Weather and Climate Data for the Automotive Aftermarket

At Aftermarket Analytics we’ve built dozens of replacement rate models for companies in the Automotive Aftermarket. In nearly every instance we find automotive part category replacement rates are influenced heavily by geography. Typically we find vehicles driven in colder climates, like in the Upper Midwest or in New England, have higher replacement rates. The opposite is also true for some part categories vulnerable to extreme heat. As a result, we’ve spoken to a number of parts suppliers and distributors who are very interested in a better understanding of the relationship between climate and demand for replacement parts.

More recently, we’ve heard that unseasonably warm or cool weather patterns, perhaps related to climate change, are making it more difficult to accurately forecast demand for a number of key replacement part categories. In response, we are offering a new service in 2020 to help the industry address these concerns.

I’m pleased to announce that Aftermarket Analytics will begin offering a Climate Data Portal (CDP) service beginning in Q1 2020. Our CDP will provide historical and current climate and weather data, including temperature and precipitation normals along with recent daily precip and temp highs and lows for all key markets in the U.S. The portal will enable Category Management professionals and inventory managers to identify unusual weather patterns, calculate anomalies and quantify relationships between climate variables and location specific part sales. Data in the CDP will be updated on an ongoing basis and will be easy to manipulate, visualize and download for further analysis.

Contact Shawn Wills shawn.wills@aftermarketanaltyics.com for more information.

Forecasting the Future of the Automotive Aftermarket with New Car Sales Data

This line chart (from the St. Louis Fed website) is one I’ve looked at many times. It tells an interesting story.

It shows monthly new car sales volume in the US going all the back to Jan 1976. Along the horizontal x-axis we have the monthly time series beginning Jan 1976 on the left moving through time until the present with last month’s car sales on the far right. Along the vertical y-axis we see new car sales volume in millions. In Jan 1976 the annual rate of new car sales, seasonally adjusted, was approximately 12.8 million. More generally, car makers were selling about 14-15 million units annually during the late 1970s. Last month, Jul 2019, the seasonally adjusted annual rate was approximately 17.3 million units, which is about where it’s been for the past 5 years. It’s been a roller coaster ride but for the domestic automotive industry volume is only up 15-20% over more than 40 years. Thankfully for the industry global growth has more than made up for relatively stagnant domestic sales.

The line chart includes shaded areas corresponding to economic recessions. You can see the dual stagflation recessions in 1980 and 1981-1982. You can see the recession in the early 1990s coming on the heels of the S&L Crisis. You can see the 2001 recession following the bursting of the Tech Bubble and you can see the recent Great Recession of 2008-2009 following the collapse of the housing and mortgage markets. Obviously, recessions aren’t great for new car sales. Usually, sales volumes decrease; sometimes they drop precipitously. It’s hard to miss the way new car sales fell off a cliff in 2009.

How is this relevant a decade later? In the Automotive Aftermarket it’s extraordinarily relevant because the “sweet spot” for automotive parts suppliers, distributors and retailers is about 10 years, more or less depending on the part category. So, while most of the economy has moved past the calamity of 2008-2009 recession, the Aftermarket is still dealing with the fallout. On the flip side, during the next decade the US aftermarket industry should experience growth mirroring the upward slope we see between 2010 and 2014. For investors or entrepreneurs looking for a silver lining in recent market volatility and increasing fears of a recession, the automotive aftermarket could provide a nice counter-cyclical investment opportunity.

Announcing IA 2

Announcing IA2 

I’m very pleased to announce the release of Inventory Analyst 2.0 (IA2). This new version has the same easy-to-use interface along with all the great features and functionality in the previous version plus a plethora of new features making IA2 the most robust inventory solution in the Aftermarket. 

New features in IA2:

  • Generate Stocking Recommendations (Add, Keep or Remove)
  • Upload and analyze Sales History data
  • Upload and analyze Inventory-on-Hand (IoH) data
  • Custom business logic for stocking recommendations incorporating IoH, Sales History, VIO, Replacement Rates, Part Rankings and other data elements
  • Upload store locations, by channel
  • Auto-select regions around a store location based on sales history
  • Convert recommendations to custom format for order/return
  • Summarize aggregate store-level order/return costs
  • Analyze part coverage for gap analysis 
  • Configure custom vehicle groupings
  • Simplified part-selection interface
  • Improved UI and query performance
  • Download data to Excel worksheet with custom functions and formatting 

We’re working hard to build the best possible inventory solution for suppliers and distributors in the Aftermarket. If you would like to learn more about IA2, please contact us today! 

Contact : Shawn.wills@aftermarketanalytics.com 218.506.8518

US New Vehicle Mix Change

By: Pete Kornafel 

General Motors announced a significant headcount reduction and closing of several plants on 11/26/18.  There are three major factors that led to this. 

One is a flat market for new vehicle sales in 2018.  Rising interest rates have largely killed the zero-rate financing deals, and have increased payment sizes and lease rates for most vehicle purchases.  Absence of major new models also contributes to the flat market. As a result, 2018 US new vehicle sales are up a tiny 0.2%vs. 2017 YTD through October.

Second, there continues to be a significant change in the mix of new vehicle sales.  Figure 1 is a chart from the Wall Street Journal article about General Motors announcement in late November 2018.

Aftermarket Echoes of the Recession

Written By: Pete Kornafel

Most people in the automotive industry are painfully aware that the US recession created huge swings in new vehicle sales a decade ago.  New vehicle sales in the US exceeded 16 million units per year from 1999 through 2007. New vehicle sales fell to about 13 million in 2008 and 10 million in 2009.  They slowly came back but did not reach 16 million again until 2014.  This Created a huge “gap” in the population of vehicles during the recession years.

It did not impact all makes and models equally.  For example Chevrolet Silverado 1500 sales fell from about 500,000 in 2007 to about 300,000 in 2009.  That was just one factor that led to GM’s bankruptcy in 2009.  Silverado 1500 sales continued to fall to below 200,000 in 2010.  As a comparison, Lexus sales dipped a lot in 2009, but fully recovered in 2010 to their 2007-2008 level of almost 300,000/year.

Technology Newsmaker Q&A

Check Out the Q&A with our CEO Justin Holman in Aftermarket Business Word.

Earlier this year, Aftermarket Analytics in Pueblo, Colo., launched its Inventory Analyst tool – a web-based software to help aftermarket companies improve inventory planning. Company CEO Justin Holman recently discussed the new product with us and talked about the challenges of inventory planning…

Read the full article here.

Discover in 90 seconds how you can increase accuracy and improve your margins.

 

10 features of Inventory Analyst, our demand forecasting software solution

 

10. Now you can generate accurate SKU level demand forecasts

9. Cloud based software. Available on the web. Anywhere at anytime

8.Simply upload your part catalog and P2V files for SKU level demand forecasts

7. Easily download your demand forecast reports (CSV)

6. VIO data comes with your IA license – no need to buy third party data

5. Includes one Replacement Rate (RR) category. Additional RR categories available

Justin Holman, CEO of Aftermarket Analytics – Lets Tech Session at AAPEX 2018

Justin Holman (CEO) was delighted to present on the Lets Tech stage at AAPEX 2018.  The topic of his presentation was  “Data Science & Technology for demand forecasting in the Aftermarket”.

Watch the 1 min 44 second summary below featured on AAPEX TV.

https://aapextv.com/list/lg1IwOCf/video/ctKqpal1/lets-tech-sessions-at-aapex-18

For more information or to arrange a demo please contact Shawn Wills, (303) 956 2848, shawn.wills@aftermarketanalytics.com

IA and CEO Justin Holman featured in Aftermarket Business World!

Take a look at the great article and interview by Brian Albright for searchautoparts.com and Aftermarket Business World.

Automotive sector expands investment in inventory analytics

With the number of SKUs expanding and more and more companies moving to an omnichannel model for parts sales, inventory planning and demand forecasting in the aftermarket has become increasingly complex. Companies are turning to advanced analytics tools to help make more accurate and faster inventory decisions. IndustryARC predicts that automotive data analytics market will reach $3.81 billion by 2023, with a compound annual growth rate of 15.4 percent. That growth will be fueled, in part, by the increasing amount of data available from autonomous and connected vehicles or telematics systems.

Read More »

Inventory Recommendations from Spreadsheet to Easy Street

One of our first clients in the Automotive Aftermarket was a manufacturer of replacement parts. They had developed their own in-house methodology for generating inventory stocking recommendations for their customers. They didn’t need us to reinvent the wheel. But, they had problems applying their methodology efficiently.

They had built their system in Microsoft Excel and it was a fairly complicated process to generate SKU-level recommendations. It required a lot of copy-paste to get the right data in the right spreadsheet cells. And the manual data input process led to frequent mistakes and frustrations. Because of this, they had 2 full-time analysts who spent almost all their time waist-deep in the spreadsheet trying to keep up with requests for data-driven recommendations.

We suggested that we take their approach and spreadsheet and convert it into a web application. This had several benefits and a rapid ROI: