Cycle time & process stability
Review molding processes, setup information, quality requirements, and machine behavior. Find opportunities to improve output while keeping the part and process within requirements.
MANUFACTURING & IMPROVEMENT
Better performance.
Built into the process.
From molding cycles and changeovers to maintenance and material flow, improve the systems your people depend on every shift.
MANUFACTURING EXPERIENCE. APPLIED AI.ADVANCE NOW / MIKE MAIER
THE WHOLE OPERATION
Find the constraint.
Make the improvement last.
Setup & changeover reduction
Use setup-reduction and Lean methods to examine preparation, tooling, staging, and handoffs. Make the next changeover more repeatable and less dependent on one person.
Lean & continuous improvement
Apply Lean, Toyota Production System methods, 5S, standard work, and structured problem solving to real production issues. Build habits that survive after the improvement event.
Maintenance & setup teams
Connect preventive maintenance, spare-parts organization, setup standards, and production priorities. Use the knowledge already held by the people who keep the plant running.
Statistical problem solving
Use Six Sigma methods, process data, capability analysis, and appropriately designed studies to separate variation from a meaningful signal and check proposed changes.
Plant systems & equipment
Evaluate presses, automation, cooling systems, material handling, and supporting infrastructure in the context of the whole operation.
AI MEETS PROCESS KNOWLEDGE
Your existing records
are a place to start.
Setup sheets. Quality logs. Downtime notes. Test results. There is often more to learn from what you already collect.
AI can help organize records, explore patterns, and prepare useful comparisons. Manufacturing experience tells us which questions matter—and which apparent patterns need more investigation.
We connect the analysis to a decision, a person responsible for the change, and a way to see whether it worked.
See examples from my experienceAI ON THE PRODUCTION FLOOR
Use what the
operation already knows.
Start with the records
Setup sheets, downtime notes, inspection results, maintenance history, and production reports can all become better starting points for investigation when their context is clear.
Connect analysis to action
Use AI to help organize, compare, and explore. Then ask what the finding means for the process, what needs checking, and who will own the next step.
WORK WITH MIKE
If you’d like
my help, I’m here.
My personal consulting is a premium service for people and organizations that want my direct involvement. If your budget is tight, start with AN Creation. The resource is there to help you make progress without hiring me.
Talk with MikeMANUFACTURING THINKING / PRACTICAL AI
Define. Measure. Analyze.
Improve. Control.
A familiar discipline for improving a process—whether the work happens on a production floor or in an AI workspace.
- 01 / DEFINE
Name the problem
Whose need are we addressing? Set the scope and describe a useful result.
- 02 / MEASURE
Establish the baseline
Find out what happens today. Check that the records and measurements are dependable.
- 03 / ANALYZE
Investigate the cause
Test explanations against evidence. A plausible AI answer is a hypothesis to check.
- 04 / IMPROVE
Try a change
Run a bounded trial and compare the result with the baseline.
- 05 / CONTROL
Make it hold
Update the procedure, assign ownership, and monitor whether the gain lasts. Revisit the problem when performance changes.
This is DMAIC, the Six Sigma framework for improving existing processes. It gives AI-assisted improvement a purpose, evidence, and follow-through.
Framework reference: ASQ’s DMAIC overview.
MANUFACTURING THINKING / PRACTICAL AI
Plan a change.
Learn from what happens.
Plan → Do → Study → Act → repeat.
The learning cycle associated with W. Edwards Deming is Plan–Do–Study–Act (PDSA). Predict what a change will accomplish, try it, study the outcome against that prediction, then use what you learned to choose the next step.
For an AI-assisted setup review, that might mean trying a new way to compare approved setup sheets, studying which discrepancies it catches or misses, and revising the instructions before expanding its use.
Keep the approved setup record authoritative. A proposed change still goes through the plant’s normal review.
Framework reference: The Deming Institute’s PDSA explanation.
Translate AI language into plant-floor languageOLD PRINCIPLES / NEW TOOLS
The principles keep coming back.
The tools keep moving forward.
My experience with Toyota Production System methods, Deming’s ideas, and Lean Six Sigma shapes how I approach AI today.
These are distinct bodies of work, but I keep returning to familiar questions: What does the customer need? How does the work actually flow? What does the evidence show? How do we make an improvement hold?
The history reaches back well before today’s AI. Deming taught Japanese managers and engineers in 1950, helping advance the use of statistical methods and learning in industry. Those lessons remain useful when we organize work around software and AI agents.
I see another opportunity to apply that discipline now. More of the execution can move to agents: gathering records, running repeatable analyses, preparing charts, and carrying a defined task through its steps. We still need clear assignments, dependable inputs, and a way to check the result.
The opportunity is to make good improvement practice easier to carry out, more often, by more people.
Historical reference: The Deming Institute’s biography.
How AI can reduce the work of analysis