Production Scheduling Optimization

Introduction

The client faced major bottlenecks on its composite parts production line. Planning had to balance many factors, such as operator skills, workstation availability, resource constraints and complex nesting problems.

With an exponential number of possible combinations, the planning team spent many hours building a weekly schedule that met the constraints, and had little room to react to a disruption in production.

Methodology

The project was set up as a joint development, to build a decision support system that extends what the planners can do. The work was organized in three milestones:

• Milestone 1: Discovery and prototyping. Process mapping, documentation of the business rules and an assessment of data quality (EDA). The phase ended with a rule-based MVP, used to validate the logic against the real particularities of the operation.
• Milestone 2: Optimization and performance. Refinement of the engine’s performance and usability. In feedback cycles with the client’s internal team, the metrics were tuned to better reflect operational quality, with adherence to business requirements, stability and performance.
• Milestone 3: Deployment and handover. System integration, technical and user documentation and a structured knowledge transfer, so the client can maintain the solution on its own.

Results

The system built by Mip Wise gives the planners a high-fidelity simulation environment to test hundreds of scheduling combinations and check feasibility in real time, before committing to production.

• System integration: built into the client’s existing infrastructure.
• Operational efficiency: a significant reduction in planning time and measurable improvements in production throughput.
• Payback: the project paid for itself within the production cycle of a single aircraft.