSTRATIVE DEMO
NEXT 4 WEEKS3,420LIKELY RANGE±6%REORDER BYTue 14 Oct
TODAY
FORECAST WITH A REALISTIC RANGE refreshed weekly
02 / STAFFING & CAPACITY
Staff for the busy hours before they arrive.
Predicting footfall, calls or bookings hour by hour helps match rotas to demand. Planners can see next week’s pinch points and quiet spells in advance, instead of finding out on the day.
- Rota & shift planning
- Call handling & front desk cover
- Bookings, footfall & delivery slots
Rotas that follow the demand, not last year’s habits.
Staffing & capacityILLUSTRATIVE DEMO
08091011121314151617181920
Forecast customersStaff capacity
- Add 2 covers 12:00 – 14:00
- Add 1 cover 18:00 – 19:00
- Start one shift later: 09:00 → 11:00
ROTA SUGGESTIONS FOR REVIEW 7 days ahead
03 / ANOMALY DETECTION
Notice the unusual in the everyday.
A model that learns the normal rhythm of your data — energy use, payments, sensor readings or orders — can flag the points that don’t fit. Leaks, faults, errors and fraud tend to surface sooner, and people only need to review what stands out.
- Energy & water use
- Payments, refunds & expenses
- Sensor readings & machine data
Problems spotted in hours, not at the month-end review.
Anomaly detectionILLUSTRATIVE DEMO
Sat 00:00–06:00 · +48%
MONTUEWEDTHUFRISATSUN
- Mon – Fri within expected range
- Unit 4 compressor left running overnight
1 UNUSUAL PERIOD FLAGGED ~£38 saved per week
04 / CUSTOMER INSIGHT
Know which customers need attention.
Using order history and engagement, a model can estimate how likely each customer is to lapse, renew or respond to an offer. Your team gets a short, ranked list with the reasons behind each score, rather than a spreadsheet to trawl through.
- Lapse & renewal risk
- Lead & enquiry scoring
- Targeted offers & follow-ups
Time spent where it is most likely to matter.
Customer insightILLUSTRATIVE DEMO
- CUSTOMERLAPSE RISK
- Harrogate Deli Co.No order in 7 weeks0.86
- Ripon Garden CentreSpend down 40%0.74
- Dales VeterinarySupport tickets up0.61
- Thirsk MotorsRenewal in 30 days0.38
- Malton BakeryOrdering as usual0.12
TOP 3 SENT TO ACCOUNT MANAGERS every Monday
05 / QUOTE & JOB ESTIMATION
Price jobs with your own history behind you.
Past jobs hold a lot of knowledge about how long work really takes and what it really costs. A model trained on them can suggest an estimate for a new job from its details, with a range and the most similar past jobs to compare against.
- Job duration & labour hours
- Materials & cost estimates
- Lead times & delivery promises
Quicker, more consistent quotes.
Quote & job estimationILLUSTRATIVE DEMO
- Job type
- Kitchen refit
- Floor area
- 14 m²
- Location
- Knaresborough
- Access
- Restricted parking
ESTIMATED LABOUR38 h32 h45 h
- Kitchen refit · 12 m²35 h.93
- Kitchen & utility · 14 m²41 h.89
- Kitchen refit · 16 m²44 h.86
ESTIMATE READY TO CHECK 3 similar jobs
02 / AND MORE
Other problems
ML can take on.
Route & schedule planning
Plan delivery rounds, visits or production runs that make better use of time, vehicles and people.
Recommendations
Suggest related products or services based on what similar customers have bought or booked.
Pricing & markdowns
Test how price changes affect demand, and time reductions on slow or short-dated stock.
Sorting & routing
Classify incoming records, forms or requests and send each one to the right person or queue.
Something else entirely
If a decision is made regularly and there is data behind it, it is worth a conversation.
03 / GETTING STARTED
What a good project
needs to succeed.
History that covers the patterns
Records going back far enough to include the cycles that matter — often at least a year for seasonal businesses.
A decision it will feed
A prediction is only useful if it changes something: an order, a rota, a phone call. We start from that decision.
A simple baseline to beat
Compare the model against what you do now, such as last year’s figures, so the improvement is clear and honest.
Regular refreshes
Businesses change. Models are retrained and checked on a schedule so their accuracy doesn’t quietly drift.
04 / YOUR DATA, PUT TO WORK
Have a problem
in mind?
Tell me about the decision you are trying to make and the data you already keep. We can talk through whether machine learning is a good fit, or whether something simpler would do the job.
Open to projects in North Yorkshire and nationwide