Case Study

Manufacturer Realizes Savings From Browser-Based Enterprise Labeling Solution

Source: Loftware, Inc.

As a leading global manufacturer of advanced plasma, laser, and water jet cutting systems, Hypertherm owns a large market share in their space, with 80 percent penetration in the plasma market. The company is a recognized leader, with Hypertherm’s products being used for cutting any type of metal, for applications in a variety of industries including shipbuilding, manufacturing, and automotive repair.  With nearly 50 years of experience in cutting, and maintaining 1,300 associates, the company’s client list boasts of brands including Ford, Volvo, Harley Davidson and Caterpillar.

With operations and partners worldwide, Hypertherm was experiencing significant growth, however their existing consumables product label printing system was not sufficiently accommodating this growth, and was presenting considerable risks to their operations in this lean manufacturing environment.  The company’s existing “home-grown” system was complex and customized with layers of third party applications, which posed connectivity issues for suppliers, and represented a single point of production failure.  Additional software licenses were no longer available to enable them to grow or upgrade their existing deployment, and there were inherent issues with mislabeling.  Further, the system was prone to breaking, ultimately causing significant downtime issues representing a loss of productivity, with five to ten hours of troubleshooting and fixing the label system each week.  All of this resulted in customer dissatisfaction, brand integrity issues, and lost revenues, equaling $25,000 – $100,000 worth of productivity each week due to labeling issues.

Challenges

Avoid Costly Downtime Due to System Failures

With their customized, “home grown,” labeling system, Hypertherm found itself in the midst of production down situations all too often.  Additionally, there were a variety of connectivity issues, which caused synching errors when trying to replicate data to local stations, which also resulted in more downtime.

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