This leading commercial vehicle manufacturer in North America, as well as a leader in the used truck market, maintains a nationwide inventory of used trucks and offers the industry’s most comprehensive used truck warranty. It oversees the resale of used vehicles that are generally acquired through fleet trade deals against the purchase of a new truck.
The majority of sales are through three channels: retail, which encompasses shops that are either fully owned; secondary wholesalers, which act as middlemen in the sales process; and auctions. A retail sale generally takes longer than a wholesale or auction, which can incur storage and maintenance costs, while fetching a higher price, making the decision about which channel to use an important one.
The used truck market is a volatile one, with the market in all three channels fluctuating on a month-to-month basis. Resale value is driven by a wide variety of factors, from prices and developments in the new truck market to vehicle mileage, age and specifications, available inventory, and growth in buildings and infrastructure. Prices are volatile as well. With little incentive for sellers of a used truck to continue owning a depreciating asset, used truck pricing reflects that day’s market-clearing price.
Against this backdrop, Concentrix Catalyst wanted to arm the vehicle manufacturer with foresight into upcoming market shifts while predicting how long a vehicle would take to see and the expected contribution, or the sales price against costs. Doing so would enable it to better control the flow of inventory and the cost of storage and identify potential downturns in the market and where to target efforts with vehicles.
To enable the client to meet its goals, Catalyst developed machine learning (ML)-powered inventory management tools and gave sales engineers guidance and metrics through forecasts and dashboards. A market trend model forecasts the average sale amount for trucks on each given date. This average sale amount is weighted by the estimated number of trucks, providing a picture of expected trends in the overall used truck market.
The model also enables the client to predict how much each truck will sell for if it is sold retail or wholesale as well as how long a truck will take to sell if it were sold retail. These are combined with the acquisition, holding, lot-rot, storage, and sale costs to estimate its contribution to the bottom line and help the sales team determine the channel that will maximize revenue.
Easy-to-use dashboards display the market trends and individual truck sale predictions, and an interactive tool allows users to understand how the market and truck prices will change based on different conditions they input. The dynamic solution sends an alert when significant data fluctuations degrade the model, enabling the organization to update the underlying data science to adjust, ensuring the accuracy of the predictions over the long term.
Catalyst’s models enable the client to anticipate changes in the used truck market and predict what an individual truck will sell for based on varying conditions, giving the company insight into the best decisions to make when selling each truck. The client can now not only predict market average prices but also optimize used truck inventory and resale by determining optimal sales revenue for each truck. In a market known for its volatility, it can now answer a key question: What is the probability a given truck will sell in the next 60 days?
Catalyst also provided a two-year rolling forecast of the expected used over-the-road transport vehicle market that will help drive executive decision-making. By considering correlations to economic indicators, it can predict possible future downturns before they happen.
The client plans to continue to invest in data- and ML-driven inventory management tools that will solidify its position as a leader in the used trucks market. By being able to better forecast shifts in the market, the company will be able to maintain its focus on quality and on helping its customers solve their transportation needs.