AI is transforming the global auto aftermarket

As is the case in many other sectors, artificial intelligence (AI) is now playing a bigger role in the global automotive industry. According to S&P Global, AI is now embedded in every process, from smart vehicle production to autonomous driving. It is also being used in the automotive aftermarket to streamline complex and time-sensitive processes involving millions of stock-keeping units (SKUs), whether for replacement of end-of-life or damaged parts, or for accessorisation.

Everything required by a vehicle customer and their dealership after that vehicle leaves the assembly plant is covered by the providers of aftermarket services, whether directly affiliated to the carmaker or by independent providers. The provision of aftermarket parts and services is the most profitable part of the business for the dealerships, meaning efficiency and innovation are key. That is where AI has become a game-changer.

Technology to cope with complexity

Before the integration of AI, the automotive aftermarket sector grappled with complexities related to high data volume and a lack of database harmonisation. There can be many versions of a particular spare part because of supplier variations, mid-production changes or differing regional regulatory requirements related to safety and emissions. To complicate matters further, manual processing could risk errors in the supply of the right part, meaning delays at the dealership or service centre. That can increase operating costs and leave customers dissatisfied. The adoption of innovative AI is helping to solve these problems.

Broader trends in the automotive supply industry also help explain why innovation in the aftermarket is increasingly necessary. Connectivity, digital transformation and AI-based tools are reshaping how suppliers develop components, collaborate with partners and respond to market shifts. These forces, from agile development processes to leveraging big data and digital platforms, are not limited to original equipment production, but also influence aftermarket forecasting, parts design and supply chain responsiveness.

For instance, AI has taken care of the challenges associated with manually identifying the correct vehicle spare parts from the multitude of SKUs that exist. With the use of natural language processing (NLP), computer vision and knowledge graph technologies, an operative can now cross-reference product types. The benefits are substantial and the digital tools have drastically reduced errors in product matching, ensuring faster delivery of the right part and a growing number of satisfied customers.

Demand forecasting

Forecasting demand for spare parts hasn’t always been the easiest job and the transition to electric vehicles (EVs) has further complicated the process because of the huge reduction in moving parts. Vehicles with internal combustion engines have a multitude of moving parts that need periodic servicing and replacement. Electric vehicles have far fewer moving parts but those parts are often more sophisticated. So, while there is a drop in the volume of ICE parts supplied in the aftermarket there are new requirements for their delivery, often by the same suppliers.

AI is helping to solve this problem: demand forecasting in the auto aftermarket sector uses AI to examine historical sales data, vehicle parc evolution across specific regions, and even EV penetration rates in different countries. The data gathered and analysed helps to ensure proactive rebalancing of inventory and even make provision for emergency shipping. In the same vein, it helps to reduce overstocking and ultimately leads to operational efficiency.

Advanced planning platforms are now being deployed across the automotive ecosystem. AI-powered and connected planning models are allowing manufacturers and distributors to synchronise demand forecasting for both original equipment and the aftermarket. By integrating real-time sales signals, vehicle parc data and service demand, these systems enable aftermarket players to rebalance inventories faster, reduce excess stock and improve service levels despite volatile demand patterns.

AI plays other supportive roles in the automotive aftermarket sector beyond warehouse management and parts delivery. It is also very useful for customer service coordination, including facilitating multilingual communication for service parts supplied across regions.

Bridging the digital gap 

The adoption of AI tools to make aftermarket services more efficient has not been without its own peculiar challenges. They range from difficulty integrating AI with legacy enterprise resource planning (ERP) systems to regulatory complexities across regions, and even poor or inconsistent data. There is also the issue of AI skill gaps because of low data literacy. In a 2024 industry report, market analyst Deloitte highlighted how traditional hiring models are struggling to keep pace with the demand for data scientists, AI engineers and supply chain specialists.

Nevertheless, AI adoption has been of great value to the aftermarket sector and considering that different industries are still at the nascent stages of AI adoption, there is no doubt that with improvements and greater training there will be greater benefits for the aftermarket in the years to come.

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