Ford's Data-Driven Approach Boosts Supply Chain Resiliency
Ed Stachowski, Ford's director of data science and machine learning, shared insights at the Automotive Logistics and Supply Chain Digital Strategies North America conference in Nashville, Tennessee. The event highlighted the growing complexity of supply chains and the need for digital tools and data-driven strategies.
Ford is shifting its approach, focusing on long-term solutions rather than quick fixes. Data engineering is key to democratizing data, enabling better decision-making across the supply chain. The demand for machine learning and AI applications has surged, driven by pandemic disruptions and semiconductor supply chain impacts.
Ford is leveraging digital twin technology in manufacturing and working on a digital supply chain replica. The goal is to enhance resiliency and risk sensing across tiers, moving away from interim containment actions. Predictive models and machine learning help Ford assess supplier risk and prioritize actions.
Consistency in data is crucial for efficient services. A common operating platform and semantic layer are needed to achieve this. Automation of processes, as highlighted by Scott Sloan of Kyndryl, will help manage increased complexity.
At the conference, Catena-X, in collaboration with AIAG and T-Systems, presented technology for automated process handling in the automotive industry, launching their North American collaboration to drive data-driven supply chain innovation.
Ford's data-driven approach is transforming its supply chain, focusing on long-term solutions, resiliency, and data consistency. The industry is embracing automation and collaboration, as seen in the Catena-X, AIAG, and T-Systems partnership, to drive innovation and manage complexity.
