IMAGINE A DECREASE IN INVENTORY COSTS OF UP TO 2 MILLION DOLLARS
Imagine a decrease of 2 million dollars in inventory costs, freeing up cash for customer acquisition or employee benefits. This is exactly what happened to Ramco when they implemented the Avercast software.
Based in Hudson, Ohio, Ramco Specialties, Inc. was founded in 1977. In their state-of-the art, 165,000 sq. ft. facility, they manufacture spacers, nuts, washers, and nearly every type of fastener you can think of. Producing such a variety of nuts and bolts can be quite the task. That’s why materials manager, Cassidy Laudadio, and her team trust Avercast to know what to produce and when to produce it.
Ramco, like many companies, is faced with the forecasting challenge of looking forward to the quick turnaround on customer orders, mixed with the long tail of raw materials purchasing. They must plan months, even years, in advance in order to get their customers what they need when they need it. Some of the benefits that Cassidy Laudadio enjoys with Avercast is her ability to make changes to the program when they need to. She is able to speak directly with the programmers who understand and work with the software, allowing her to work how she wants by getting customizations to the Avercast software.
As part of a deal with General Motors, Cassidy is responsible for ensuring 50 GM plants weekly forecast data is automatically interfaced into Avercast to assist in executing their orders. She also contrasts the GM forecasts and other company’s forecasts to Avercast generated forecasts with their robust 208 formulas. By comparing and monitoring forecast accuracy, they are able to create a more precise purchasing plan. This decreases inventory costs and frees up capital that can be used in other ways. Another benefit of Avercast is that Cassidy and her team can use a forecast of finished parts they are projecting to trace it down to each component needed to make that finished part. The long tail of planning for future demand in the automotive industry makes it imperative that this forecast of component parts is exact. With a forecast she trusts, Cassidy is able to rely on the fact that she won’t have a massive surplus when it comes time to fill orders for her clients.