Digitalisation can help manufacturers save time, reduce costs and respond more effectively to customer demand, all as part of the culture of innovation and continuous improvement embedded within the DNA of the automotive sector.

By fully embracing digitalisation, the automotive sector in the for example the UK, stands to gain £6.9bn every year by 2035. The cumulative total benefit to the UK economy could be £74bn by 2035. This is a significant prize, but there are challenges that need to be overcome, by the sector and by government if it is to be realised. The UK’s digital infrastructure needs to be improved, clear policies on cyber security must be developed, the skills gap must be addressed and investment in digitalisation must be accelerated.

Digitalisation technologies have found applications across the manufacturing value chain and have had beneficial impact on suppliers, OEMs and end-customers. While some technologies have more focused applications (e.g. robotics on production), others such as cloud computing, analytics and cybersecurity are progressively leading to an unprecedented sharing of information and new applications across the value chain.

The digitalisation of manufacturing is already underway. Both manufacturers and their suppliers benefit from productivity gains, quality improvements, greater flexibility and shorter times to market. Customers are also likely to benefit from more personalised, higher-quality vehicles with a greater level of product content and connectivity.
In truth manufacturing has been increasingly using data to raise productivity for decades, but many commentators now foresee an exponential growth in the use of this data, driven by five new disruptive technologies.

1. Firstly, connected devices and sensors using Radio Frequency Identification (RFID) technology have become sufficiently affordable allowing a physical system to be replicated in digital form and visualised in real time.
2. Secondly, predictive analytics, cognitive computing and artificial intelligence powered by algorithms that have become sufficiently sophisticated and validated through real-world examples are now able to make decisions and predictions based on this real time data. In the future, the advent of deep learning – a high performance, dynamic way of computerised decision-making that can learn patterns automatically and more accurately with the more data you give it – will enable further augmented decision-making.
3. Thirdly, the human-machine interface has developed to a point where widespread adoption of mobile, touchscreen and now virtual reality allow for more intuitive interaction between physical and digital worlds.
4. Fourthly, the ability to directly produce from a digital construct through technologies such as 3D printing and intelligent robotics have enabled an entirely new flexible system of production to be imagined.
5. Finally, despite a rise in cybercrime, significant improvements in cybersecurity technologies and the blockchain are giving companies the confidence to connect their factories and store vast amounts of intellectual property-sensitive data in the cloud.

Ultimately, digitalisation applications often involve the creation of a “digital twin” of a physical product, manufacturing process, factory or supply chain. Once the digital twin is created it can be analysed for many purposes. Changes can be made easily in digital form allowing for the simulation of different scenarios.


The previous scenarios can help in a multitude of applications that span the value chain to:

1. Design production lines move quickly and with greater certainty through the use of virtual reality and analytics to optimise the flow of materials and movable assets;
2. Better execution of new vehicle model launches and exchange of product development and pre-production data;
3. Optimise throughput in a factory by creating a digital twin and then simulating alternative production processes and techniques in alternative scenarios to better plan production and remove bottlenecks;
4. Eliminate defects with in-vehicle diagnostics to better understand the factors leading to component failures leading to faster root-cause identification;
5. Better plan plant maintenance and algorithms to predict future usage, substantially reducing unplanned machine downtime;
6. To reschedule production and automatically communicate changed production plans to suppliers in response to crises such as a major logistics disruption or supplier failure;
7. Reduce inventories and lead times through track-and-trace inbound supplies which give real-time estimated times of arrival.

SOURCE The Digitalisation of the UK Automotive Industry KPMG