Optimizing Casting Delivery with Process Simulation

When an airplane needs a vital part, who does it call? When a hospital needs a mechanical substitute for a knee or hip, to whom does it turn? They look for a material casting company. The company we will talk about is one of the largest casting producer in the United States, they produce key components for both the aerospace and medical industries, including aircraft turbines, impellers, industrial turbines, prosthetic implants, airframes, and other commercial products. Headquartered in Portland, Oregon, it maintains successful casting and forging factories throughout the United States and Europe, and enjoyed sales of $5.67 billion for fiscal year 2019.

Problem

With many customers waiting to be serviced, it was very frustrating for the company’s castings facility not be able to project ahead of time how many resources they would need, when they would need them, or when the job would be completed for their customers. This was extremely hard on both the company and the customers. Sometimes, due to limited capacity, inventory would pile up. Other times there would be an extreme demand for certain parts and the company would have to scramble to fulfill their orders. Timing was crucial in filling orders. Sometimes an order would involve a thousand dollar part for a 30M engine for a $500M airplane that needed to be shipped within a certain time frame or contract penalties would apply. The current system greatly impacted the way orders were taken, and also on employees who may have to work long and extra hours to make sure orders were turned out in a timely manner.

The company’s castings facility wanted to better serve their customers, reduce overtime for their employees, and at the same time, reduce the amount of inventory on their manufacturing floor to make space for new business.

Solution

The castings facility investigated several solutions. They looked at fairly large corporate systems designed for prediction and analysis, but the attendant corporate price tags and system maintenance requirements (i.e. MIS and corporate horsepower) made them look elsewhere.

One day, after discovering ProcessModel, The castings facility decided that this process simulation model would greatly improve their work flow. They weren’t disappointed.

After all data was input, ProcessModel helped them identify bottlenecks and provide feedback, pinpointing areas for improvement. For instance, it helped reveal bottlenecks in the mold-making portion of the casting process (i.e. backlogged inventory). ProcessModel would also demonstrate why there was excessive inventory, and test solutions for the problem.