This supply chain control tower allows the manufacturer to capture real-time information on the flow and consumption of production materials. The aim is to know exactly where each part is at any given time, but also to predict when it will arrive and whether there will be any delays in delivery. To this end, the system continuously processes a puzzle-like patchwork of 200,000 key data points, encompassing components and parameters ranging from supplier inventories to processes in transport and logistics centers. As Castilla – who is also an engineer, management expert and supply chain manager at SEAT – comments, “Previously, it took several hours and multiple telephone calls to update this information. Now, it’s a matter of seconds.”
With this project, SEAT is digitizing its production processes and enabling better control of each manufacturing step, with the ultimate goal of offering innovative services and even launching new business models. As Castilla remarks, “This project puts the foundations and control mechanisms in place to offer our customers a greatly enhanced purchasing experience. The objective is not just to guarantee a delivery date right from the start, but actually to speed up delivery as much as possible – while also giving the customer the opportunity to make adjustments during the vehicle manufacturing process.” Moreover, Castilla is quick to underline the vital role that SEAT’s customers play in vehicle planning and production: “Our customers are fundamental to us. By better understanding their expectations, we are more able to anticipate market needs, adjust our production schedules and optimize our purchasing plans.”
Logically, big data has been an indispensable part of this project. As Castilla explains, “Data is our raw material to build digital projects. A crucial element of our success has been establishing a clear program to ensure sufficient data quality.” In this regard, Eva Pueyo, Account Manager for SEAT at T-Systems, has been integral in getting this ‘program’ up and running. “One of our key tasks in this project has been to provide the platform’s intelligence,” Pueyo reflects. “If data quality is inadequate, decision-making based on the visual and analytical reporting of the solution would be impossible – or simply wrong.”
The Deutsche Telekom subsidiary has developed a system that collects data from several hundred reference sources in SEAT’s systems and processes it for further analysis and the display of valuable business information. The solution identifies the relevant sources, selects the specific data to be collected and processes this information in a way that allows production teams to gain insights. In turn, transporter teams now work with an app-based tracking system that is connected to the data entry platform.
But how do you guarantee adequate data quality? Eva Pueyo is happy to explain: “On the one hand, we have to compare and contrast the origins of the data to verify that the data in each process is providing precise, relevant information. On the other hand, this information must be fed with context data that provides the necessary multi-dimensional vision. Finally, it is necessary to check that the reports generated in the visualization and analysis platforms are the ones expected. All of this is done at each stage of the project in an integrated way – and later, it is verified with the users of the platform.”
The solution is being rolled out gradually. Currently, around 20 Tier 1 production material suppliers have been integrated into the logistics control system, in addition to the transport companies associated with the logistics routes. As Pueyo adds, “In parallel, some functional improvements to the current solution will be deployed in the course of 2020. Subsequently, implementations will continue in the logistics processes associated with purchasing and dispatch of finished products.”
Agile methodologies have been instrumental in enabling T-Systems to deploy this solution in close alignment with SEAT’s business goals. As Eva Pueyo comments, “In each sprint, the target deliverables have been defined and agreed upon, in terms of integrating what type of data and from what sources. This has always started with an MVP (minimum viable product) that provides the system’s users with functionality from the very first cycle – enabling them to evolve with the new data, reports and functions in an iterative way.”
The system is currently analytical and reactive in nature. The next objective is to develop a platform that performs predictive analysis and can anticipate, for example, material supply requirements for each step of the manufacturing process – from the pressing of vehicle body parts, to the welding and paint shops, all the way through to the assembly lines. In addition, traceability and monitoring will also be extended to the dispatch of the finished product, to aid the optimization of SEAT’s vehicle manufacturing processes. This will pave the way for cost reductions and improved margins, while avoiding the expenses and corrective action costs arising from errors in the supply of parts.
In a final step that will close the circle of the project, the team plans to extend the traceability model to purchasing. This will enable SEAT to employ artificial intelligence (AI) to cut its manufacturing costs even further. The basis of this approach will be a predictive system that chooses the most cost-efficient times to buy parts and materials. This system will be able to plan and adjust production needs in line with cost developments – without ever running out of stock of any single component.
With this last phase, the project in Martorell will have covered the entire SEAT logistics cycle, re-shaping the supply and delivery model to optimize and boost the flexibility of the manufacturer’s vehicle production and marketing processes.
David Castilla firmly believes that this type of technology will revolutionize SEAT’s process management and the way the company interacts with its customers. At the same time, he is confident that it will strengthen relationships between the key players in SEAT’s logistics chains, while partnership models will develop that will give customers access to even better service offerings.
As Castilla remarks, “Our current suppliers will evolve towards a ‘hyper-connection’ with SEAT, where their roles in the supply chain will become more and more reliable for us. Connectivity in information flows, processes and incident resolution will be the basis to provide quality, speed and efficiency to the supply chains.” In Castilla’s opinion, this is exactly where the “new frontier of competition” will lie in the future: “We must be the ones who get there first, with a better product and the best experience for our customers.”
But as far as Castilla is concerned, these advances would certainly not mark the end of this particular road for SEAT. “Information management, partnership models and the inclusion of our customers sit at the absolute center of all the process-related decisions we take. In that sense, our model can be perfectly scaled to any area of the company’s activities.”