Guest commentary: What BMW, Bavarian soccer club have in common: Using data, AI to win

Two of the most successful organizations in the German state of Bavaria excel in very different businesses. The 1,200-employee FC Bayern Munich fields 18 soccer players on game days whose favorable outcomes have led to a record 33 domestic titles and a slew of European honors. BMW Group employs 155,000 people who team up to conceive, design, produce and sell more than 2.5 million vehicles and 200,000 motorcycles a year across 31 manufacturing facilities in 15 countries.

Besides facing elite competition in environments with razor-thin margins, Bayern Munich and BMW share a commitment to applying artificial intelligence and other technologies to stay at the top of their games. Each has lessons to offer others plying its trade.

As different as the trades of these two organizations are, data related to their locations and interactions and the condition of their critical assets increasingly fuels their perennial success. Bayern tracks player movement, fatigue levels and overall performance to avoid overtraining, reduce injury risk and develop personalized training programs to maximize each player's potential. On game days, cameras and sensors in soccer balls and on players monitor the physical status of team members and provide critical insights into the positioning and movement of Bayern and its opposition over time. Those feed the coaching staff's immediate tactical decisions and factor into longer-term planning for future contests.

BMW is harnessing data to improve operations within individual production facilities — and, increasingly, across its global production network. Just as Bayern does with its players in the arena, BMW uses real-time, accurate position data to improve production efficiency and reduce costs in a few ways.

For example, the company is using ultra-wideband location data to track the location and status of tugger trains, sequence cars and materials in real time, boosting transparency and enabling more accurate production planning and management. The automaker uses the centimeter-level accuracy of ultra-wideband to define specifications for tools, parts and vehicle types. Ultra-wideband data guides the tightening sequence and torque settings through various assembly stages, which speeds assembly, improves quality and safety, eliminates human error and rework, and saves money.

In addition, BMW's cloud-based Car2X system turns vehicles on the production line into active participants in their own creation. As it moves along the line, a vehicle interacts with production resources as it monitors its own intended and actual assembly status. Car2X AI identifies variances such as faulty plug connections, saving substantial rework. Similarly, BMW's Artificial Intelligence Quality Next uses camera systems and sensors to automate quality processes. AI analyzes the data and notifies the appropriate workers if something slips out of spec.

BMW is also using location intelligence and AI to improve coordination throughout a production network spanning its own facilities and those of about 13,000 Tier 1 suppliers. With data from smart systems tracking the locations of thousands of parts and cargo trucks, the company is using AI to coordinate the delivery of supplies to the right production lines at the right time. That's making for the seamless management of inputs from semiconductors to major assembly components. Real-time documentation of the pathways of production inputs is enabling better inventory management, reducing errors and lowering costs.

Just as a great soccer team is more than a collection of talented players, a great automotive company is more than a collection of factories linked through logistics systems. BMW recognizes as much and, in addition to the extensive use of digital twins, employs its iFACTORY strategy to standardize, digitize and harmonize global production processes. Doing this allows the automaker to link product, process, quality and data, enabling data consistency and transparency across all of its production. This approach is already helping the team improve through mutual plant support, flexible employee deployment, and synergies in support and training.

Both Bayern and BMW are using data and advanced AI technologies in other ways, too. To streamline its global scouting efforts, Bayern has implemented a generative AI copilot that instantly distills and relates desired player-evaluation metrics based on specific criteria and across multiple languages, drastically simplifying and improving the quality of information transferred. BMW uses AI to analyze buyer preferences and market trends to identify likely customers and tailor products to their evolving tastes.

FC Bayern and BMW share more than their home city of Munich. They're embracing data to ensure that their historic successes continue far into the future.