Use case · 99%+ record accuracy, counted continuously
Make the system match the shelf - and keep it that way.
Most shops carry inventory records they don't fully trust, so planners hedge with excess stock and jobs still stall on missing parts. Cortrova keeps the perpetual record honest with barcode and RFID scanning on every move, cycle counts scheduled by ABC class, variance reconciliation on a full audit trail, and AI engines that predict shortages and recompute safety stock before a count ever drifts.
The challenge
- !On-hand counts in the system never match what's actually on the shelf, so planners hedge with excess stock and still get caught short
- !Accuracy is only checked at the annual physical, so the perpetual record drifts unnoticed for months and forces a plant-wide shutdown to fix
- !Stockouts surface only when a job hits a missing part - too late to expedite without breaking schedule
- !Dead and slow-moving stock ties up cash because nothing flags it until the annual write-off
- !Lot and serial history lives across paper and spreadsheets, so a recall, trace, or audit pull turns into a multi-day reconstruction
How Cortrova answers
- ✓Barcode and RFID scanning on every receipt, putaway, pick, move, and count keeps the perpetual record live instead of letting it drift between physical counts
- ✓Cycle counts are scheduled by ABC class so the highest-value and fastest-moving items are checked most often, and run as blind counts to remove bias
- ✓Every variance is reconciled with a documented adjustment on a full audit trail of IN, OUT, and ADJUSTMENT movements, so accuracy improves with a clean financial trail rather than a black-box correction
- ✓Lot and serial genealogy follows material from receipt through inspection, stock, and ship, so any unit can be traced forward and backward in seconds
- ✓Stockout Prediction, Turnover AI, and automated Safety Stock engines forecast shortages and recompute reorder points as demand shifts, so buffers stay right and counts stay meaningful
- ✓One shared balance with purchasing, quality, and shipping means available-to-promise reflects only approved, on-shelf material - and the same unit is never promised twice
How accuracy holds
Three things that keep the record honest.
Scan every move
Barcode and RFID capture on receiving, putaway, picking, transfers, and counts updates the perpetual record at the moment material moves, so there's no gap for the count to drift into.
Count continuously
Blind cycle counts scheduled by ABC class check high-value and fast-moving items most often, closing the gap between system and shelf without ever shutting the plant down for a full physical.
Reconcile with a trail
Every count variance posts a documented adjustment on the audit trail, so accuracy climbs with a defensible record finance can reconcile against - not an untraceable correction.
Beyond the count
Accurate stock is only useful if it's the right stock.
Counting correctly keeps the number honest; the predictive layer keeps the number right. The same balance that cycle counting protects feeds three AI engines that watch for the shortages and the dead stock a snapshot count would never reveal.
Stockout Prediction reads consumption, open demand, and lead times to flag parts trending short while there's still time to expedite
Turnover AI surfaces slow movers and dead stock so working capital isn't frozen on shelves nobody pulls from
Safety Stock recalculates reorder points as demand and variability shift, so buffers stay sized to reality instead of a number set once and forgotten
When it counts
Traceability that survives an audit or a recall.
Accuracy isn't only about quantity - it's about knowing exactly which lot and serial is where. Genealogy links every unit back to its supplier, receipt, and inspection result and forward to the orders it shipped on.
Quarantine and hold segregate nonconforming or controlled material automatically, so held stock can't be counted as available
Expiration and shelf-life control pull aging lots before they ship
An AS9100 or 21 CFR Part 11 trace becomes a query that returns in seconds, not a paper chase across spreadsheets
How it works
Adopting inventory accuracy.
Baseline the record
Establish SKUs, bins, and costing, then bring the perpetual balance to a known state with an opening count so every later variance is measured against a trusted starting point.
Scan at the point of movement
Barcode and RFID capture every receipt, putaway, pick, transfer, and adjustment, so the system reflects the shelf in real time instead of at the next physical.
Cycle count by class
Counts are scheduled automatically by ABC/XYZ classification - A items most often - and run blind, so the highest-impact stock is verified continuously without a shutdown.
Reconcile and learn
Variances post documented adjustments on the audit trail, while the predictive engines fold consumption and demand back in to keep reorder points and safety stock accurate.
FAQ
Questions, answered.
What inventory accuracy can a shop realistically reach with Cortrova?
Manufacturers that scan at every movement and run scheduled cycle counts typically operate at 99% or better record-to-shelf accuracy, instead of the 80-90% that's common when accuracy is only checked at an annual physical. The combination matters: barcode and RFID keep the perpetual record live between counts, and ABC-class cycle counting verifies the highest-value items most often, so drift is caught in days rather than discovered at year-end.
How does cycle counting keep the record accurate without shutting down production?
Counts are scheduled by ABC class so your highest-value and fastest-moving items are checked most often, and they run as blind counts against barcode or RFID scans - a few locations at a time, woven into normal operations. Each variance is reconciled with a documented adjustment on the audit trail. That keeps the perpetual record accurate continuously, so you no longer need a plant-wide shutdown for a full physical inventory.
Why does inventory accuracy matter beyond the count itself?
An accurate balance is what every downstream decision relies on. MRP only raises the right purchase orders if on-hand is correct; available-to-promise only protects a ship date if committed material is real; and a quality hold only works if quarantined stock can't be counted as available. Inaccurate inventory forces planners to hedge with excess stock and still leaves jobs stalling on missing parts - so accuracy directly drives both working capital and on-time delivery.
How do the AI engines support accuracy rather than just reporting on it?
Accuracy isn't only a quantity-matching problem - carrying the wrong stock is its own form of inaccuracy. Stockout Prediction flags parts trending short inside their lead time so a shortage is expedited before it becomes an emergency adjustment; Turnover AI identifies dead and slow-moving stock so it's worked off rather than miscounted year after year; and the Safety Stock engine recomputes reorder points as demand shifts so buffers stay sized to reality.
How is lot and serial traceability tied to inventory accuracy?
Every unit carries lot and serial genealogy from receipt to ship, linked back to its supplier, receipt, and inspection result and forward to the orders it shipped on. That makes accuracy auditable: quarantine and hold segregate nonconforming or controlled material so it's never counted as available, expiration control pulls aging lots, and a recall or an AS9100 or 21 CFR Part 11 trace returns in seconds instead of taking days to reconstruct.
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