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Coca-Cola

As the AI leader at Coca-Cola, I led the creation of an AI-powered chatbot for the Transportation & Logistics (T&L) department to optimize operations, enhance efficiency, and improve real-time decision-making. Here’s how the project unfolded:

1. Defined Business Objectives & Use Cases

  • Identified Pain Points: Collaborated with T&L stakeholders to pinpoint challenges such as shipment delays, inefficient carrier communications, and lack of real-time tracking visibility.
  • Prioritized Key Use Cases:
    • Real-Time Shipment Tracking: Enabled queries like, “Where is shipment #XYZ?”
    • Carrier & Route Recommendations: Supported requests like, “Suggest the best carrier for a Miami-to-Chicago route.”
    • Exception Alerts: Automated notifications such as, “Alert me if a shipment is delayed by more than 2 hours.”
    • Freight Cost Analysis: Answered questions like, “What were last month’s freight expenses for Mexico?”
    • Document Automation: Generated bills of lading and shipping labels on demand.

2. Integrated Data & Selected AI Models

  • Connected Key Systems: Pulled data from ERP (SAP/Oracle), TMS, GPS/telematics, and external APIs (weather, traffic).
  • Built the AI Stack:
    • NLP Engine: Fine-tuned an open-source LLM (Llama 3, Mistral) and combined it with RAG (Retrieval-Augmented Generation) to ensure accuracy.
    • Knowledge Graph: Developed a logistics-specific ontology covering carriers, routes, and SKUs for contextual understanding.
    • Multilingual Support: Enabled voice and text interactions in key regional languages.

3. Developed & Deployed the Chatbot

  • Ran a Pilot in North America: Tested the chatbot with a small logistics team and refined responses based on feedback.
  • Key Features Implemented:
    • Predictive Alerts: Proactively flagged disruptions (e.g., “Hurricane X may delay Houston shipments next week.”)
    • Automated Rescheduling: Allowed dynamic reassignments (e.g., “Reassign this shipment to Carrier Y.”)
    • IoT Integration: Monitored cold-chain shipments via real-time temperature tracking.
  • Ensured Compliance: Applied GDPR/CCPA protocols to protect shipment data.

4. Drove Adoption & Measured Impact

  • Trained Teams: Conducted workshops for dispatchers, drivers, and planners to ensure smooth adoption.
  • Iterated Based on Feedback: Used sentiment analysis on user interactions to refine responses.
  • Tracked ROI: Measured a ~40% reduction in manual inquiries, faster decision-making, and freight cost savings.

5. Scaled & Expanded AI Capabilities

  • Extended to Last-Mile Logistics: Integrated the chatbot with Coca-Cola’s direct-to-consumer delivery network.
  • Explored Autonomous Logistics: Began testing AI-driven dynamic routing optimizations.
  • Added Sustainability Insights: Enabled queries like, “Which routes minimize carbon emissions?”

Outcome

The AI chatbot became the single source of truth for Coca-Cola’s T&L team, reducing operational friction, cutting costs, and improving delivery reliability.