The purchase order that will arrive late is usually predictable — from the vendor’s own delivery history and the shipment’s current state. In this demo, TEKAI reads live SAP Business One purchasing data and flags the deliveries at risk, so the expedite call happens before the line stops instead of after.
A late shipment is never just a purchasing problem. It becomes a production stop, a missed delivery and an awkward customer call — and by the time it shows up as an overdue line on a report, every one of those consequences is already in motion.
Every open purchase order carries a promised date; your history carries the truth about which vendors keep them. A vendor who delivers twelve days late on average will, in all likelihood, do it again — and no promised date on a new PO changes that pattern.
That pattern is knowable from data SAP Business One already holds: promised date versus goods-receipt date, purchase order by purchase order, vendor by vendor, going back years. Multiply it across every open PO, then read it against what each late delivery would block — production orders waiting on the material, customer commitments waiting on the production — and you have a revenue-risk report that nobody compiles manually, because compiling it manually would take days and be stale on arrival.
So most teams run on promises. The report that says “these deliveries are on time” is really saying “these vendors said they would be on time” — a very different claim.
On a demonstration database, TEKAI ranks the open purchase orders by delay risk. The ranking draws on vendor punctuality history read against each PO’s promised date: which vendors habitually slip, by how much, and on what kind of order.
Then it answers the question that matters more than “which POs are late” — the downstream exposure. For each risky delivery, the conversation traces which sales orders and production plans that supply line feeds. The output is not a list of late vendors; it is a list of at-risk commitments, each one connected to the PO that threatens it.
Every figure in the video comes from a demonstration database, not customer data. On your own system the same conversation runs against your vendors, your promise-versus-actual history and your open commitments.
Follow-ups go where a planning meeting would go: which of the risky POs have alternates in the vendor master, which production orders can re-sequence around the gap, which customer commitments need an honest revised date. The point of days of warning is that all three of those moves are still available — none of them survives to the day the material was due.
The ranking is by consequence, not lateness. A commodity item with buffer stock can slip a week and nobody notices; the imported component gating next week’s dispatches cannot slip a day. A plain overdue-PO report treats those two identically. A consequence-ranked view puts the second one at the top with the reason attached.
That is the difference between a delay report and a revenue-protection tool. The first tells purchasing what is late; the second tells the business what is at stake — and gives the team days of warning to act on it: expedite the critical PO, arrange alternate sourcing, re-sequence production, or reset the customer’s expectation honestly while there is still goodwill to protect.
SAP Business One (SQL or HANA) with purchasing history · TEKAI at ₹5,000/company/month India, $150 global · your own AI subscription (Claude, ChatGPT or Perplexity) · permission-scoped access, nothing trained on your data (/tekai/security/).
Primarily from your own vendor delivery history — promised date versus actual date, learned per vendor from the purchasing records SAP Business One already holds.
Yes — the risk maps through to the open sales orders each supply line feeds, so purchasing sees the revenue exposure, not just the late PO.
Alerts go to your team on the WhatsApp rail; chasing vendors stays your call. The AI surfaces the risk — the expedite conversation remains a human decision.
Yes — one TEKAI subscription per company covers the query and alert patterns shown here.
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