Future-State Facility Design for Metal Tooling Manufacturer

As part of a broader corporate initiative, a metals manufacturing company is modernizing a metal insert manufacturing facility. The company is replacing legacy equipment throughout the facility with new high-speed machinery, and streamlining manual operations with automation in order to increase production throughput and maximize the efficiency of the facility. The company wanted to evaluate the performance of the proposed-future state of the facility, and needed a solution that addressed the following objectives:

• Obtain general manufacturing facility design direction and recommendations from a seasoned industrial engineering consulting firm
• Determine if the proposed equipment configuration provided sufficient production capacity to meet forecasted demand
• Identify and alleviate bottlenecks in the future-state production system
• Determine the best order dispatching rules for each work center
• Determine the ideal tray types and sizes for transporting product around the facility
• Determine spatial requirements for staging WIP at over fifty work centers
• Determine labor requirements for over thirty work centers in order to meet target production throughput
• Determine whether the product qualifications for batch processing machines were adequate based on the forecasted demand and product mix
• Maximize machine throughput and utilizations, particularly for sintering and coating batch processes
• Determine the ideal operating parameters for minimizing flowtimes between pre-coating surface treatment and coating work centers
• Determine the impact of implementing crossover logic at several work centers (i.e., allowing orders to conditionally deviate from predefined routing sequences based on real-time system conditions)
• Determine the impact of alternative production plans on facility design
• Experiment with using simulation-based scheduling to automate production scheduling

Additionally, the company wanted a solution that could serve as a digital twin of the facility and provide them with a means of continuously experimenting with and improving the design and performance of the facility. These project objectives carried the additional challenge of collecting the data necessary to accurately define the system state, equipment specifications, scheduling rules, and operating policies. Because of the complexity of the facility, the amount of data required to drive the model was substantial. The data requirements and system complexity made it critical to design the model using a generic and data-driven approach, and required the development of model input and output data interfaces that are easy to use and familiar to company key personnel. This also allows company key personnel to conduct experiments and analyses without being intimately familiar with the simulation software.