All Sessions /

Education

Education

Jonathan Smalley
  • Workshop

How AI Is Changing Manufacturing from the Ground Up

A 30-Day Production Case Study Using RotoEdge Pro

Jonathan Smalley

CEO, SmaK Plastics and RotoEdge Pro

Can you hand day-to-day production management to AI, and trust it?

This session presents a structured 30-day production study testing how far AI can go in guiding manufacturing decisions on the shop floor. The premise: focus your team on making good parts. AI does the rest.

The Foundation

RotoEdge Pro, a real-time manufacturing data platform, captures hundreds of variables during every logging operation across all key production activities. Its AI module uses that data to analyze conditions, forecast outcomes, and generate production-level instructions in real time. This study puts that capability to the test under live, changing production conditions.

The Study

Over 30 days, AI took an active role in production guidance, tracking three parallel data threads to isolate the specific value AI guidance adds, rather than crediting AI for improvement that would have happened anyway:

  • A baseline forecast: what AI predicts if nothing changes, built from the prior month’s KPI data
  • AI-guided actuals: real results as shift supervisors and production leads implement AI’s recommended changes
  • An updated AI forecast: AI’s revised predictions as changes are adopted, shift by shift and day by day

What’s Measured

Five core KPIs: Machine Productivity, Machine Scrap Rate, Operator Performance, Number of Parts Molded, and Sales Value of Parts. Together they cover throughput, quality, labor, and commercial output.

What Attendees Will See

Actual production data, AI recommendations, and management decisions from the 30-day plant study, including when managers followed the guidance and when they chose a different course.

The presentation covers:

  • What real-time data AI needs to produce useful recommendations
  • How AI converts labor, machine, material, scrap, inventory, and order data into shift- and day-level production guidance
  • Where the AI baseline differed from actual results, and what changed when managers applied its recommendations
  • The effect of AI-guided decisions on productivity, labor performance, scrap, inventory, revenue, and customer-order fulfillment
  • Where manufacturers can begin, how to evaluate AI readiness, and which results to measure

This case study took place in a rotational molding plant. The pressures it measured (labor changes, machine delays, material shortages, scrap, shifting customer priorities) apply across labor-intensive manufacturing.

Back to Top