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    Transforming Demand Management for a leading Automotive Manufacturer with Agentic AI

    Client : A Leading Automotive Manufacturer
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    case study
    • Agentic AI
    • Automotive
    • Manufacturing
    Problem Statement Problem Statement

    Our client is a leading global automotive manufacturer, known for its vast supply chain network and a wide-reaching customer base. The company came across challenges related to managing sudden demand spikes. With operations spread across numerous distribution centers and production lines, accessing real-time insights from the massive flow of data across the company was becoming increasingly complex and time-consuming.

    Key Challenges Key Challenges
      • Lack of real-time systems to flag demand spikes and inventory shortages.
      • Inefficient coordination between inventory, logistics, and production teams.
      • Bottlenecks in approval workflows, causing slow response to market needs.
      • Fragmented data sources slowing decision-making.
      • High operational costs due to poor production planning or last-moment expedited shipments.
      Solution Implemented Solution Framework
      Polestar analytics helped them to set up a connected system of AI agents, each putting focus on disparate parts of the supply chain to keep things running in a smooth way. We deployed:

      • Orchestrator Agent: Acts as central coordinator for large or urgent orders.
      • Inventory Agent: Checks stock and suggests fastest delivery from warehouses.
      • Production Agent: Reviews capacity and adjusts schedules or overtime if needed.
      • Decision Support Agents: Provide fulfillment options with cost and time insights.
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    Business Impact
    • 70% of urgent demands met through smarter inventory reallocation
    • 25% faster decision-making on demand fulfilment
    • 15% increase in on-time deliveries
    • 20% reduction in logistics costs

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