Digital Transformation in the Automotive Industry: Pressures, Use Cases and Where to Start

Digital transformation in the automotive industry means connecting machine, process, quality and energy data. With that data connected, plants can build several powertrains on the same lines, trace every critical part and deal with losses before they reach the customer. In 2026, the pressure comes from six directions at once: a mixed powertrain market, high European energy prices, fragile supply chains, new traceability rules, cybersecurity obligations under NIS2 and cheaper competition from China. The plants that respond best do not start with a technology. They start with a measurable loss, connect the data behind it once, and build each new use case on the same foundation.

Key takeaways

• Electrified and combustion vehicles will share European production lines for years. In the first half of 2026, battery-electric cars took 20.7% of new EU registrations, hybrid-electric cars took 37.3%, and petrol and diesel cars together still took almost 30%.
• An idle production line in a major automotive plant can cost up to $2.3 million per hour. That makes downtime the fastest place to find a return.
• From 18 February 2027, EV batteries placed on the EU market need a digital battery passport. Traceability becomes a data requirement, not only a quality practice.
• The most scalable approach is a shared data foundation. Connect machines once, then add OEE, predictive maintenance, predictive quality and traceability on top.

What digital transformation means for automotive manufacturers

Digital transformation in automotive covers three areas: how vehicles are engineered, how they are built and how they are serviced. This guide focuses on production, where cost, quality and delivery performance are decided shift by shift.

On the shop floor, digital transformation replaces isolated systems and manual reporting with connected data. Machine signals, process parameters, measurement results, tool data and production events are collected in real time. They are then placed in a shared context and made available to the people who run the lines. The enabling technologies are Industrial IoT (IIoT), industrial connectivity, analytics and augmented reality (AR). The goal is practical: fewer stoppages, fewer defects, lower energy use per vehicle and faster problem solving.

Why automotive production is under pressure in 2026

The pandemic shutdowns and the first chip shortage have given way to a more permanent set of pressures, and most of them are geopolitical. The war in Ukraine reset European energy costs. Export controls, tariffs and a chip dispute now shape what a plant can source and where it can sell. Chinese manufacturers compete on price inside Europe, and new rules on emissions, traceability and cybersecurity land on the same production lines. Atradius forecasts that global automotive production will fall by 0.2% in 2026, with output in Germany expected to drop by 2.6%. Six pressures shape what plants need from digital technology.

Pressure	What it means on the shop floor	Digital response
Mixed powertrain demand	More variants and changeovers on the same lines	Production monitoring, digital work instructions
High energy prices	Energy becomes a cost per part, not an overhead	Energy monitoring by line and product
Fragile supply chains	Late parts, substitute components, resequenced orders	Connected production data, traceability
Traceability regulation	Part-level data must be complete and auditable	Genealogy linking parts, process and quality data
Cybersecurity regulation (NIS2)	OT networks must be segmented, monitored and documented	Secure connectivity with a record of every data flow
Competition and skills gaps	Pressure on cost per vehicle and on retaining know-how	OEE, predictive maintenance, assisted work

Mixed powertrain production is here to stay

The EU market is split across drive types. According to ACEA, battery-electric cars accounted for 20.7% of new EU car registrations in the first half of 2026, up from 15.6% a year earlier. Hybrid-electric cars accounted for 37.3%, plug-in hybrids for 9.8%, petrol cars for 22.2% and diesel cars for 7.5%.

Regulation points the same way. In December 2025, the European Commission proposed replacing the 100% CO2 reduction target for new cars in 2035 with a 90% target. Under the proposal, plug-in hybrids, range extenders, mild hybrids and combustion vehicles could still be sold after 2035. The proposal is still being negotiated. In June 2026, EU environment ministers reviewed progress without agreeing a common position, and the European Parliament expects to vote on it in plenary in November 2026.

For plants, this means several drive types on the same lines for years to come. More variants bring more changeovers, more work instructions and more quality checks per shift.

Energy costs remain a competitive handicap in Europe

According to the IEA, electricity prices for energy-intensive industry in the EU were roughly double those in the United States in 2025, and more than 50% higher than in China and India. The gap goes back to the energy shock that followed the full-scale invasion of Ukraine, and it has not closed. In a June 2026 VDA survey of medium-sized automotive companies, 53% named high electricity prices as a burden. In the same survey, 67% said they had been forced to postpone, relocate abroad or cancel investments planned for Germany.

When energy is this expensive, plants have to measure it like any other production cost: per line, per shift and per part.

Supply chains stay fragile

In October 2025, a governance dispute at chipmaker Nexperia disrupted the supply of basic automotive semiconductors. Honda’s production in North America fell by 110,000 units as a result. In the same year, Chinese export controls on rare earths, which are used in electric motors and sensors, caused supply disruptions for carmakers in the United States, Europe and Japan. The licensing regime introduced in April 2025 for seven heavy rare earths and their magnet materials is still in force. China suspended its broader October 2025 controls for 12 months, but that suspension expires on 10 November 2026, and no extension had been announced by early September. Tariffs add a further layer of uncertainty.

Each disruption forces plants to resequence orders, qualify substitute parts and prove that every vehicle was still built to specification. They can only do this with production data that is current and connected.

Traceability is becoming a legal requirement

Automotive suppliers already work to IATF 16949, which requires product traceability. The EU Battery Regulation goes further. From 18 February 2027, a digital battery passport will be required for EV batteries, batteries for light means of transport and industrial batteries above 2 kWh placed on the EU market. The passport must hold static data, such as composition and carbon footprint, and dynamic data, such as state of health. The organisation that places the battery on the market is responsible for keeping it complete and accurate.

In practice, every serial number has to be linked to its components, process parameters and inspection results, across suppliers and plants.

Cybersecurity rules now reach the shop floor

The NIS2 directive brings vehicle production into scope. The manufacture of motor vehicles, trailers and semi-trailers sits in Annex II, so many plants and suppliers are treated as important entities. The obligations are concrete: risk management measures that cover the supply chain, accountability at management level, and incident reporting with an early warning within 24 hours, a notification within 72 hours and a final report within one month. By May 2026, 22 of the 27 member states had adopted transposing legislation, and the European Commission had opened infringement proceedings against those that were late.

For a plant, this changes how connectivity projects are designed. OT networks have to be segmented, monitored and documented, and every new data flow from a machine needs an owner and a record. We cover what this means in practice in NIS2 and OT networks and in 42 days to detection, 24 hours to report.

Competition and the skills gap squeeze margins

Chinese brands accounted for around 6% of EU car registrations between January and April 2026, almost double the 3.2% share a year earlier. At the same time, experienced people are hard to replace. In the United States alone, Deloitte and The Manufacturing Institute estimate that manufacturers may need up to 3.8 million new employees by 2033, and that 1.9 million of those roles could remain unfilled.

Price is the sharp end of that competition. An analysis published by Transport & Environment in July 2026 found that Chinese electric cars remain on average 21% cheaper than comparable European models, even after the EU tariffs. Chinese-made cars took 17% of EU battery-electric sales in the first quarter of 2026, and Chinese manufacturers are now building plants inside Europe, mainly in Turkey, Hungary and Spain. European plants cannot answer that with labour costs, so they have to answer it with throughput, quality and energy use per vehicle.

Plants therefore need to lower the cost per vehicle and keep expert knowledge available on the line, even when the expert is not there.

Expert tip: start with the loss your plant already reports. Regulation, energy and supply risk all matter, but they rarely make the best first project. Start with the loss your plant manager already reports every week, such as unplanned downtime on a bottleneck line or scrap on a critical machining step. It has a clear cost, a clear owner and data that already sits in the machines. A first project with a visible saving builds the trust and the budget for the next ones, including traceability and energy.

Jakub Kaczyński, Industrial Portfolio Director, TT PSC

7 digital manufacturing use cases for automotive plants

Each use case below targets a loss that automotive plants can measure. They deliver the most when they run on the same data foundation. For an overview of all solution areas, see our Digital Manufacturing page.

1. Production monitoring and OEE

Production monitoring shows the real-time status of every line. OEE (Overall Equipment Effectiveness) measures how much of the planned production time turns into good parts. In automotive plants, a single bottleneck can stop a whole assembly sequence, so this visibility comes first. Connected monitoring replaces manual shift reports with automatic data on stops, cycle times and scrap. Teams can see where capacity is lost and act within the same shift.

Learn more about production monitoring with OEE.

2. Predictive maintenance

Predictive maintenance uses machine data to detect failures before they stop production. The Siemens report True Cost of Downtime 2024 puts the cost of an idle production line in a major automotive plant at up to $2.3 million per hour. Sensors on spindles, drives, pumps and robots feed condition data into analytics models, which flag abnormal patterns early. Maintenance teams can then plan repairs during scheduled breaks and find the root cause of recurring failures instead of reacting to breakdowns.

Learn more about predictive maintenance.

3. Predictive quality

Predictive quality links process parameters with inspection results to catch deviations before they become scrap or recalls. Automotive machining and assembly combine tight tolerances with high volumes. A small drift in temperature, torque or tool wear can affect thousands of parts. Machine learning models trained on historical process and quality data warn operators while there is still time to adjust. At BMW Group, predictive quality is one of the applications running on a shared production data platform, as described in the case study below.

Artificial intelligence speeds this up rather than replacing it. A model can watch signals that no operator has time to track, and the most useful ones are often the signals nobody was looking at. MFG Copilot, an agent-based interface that TT PSC builds on ThingWorx, lets production teams ask questions about their own data and get an answer with the context behind it. Our article on AI on the factory floor shows how such a co-pilot is put together.

Learn more about predictive quality analytics.

4. Part-level traceability

Traceability connects each part or vehicle serial number with the machines, tools, process parameters and quality results involved in making it. When a supplier issue or defect appears, plants can limit containment to the affected parts instead of a whole production period. With the battery passport deadline approaching, the same genealogy data also becomes the basis for regulatory reporting.

5. Digital work instructions and remote assistance

Digital work instructions guide operators step by step on tablets, smartphones or head-mounted devices, using 3D models, images and videos. They reduce errors when variants change and shorten training for new staff. For an international automotive technology company, TT PSC built an AR solution on SkillWorx. With it, production staff can create machine operation and maintenance instructions without writing code.

When a problem is not covered by an instruction, remote assistance connects the technician with an expert, who sees the situation through the technician’s camera. Solaris Bus & Coach uses AR remote support in after-sales service to speed up diagnostics, reduce bus downtime and cut business travel costs.

Learn more about SkillWorx Assisted Worker and Remote Expert.

6. Energy monitoring

Energy monitoring measures electricity, gas and compressed air consumption per line, machine and product. Energy stops being a monthly invoice and becomes an operational KPI. Paint shops, compressed air systems and machining centres are typical places to look for waste, such as equipment left running during breaks. The same data supports carbon footprint reporting for customers and regulators.

Learn more about Energy Advisor for manufacturing.

7. Tool management and paperless shop-floor workflows

Tool management tracks tool life, usage and traceability, so tools are changed at the right time rather than too early or too late. Paperless workflows replace printed forms for shift handovers, component routing and inspections. At BMW Group, tool management, shift management and paperless component management run on the same platform as predictive quality.

The foundation: connect data once, use it many times

Most automotive plants already produce the data they need. The problem is that it sits in separate systems: PLCs and CNC controllers, MES, quality databases, tool management and ERP. Every use case that builds its own integration adds another silo.

A scalable architecture has three layers:

• Connectivity. Industrial connectivity software such as Kepware collects data from machines and controllers made by different vendors through one standard interface. TT PSC is both a Velotic Authorised Reseller and a certified Kepware integrator.
• A shared data model. A Unified Namespace (UNS) or an integrated data model gives every data point a clear context: which line, which machine, which part and which order.
• Applications. OEE dashboards, predictive models, traceability views and work instructions all use the same data rather than their own copies. See how we approach Industrial IoT.

With this approach, the tenth use case is quicker to deliver than the first, because the data is already integrated.

Case study: BMW Group shared data platform for powertrain machining

For more than 20 years, TT PSC has supported BMW Group in the machining of powertrain components. This environment is defined by high production rates, extremely tight tolerances and extensive product variation. Every line generates machine and process signals, quality and measurement results, tool data and production events. That data used to sit in several systems, and viewed in isolation it offered limited value.

TT PSC designed and implemented a production data platform with an integrated data model that connects components, machines, tools, process parameters and quality information. The platform follows three principles:

• Contextualise, do not just collect. Production and quality data are connected across the whole manufacturing process.
• Navigate, do not search. Users can perform root-cause analysis without switching between systems.
• Integrate once, use many times. New applications are built on the existing data foundation instead of creating new silos.

Applications built on the platform with BMW Group business teams include Predictive Quality, Process and Bottleneck Analysis, Tool Management, Digital Inspection Management, Shift Management, Paperless Component Management, Shopfloor Visualisation and a Production Analytics Portal.

Today, the platform runs across multiple plants and in multi-shift operations. It is embedded in existing IT and security environments. TT PSC manages its full technical lifecycle, from architecture and development to rollout, operation and continuous improvement. New use cases can now be delivered significantly faster, because the data and its relationships are already in place.

The greatest value does not come from individual tools, but from a shared data foundation.

Bastian Sacher, Sales Director, TT PSC

Read the full BMW Group success story.

Where PLM and the digital thread fit

Production data is only part of the picture. Product lifecycle management (PLM) holds the engineering definition of every vehicle and component. A digital thread connects that definition with manufacturing and service data. When a quality issue appears on the line, the thread helps engineers trace it back to design decisions and change requests. Schaeffler, for example, used RFLP traceability as its first step into the digital thread.

To go deeper, read about PLM in the automotive industry and our digital thread approach.

How to start: five steps for automotive plants

Our step-by-step digital transformation roadmap describes the full method. For automotive plants, the short version is:

1. Pick one measurable loss. Choose the line or process with the highest downtime, scrap or energy cost.
2. Set a baseline. Measure OEE, scrap rate or energy per part before changing anything.
3. Connect the relevant machines. Use standard connectivity so that the integration can be reused later.
4. Run a focused proof of concept. Eight to twelve weeks is usually enough to prove the value on one line.
5. Scale on the same foundation. Roll out to more lines and plants, then add new use cases to the shared data model.

Expert tip: do not let the pilot become a one-off. The most common mistake when scaling is treating the pilot as a standalone project. If the first line gets its own custom integration, every new plant starts from zero. Before you roll out, agree on a common data model: how lines, machines, parts and orders are named, and who owns each data source. Then a use case built in one plant can run in the next one through configuration, not a new project.

Jakub Kaczyński, Industrial Portfolio Director, TT PSC

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