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Case Studies – Metal Automotive Parts Industry​

Optimizing Production with Advanced Planning and Scheduling (APS) and Manufacturing Execution System (MES) at medium size automotive company

Medium sized company producing metal parts for automotive industry hired MSF for their references in complex production understanding and optimizations, and adaptation to their production complex rules. The company assembled a team of IT, project leader, planner, lean manager in order to execute project correctly. Project was done in stages, both MES and APS was done in paralel.


  • Full shop floor visibility with MES
  • Complex scheduling process leading to inefficient resource utilization.
  • Difficulty in meeting delivery deadlines and customer demands.
  • Lack of real-time visibility into production and resource availability.
  • Inability to prioritize urgent orders and manage changes effectively.
  • High setup times and suboptimal tool changeovers impacting efficiency.

Scenarios and Solutions:

  • Full shop floor visibility using MES, bottleneck detection, paperless factory
  • MES and ERP (IFS) integration in real time, MES-APS communication in real time
  • Real-time Integration: Seamless integration of APS with IFS ERP, enabling real-time transfer of work orders for scheduling.
  • Multi-Objective Optimization: Utilizing advanced algorithms to optimize various parameters and achieve the best scheduling results.
  • MES Integration: Connecting APS with MES for real-time data collection from the shop floor, enhancing accuracy and responsiveness.
  • Maintenance Module Integration: Factoring planned maintenance into scheduling to ensure realistic production plans.
  • Priority Assignment: Automatically assigning priority to work orders based on demand from sales orders and semi-finished products.
  • Setup Time and Tool Changeover Optimization: Planning orders with similar tools consecutively to reduce setup times.
  • Purchase Order Consideration: Incorporating integrated purchase orders for more accurate scheduling based on delivery times.
  • Outsource Operation Handling: Managing outsource orders by planning operations based on the capacity of outsourcing companies.
  • Labor Scheduling: Assigning skilled employees to workstations based on their capabilities and skill matrix.
  • Combining of Work Orders: Automatically combining work orders for laser machines based on predefined rules.


  • Efficient Scheduling: Work orders from IFS are scheduled within minutes, optimizing multiple parameters to achieve optimal results.
  • Alternative Routings: APS suggests alternative routings for work orders, considering the lowest cost.
  • Realistic Plans: Integration with MES and maintenance module leads to more accurate and realistic production plans.
  • Chaos Elimination: Chaos is eliminated with streamlined priority assignment, ensuring urgent orders are managed effectively.
  • Setup Time Reduction: APS optimizes setup times and tool changeovers, reducing downtime and enhancing efficiency.
  • Resource Utilization: APS ensures optimal use of workstation capacity, taking into account various constraints.
  • Delivery Time Accuracy: Integration with purchase orders leads to more precise scheduling based on materials availability.
  • Outsource Efficiency: Outsource operations are efficiently planned based on outsourcing company capacity.
  • Labor Optimization: Skilled employees are assigned to appropriate workstations, ensuring efficient resource allocation.
  • Integrated Scheduling: Orders for laser machines are automatically combined, improving overall integration and scheduling accuracy.

Business Intelligence and Insights:

  • BI Reports: APS provides comprehensive BI reports and KPI metrics for data analysis and insights.
  • Execution vs. Plan: Comparison report highlights the adherence of execution to the planned schedule.
  • Resource Allocation Reports: Reports detail capacity hours, employee allocation, setup times, and scheduling cost.
  • Simulation Management: APS offers a simulation environment where factory conditions can be modified, such as shift hours, working days, and workstation capacities. Users can simulate various scenarios by altering parameters in the simulation, enabling in-depth “what-if” analysis. Results of simulations can be compared to the default plan, allowing users to assess the impact of different conditions on scheduling outcomes.

Results and conclusion:

Incorporating the APS and MES solutions into Company’s operations has led to substantial improvements in scheduling efficiency, resource utilization, and delivery time accuracy. By addressing challenges and providing innovative solutions, the APS system has become an essential tool for optimizing production processes and enhancing overall operational performance, the MES provided with full production visibility and Workorder-Operator-Maintenance-ERP execution and traicebility.

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