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How Molex 'Plans for Every Part'

"Plan for every part" is a key concept that's helping companies do a better job of forecasting. Aaron Simms, vice president of global customer service operations with Molex, is joined by Jonathon Karelse, partner with NorthFind Partners, to discuss how Molex applies the fundamentals of PFEP to its demand-planning function.

The notion of “plan for every part” (PFEP) signals “a fundamental recognition that every part has its own personality, unique behavior and signature,” says Karelse. Many companies make the mistake of treating all parts the same. The result is inaccurate demand planning.

In theory, there’s a need to create the best possible forecast for every item. The problem, says Karelse, is that companies have finite resources. Molex simply doesn’t have the time to put equal effort into every one of its tens of thousands of parts. It needs to focus on those items that make the biggest financial contribution, and highest return on investment.

“There’s no sense spending a lot of time on a part that’s impossible to forecast, and whose marginal contribution might even be negative,” says Karelse.

Companies will argue that their situations are unique. Indeed, says Karelse, “one cookie-cutter approach isn’t applicable to every part. Because industries differ is why companies should be looking at PFEP.”

Molex is a multibillion-dollar manufacturer of electronic components, with more than 40 plants in 20 countries. It produces more than 50,000 SKUs. “The challenge is to understand how we’re going to forecast with such a vast number,” says Simms.

Molex first came to understand the concept of PFEP by working with NorthFind Partners, which helped it to understand the “personality” of its various SKUs, and how to segment demand by industry and region.

The biggest challenge that the company faced, says Simms, lay in “taking what appeared to be too complex, and simplifying it.” What it learned, however, was that there was actually less complexity to its business than it had thought. In the process, demand planners could begin focusing on where they could add the most value. And Molex could gain an understanding of which forecast inputs were helping it the most.

Not only has the company achieved greater accuracy in its demand planning, Simms said, “we’ve been able to do it with a staff that is operating a lot more efficiently.”

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