Use Case: Optimize Production with MES & Scheduling | On Time Edge

This discrete products manufacturer specializes in high-complexity, high-mix components. Every production facility is characterized by multiple assembly lines and frequent schedule adjustments, driven by constantly shifting customer demand and part availability. The business is known as a highly agile, dynamic manufacturer with a global reach. The company’s ability to pivot quickly has fueled its reputation and growth over the years. Considering intensified competition and constant shifts in market dynamics, the leadership team wanted to build on its pillars of precision and adaptability to thrive amid accelerated market turbulence.

Great reputation, yet ready to address recurring obstacles

Despite the company’s excellent reputation, the manufacturer faced persistent issues in foundational metrics:

• Production order delays due to uncoordinated planning and execution processes
• Underutilized capacity across production lines caused by manual scheduling constraints
• High rescheduling effort, with cascading impacts across upstream and downstream work centers
• Low on-time-in-full (OTIF) performance, eroding customer confidence and increasing penalties

Unlocking performance gains with closed-loop scheduling and execution framework

The company established a closed-loop scheduling and execution framework by integrating MES with detailed scheduling. The MES provided real-time visibility and control of shop floor operations, including WIP tracking, machine status, labor availability, and process traceability. The detailed scheduling ingested real-time constraints (machine, material, labor, tool availability) to generate feasible, optimized schedules that adapt to disruption and minimize idle time. Execution feedback from the MES was sent back to the scheduler for continuous, adaptive planning.

The integration meant an about-face from reactive scheduling and chaotic operations to predictive and constraint-aware scheduling, while grounding all decisions in real-time execution data. Through their effort, the manufacturer created a more resilient and scalable manufacturing operation—prepared to meet demand volatility, minimize delivery risk, and elevate overall customer satisfaction.

The value-based outcomes this discrete products manufacturer achieved included:

Improved throughput | +18% increase by identifying bottlenecks and optimizing sequencing

Increased schedule adherence | From 72% to 94% by synching plan with actual floor capacity and constraints

Reduced expedites | 40% drop in rush orders through better visibility and proactive load balancing

Enhanced OTIF performance | From 85% to 97%, resulting in fewer penalties and stronger customer trust

Accelerated scheduling | Reduced planning cycle time by 60% with dynamic scenario modeling

Boosted asset utilization | +15% utilization gain across under-performing cells