Production Optimization System

Context

A precision manufacturing company producing custom components faced increasingly complex scheduling challenges. Each job had unique specifications, and the shop floor had dozens of machines with different capabilities and constraints.

The Problem

Manual scheduling couldn't handle the combinatorial complexity. Changeover times varied dramatically based on job sequencing. Rush orders disrupted carefully planned schedules. The planning team spent days creating schedules that were obsolete within hours.

Why generic AI wouldn't work

Standard scheduling software assumes uniform jobs and resources. This shop had highly variable setup times based on job-to-job transitions, machine-specific capabilities, and operator skill dependencies. Generic optimizers couldn't model the real-world constraints that determined actual throughput.

The System We Designed

• Detailed constraint modeling of machines, tooling, and operator skills
• Historical analysis of changeover times by job-pair characteristics
• Multi-objective optimization balancing throughput, on-time delivery, and setup costs
• Scenario planning for rush orders and machine downtime
• Real-time schedule adjustment based on actual progress

Human-in-the-Loop & Explainability

Planners review and approve generated schedules. System explains tradeoffs and allows manual overrides. Shop floor supervisors can flag constraints the system doesn't know. Continuous calibration against actual execution times.

Outcomes

• 40% reduction in total changeover time
• On-time delivery improved from 82% to 96%
• Planning time reduced from 2 days to 4 hours
• Throughput increased 15% on same equipment