ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Insight into Aftermarket Automotive Sales, Factory Standards and Predicting Autopart Replacement

4 September 2018, 10:10 – 10:30

Abstract

Submitted by
Shirley Coleman
Authors
Wayne Smith (Rain Data / Newcastle University), Shirley Coleman (Newcastle University), Jaume Bacardit (Newcastle University)
Abstract
The automotive aftermarket sector collects large datasets on every aspect of buying car parts online via web hosted catalogues. These datasets contain a lot of information about the transactions of buying automotive parts (when, where, what) and details of the parts themselves. From this it is possible to derive patterns of buying habits, preferences, return rates and product attributes from the data. Using datasets collected from a software company (who provide both cataloguing and invoicing systems) this presentation will demonstrate three statistically applied techniques to this type of data that have provided both insight and benefit to business.

Returns are an important consideration in any business that supplies goods. Returns can cost a company for many reasons, including, the cost of posting and identifying if a product is fit for re-sale. Here, the return rates of frequently bought car parts are plotted as a Shewhart chart, using a funnel plot with statistical limits to identify which return rates require investigation. This allows a method to prioritise attention to parts according to whether (and by how much) the return rates lie beyond the 3 standard deviation limits of the control chart.

When referring to car parts, the OEM standard refers to the manufacturer of the original equipment - that is, the parts assembled and installed during the construction of a new vehicle. The aftermarket parts are those made by companies trying to match the OEM factory standard. There is considerable uncertainty about which parts match to which OEM numbers. The analysis presented here uses fuzzy matching to compare supplier parts to typical OEM factory standards. This process reveals those supplier parts which can be matched against other cars to which they weren’t originally intended, and in doing so highlights potential revenue for a supplier, in which they can sell their part against cars without modifying their product.

Comparing invoices for replacement car parts and noting the mileage of the car at the time of replacement highlights the potential failure rate of non-serviceable car parts. Inspecting those parts that were replaced (along with the mileage at the point of replacement) and comparing these across different car models also demonstrates the reliability different car manufacturers. Function fitting of the part replacement per mileage aids in the process of identifying key points in a car’s history when failure may occur. This presentation explains this method in further detail.
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