๐ŸญManufacturing

Process experiments that reduce waste and increase throughput.

Maintenance schedules, tooling parameters, quality control methods, shift structures โ€” manufacturing improvements are often opinions. Decision Process turns them into controlled experiments with measurable outcomes.

Experiment Templates

Ready-to-run experiments

Preventive Maintenance Schedule A/B

โ€œWeekly PM reduces unplanned downtime more than bi-weekly PM at acceptable cost.โ€

Conditions

  • Bi-weekly PM (control)
  • Weekly PM
  • Daily inspection only

Metrics

  • Unplanned downtime (hrs/mo)
  • Defect rate (%)
  • PM labor cost ($)
Production linesWeekly PM โˆ’31% downtime

Cutting Speed Optimization

โ€œA 15% increase in cutting speed maintains surface finish quality while improving throughput.โ€

Conditions

  • Standard speed (control)
  • +10% speed
  • +15% speed
  • +20% speed

Metrics

  • Parts per hour
  • Surface finish Ra (ยตm)
  • Tool life (parts/edge)
CNC batches+15%: throughput +11%, finish OK

Quality Control Method Comparison

โ€œStatistical process control catches defects earlier than end-of-line inspection.โ€

Conditions

  • End-of-line inspection (control)
  • SPC โ€” every 20 units
  • SPC โ€” every 10 units

Metrics

  • Defect escape rate (%)
  • Scrap cost per 1,000 units ($)
  • Inspection labor (min/unit)
Production shiftsSPC-20: โˆ’44% defect escape

Shift Structure Comparison

โ€œThree 8-hour shifts produce higher throughput than two 12-hour shifts due to fatigue effects.โ€

Conditions

  • Two 12-hr shifts (control)
  • Three 8-hr shifts

Metrics

  • OEE %
  • Units per labor hour
  • Error rate in last 2 hrs of shift
Production weeks8-hr shifts: OEE +4.2 pts

Assembly Jig Comparison

โ€œA new pneumatic jig reduces assembly cycle time without affecting defect rate.โ€

Conditions

  • Manual jig (control)
  • Pneumatic jig

Metrics

  • Cycle time (sec/unit)
  • Defect rate (%)
  • Operator fatigue rating (1โ€“10)
Assembly stationsPneumatic jig โˆ’19 sec/unit

Worked Example

Maintenance schedule experiment across 6 production lines

A plant manager tests two preventive maintenance schedules across 6 identical production lines over 3 months. 3 lines stay on bi-weekly PM; 3 switch to weekly PM. Unplanned downtime and defect rate are tracked weekly.

Results: unplanned_downtime (hrs/month)

Bi-weekly PM (control)

mean: 14.2 hrs/month

95% CI: 11.8โ€“16.6

Weekly PM

mean: 9.8 hrs/month

95% CI: 7.9โ€“11.7

P(weekly PM better) = 94%

Weekly PM reduces unplanned downtime by 31% (d = โˆ’0.83, large effect). At 94% posterior probability this exceeds the plant's 90% threshold. The added PM labor cost ($420/line/month) is offset 3:1 by reduced downtime cost ($1,280/line/month saved). Recommend switching all 12 lines to weekly PM.

Run your first manufacturing experiment

Private beta โ€” tell us about your use case and we'll get you set up.