Common challenges
Six systems.Six different Tuesdays.
Scans, telematics, sensors, WMS and ERP each hold a version of the same shift, and by the time someone tries to reconcile them the story has already diverged. The fix isn't another dashboard bolted on top — it's landing every source in one event model, so a truck's dwell time sits next to the pick order it was waiting to serve.
See the connected warehouse
Is this your problem?
How to tell whether your data is connected, or just adjacent.
The measure that tells you is whether a single shift can be reconstructed end to end — pick to putaway, dock to dispatch — from one model, without anyone touching a spreadsheet. If getting there still means a chain of manual joins across separate exports before yesterday's shift reads as one story, the systems aren't disconnected by accident; nothing was ever built to make them agree.
The gap is a familiar one. Operational visibility stays one of the persistent gaps in warehousing despite years of heavy digital investment — precisely because a new system usually adds another version of the truth rather than reconciling the ones already in the building.
WareBee scores how many of those manual joins your operation still needs against a peer set matched on order profile, SKU count and footprint — never an industry median — so 'our systems don't talk to each other' becomes a number you can compare, argue with and act on.
What's actually causing it
Five reasons the picture won't hold still.
Each one is measured on your digital twin from data you already generate, so you can see which applies to you before starting another integration project.
Each system keeps its own truth
The WMS, the ERP and the telematics platform each hold a version of the same event, and none of them defer to the others.
A pick recorded in the WMS, a dwell time recorded in telematics and a temperature reading recorded by a sensor can all describe the same minute of the same shift, but nothing forces them to agree with each other. A knowledge graph resolves them into one record instead of leaving three systems to disagree quietly in the background.
Events timestamped differently
A scan, a telematics ping and a sensor reading can log the same moment on clocks that don't quite match.
Small clock drift between systems is invisible in any one of them alone, but it breaks any attempt to line up a scan against the truck it was waiting on. Landing every source in the same event model is what lets a truck's dwell time sit next to the pick order it was serving, regardless of which system's clock recorded it.
Manual spreadsheet joins
Someone still exports two systems, pastes them into a third, and calls the result a report.
The join gets redone by hand every time someone asks the question, so the answer takes as long as the analyst has patience for and stops the moment they're pulled onto something else. What should be one query becomes a recurring chore nobody actually owns.
Hardware data dying in its own app
Forklift telematics and sensor readings sit in a manufacturer's own dashboard, cut off from the rest of the operation.
A truck's dwell time or a sensor reading only means something next to the pick order or the stock it relates to, and a manufacturer's standalone app was never built to hold that context. Once the same feed lands in the twin, one truck's dwell time reads the same way as another's, regardless of who built it.
No shared ontology across sites
Two sites use the same WMS field for two different things, and the network total quietly means nothing.
Without one shared definition of an item, a location or an event, a network rollup is really several sites' worth of assumptions added together and mislabelled as a total. One ontology applied everywhere is what makes a number from one site genuinely comparable to the same number from another.
One event model, not one more export.
WareBee's Data Engine ingests every source you already have — scans, wearable events, forklift and MHE telematics, IoT sensors, WMS and ERP feeds, even photos and spreadsheets — and a knowledge graph cleanses, deduplicates and unifies them into a single trusted picture. Nothing gets ripped out to get there: WareBee starts read-only, running alongside the systems you already have, and can begin from a plain CSV if that's the only source a site has ready.
Once every source lands in the same event model, a truck's dwell time sits next to the pick order it was waiting to serve, and a sensor reading binds to the exact location it describes — the reconciliation a spreadsheet was doing by hand becomes something the twin already holds. Ask a question about yesterday's shift and get one answer, not five exports and an argument about whose number is right.
- Every source — scans, telematics, sensors, WMS, ERP, spreadsheets — in one event model
- Read-only to start, no rip-and-replace, a plain CSV is enough to begin
- One shared ontology, so the same field means the same thing at every site
Questions
Data silos, answered.
What counts as a data source?
Six kinds, and none of them need to be perfect first. Barcode and wearable scans, forklift and MHE telematics, IoT and environmental sensors, WMS and ERP feeds, photos and documents read by AI vision, and plain manual or spreadsheet uploads all land in the same knowledge graph. Where a live feed doesn't exist yet, a spreadsheet upload still builds a working model — it isn't a placeholder waiting to be replaced, it's the same twin waiting for a better feed to slot into.
Do I need an integration project?
No. WareBee starts read-only, sitting alongside your WMS and ERP rather than replacing anything, and it can begin from a plain CSV export if that's the only source a site has ready. There's no rip-and-replace and no lengthy IT project gating the first answer — connect what you already have, and better feeds can slot in later without rebuilding the model underneath them.
How are different manufacturers' telematics reconciled?
Telematics land in the same event model as every scan, regardless of which manufacturer built the truck. A dwell time, a travel path or a utilisation reading is normalised into the schema the twin already runs on, so a mixed fleet of different brands and ages reads as one comparable picture rather than several dashboards that don't speak to each other. One truck's dwell time means the same thing as another's.
What if some data is only in spreadsheets?
That's still a usable source. A spreadsheet upload maps into the exact schema an automated feed would use, so a site with no live integration yet still gets a working digital twin from day one instead of waiting on IT. When a proper feed eventually arrives — a live connection or a scheduled export — it slots into the same model the spreadsheet was already feeding, so nothing has to be rebuilt.
How long does it take to land the first model?
Setup takes under a day, with no IT project and no code, and the first pass over your data completes in around an hour. Reconciling every source into one working model happens in hours rather than weeks, which is usually faster than the manual joins it replaces would take just once. You find out whether the sources agree before you've committed to anything longer.
Is this read-only to start?
Yes, by default. WareBee ingests and models your data without writing anything back until you decide otherwise, so nothing about your WMS, ERP or telematics platform changes just by connecting it. When you're ready, approved recommendations flow back through the WMS you already run — the same supported path every other change uses — rather than WareBee writing to systems on its own initiative.