stockouts
−25%
Fewer stockouts across the catalog once forecasts drive replenishment.
Demand
Forecasting
Predict demand for every SKU at every location, throughout the year. Position the right stock proactively to increase fill rate while keeping working capital under control.
sales uplift
5%+
Sales uplift per store once the right parts are in place before demand hits.
Product features
Demand modelled with unprecedented precision
01
Inventory data ingested and standardized
Sales, stock, open orders and lead times are read in real time from your DMS or ERP. Interchangeable part numbers are tracked and mapped, leaving one clean history per part, per location.
PartNumber
Transmission, 6F35TRN-6F35/AT2KQV-482137
Water pump, 2.5L8g1z 6c032aZRB-701645
Headlamp, projector LHhdlmp proj lXMD-219873
Alternator, 150AALT150A-REMJFN-590428
Brake caliper, front RHcal fr rh (a)PTH-137562
02
Model demand
Fast movers, seasonal lines and parts that sell twice a year are each modelled differently. Local vehicles on the road, season and customer inquiries feed the number, and every forecast is scored against what actually sold.
03
Recommended stock orders
Forecasts become stock levels and order quantities in your own purchasing screens, each with the reason behind it, then get checked against what actually sold.
04
Each part forecasted, branch by branch
A separate forecast for every part at every location, in real time. React quickly to surge demand and only stock the parts needed for each location's market and share.
What it solves
The right part, in the right branch.
Problem 0101
Sales data is only part of the story
Predicting stock based on sales alone caps your fill rate at its current level. Capturing lost sales and wider market demand means higher conversion and stock turn.
Solution
Every part is forecast at every branch
Each branch is modelled against the vehicles on the road in its catchment, so stock lands where the demand is.
Problem 0202
Slow movers get averaged into nothing
A part that sells four times a year averages out to nearly zero a week, so the reorder point never trips. It only gets noticed when a customer asks for it and it is not there.
Solution
Intermittent demand is modelled as it behaves
Forecasts run on how often a part sells and how much when it does, rather than smoothing it to an average.
Problem 0303
New and superseded numbers start from zero
When a part number changes, its history stays with the old number. The replacement looks brand new, so it is under-ordered for months while demand continues unchanged.
Solution
Supersessions and equivalents inherit demand
History carries through the fitment graph, so a new part number is forecast from day one.
EXAMPLE DASHBOARD
Forecasts that drive the reorder.
Recommended stock per location, plus automated PO creation and in-network stock redistribution.
Stock Profile
Recommended stock profiles by location
Orlando▾
Recommended▾
ORLANDOFL 32801
+−
Overall demand forecast
Budget$1.24M
Stock value$487,300
$402k – $533k
Expected sales$126,415
next 2 weeks
COGS$71,208
of expected sales
Stock turn4.7×
per year
Product range breakdown
Search range, OEM no, part no…
Category
| Product | Part number | Stock | Value |
|---|---|---|---|
| RangeBrake padsFittingFront | OEMVGL-408821AltQJP8814 | Min604Max1,742 | Cost$14,905Opp$22,358 |
| RangeAlternatorsFitting— | OEMNDR-915374AltLTM24907 | Min488Max9,164 | Cost$3,418Opp$5,127 |
| RangeTransmissionFitting— | OEMQSW-221609AltPLV60318 | Min48Max126 | Cost$2,860Opp$4,290 |
| RangeRadiatorsFitting— | OEMHZM-771253AltWFN08R4471 | Min14Max14 | Cost$1,974Opp$2,961 |
| RangeShock absorbersFitting— | OEMBFT-391847AltORQ77142 | Min7Max7 | Cost$1,706Opp$2,559 |
| RangeWheel bearingsFitting— | OEMLCX-104477AltMJU91-628 | Min36Max36 | Cost$1,588Opp$2,382 |
| RangeOil filtersFitting— | OEMWPN-903116AltHKPZ4471 | Min71Max82 | Cost$1,502Opp$2,253 |
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TAKE CONTROL OF YOUR PARTS BUSINESS WITH AI
We'll run your own sales history and parc data through the engine and show you the stockout risk on real SKUs, before you commit to anything.