Simulation Modeling Helps Bring Pain Reliever to Market
ProcessModel

Pharmacia’s Searle plant in Caguas, Puerto Rico, produces more than 20 brands of pharmaceuticals. Faced with the responsibility of undertaking a launch of the new prescription drug Celebrex, Searle Caguas had the daunting task of producing launch requirements in three months, plus packaging and shipping the capsules in a timely manner. Searle assembled an ad hoc task force—the Celebrex Readiness Team—to manage the introduction and commercial production of the new product.

The team searched for a simulation- modeling solution that was affordable, user-friendly and had animation capabilities. Additionally, it had to be powerful enough to handle different process details without losing model credibility and validity. The animation feature was necessary in order to depict the process operation for the team members to verify model logic in operation.

Searle Caguas turned to ProcessModel for all these reasons.

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Data Analysis Software Integrates with Shop-Floor CMM
Statsoft's STATISTICA

Steelcase Inc., an international work-effectiveness company, produces interior architectural products, including furniture systems, technology products, seating, lighting, storage and related services to individuals and organizations around the world. The company currently implements lean manufacturing, and Steelcase engineers and quality professionals are charged with designing and maintaining processes with the objective of producing consistently conforming equipment that meets customer requirements while minimizing associated costs.

Realizing that variations were apparent in the inspection process, a team was created to improve process control for automatic die presses and manual press brakes in the Steelcase basic division.

Because of the noted success in its other manufacturing facilities, Steelcase decided to use StatSoft’s STATISTICA Enterprise-wide SPC System Interactive Quality Control Charts to monitor inspection results. SEWSS helped to quickly identify that measurement error was a key source of variation.

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