Digital tools for a more dynamic workforce
Seat, Toyota and Schaeffler are leveraging digital technologies, including machine learning, AI and automation, to optimise supply chain processes and empower their workforce.
While the automotive industry might have lagged on the adoption of AI to solve supply chain disruption and optimise logistics processes, carmakers and tier one suppliers are now looking at a range of strategies to leverage digital technologies to improve their supply chain logistics. Central to that is how the technology can empower employees and cut down lead times.
Speaking at this year’s ALSC Digital Strategies Europe conference, representatives from Seat, Toyota and Schaeffler are focusing on the use of digital technology, including agentic AI, to help their logistics teams optimise processes and work faster to solve supply chain problems for both inbound parts and finished vehicle deliveries.
Seat’s manager of customer-driven supply chain, Rafael Sanchez Aviles, outlined the carmaker’s AI roadmap and its aim to use the latest digital technology to make Seat an efficient, productive and customer-centric organisation. Seat is adopting AI as part of its digital evolution, using it to overcome the challenges it is facing and introduce a new working model between IT and its logistics people.
That digital evolution began in 2020 and at previous ALSC Digital Strategies conferences in Europe, Seat has revealed how its control tower technology is improving supply chain transparency and reducing lead times for a better customer experience. Cultivating customer loyalty is crucial at a time when Chinese vehicles are flooding into Europe, according to Sanchez.
He also reviewed Seat’s HAI Project aimed at accelerating the adoption of AI by through training and improving personal productivity. Through the project Seat is introducing new data and AI platforms, ensuring compliance with data regulations and AI governance, and getting ROI through business processes.
Digital accelerator teams
Seat is now deploying new platforms for data, hyperautomation and AI. Sanchez said the plan is to unify high-quality, real-time data in the cloud using software platforms such as Snowflake and Azure, as well as governing that data and building relationships using platforms such as Collibra. Seat wants to enable its people to quickly build solutions without coding, including custom applications that automate workflows and analyse data (with Power Platform), as well as developing web and mobile apps for internal and external users (using Outsystems).
Seat is also using an AI-empowered assistant embedded in MS 365 applications to make employee tasks more interesting. That is part of bringing the company demographic along on the digitalisation journey.
“A lot of people are not very into new technologies and it can be difficult for them sometimes to get used to it but we have created digital accelerator teams (DATs), which are a mix of IT and logistics experts,” Sanchez explained. “They have meetings and discussions to boost the use of AI in the company.”
It is not just logisticians receiving training in digital tools at Seat, according to Sanchez. He said the whole company is involved in awareness sessions which highlight the benefits of AI. After that, there are training sessions in which employees can go deeper into the applications and understand their practical use.
Training then divides into two grades: Citizen Developer and Data Steward. Citizen developers are taught to use basic tools to automate non-added valued processes, while data stewards are trained to understand what to do with custom applications, including Snowflake and Collibra.
“They understand data governance and what to do with the data, and why it has to be good data,” said Sanchez. “Those data stewards are present across logistics departments helping everyone with the information they need.”
There is also a training path at Seat aimed at understanding AI and regulations, including EU data and AI acts, which are important for the future.
Seat is placing great importance on personal productivity with this digital know-how through better task optimisation. “We are trying to reduce the administrative burden and define the critical business units and processes; the use cases,” said Sanchez. “This is linked to our KPIs for stock reduction, better revenues, less working capital and customer satisfaction.”
Digital quality circles
Supporting teams with digital technology is also something Toyota is embracing and Jean-Christophe Deville, vice-president of supply chain at Toyota Motor Europe (TME), explained that the company is applying has evolved the Toyota Quality Circle for the digital age. Toyota introduced the Quality Circle concept to the manufacturing shopfloor in the 1990s.
They are small, voluntary groups of frontline workers and a leader who meet regularly to identify, analyse and solve work-related quality problems in their immediate area, fostering a culture of continuous improvement (kaizen) by empowering employees to improve processes, products and their own skills. It is a core part of the Toyota Production System (TPS).
Toyota now has close to 300 people that have been trained in ‘digital quality circles’ and close to 100 solutions that are being implemented on different levels, according to Deville. Some apply to small local processes, and some to “multimillion-euro large systems”. Deville said this is about making the most of the existing workforce, not squeezing them.
He pointed out that there are always abnormalities in the supply chain and there is great scope to solve those abnormalities with brainpower – talent supported by the latest technology, bringing excellence in terms of safety, quality, delivery and cost saving.
Just-in-time for a crisis
Deville said that its core just-in-time (JIT) delivery principle is a way of revealing supply chain problems that are otherwise hidden by an excessive stock of parts. “We want the organisation to be permanently under the positive stress of finding problems, fixing problems and therefore elevating our skills level,” he said. That problem solving is now being helped with tools that provide greater visibility.
Deville used two examples of where Toyota had to deal with sudden and dramatic supply chain problems. One was the hailstorm that damaged 4,700 of its vehicles that were due for export to the US and Europe. The other was a severe storm in Brazil that badly damaged its engine plant in Porto Feliz and caused production downtime at the Sorocaba assembly plant. Deville said whole Toyota organisation had to react quickly to both problems because it did not retain buffer stocks of vehicles or engines.
In reaction to the hailstorm damage, Toyota set up 11 repair centres in Europe within 20 days and got 350 people from factories in Japan and Europe together. All of the vehicles were repaired to brand new in 90 days. For the engine plant, Toyota connected parts experts from around the world and set up new supply chains to deliver the engines from alternative sources to the vehicle assembly plant in 45 days.
On the service parts side of the business, Deville also pointed to an initiative in which Toyota replaced the traditional outdated standard lead times with more accurate ones provided by Shippeo, a transport visibility provider in which Toyota has invested through its Woven Capital growth fund. The Shippeo platform is bringing accurate ETAs based on real-time data. That is enabling Toyota to reduce the buffers, lowering stock to reveal any problems that Toyota can then solve.
“We want our people to skill up their ability to problem solve,” said Deville. “It's a proactive problem-solving spirit: review the problems, progress the JITs and reveal more problems.”
Supply Chain 2030+
Toyota’s adoption of digital technology is one of three key enablers of its Supply Chain 2030+ model to meet customer demand. The aim of the model is to meet customer demand on time (in terms of ETAs), in time (referring to lead times) and every time ie. in the best quality for the customer at the lowest cost.
Alongside digitalisation in enabling these goals are a focus on the network – moving parts and cars across the right locations in the market – and sustainability. Deville used the example of its rollout of hydrogen trucks in the Netherlands, Belgium and Germany for the latter case.
Deville admitted frustration with the low level of partnership in logistics operations compared to areas such as parts procurement. He said there is a lot of room for improvement in collaborating more comprehensively and Toyota wants to engage more at an executive level with its logistics partners to hit carbon-neutral technology goals as well as establish mutual trust.
One of the ways of doing that is promoting JIT and facing the problems that are revealed together. He said logistics providers are asking for more visibility and Toyota needs to open the door to them to bring their experience and question processes and results. He added that digitalisation is not so much about the technology as it is about skilling up the workforce together and developing collaboration with its logistics partners.
Humans in the loop
Schaeffler is also looking at how digital technology, including AI agents, can help its workforce. Fabian Pobantz, vice-president of operation digitalisation and IT for the Supply Chain and Purchasing division at the tier one supplier, said it is only in the last five years that the automotive industry has started to talk about a human-centric approach to AI and the ‘human in the loop’.
“AI is here to help us,” he said. “We are focusing on the people, the individuals at work. AI has to help them at some point. That still is the most realistic view we have today.”
Referring to Schaeffler’s IT strategy (see box), Pobantz picked out applied AI in production, where Schaeffler aims to level up operational efficiency and flexibility by utilising the intelligence of AI solutions and agents. He said it is now widely understood that an AI assistant can extract data and provide a logical insight.
With that Schaeffler employees can better understand the production problem in front of them and make a decision on how to move forward. AI agents are a step on from this because they can make decisions and derive insights from the data based on the tasks and responsibilities given them.
“An agent is a goal-oriented AI that knows what are the main tasks, what are the main activities and what is the target that needs to be achieved and it will execute that,” said Pobantz.
There remain challenges to execute the full potential of these agents because of necessary access restrictions to the core systems such as the ERP, manufacturing execution system (MES) or transport management system (TMS).
Pobantz said that it is important to understand the tolerances and responsibility of AI agents when empowering them to act and execute tasks.
“This is a little bit exciting on one side, but also scary from the other side because we need to accept the fact that we need to delegate the responsibility to a technology to do our work,” said Pobantz. He added that Schaeffler is just starting to slowly delegate actions to its AI agents and he advised that any implementation start with non-critical processes.
Alignment and awareness
In drawing up an AI roadmap it is important to build at scale and establish a foundation by function, though AI agents can be used across functions, according to Pobantz.
“Align the priorities with the business need,” he advised. “Where are the paper-heavy and more labour-intensive areas where you can bring these agents to help the organisation to react faster.”
He also said companies need to align technology according to region, indicating that firewalls in countries such as China can affect the performance of the technology platform used. “Accept the fact that Greater China will have its own technology. What is important is that delivers the same benefit,” he said.
Data quality is important and so is the infrastructure and interfaces on which the technology is going to scale. Pobantz also said it is important to look at whether the process is stable, whether is it harmonised or heterogenous.
However, what is most important, according to Pobantz, is validating every use case for the technology and asking whether the people and the culture in the organisation are ready to accept it? If not, it is a case of investing in training and awareness.
“Always ask the questions that help you to answer whether the use case fits or not,” he said. “If one of these fails, at least you can put in your countermeasures to change and improve it.”
