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Auto Aftermarket: Save Millions on Inventory Forecasting With AI on VIO Data for Parts Replenishment


Using VIO data for inventory forecasting - automotive aftermarket
Source: S&P global - VIO Data

There is a lot of buzz about Artificial Intelligence (AI) these days, yet not enough is focused on its positive impact on supply chains of the automotive aftermarket. To the un-initiated, AI might seem like a fancy word for robot, and veterans of the industry have every right to be skeptical about how AI can benefit them. And since they’ve likely been in the aftermarket for decades, who can blame them?


However, one thing is for sure, the aftermarket has a lot to gain and nothing to lose in exploring the usefulness of AI in their supply chain. If understood and used properly, AI can save the aftermarket billions in overstock every year and lead to a more reliable supply chain that delivers to its customers, regardless of if another pandemic or Ukraine war occurs.


More specifically, Vehicles in Operation (VIO) data can be used with artificial intelligence to make the inventory forecasting of parts more accurate and more reliable than ever before.


In this article, we’ll explore the benefits using AI on VIO data for inventory forecasting in the aftermarket. Let’s get started with understanding more about AI.


What Exactly is Artificial Intelligence?


To keep it very simple, Artificial Intelligence (AI) is nothing more than the ability to program a computer with an algorithm trained to think and learn similar to a human. To make an AI model, organizations usually need access to a large repertoire of data to analyze and clean that data prior to creating an algorithm that will train the model. Finally, training an AI model usually requires expertise from a data scientist with experience in the aftermarket. It’s important to note that training an AI model on incomplete data will yield inaccurate results.


The good news for the aftermarket is that most players have been in business for decades (i.e. lots of historical data), so there’s no shortage of data.


How Most Part Makers in the Aftermarket Do Inventory Forecasting


Without AI in their supply chain, supply and demand planning teams are likely forecasting inventory using a combination of ad-hoc tools (Excel) and their company’s Enterprise Resource Planning (ERP) tool. In between these two tools, multiple departments from sales & operations to finance create their individual inventory forecasts using their own inputs and analysis of the market. The planning team is then tasked with creating a consensus demand forecast for the entire organization to act as a sanity benchmark.


Generally, given the web of complexities in utilizing VIO data, teams usually don’t factor VIO into their inventory forecasts when using ERPs or spreadsheets.


With an AI powered forecast engine, the consensus inventory demand forecast is essentially automated, as the AI model can factor in various input assumptions and forecasts to come up with the most likely prediction of demand.


I Get It! So… How do I Leverage AI on VIO Data?


Vehicle in Operation (VIO) data, otherwise known as Vehicle Identification Number (VIN) can help auto part markers forecast demand more accurately for their thousands of SKUs. While incorporating VIO data in inventory forecasting is beneficial in many ways, we’ll focus on the two immediate benefits for part markers:

  • Inventory demand forecasting.

  • Inventory management & control.

Inventory Demand Forecasting Using AI with VIO Data


Training an AI model on VIO data can help part makers not only forecast which parts will be needed but also when they will be needed. Think back to when your company over-under- estimated the demand of a particular SKU and reflect on why that occurred – the answer is most likely due to market-timing issues, either forced from a supply chain disruption or an understaffed replenishment team. Ultimately, a lack of visibility into which specific SKUs are needed and when leads to either a loss of potential sales or overspending on inventory that won’t sell.


Moreover, it’s especially important to understand why manual labor used to monitor, analyze,

and forecast using VIO data might lead to overproduction or a loss of sales in comparison to AI...

Scale

There is far too much VIO data available in the aftermarket and analyzing it manually or semi-manually (using Excel) is too time-consuming and prone to human error & bias. AI can speed up the exploration of large amounts of VIO data quickly and consistently because the algorithm is trained to do so.

Speed

It’s not a secret that speed is the name of the game in the aftermarket because demand for auto parts fluctuates according to weather conditions, accidents, new regulations, etc. No human can reasonably keep up with the rapid changes occurring in 2023 (imagine how much has changed in 3 years?!), who’s to say manual intervention will be able to keep up in the next 3 years?


AI on the on the other hand can consume VIO data in real-time, like Google’s search engine, leading to insights that are more timely, accurate and consistent with reality.

Accuracy

As humans, we all make mistakes, often because of bias. And it’s too easy to subject ourselves to confirmation bias when analyzing historical data to assess the future. If the last three years taught us anything, it’s that history rarely, if ever, repeats itself.


An AI model trained on VIO data does not simply analyze historical trend data but detects patterns within the historical trend data to make assumptions about a future period. This is where AI can really make a difference in driving the accuracy of your replenishment models.


Still not convinced? In 2022, McKinsey found that applying AI in supply chain forecasting can reduce errors between 20-50 percent!

Complexity

Finally, there’s a reason why most part makers don’t incorporate VIO data into their forecasts: it’s way too complex and has way too many variables.


AI can sift through that complex data set, typically in minutes, which translates to faster insights delivered to your supply chain team.


Right Parts, Right Place, Right Time


VIO data is extremely useful for part markers to forecast their replenishment needs, in near real-time. Beyond the inventory forecast accuracy benefits, combining AI and VIO data can help management identify emerging patterns in the market, which parts are replaced more often than others and when according to their respective lead time gaps. From there, AI can help model out future demand for that specific part based on a variety of factors like the age and make of the vehicles in operations.


Finally, it goes without saying that AI can inform part makers about which parts to produce, when and how much to avoid either overproduction or underproduction.


Imagine how much lead times and customer satisfaction levels can improve when, through you, they have access to the right parts in stock and at the right time…


Now think of how your top and bottom line will look.


Start Inventory Forecasting with VIO Data


While the age of AI is making headlines, it’s important for every auto part maker to educate themselves on incorporating AI into their inventory demand forecasting. In other words, avoid making the headlines in a few years when your company cannot keep up with competitors and/or customer demands.


We hope that you’ve appreciated this article. For more information on incorporating VIO data into your inventory forecasting models, read our case study on how the largest chassis parts distributor in North America leveraged SkuCaster's machine-learning model on VIO data.

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