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Manufacturing playbook

AI for manufacturing — without another QA hire on the payroll.

Computer-vision triage, predictive maintenance, operator handoff redesign. The Handover Method applied to the workflows that move OEE, scrap rate and capacity-per-shift.

Manufacturing — operating reality

The patterns we keep walking into

Four operational patterns that show up across the sector, regardless of plant size or product line.

QA bottleneck masked as a hiring problem

Defect triage piles up at end-of-line. Adding inspectors moves the queue but not the rate. The fix is upstream — flag patterns 30 seconds earlier, not 30 seconds slower.

OEE numbers your operators don't trust

If the floor overrides the dashboard daily, the dashboard is wrong, the data pipeline is broken, or the metric formula doesn't match operating reality. AI on top of distrusted data inherits the distrust.

Maintenance scheduled by calendar, not by signal

Calendar-driven maintenance buys insurance you don't need on machines that are fine and skips the ones that are silently degrading. Signal-driven maintenance trades calendar predictability for capacity.

Operator knowledge that walks out the door at retirement

The senior operator who knows when the line is about to drift takes 15 years of pattern recognition with them. That pattern recognition is exactly what an AI workflow encodes — if you capture it before they leave.

The Handover Method

Why The Handover Method fits manufacturing

Production environments don't tolerate consultants who linger. The Handover Method's close-on-handover, built-in progress reviews and runbook transfer map directly to how plant managers think: pilots that finish, ownership that transfers, and a written SOP your shift supervisor can run.

  • Work focused on one workflow, from the floor map to the handover
  • Runbook handover hands operating control of the workflow to your team at close
  • Built-in progress reviews, with the calls recorded in the decision log
What the engagement looks like

What a manufacturing pilot looks like

Each phase ends with a progress review that checks baseline and direction against what discovery showed. The decision log captures the call.

  1. Phase 1

    Setting the baseline + workflow map

    Walk the floor, align the KPI baseline with finance, document the workflow as it runs today (not as the SOP claims). Decision log opens.

  2. Phase 2

    AI workflow design + progress review

    Design the AI workflow against the documented baseline. The progress review checks whether the baseline and the direction of the work still match what discovery showed. If needed, we adjust direction, and the decision log captures the call.

  3. Phase 3

    Build, integrate, parallel-run

    AI workflow built and integrated with existing systems. Parallel-run alongside the current process so operators see both readings in real time.

  4. Phase 4

    Switchover + runbook draft

    Operators take the AI workflow as the primary path. Manual runbook drafted in parallel — the version your shift supervisor will own.

  5. Closeout

    Handover + close

    Runbook taken over by the operating team. Decision log handed over. Engagement ends.

Manufacturing — common questions

Operational questions before week one

The three questions that come up before every kickoff.

  • No. The first phase is a baseline + workflow walk; we work from your existing data exports and operator interviews. Wider plant access is arranged only once it is clear what the pilot needs.

Want this run on your floor?

30-minute call, written agenda, no follow-up unless you ask.

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