When I saw the news item that NPD was no longer going to report point of sale data for hard parts, the first thing that jumped into my head was, "how are the scores of category managers around the aftermarket going to be able to be effective doing their jobs?"

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To gain perspective, I called one of the best category managers I know, Nick Jacquez at Affinia, and asked him how this change will affect practicing category management in the aftermarket.

"The absence of the POS data for application hard parts will absolutely change the practice of aftermarket category management," said Jacquez, "however from my perspective, that may not be a bad thing."

He explained that something got lost in the translation of category management from consumer products to aftermarket hard parts. Jacquez explained that the practice of category management in package goods focuses almost exclusively on two issues, 1) POS issues; like planograming, shelf signage, promotion and packaging and 2) competitive market share issues (from what retailer the consumer is buying the brand). When the practice was translated to aftermarket hard parts, the first element logically went away since application parts are rarely displayed front of counter. As such, nearly all the focus was on competitive opportunity analysis and market share.

"In my opinion that was not necessarily a good thing, because it depended on historical data and took our collective eyes off the core issue that we need to focus on -- better inventory management to be sure we have the right parts in the right places at the right time," Jacquez said.

"We are all struggling with the millstone of excess inventory and focusing on only market share doesn't do anything to address critical issues like the capital consumed by the inventory or the sales we lose by not having the part in stock."

We have all relied too heavily on historic data to predict the future. Adding, with a discernible pang in his voice, that if history were a reliable predictor of the future, "My house would be worth a lot more than I paid for it."

What does Jacquez suggest is the better approach?

"We need to more precisely match supply and demand. We need to better understand the multiple variables that impact requirements at every stocking location, like vehicle populations, replacement rates, POS history, seasonality, along with market share data. And we need to have the wherewithal to define and measure those variables and use them to predict sales at the store level."

"At the store level? Isn't that something of a tall order?" I asked.

"Yes, it is aiming high, but with the technology available today it is very doable. It is already being used widely in other industries."

He spoke of a collaboration he had undertaken with a technology provider called TerraSeer. They had been able to help him create the ability to use data points from conventional aftermarket sources like Polk, Experian, IMR and others and shape that data into a predictive model. They then built an Internet portal that allowed both manufacturer field sales people and channel partners (headquarters, district and store managers) to participate in optimizing inventories at the store level.

So, how does that work?

According to Justin Holman, CEO of TerraSeer, "We applied best practices from supply chain optimization research and retail location analytics to create predictive sales forecasts and inventory recommendations."

It is the first aftermarket predictive model I have seen that is based on zip code vehicle registrations and also creates part-specific replacement rates, based on data as reported by IMR.

By applying that predictive model to the trading area defined around each store location, predictive inventories can be compared with actual store inventories through an Internet portal that can be accessed with a secure password. The manufacturer and channel partner designate who gets access to the data, most importantly the store manager and field sales personnel.

"We have found that by allowing the field people to be able to comment and recommend adjustments to the forecast, they feel a part of the process and are infinitely more comfortable with adjustments to their inventories," said Holman. "This has been especially true in the case of changeovers."

The market science of predictive inventory planning at the store level is a fascinating subject and one whose scope is too broad to adequately cover here. Thankfully, the planners of the Global Automotive Aftermarket Symposium have had the foresight to have Jacquez and Holman present at their upcoming event.

It is a must see, especially for those category managers around the aftermarket who find themselves with a scarcity of category data to manage.