- Industry: Manufacturing
- Product(s): Evolver, @RISK
- Application: Enterprise Resource Planning
Consultant Don Mettler specializes in integrating software systems with Palisade risk analysis models to help large manufacturing companies make the most efficient use of their plant and material resources.
Every manufacturing company should use scheduling optimization. You have a finite set of resources, and you want to maximize gain while minimizing the cost of handling those resources. It’s a problem just made for genetic programming and Evolver.Don Mettler, Consultant
With Enterprise Resource Planning a nearly universal but continually evolving business model, consultant Don Mettler fulfills a vital role for the large manufacturing companies that are his clients. He specializes in integrating software systems to help these companies make the most efficient use of their plant and material resources. For the past fifteen years he has worked with companies in businesses that range from pharmaceuticals to aerospace and automotive to high-tech hardware--and Palisade’s Evolver, @RISK, and the @RISK Developer Kit have been essential to much of this work.
To create the models that guide company efficiencies, Mettler needs to link the company’s backend database, business intelligence software, and, sometimes, legacy systems to Palisade’s tools. His goal in developing a custom system is to build an optimization model that he can eventually turn over to his client to manage. “That’s one reason I like to use the Palisade tools. They run in Excel, and clients can easily understand the results.”
A Typical Manufacturing Example
For one recent assignment, Mettler developed a model to optimize the production and minimize inventory costs of corrugated cardboard boxes for fresh produce. Demand for the product was highly volatile, while plant capacity and the number of manufacturing machines were fixed. Ideally, inventory levels would respond to ordering information, and his model would have to account for thousands of inputs from the company’s ERP and business intelligence software.
In response to these challenges, Mettler first built a prototype model using RISKOptimizer and later used @RISK and Evolver to build the final model installed on his client’s system. He used @RISK to forecast sales data and Evolver to optimize the model outputs for production. The optimization results were stored in the company’s database and could be re-accessed by his model during a simulation run.
Real-Time Optimization for Real-Time Decisions
Mettler’s model ran--and under the supervision of plant managers is still running--every night on a fifty-two week schedule, continually adjusting box production and inventory to daily orders. The company is manufacturing only boxes it will ship that week and has reduced the costs associated with inventory.
“Every manufacturing company should use scheduling optimization,” says Mettler. “You have a finite set of resources, and you want to maximize gain while minimizing the cost of handling those resources. It’s a problem just made for genetic programming and Evolver.”