Automakers: Protect manufacturing operations from global disruptions by accelerating efficiency enhancement

Traditional methods of enhancing automotive manufacturing efficiency have delivered results over past decades. However, to deal with today’s pressures, these methods must now be complemented by digital approaches that accelerate implementation.

Lasting results depend on the right mix of organization, processes, methodologies, technologies – and partnerships. How can automotive companies combine these ingredients fast and effectively enough to shield their manufacturing operations from ongoing disruption?

The challenge for automakers: Deal with multiple disruptions while rapidly reducing costs and increasing productivity

Automotive manufacturing is seeing unprecedented levels of disruption, with impacts that are both complex and global in scope. Companies need to respond fast to keep their business plans on track.

For example, disruption to the global business environment is unremitting and often has a sizeable impact on manufacturing operations. Currently, changing tariff policies are creating uncertainty, and may necessitate relocation of production and reshaping of the supply chain. (This is the topic of a recent Capgemini report.)

Another source of disruption is the speed at which new products are appearing. This can increase complexity in the factory as manufacturers strive to produce multiple models simultaneously. It can also necessitate accelerated time-to-market: For example, new electric vehicles generally need to be launched much faster than traditional models if they are to provide a competitive advantage. Yet another disruptor is regulatory change. Tightening regulations regarding sustainability, together with consumer preferences, are driving reappraisal of the carbon footprint across the value chain.

Finally, the industry is undergoing a significant change in its business models. New models are influenced by recent market entrants, whether digital-native EV companies or tech companies, as well as by consumer expectations in areas such as hyper-personalization and by changing buying patterns. The result is that established players are being pushed to rethink and redesign their existing models. Many of the new models are software-intensive, which will mean additional demand for already scarce talents.

Automakers need to innovate under pressure

Dealing with all this disruption is expensive, but heightened competition from new entrants means many companies must operate on single-digit margins. Automakers therefore need ways to bring costs down and increase productivity while still achieving strategic goals such as electrification.

As they do so, they are also under mounting pressure to leverage technological change. To remain competitive and satisfy stakeholders, they must keep ahead in areas such as automation and the use of AI. And of course, other challenges – such as shortages of talent and of raw materials – make it imperative to do so.

However, all this is dependent on having the right manufacturing talent, and unfortunately that talent is currently in short supply – particularly in relation to newer technologies. Automotive companies struggle to attract and retain this talent, and to scale their workforce at the speed required. Training existing staff in the necessary skills may be a more viable option, but is still challenging.

Existing methods must now be complemented with physical AI, gen AI, & agentic AI, powered by AI-ready data

Rapid improvements to manufacturing efficiency are essential if automotive companies are to overcome these challenges and maintain their competitiveness. While approaches like Lean manufacturing have successfully driven up efficiency over the past 30 or so years, they cannot on their own deliver the pace of improvement that today’s environment demands.

These traditional approaches now need to be complemented with digital technologies and data-driven solutions tailored to the automotive industry. Far from replacing traditional efficiency enhancement methods, these newer approaches enhance the reach and impact of the traditional methods, and make them more powerful.

For example, 10 years ago improving performance on a rubber extrusion line was a laborious process that could take weeks. Today, the same improvements can be achieved in hours by analyzing data collected in real time from the line.

We can refer to these new technologies and solutions collectively as “intelligent manufacturing.” Our recent reindustrialization report includes several real-life success stories demonstrating the benefits of this approach. For example:

• An automated system for component production has been implemented by a German aerospace manufacturer. Operators need only load blank parts, and then robots handle the rest of the process. The system can run autonomously for up to 66 hours, with predictive maintenance and self-correction ensuring stability. Downtime for retooling has been reduced from 7-8 hours to just 15 minutes.
• A leading European OEM has upgraded a plant that is over 100 years old to create an advanced manufacturing facility, where robotic arms carry out an integrated painting process. This approach has reduced natural gas consumption and CO₂ emissions by 50%, and electricity consumption by 25%.
• A major US automotive OEM has modernized a large plant by implementing an AI-powered platform to enhance the installation of torque converters into transmission cases. The new AI-driven system has optimized efficiency and improved throughput by almost 5%.

How can we shape intelligent manufacturing for the needs of global automakers?

The process of applying intelligent manufacturing in automotive can be understood in terms of three main steps: Develop converged intelligence, target the pain points where intelligent manufacturing techniques can provide differentiation, and confront software-enabled manufacturing transformation.

Develop converged intelligence

Shaping intelligent manufacturing for global automakers depends on the convergence of several types of intelligence, as shown in the table.

This powerful converged intelligence must, of course, be carefully secured. Indeed, security is a vital aspect of digital manufacturing generally, as we’ve discussed elsewhere. 

Turn yesterday’s pain points into today’s differentiators

Each company needs to create its own roadmap for intelligent manufacturing adoption, carefully prioritizing pain points for resolution through the application of converged intelligence. This is an effective way to identify opportunities for differentiation.

For example, the reindustrialization report describes how a leading aerospace company used this approach to overcome difficulties in accessing technical instructions in a timely way, which were reducing its efficiency. This company has transformed its operations and innovation processes with gen AI. AI assistants provide aircraft manufacturing instructions, enhance accessibility to technical data, and facilitate precise task guidance.

For example, a high-priority pain point might be the need to accelerate time to market in order to accommodate fast-evolving consumer expectations. This could be addressed by implementing scalable intelligent manufacturing platforms enhanced by semantic models and collaborative data hubs which build the base for simulation and virtual commissioning. In addition, copilots powered by agentic AI could enable faster line design and integration, as discussed in an earlier blog post.

This approach would mitigate another pain point at the same time: the need to overcome productivity plateaus, increase overall equipment effectiveness (OEE) and yield, and reduce manufacturing costs.

The need to improve material flows may be another priority. Intelligent manufacturing can make sourcing more robust, resilient, and flexible through rapid assessment of supply chain risk; qualification of additional sources using engineering specs; real-time revalidation of the bill of materials and standardization of components; and seamless integration of new flows into existing manufacturing systems.

If the company needs to enhance its ability to meet “right first time” goals and other quality objectives, MES enhanced with AI can reduce the risk of errors. AI can also accelerate quality assurance and validation, as well as enabling plant-wide traceability and smarter root cause analysis.

These are just a few of the possible priorities. Intelligent manufacturing techniques can be applied to a whole range of pain points in automotive manufacturing, operations, and engineering – but also across the wider business, including general management. It can even help IT and digital functions with adoption of leading-edge technologies such as agentic AI, or with improving data quality.

Confront software-enabled manufacturing transformation

The third step is in some ways the most crucial. Many aspects of intelligent manufacturing rely on mature software capabilities.

Automakers are well aware of the central role of software in the future of their business. In a recent report from the Capgemini Research Institute, several examples show how automakers are merging software-based and physical technologies in the context of manufacturing. For example:

• A major automotive manufacturer is improving the efficiency of its manufacturing operations using an in-house-developed AI platform.
• Another automaker will deploy humanoid robots and develop a physical AI ecosystem at manufacturing and logistics bases worldwide.
• An EV specialist plans to develop 10,000 humanoid robots to tackle industrial automation, logistics, and consumer services.

To develop the necessary capabilities, companies need to undertake an enterprise-wide software-driven transformation. This will involve transforming at pace into a software company, laying the foundations for a software platform, and streamlining industry-grade software delivery. More details are in the CRI report.

Successful adoption depends on collaboration

The other essential ingredient in adoption of intelligent manufacturing is the partner ecosystem. As we have previously argued, no one company can or should master all the techniques and technologies required.

Automakers have unrivalled knowledge of their own business, processes, and products, but know much less about, for example, machine learning, edge-to-cloud connectivity, and unified name spaces. That knowledge is available from partners, whether in leading technology or engineering companies, startups, or universities. Automakers can win by combining their own expertise with that of these other experts.

“Amid intensifying competition and turbulent trade conditions, manufacturing efficiency and agility is more important than ever. Our Intelligent Manufacturing Services for Automotive approach blends deep industry and manufacturing expertise with advanced engineering and software capabilities to unlock new levels of manufacturing efficiency and flexibility, at an accelerated pace. We do this by combining solutions and capabilities from across Capgemini with technologies from our partner ecosystem – so you can become leaner, more agile, faster to market, and better equipped to maximize the game-changing opportunities created by AI, Gen AI, and robotics.”

Laurence Noël, EVP, Head of Global Automotive Industry, Capgemini

Capgemini has the capabilities, experience, and ecosystem to help you achieve intelligent manufacturing objectives at speed

Capgemini is ready to help automotive organizations leverage the benefits of intelligent manufacturing. To accelerate adoption of intelligent manufacturing, Capgemini offers multiple value propositions across each stage of the full manufacturing lifecycle, each one enabled by converged intelligence.

For example, we have helped OEMs launch greenfield EV plants with accelerated time-to-value, standardized global operations for smart factory rollouts, and reduced carbon footprint per vehicle. In every case, efficiency was a key outcome – whether through reduced engineering costs, better OEE, faster commissioning, improved quality, or enhanced application stability.

• When one of our automotive clients needed to validate SCADA and MES applications virtually to secure their delivery for a greenfield site, we produced an architecture, solution, and framework that reduces commissioning time by up to 20%.
• For a major French OEM undergoing a software-driven manufacturing transformation, we helped implement scalable digital manufacturing platforms enhanced by semantic models and collaborative data hubs. The result is faster time-to-market and a 15% reduction in manufacturing engineering costs.

Our solutions and services are helping clients overcome the talent shortages that bedevil important emerging technologies such as AI. As well as offering our own expertise and readymade accelerators, we can help with upskilling the workforce through our Intelligent Manufacturing Academy, and with attracting and retaining specialist talent. These accelerated adoption processes enable automakers to boost manufacturing efficiency dramatically and with the urgency that their situation demands.

Clients benefit from our unrivalled ecosystem, which includes hyperscalers and a wide range of other outstanding technology partners, as well as leading universities and industry organizations. We willingly share these connections with our clients, and advise them on the right partnerships to support their intelligent manufacturing initiatives.

Recently we’ve launched a fully integrated offer in this space, Intelligent Manufacturing Services for Automotive by Capgemini. This solution is designed for automotive OEMs (traditional and EV), tier 1 suppliers, and related manufacturing functions across passenger and commercial vehicles. It addresses the fundamental transformation currently underway in automotive manufacturing, and empowers clients to deliver agility, cost efficiency, and uncompromising quality in an environment of constant change. The offer:

• Supports organizations in transforming automotive manufacturing operations by addressing challenges across all relevant manufacturing phases – design, build, execute, and optimize – on shop floors and production/assembly lines.
• Deploys converged intelligence – IT, OT, network technology, and AI – through digital applications, AI-enabled management systems, and smart automation.
• Enables intelligent manufacturing operations with higher throughput, improved quality achieving “right first time,” and faster decision-making, driving cost reduction and productivity gains.

Ready to future-proof your manufacturing? Explore Intelligent Manufacturing Services for Automotive by Capgemini.