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How digital twins enable data-driven automotive supply chains

This article is more than 1 year old.

The automotive industry is facing one of its biggest revolutions since the advent of automation. In this post, we will go through the Industry 4.0 aspects and how OEMs can turn these challenges into opportunities.

To put it simply, the first Industrial Revolution relied on steam power, the second one on electricity and the third one on computers. What about the fourth Industrial Revolution everyone is talking about? I would describe it as a data-driven revolution. 

Data is a wide theme, as it enables a multitude of production processes that were not possible before. In order to leverage this data, the industry will use internet of things (IoT) technologies allowing the upload, use and sharing of said data. Data will enable the automotive industry’s digital transformation: in the vehicle, in the factory and beyond. 

In a previous post, we discussed digital twins. While the most obvious use case for digital twins in automotive is optimising vehicle development, there’s a lot of value in using digital twins for supply chain and factory modelling and optimisation.

Supply chain use cases

In order to build a vehicle today, more than 3,000 parts need to be integrated per vehicle! These parts come from hundreds of suppliers around the world that need to work hand in hand with the OEM.

From designing the part, to sourcing it, producing it and delivering it, the entire supply chain needs to behave like clockwork. On top of that, there are environmental and social commitments that have to be considered, mainly related to worker well-being, for example.

Logistics and inventory management

Building and using a digital supply chain twin helps monitor the status of each part in the supply chain, while ensuring the previously stated constraints are respected.

For instance, data sharing can enable both suppliers and OEMs to have a clear and up-to-date view of where parts are coming from and where they are. This implies connectivity either on the shipment itself or with scanning solutions each step of the way.

An analysis of these elements can help with unexpected events that could occur during the delivery – re-routing specific parts, for example. This knowledge also enables cost savings and a reduction in environmental impact, as the routing of each part can be improved.

A digital twin can also help companies avoid inventory scrapping and better balance part purchases based on the stock on hand. Digital twins can also be a powerful tool to avoid inventory shortages, as they provide visibility into stock levels.

Supply chain automation 

Digital twins are useful on their own but the end goal is supply chain automation. Having a digital supply chain twin enables the use of AI/ML solutions for most tasks. The rest can be handled based on event and analytics-driven alerts. Most issues can be anticipated with preventive feedback based on your supply chain’s data. Your experts can focus on making sure these events are treated correctly.

In our upcoming 2023 Automotive Trends white paper, we describe the semiconductor shortage. With the right technology and data, OEMs can have better visibility on their inventory levels as well as forecasted inventory information based on suppliers’ inputs. While it wouldn’t have avoided the chip shortage, it would have reduced the supply chain disruptions.

We have explored supply chain use cases. But what about applying digital twins in the factory?

Factory use cases

The digital twin approach that we mentioned for vehicles and for the supply chain, has a lot of potential in the factory itself. Imagine all the robots on the factory floor sharing their data in real time and adapting their actions based on the context of the factory and of the supply chain.

Downtime prevention

Digital twins make it possible to anticipate downtime and use downtime for planned and not unexpected maintenance. Some tasks can be kept at the edge for better response time and security, while more power-intensive processes like factory floor optimisation can be pushed to the cloud and handled by HPC clusters.

Factory design

Having a factory digital twin also allows for an optimised view of factory scalability. For example, finding the best position for additional robots, in case the manufacturing chain needs to expand. Using advanced simulations can contribute greatly to the improvement of your manufacturing process and assembly lines.

Quality assurance use cases

Automations based on aggregated data can provide accurate information on potential quality issues too. Some defects aren’t immediately visible to the human eye. Catching quality issues beforehand avoids warranty costs related to maintenance and vehicle recalls.

Digital twins allow the entire manufacturing process to be monitored from teams all around the world, allowing OEMs to leverage their talent wherever they may be. Most, if not all, the automotive Industry 4.0 innovations rely on data, but data alone is not sufficient for transforming the industry, some technologies are becoming essential.

Addressing related challenges with digital twins

The right data makes it possible to build and effectively run a factory, but there are always challenges to address. Worker enablement is one of them. Training factory workers is difficult: it takes time, and commitment from both sides and retention tends to be low. Ensuring the workers are trained for new machines, tools, and software have a high cost.

Combined with digital twins, virtual reality (VR) and augmented reality (AR) make it possible to train factory workers outside the factory with very realistic VR 3D environments in which specific scenarios are played out. With AR, the worker can see indications related to specific scenario steps being displayed while they are interacting with machines and parts.

We talked about the importance of using transparent and collaborative data so that OEMs could leverage its traceability. This is all the more true when feeding said data into AI/ML data exploitation solutions. From the design to the servicing, AI solutions are a key enabler of supply chain automation.

One of the issues that factory digital transformation is facing is the fact that factories are still using old systems that cannot be integrated with new ways of working. Therefore, they cannot share data in the expected manner. The transition from these non-connected machines to sensor-packed, smart, collaborative robots might take a while due to cost and profitability constraints. We anticipate that the investments will be worth the cost when taking into account the potential CO2 reductions that a smart supply chain and factory can provide.


In conclusion, Industry 4.0 is ushering in a new era of automation and digitalisation that brings with it a host of challenges for manufacturers. However, by leveraging the power of digital twins, companies can effectively navigate this evolving landscape and stay competitive in the marketplace.

Ubuntu Core provides a secure and reliable foundation for industry applications, which enables companies to easily connect, manage and update their devices and machines remotely. This means that manufacturers can easily monitor and control the devices that power up their production lines, ensuring that they are running smoothly and efficiently at all times.

Additionally, Canonical enables companies to leverage the power of artificial intelligence and machine learning to optimise their production processes, leading to improved efficiency, cost savings and more advanced factory and supply chain related digital twins.

In short, Canonical empowers OEMs and suppliers in their industry 4.0 transition. By embracing software, OEMs can successfully face new industry challenges by having a secure and reliable connected factory and supply chain devices. Whether it is for your connected OT, your factory robots, or harnessing the value of your data using HPC clusters, we have the solutions ready to help you improve your supply chain and factory efficiency.

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