Who buys it
Supply chain or operations leadership accountable for line stoppages and expedite spend.
Plant and Procurement Intelligence Pipeline
Shortage module business case
The exception layer inside the integrated plant/procurement pipeline.
Who uses it
Material planners and buyers working a weekly shortage queue.
The problem
What this costs you today.
Every ERP emits a shortage report. Most emit a wall of red that planners learn to skim, because the report cannot distinguish a shortage someone can fix this week from one that was already unfixable when it printed.
That produces two costs at once: time spent triaging alerts with no available action, and genuine shortages missed inside the noise, which resurface as expedite freight or a stopped line.
The mechanism
What changes.
Ending on-hand is projected per part per week, classified against that part's own thresholds rather than a global rule, and reduced to three numbers a planner can rank: when it first breaks, how long it stays broken, how deep it goes.
Alerts are then triaged by whether anything can still be done. A ten-week lead time against a week-37 breach cannot be expedited, so it routes to planning instead of wasting a buyer's morning.
Parts with master data but no projection are classified separately and surfaced, instead of silently vanishing from the report.
Measured
What was actually observed.
These figures describe the scale of the live data foundation this approach was built against, not a measured alert volume or time saving — no production run of the shortage-classification logic has been executed against real thresholds, and the specific alert counts in the commercial argument are illustrative, not observed. The calculator below runs on figures you supply.
| Metric | Observed | Basis |
|---|---|---|
| Parts carrying weekly positions in the live data foundation | ~6,200 | Shared with the Hybrid Supply Chain Chatbot, same graph, verified 2026-09-13 |
| Vendors | 282 | Same instance |
| Weekly position records | ~295,000 | Same instance |
Where the money is
Your numbers, not ours.
Defaults are illustrative placeholders. Use your own alert volume and rates.
Every figure above is arithmetic on the values you entered. Nothing is a measured result from a prior deployment, and no customer savings are claimed anywhere on this site.
The honest part
When not to buy this.
- Operations small enough that a planner already knows every part by name. The triage layer solves a problem you do not have.
- Where requirement and receipt data is unreliable. Better ranking of bad projections produces confident wrong answers faster.
- If nobody is accountable for working the queue. A better alert list that nobody reads changes nothing.
Adoption
What it actually takes.
Prerequisites
Reliable build requirements, open order quantities, and per-part thresholds. Threshold quality determines whether the output is useful.
Setup effort
Projection and classification are straightforward. The work is threshold review and lead-time accuracy, which is a data exercise rather than an engineering one.
Ongoing cost
Thresholds and lead times drift. Someone must own them or classification quietly degrades.
Engagement
How this would be delivered.
- Build against your existing planning data and hand over.
- Run as a weekly managed cycle with the queue delivered to your planners.
Caveat: No claimed savings from prior deployments. Every figure here is arithmetic on numbers you supply.
Next step
Run the demo, then tell us your numbers.
The fastest way to test any of this is against your own volumes rather than the placeholders above.