Big data in the aftermarket

Aug. 28, 2014
Big data has arrived in the aftermarket, with the potential to improve supply chain operations from inventory forecasting to pricing.

Big data has arrived in the aftermarket, with the potential to improve supply chain operations from inventory forecasting to pricing.

More and more companies in the aftermarket are using data analytics and big data tools to corral disparate data sets and transform the information into a more accurate forecast. A Gartner survey in 2012 found that 51 percent of companies said that forecast accuracy and demand variability were the top obstacles to achieving their supply chain goals. Lack of visibility across the supply chain was cited by 29 percent of respondents. Big data can help overcome those obstacles.

SNS Research estimates that big data investments will account for $30 billion in spending in 2014, and the market is expected to grow at a CAGR of 17 percent in the next six years. Big data refers to datasets whose size is beyond what traditional databases can store, manage and analyze. The term also encompasses analytical technologies used to crunch and utilize that data, and turn it into actionable business information. However, most companies don't leverage the bulk of the data they collect.

One vendor, PROS Holdings, provides big data software to help clients with sales effectiveness. TRW Aftermarket uses the solution with its own sales team, helping them to make tailored offerings and improved pricing strategies. PROS has worked with a number of retailers and distributors in the aftermarket, and Sean Duclaux, manager of industry and solutions marketing at PROS, says that growth is the key driver.

"New growth is paramount," Duclaux says. "You have cars and parts for existing customers, but how do you get new customers? How do you find where they are? Sales people typically aren't maximizing deals. They may be discounting too heavily, or not looking at ways to cross-sell or upsell."

The automotive aftermarket is no stranger to the concept of data analytics. A number of companies are leveraging vast amounts of internal and external data to improve forecasting.

Suppliers and distributors are using data warehousing tools to better manage and forecast inventory across the supply chain, leveraging vehicle registrations, sales data, and failure rate data to create geographic forecasts and stocking recommendations. But aftermarket companies are often starved for data on specific SKUs because no single location generates a high level of sales for a given part. That's required data pooling among supply chain partners.

In an effort to help jobbers match inventories to demand, companies have developed forecasting tools that also integrate regional knowledge and even search engine data. Use of these tools not only reduces unnecessary inventory, but also increases sales for specific SKUs.

That's the concept behind Goodyear Engineered Product's DataDrive Market Intelligence System and Delphi's Intelligent SKU Management solution. The Automotive Distribution Network (ADN) uses a tool called Network Intelligence that it developed with Activant, while the Auto Parts Alliance has the Inventory Optimization Tool for inventory realignment.

According to Duclaux, big data analytics can also help adjust pricing to make sure those sales are as profitable as possible. "We see this across the service parts industry," Duclaux says. "Companies want the agility and analytics to identify opportunities so they can change pricing strategies effectively across thousands of parts, and know where to make those changes and roll them out."

Companies need accurate data not just on sales volume, but also on pricing, discounting, shipping and freight costs, currency fluctuations, raw materials, and other costs that affect margins. "If you're a tire manufacturer, you know the price of raw materials for rubber as a manufacturing cost, but those changes in cost don't always make it into a strategic pricing decision, because the information is in a silo," Duclaux says.

"If you take all of that data and use that as a backdrop, then for service parts companies there is a wealth of opportunity to use that un-leveraged asset of data," Duclaux continues. "You have to start thinking of data as an underutilized asset. Once these islands of big data are leveraged, you can drive very important decisions from them."

However, a survey by The Economist last year found that while most companies see the value in using predictive analytics and big data in the supply chain, the cost is still considered too high.

There are indications that use of big data is expanding in the aftermarket. In the 2012 Automotive Aftermarket Suppliers Association (AASA) Spend & Trends survey, 61 percent of respondents said that enhancing big data/business intelligence/decision-support systems was a top IT initiative, and big data was the second highest area for new aftermarket IT spending in 2012.

The increasing use of "connected car" technology will also add more data to the rapidly expanding pool of information available to parts suppliers, distributors, retailers and repairers. According to Frost & Sullivan, OEMs are poised to take advantage of this connectivity, combined with big data analytics. The research firm expects 60 percent of OEMs to devise big data strategies and offerings over the next two years.

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