The map above shows temperature anomalies across the Conterminous US for calendar year 2019 through November. These anomalies relate specifically to daily Low temperatures. Where you see red, daily low temperatures were warmer than 20th Century averages (darker reds indicate much warmer daily low temps). Generally speaking, 2019 has been a much warmer than average (vs 20th Century) year, especially in the Eastern US and along the Pacific, Gulf and Atlantic Coasts (where the vast majority of Americans live). The Northern Rockies, Dakotas and western Great Plains areas have seen cooler temperatures but this isn’t as significant for most consumer products businesses because there aren’t as many people in the region.
Why look at daily low temperature anomalies? There could be many reasons but one reason is that some consumer products sell a lot more or a lot less depending on weather. For example, the starter motor in your car is far more likely to fail when the nightly low temperature is really low. So, companies in the automotive aftermarket are accustomed to selling lots of starters in the late fall and winter months when temperatures plummet.
But, what if they don’t plummet? Well, that might lead to lower sales volumes for the starter produce category. It might also leave a lot of starter inventory sitting on shelves at distribution centers. In other words, this can have a major impact on the starter motor supply chain. This appears to be the case in a large portion of the Eastern US where aftermarket companies may have expected lower sales due to milder overnight low temperatures.
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.
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.
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.
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
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:
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.
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.
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.
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.
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”.