Auto dealerships face a perpetual challenge: balancing inventory to meet customer demand without overstocking, which can lead to carryover inventory. Carryover inventory—vehicles that remain unsold for extended periods—ties up capital, incurs holding costs, and often requires discounting and markdowns to clear off the lot. This scenario impacts profitability and creates a cycle of reduced margins in today’s highly competitive market. 

Traditionally, dealerships relied on historical sales data, market trends, and personal experience to manage inventory. However, these methods often lack precision and fail to account for rapidly changing market dynamics. A “back-to-basics” mentality, combined with partners that offer advanced data analytics, AI, and predictive modeling - technological advancements that offer granular insights and actionable intelligence – are helping dealers ensure proper inventory risk management. 

Understanding inventory risk management in the data age 

Car dealers today have a wealth of data to help sell vehicles from their lot. It’s no longer just about identifying the VIN number on a vehicle. Today, dealers are leveraging 190 different data points to help sell a car. Dealers can even tell what type of weather a car sat under, which can help pinpoint slow sales during bad weather weekends, for example. 

Every dealer has a different success rate with different models. Deep data and machine learning are helping dealers determine their true competitive levels against other dealerships down the road to move vehicles. 

Because of this highly competitive landscape, dealers for a long time have been focused on their days supply rates and days-to-sales rates for internal benchmarkings for their monthly goals.  

This is extremely critical data, especially as dealers try to constantly identify the key sales trends in the new versus used side of their business. Inventory is growing faster for new compared with used. Total new vehicle sales for June, including retail and non-retail transactions, were expected to reach between 1.3 million and 1.2 million units, a roughly 2.6% decrease from a year ago. 

Legacy philosophies in managing inventory 

The traditional thinking for many dealers is that you have to move a used car in 30 days, and move a new car or truck in 45 days. And this is why it’s important to be focused on the carryover rate, because it can be a significant leading indicator to help a dealer drive profitability when they can drill down into the correlation between length of carryover (or no carryover) versus the rate and severity of any markdown activity on a particular vehicle. 

Profit levels are always great if a vehicle moves within its 30- or 45-day expected period, but profit levels are significantly diminished if it carries over past that time frame. When you can show how this timing is affecting the bottom line of a dealer, all of their old discipline comes back, and they return to the basics and fundamentals of selling a vehicle. 

There are no guarantees with markdowns 

It’s important to understand that carryover rates, when they rise, can be different depending on each brand. When a vehicle does carry over past its prime selling days, this is when dealers begin to panic and start slashing prices in markdown mode. However, while markdowns have been traditionally viewed as a surefire method to boost sales, the complexities of modern markets reveal that this strategy is fraught with misconceptions. 

One of the most general beliefs in the auto industry is that reducing a vehicle’s price will automatically attract more views to its Vehicle Detail Page (VDP) and directly translate into sales.  

However, real-world data paints a different picture. Approximately 69% of used car listings and 76% of new car listings do not experience a significant increase in organic or direct views following a price reduction. This suggests that merely lowering the price does not necessarily make the vehicle more attractive to potential buyers. Additionally, any brief spike in activity often comes from repeat visitors who are already familiar with the vehicle and are alerted to price changes rather than new shoppers.  

Another legacy viewpoint is that a single price reduction will secure a sale. In reality, vehicles that are eventually sold often undergo three-to-four price reductions. This pattern demonstrates that relying on one markdown to close a sale is far from realistic, which is why dealers need to adopt a more sophisticated approach, integrating multiple factors such as market trends, customer behavior, and competitive pricing. 

AI and Machine Learning: enhancing decision-making 

Artificial intelligence and machine learning (ML) take data analytics a step further by enabling predictive capabilities in managing carryovers. AI systems can process vast amounts of data much faster than humans, identifying trends and making predictions with remarkable accuracy. In the context of auto retailers, AI can predict which vehicles are likely to sell quickly and which might be carried over. 

Machine learning algorithms can analyze factors such as past sales data, market trends, economic indicators, and even social media sentiment to forecast demand for specific models. These predictions help dealerships make informed decisions about which vehicles to order and in what quantities. By aligning inventory with predicted demand, dealerships can minimize the risk of overstocking and understocking, leading to an imbalance of carryover inventory. 

Predictive Modeling: anticipating market dynamics 

Predictive modeling is a statistical technique now being used alongside historical data to forecast future outcomes. In auto dealerships, predictive modeling can be applied to various aspects of inventory risk management. For example, it can predict the depreciation rate of different vehicle models, helping dealerships price their inventory more effectively. 

One practical application is predicting the optimal time to markdown a vehicle. By analyzing data on how long similar vehicles have taken to sell at different price points, especially in local regions, predictive models can recommend when to reduce prices to accelerate sales without eroding profitability. What’s more, this data can be used to inform a dealer when a vehicle should NOT be marked down, ensuring better profitability from analytical visibility that prevents an over reliance on traditional gut instinct. This ensures that dealerships can clear aging inventory at its maximum profit potential before it becomes a financial burden. 

As technology continues to evolve, the role of advanced data analytics, AI, and predictive modeling in auto dealerships will only grow. Future advancements may include more sophisticated AI algorithms that can predict market trends with even greater accuracy, as well as enhanced data integration platforms that provide real-time insights across all dealership operations. With this level of visibility, dealers will better understand the carryover risk on each vehicle – new and used – on their lot, and will have the tools they need to properly manage their inventory risk levels to maximize profitability. 

About The Author: Len Short is the executive chairman of Lotlinx, who offers the only inventory platform that enables dealers to automatically adapt to market dynamics, mitigating inventory risk through VIN-specific strategies. For more information, visit www.lotlinx.com.