Maximize Margins, Retain Customers, and
Grow Market Share ThroughOptimal Pricing
The Challenge
Aftermarket part sales present unique challenges due to supply cost constraints & variability, OE pricing volatility, party quality, and a demanding customer base.
The Solution
QueBIT implemented a robust solution, combining IBM Planning Analytics (TM1/PAW) connected to
a powerful GUI-based modeling engine endowed with faster and more accurate algorithms/nodes (SPSS Modeler and QueBIT DataWORQ).
The Benefit
Manual pricing efforts were reduced by 3 days per week while simultaneously fostering greater visibility, flexibility and structure.
About: Global Distributor
As a leading global distributor of specialty automotive repair parts and accessories, with operations in North America, Europe, and Taiwan the company offers its customers a broad range of replacement systems, components, equipment and parts to repair and accessorize automobiles, trucks, and recreational and performance vehicles.
The Challenge
The company must respond quickly to fluctuating market conditions to effectively support customers in their Retail and Commercial sales channels. Aftermarket part sales present unique challenges due to supply cost constraints & variability, OE pricing volatility, part quality, and a demanding customer base. Amongst these challenges was also the need to have a cost-effective way to systematically manage pricing 200,000 parts, across 10 cost regions. Their legacy Excelbased process made it difficult to dynamically set prices on parts while adherence to existing category management goals. Additionally, the company was unable to automate data loads and consistent calculations.
“Doing a price review on existing parts and pricing new parts used to take 2-3 days every week, now it takes less than an hour.”
- Product Pricing Manager
Delivering the Solution
The company’s pricing process consisted of a series of excel spreadsheets spread across multiple users who leveraged their own unique variables and logic which drove inconsistent pricing decisions. In these spreadsheets, the pricing team manually edited Excel formulas for hours each week to calculate costs and prices for all products in the company, followed by a manual export to the external pricing system. This resulted in time lost due to repeating the same task multiple times, inconsistent results, and no traceability to why a price or calculated value might be incorrect. The process was largely rule based, with hundreds of ‘exceptions to the rule’ that needed to be considered. QueBIT’s pricing solution leverages SPSS Modeler to build visual processes to replicate the complex pricing logic in a consistent, maintainable, and traceable way. SPSS is also used to schedule jobs to automatically run these processes every day to ensure new data is loaded and priced every morning before the team arrives.
Once prices are calculated in SPSS, they are fed into IBM Planning Analytics so that users can graphically view and interact with the output of the system in PAW (Planning Analytics Workspace). Here users can review the calculated pricing recommendations and make any necessary adjustments. To aid decision making, users can report on both historical and projected sales & margin, in addition to explanatory variables ensuring visibility into the underlying pricing process. Users
can then leverage dropdowns and filters to view or export subsets of parts they are most interested in. If an assumption used in the streams needs to be changed, parameters used in the pricing calculations can be adjusted within these reports. This ensures usability while enforcing a standard process to maintain consistency in the pricing process.
Products Used:
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