Nissan Deploys AI for Predictive Procurement

- The deployment across Nissan's U.S., Canada, and Mexico operations is a response to ongoing supply chain pressures, including recent semiconductor shortages and rising raw material costs that have impacted production. - Arkestro's platform utilizes a combination of AI, behavioral science, and game theory to inform its predictive capabilities. Game theory is specifically applied to anticipate suppliers' actions during negotiations, aiming to create more favorable and strategic outcomes for Nissan. - The AI system is intended to accelerate sourcing and cut event cycle times, with Arkestro reporting that its platform can make sourcing cycles 60% faster. This is critical for an industry where production delays due to parts shortages have been a significant issue. - A key goal for Nissan is to enhance transparency and competitive opportunities for its suppliers across both direct and indirect spending. The AI platform automates parts of the tendering and evaluation process, creating more consistent and data-driven interactions. - Arkestro claims its customers see an average of 18.8% in cost savings for every $1 million of spend. One case study with a U.S. automotive manufacturer reported an average savings of 34% per sourcing event. - This move reflects a broader automotive industry trend where companies are using AI to shift from reactive to predictive supply chain management. Other major automakers are also implementing AI to improve demand forecasting, manage inventory, and reduce production disruptions. - The platform is composed of three proprietary technologies: "Negotiation Science," "Supplier Science," and "Process Science," which work together to automate workflows and provide data for supplier selection. - This initiative is part of a larger digital transformation at Nissan, which includes a multi-year effort to centralize data from logistics, purchasing, and manufacturing to improve resilience and agility.