What is de-costing? How can it help manufacturers?

While it's not a direct component of spend management, but rather a residual insight, identifying expenses that present little to no return on investment is a growing responsibility among purchasing departments.

Acknowledging a cost when it's realized is one thing, but analyzing why that expense occurred in the first place (i.e. deducing which assets led to this marginal waste) is another. Essentially, there was a reason why money was spent on a particular piece of equipment, property or department in the past. Now, that machine is no longer providing value to the operations as a whole, so it is removed.

But first, the procurement department must understand why that asset is no longer performing. This step may involve asking the following questions:

  • Is there a more efficient, updated type of machine that can perform better than this asset?
  • Has the business model shifted in a way that renders this department responsibility obsolete?
  • Why is it that a factory is now a cost instead of a profit driver? Are larger elements at work?

Welcome to the process of de-costing

De-costing, a practice acknowledged by SKF USA Senior Vice President of Sales and Channel Bill Moore in a piece for My Purchasing Center, is a collaborative process in which purchasers, distributors, manufacturers and suppliers improve productivity by reducing "actual expenditures from one period to the next." Moore asserted that achieving greater efficiency requires that the aforementioned participants work together to collectively lower consumption.

Big data analytics could be used as a de-costing tool. Predicative maintenance is a medium associated with data analysis technology, enabling manufacturers, energy companies, health care organizations and other types of firms to consistently monitor equipment performance and activity. This in turn allows professionals to identify potential flaws within machinery or other assets and prevent full-scale breaks from occurring.

How does predicative maintenance correlate with de-costing? If specialists can use data find methods of keeping equipment operational for as long as possible, then the companies they work for won't have to spend the capital necessary to buy new machinery.

De-costing doesn't necessarily mean "spending less"

Because the applications of de-costing are so diverse, it's easy for some people to misinterpret the practice's meaning. In a post for IndustryWeek, Moore highlighted a hypothetical situation that emphasizes the advantages of de-costing perfectly.

"For example, a manufacturing company might purchase a premium bearing that costs 30 percent more than a bargain-priced bearing. But if the premium bearing lasts twice as long, the plant saves 50 percent of its bearing procurement cost alone," wrote Moore.

In other words, if it's going to cost a company more to purchase a piece of equipment of higher quality, but that particular asset will be more robust than cheaper alternative, it makes sense for the organization to procure it from a spend management perspective.

Transparency as a key component

What about the collaborative aspect of de-costing initiatives? How do suppliers and other participants of the supply chain help purchasers apply de-costing principles to their procurement practices? Moore emphasized the importance of working with these partners to make a full assessment of which assets are behaving as costs or value drivers.

Think about how a manufacturer can apply de-costing to the supplier relationship management process. The production company systematically replaces equipment as it's outdated. When it comes to purchasing new machinery, the procurement officer does everything he or she can to subvert the typical sales jargon: "The latest and greatest ..." Instead, he or she asks interrogative questions regarding certain machines. Routing out any potential problems is a key responsibility.

De-costing, in the grand scheme of things, has been around for some time, but Moore seems to have found a name for the practice.

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