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

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:

Understanding Vehicle Prevalence

In my last post we looked at the population of Honda Accords in New York. We calculated a few descriptive statistics and examined a few data distributions. This helped us get to know the Honda Accord population in New York but there’s a lot more we can do to understand this potential population of aftermarket part customers.

We now know the size of the Accord population but what about the rate of Accord ownership? Where are Accords popular or unpopular? Of course there are more Accords in the New York City metropolitan area where there are millions of people. But where do people own Accords at higher or lower rates? And how do these ownership rates compare with similar vehicles? This may help us avoid having too many or too few parts on the shelf in certain locations.

How Many Honda Accords are there in New York?

This simple question is at the core of what VIO data are all about.

Imagine the entrepreneur who has invented an innovative aftermarket replacement part. The part might fit dozens of different models but, for the sake of a simple illustration, let’s say it only fits the Honda Accord. So, finding out the vehicle population is the same as finding out the size of the market for this new part. And this is the denominator in the equation to calculate market share. Pretty important number, explaining why companies are willing to spend money on VIO.

According to data we downloaded and processed from data.ny.gov, the answer to the question is there are approximately 300,000 Honda Accords in New York state.

Hey, why not give the exact number? Because all VIO data are estimates.

Even if we retrieve the data today, this minute, it would be just a static snapshot of registration records. New vehicle sales in the past month won’t show up. Recently purchased vehicles registered in a neighboring state won’t show up. Recently scrapped vehicles will still be counted. Occasionally people drive to another state and need a repair. Heck, some people register their vehicle in another state…for tax reasons or whatever. Sometimes vehicle registrations are clustered in a corporate or government office location even though the vehicles themselves are spread around the state or the country. It doesn’t matter how hard you try, the best count you can get is an estimate. If you’re thinking you’ll gain significant advantage from obtaining higher levels of precision you may be missing the mathematical forest for the trees.

The Future of VIO

In a recent article the Economist declared that Data has surpassed Oil as the world’s most valuable commodity. Ironically, this value transition is also occurring in the automotive industry where oil still fuels the cars on the road but key data, such as vehicles in operation (VIO), now fuel decisions faced by automotive parts suppliers, distributors and retailers who are increasingly competing on analytics and supply chain efficiency.

For those not familiar, VIO is the name used to describe the census of vehicle ownership registrations which include details such as year, make, model and other attributes. Most large companies in the $300B+ Automotive Aftermarket market license these data in one of two flavors, “Experian” or “Polk”. I don’t know who is winning the market share battle between the two but I do know the Aftermarket is paying a hefty premium due to inadequate competition. Buyers can choose the geographic unit of reporting (national, state, county, ZIP, census tract or perhaps even block group) with prices going up with each increase in geographic resolution. I don’t have details on pricing but, from what I can gather, you’ll pay around $20-30k for County level, maybe $40-50k for ZIP and possibly over $100k for data at census tract or block group level. In exchange for this hefty price tag you get a pile of DVDs or a monster CSV file to download, and not much more.

Originally, R.L. Polk & Company had, essentially, a monopoly on the VIO data licensing business and they used their profit margins intelligently, acquiring Carfax in 1999. Then, around 10 years ago Experian (yes, the same Experian who recently exposed the private data of 123 million American consumers) entered the market. I assume this duopoly improved the competitive landscape a bit for VIO buyers but I think there’s still plenty of room on the field. In 2013, IHS-Markit acquired R.L. Polk & Co for the tidy sum of $1.4 Billion. Having been through a couple of acquisitions myself I suspect some of the Polk brain trust has cashed-in most or all of their Earnout and moved on, either literally or figuratively.

Replacement Rate Models for Category Management in the Automotive Aftermarket

An important piece of the automotive aftermarket category management puzzle involves an understanding of your category’s replacement rates.  Replacement rates, which are also referred to as repair rates or failure rates, are essentially an estimate of the likelihood that a vehicle will need to a replacement part due to failure or normal wear and tear.

So, how should replacement rates be calculated?

Well, it starts with determining an appropriate numerator and denominator.  The denominator should represent an estimate of the total population of vehicles.  The numerator should represent an estimate of the total number of vehicles that required a particular part replacement.

Miles Driven Forecasting: Not So Fast My Friend

When it comes to forecasting the future I like to think of two quotes from one of the smartest people I’ve ever worked with (the quote may not be precise but hopefully you’ll get the idea).

1. Forecasts are always wrong.
2. Forecasts with longer time horizons are always worse.

David Simchi-Levi brilliant MIT Professor and my former boss at LogicTools (now part of IBM), told me this in person and I’m pretty sure he’s expressed the same idea in one or more of his many now-famous supply chain related publications.

I thought of these quotes immediately when I read a special report published in November by the Automotive Aftermarket Supply Association titled, “Don’t Discount Miles Driven in Long Term Forecasts”.

Finding Your Way to a Healthy Bottom Line: Applying Geospatial Visualization to your Aftermarket Business

A severe outbreak of cholera was ravaging the Soho neighborhood of London during the summer of 1854. While city leaders and health officials struggled to determine the source of the outbreak, a physician named John Snow had a groundbreaking idea: he used a map of the neighborhood to plot cases of cholera. Ultimately, his method not only revealed the source of the outbreak, but also provided new information about the nature of the disease itself.

Data mapping photograph
Dr. Snow’s work is often considered to be the founding event in modern epidemiology. For me, it’s also a great illustration of the power of a simple map and its ability to illuminate patterns in data. Much like the citizens of London, business analysts throughout the world struggle to understand and diagnose pain points in their businesses. Why are we having trouble keeping SKU# 123 in stock in Morgantown, West Virginia? How can we minimize the cost of getting parts to installers in Wyoming? How can we avoid costly product returns? Sound familiar?

Estimating Category Market Demand in the Automotive Aftermarket

Until recently the Automotive Aftermarket was provided data from key channel distributors indicating monthly sales activity and market share for various vehicle part categories. At the beginning of 2012 the consortium of companies that provided these data collapsed.  Since then, parts suppliers and others in the Aftermarket have been searching for a new source of data to fill the void and this very issue is being discussed by industry representatives at the AAIA Fall Leadership Days conference in San Francisco.

Market Demand Forecasting Example

In this post I propose a methodology for estimating market size and discuss how this estimate of total demand can form the basis for replacing, and perhaps improving upon, the market data previously provided by NPD.