Why the Automotive Aftermarket Needs Digital Infrastructure

The automotive aftermarket has always been a market built on people’s competence and operational flexibility. Workshops can “pull through” complex repairs under tight time constraints, while distributors can meet demand through a mix of warehouses, partner channels, and experienced managers. But the industry has entered a phase where complexity is growing faster than operating models can adapt. The number of vehicle variants and fitments keeps expanding, SKU ranges are widening, customers are becoming more sensitive to lead times and transparency, and margins increasingly depend not on volume, but on process quality.

In this environment, the market loses efficiency not because parts are unavailable or expertise is weak, but because there is a gap between the physical supply chain and the information layer that governs daily operations. In many segments, the automotive parts supply chain is still managed manually: via messenger requests, phone calls, price lists, fragmented portals, and “spoken rules.” This works while scale remains limited. But when the pace rises and accuracy requirements increase, the manual model becomes a systemic source of losses.

That is why the discussion about “digitalization” in the aftermarket is no longer about interface convenience or adding another tool. It is a discussion about foundations: how the market organizes data exchange, how it connects participants, and how it turns a chaotic parts sourcing process into a controlled flow. In this sense, automotive aftermarket digital infrastructure is the next logical stage of industry development, comparable in importance to the shift from local warehouses to distributed logistics.

Process fragmentation has become the main constraint on growth

Fragmentation in the automotive aftermarket looks familiar and is therefore often underestimated. A single order can start in a catalog, continue through calls to suppliers, be clarified in a messenger chat, confirmed by e-mail, and then entered into an accounting system manually. Each supplier uses different rules for indicating availability, different statuses, and different confirmation timeframes. As a result, even a simple task—selecting a part and being confident in delivery timing—turns into a multi-channel negotiation process.

For workshops, this means lost time at every step: from verifying fitment to searching for alternatives when a part cannot be confirmed. In practice, “repair time” includes not only the mechanic’s work, but also waiting for supply decisions. The more waiting there is, the lower the garage efficiency, the higher the risk of a stalled bay, the harder it becomes to plan workload, and the more difficult it is to give customers a forecast you can stand behind.

For auto parts distribution, fragmentation creates a different kind of load: managers become “bridges” between systems. They synchronize stock, statuses, prices, and terms, and then manually assemble a final offer. This increases operational cost, reduces scalability, and makes service quality dependent on specific people. During peak hours, the market does not “speed up”—it overheats: request queues grow, errors increase, exceptions multiply, and repeated follow-ups become routine.

The issue is that fragmentation produces not one type of loss, but a cascade. A stock error becomes a lead-time failure, a lead-time failure triggers an urgent substitution, the substitution leads to a return, the return turns into a dispute, and the dispute damages trust. In financial reporting, these losses break into small pieces, but at the business level they appear as constant “friction” that consumes margin and time.

Why tools are not the same as infrastructure

The market is actively adopting tools. Workshops use catalogs, CRM systems, accounting software, and supplier chats. Distributors implement WMS and ERP, pricing systems, and proprietary customer portals. All of this improves individual parts of operations, but it does not solve the core problem: the lack of a shared standard and a shared interaction process. Tools optimize local operations, while infrastructure defines a common language and common rules for data exchange between parties.

The difference is fundamental. A tool answers the question, “How can I work more conveniently inside my company?” Infrastructure answers, “How does the market work between companies?” And it is the intercompany zone that is most costly: confirming availability, agreeing lead times, selecting substitutes, fixing statuses, managing returns, and tracking execution. When this zone is not standardized, any internal improvement runs into external uncertainty.

That is why even well-configured workshop accounting does not speed up the parts sourcing process if availability responses come through different channels and in different formats. And even a strong distributor ERP will not improve service if the customer receives delayed stock data and cannot rely on stable statuses. In such a model, a digital workflow exists, but it is broken: some steps are digital, some are manual, and the transitions between them create errors and delays.

An infrastructure approach assumes something different: data about products, availability, lead times, and statuses becomes a connected circuit rather than a set of messages. This does not replace internal systems—it connects them. That is the meaning of automotive aftermarket digital infrastructure: ensuring process compatibility at the market level, not only within a single company.

Where exactly the industry loses efficiency and money

Losses in the aftermarket rarely look like one big problem. They show up as many small delays and “clarifications” that feel normal. But in operational economics, these small frictions determine a company’s ability to grow. On the workshop side, the key loss zone is waiting and uncertainty. Every unverified stock line, every “I’ll check and get back,” every gap between promised and actual lead time increases the total repair cycle time.

The second zone is mistakes in selection and substitution. When fitment and replacement information is scattered and decisions are made under time pressure, the probability of choosing the wrong option increases. Even if the mistake is corrected quickly, it still causes returns, extra logistics, and lost bay time. That is a direct hit to the service economics: a bay generates no revenue while the car is sitting, and the customer does not see “internal reasons”—the customer sees a delay.

On the distributor side, losses appear in request processing and promise management. The more time a manager spends manually assembling offers and confirmations, the fewer customers they can serve without sacrificing quality. This leads to typical symptoms: headcount growth does not increase throughput, service quality fluctuates, and real-time stock visibility remains unattainable because data and statuses “live” across different layers.

Returns and claims are another critical zone. Returns are inevitable, but their cost rises sharply when they are poorly structured. If the order status, part confirmation, return terms, and error reason are not captured within a single process, a return becomes a negotiation. This burdens both sides, worsens relationships, and adds operational “noise.”

Why digital infrastructure is the next logical development stage

As industries mature, they standardize what used to be solved “through experience.” That is how logistics, finance, payment systems, and telecom evolved. In the automotive aftermarket, the next step is standardizing and digitizing intercompany interaction. Not because the market “wants innovation,” but because otherwise it cannot maintain quality as complexity and pace increase.

Automotive aftermarket digital infrastructure is a shared layer where data about products, availability, lead times, statuses, and interaction rules becomes consistent. This means participants stop “negotiating from scratch” each time and can rely on a predictable circuit. In this model, the priority is not only speed, but process reliability: the ability to plan work based on statuses that do not require constant manual re-confirmation.

This shift is especially important for auto parts distribution because the distributor model increasingly depends on service quality and execution speed, not only on assortment. If a system provides transparent data exchange and a predictable parts sourcing process, the distributor can serve more customers without increasing manual workload, and workshops gain a more stable repair cycle.

In an infrastructure logic, both sides win: workshops reduce communication time and improve decision accuracy, distributors reduce processing cost and decrease the number of disputes. Most importantly, the market begins to operate as a system rather than a set of parallel channels.

How a structured workflow changes the economics of workshops and distributors

A structured digital workflow does not “add a feature”—it changes how time and responsibility are distributed within the process. For workshops, the key effect is reduced uncertainty. When real-time stock visibility and execution statuses are available in a standardized format, a workshop spends less time on clarifications, makes substitution decisions faster, and plans bay utilization more accurately.

Service economics are built on throughput. Every bay hour spent waiting is lost revenue. When the parts sourcing process becomes more predictable, a workshop can finish repairs faster, promise timelines more accurately, and reduce the share of returns. This directly improves garage efficiency without requiring additional administrative headcount.

For distributors, the effect shows up in lower processing costs and increased scalability. A standardized stream of inquiries and orders reduces dependency on manual manager work, decreases repeat contacts and errors, and improves promise control. Over time, auto parts distribution becomes closer to an industrial model: fewer “manual exceptions,” more controlled scenarios, and transparent statuses.

Importantly, an infrastructure workflow improves data quality management. When the market operates within a unified process circuit, it becomes possible to measure bottlenecks, understand return causes, detect delays, and improve standards. Without that, improvements remain local and do not extend across the full supply cycle.

CARMA as an example of an infrastructure approach in the aftermarket

Infrastructure differs from a storefront because it is designed around processes, not around displaying products. CARMA positions itself in this category: as digital infrastructure for the automotive aftermarket that connects market participants into a structured interaction circuit. It is not a marketplace in the classic sense and not just a catalog, because the objective is not to “show assortment,” but to eliminate fragmentation and manual breaks in intercompany operations.

An infrastructure approach means value is created where the market loses time: availability confirmation, lead-time alignment, status management, substitution handling, and reducing repeated clarifications. When these elements are assembled into a single process, chaotic communication decreases, and participants can rely on a predictable digital circuit without dismantling their internal systems.

This approach is especially relevant for companies that think in terms of scale and controllability. It allows interaction to be built not on individual “heroics,” but on standardized scenarios where data and statuses are part of the process. That is the practical meaning of infrastructure: it removes pressure from communication and shifts it into transparent rules and data exchange.

For the market, this represents a gradual shift from “manual production” to a managed process where digital workflow becomes the base layer of interaction. Not as a fashionable layer on top of the aftermarket, but as a working environment in which workshops and distributors can raise productivity without increasing chaos.

The future of the aftermarket will be defined by how data and processes are organized

In the coming years, the automotive aftermarket will continue to grow in complexity: in SKU ranges, fitments, customer expectations, and speed requirements. This is not a temporary spike; it is the new normal. In such conditions, competitiveness will be determined not only by assortment and price, but by how predictably and transparently intercompany processes work.

The market has already shown that local improvements do not solve a systemic problem. You can build new portals, add new channels, and introduce internal automation, but if the intercompany circuit remains fragmented, overall speed and quality will be limited by the weakest link. That is why automotive aftermarket digital infrastructure is not “the next trend,” but a necessary condition for industry maturity.

Moving toward infrastructure is essentially moving toward controllability. It reduces the cost of errors, shortens downtime, improves promise quality, and increases distribution scalability. For workshops, it means a more stable repair economy and higher throughput. For distributors, it means the ability to serve more customers with the same service level and fewer operational losses.

The market’s future will not be shaped by loud statements, but by practical process discipline. Companies and ecosystems that can make data and status exchange a standard will gain an advantage in speed, reliability, and trust. That is why the conversation about digital infrastructure today is a conversation about the foundation on which the next stage of aftermarket development will be built.