Start Small With AI Production Planning: A Pilot Checklist
Plan one supervised manufacturing workflow with a clear scope, checked data, baseline measures, exception tests and evidence for deciding whether to expand.
Start an AI production-planning pilot with one complete workflow, named users and a measurable decision, while the planner retains release authority. A material-readiness check for one product family is a useful candidate when planners repeatedly chase stores and purchase before releasing jobs.
Keep the scope small enough to investigate each exception, but complete enough to reach a decision. A dashboard disconnected from the action owner cannot show whether the proposed workflow works.
Write a pilot charter
| Decision | What to agree |
|---|---|
| Workflow | For example: review material readiness before releasing jobs in one product family |
| Users | Named planner, stores owner, purchase contact and supervisor |
| Inputs | Accepted orders, approved BOMs, usable stock, reservations and expected receipts |
| Authority | Who may recommend, approve, release and change a job |
| Baseline | Measures, definitions, observation period and relevant product mix |
| Exceptions | Late receipt, inspection hold, wrong unit, stale stock update and changed order |
| Exit decision | Evidence required to expand, extend or stop the pilot |
Choose enough operating cycles to include the exceptions you need to test. Set the review date with the people doing the work; there is no fixed implementation duration that fits every factory.
Prepare and check the source data
Have engineering approve the BOM and revision. Stores checks item identity, units, locations and status. Purchase confirms expected receipts and their evidence. Planning defines the actual decision cutoff.
Trace one proposed job from demand to its component requirements. Compare the result with a manual calculation and investigate differences. ERPNext’s production-plan documentation provides an example of connected planning records; the pilot should verify the configuration your team will use.
Retain data freshness beside the answer. A recommendation based on yesterday’s stock should not look like a current stores confirmation. Test what the system does when an input is missing rather than assuming an empty field means zero.
Run supervised decisions and record disagreements
Compare each recommendation with the planner’s review during the pilot. Keep one authoritative release process so two systems cannot independently commit the same stock or machine time. Record the recommendation, the planner’s decision, any override and the reason.
For a material hold, ask whether the proposed job quantity excludes unavailable stock. For a late purchase receipt, ask whether the recommendation changes and who receives the exception. If the tool cannot explain the source of a quantity, record that as a gap to resolve.
| Measure | Definition to keep consistent |
|---|---|
| Material-related late starts | Jobs starting after the agreed plan because required usable material was unavailable |
| Missing-update incidents | Decisions delayed or incorrect because a required stock/receipt update was absent |
| Exception response time | Elapsed time from recorded exception to the responsible person’s recorded disposition |
| Decision agreement | Reviewed recommendations accepted or overridden, with the reason for each override |
Decide whether to expand
Review both successful and failed cases. Confirm that quantities reconcile, users know how to correct a record, exceptions reach an owner and the team can export the tested records. Compare the measures with the baseline under a similar workload; a quieter week can improve the numbers without improving the workflow.
Expand after the agreed acceptance checks pass. Extend the pilot if a fix is promising but needs another operating cycle. Stop or redesign it if the inputs or decision boundary cannot support the intended task.
Use the planning cost worksheet to separate pilot charges from later rollout costs. For an Optiwise pilot discussion, bring the charter and one job that currently needs several manual follow-ups.
Questions from manufacturing teams
What is a good first AI production-planning pilot?
Choose a recurring, checkable decision such as material readiness for one product family. Include the users and records needed to take the decision through an approved action.
Should AI release or reschedule jobs during the first pilot?
Keep release authority with the responsible planner while recommendations and exceptions are evaluated. Any later automation needs an explicit authority boundary and tested recovery process.
When should the pilot expand?
Expand when the agreed data, reconciliation, user, exception and export checks pass, and the evidence supports the next scope. A fixed number of days alone is not an acceptance criterion.
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