Curriculum
- 4 Sections
- 24 Lessons
- 1 Day
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- Foundations of Predictive Logistics & Route Optimization5
- 1.1Transition from fixed routing to predictive, data-driven logistics
- 1.2Core drivers: Demand visibility, ETA reliability, responsiveness to disruptions
- 1.3Key optimisation levers: Service windows, path constraints, vehicle-class rules, cost per kilometre
- 1.4Data inputs: GPS streams, telematics, order density patterns, driver behaviour analytics
- 1.5Cost drivers: Empty miles, dwell time, detours, poor utilisation
- Practical Operational Challenges & Situational Decision-Making6
- 2.1Unpredictable daily order volumes and vehicle underutilisation
- 2.2Delays from rigid routing, choke points, and inaccurate service time assumptions
- 2.3Route inaccuracies due to weak geocoding or map-data gaps
- 2.4Driver-level variability affecting ETA and fuel efficiency
- 2.5Cost leakage through unnecessary detours and missed consolidation
- 2.6Visibility gaps leading to reactive exception handling
- Real-World Applications, Technologies & Case Studies9
- 3.1Case Study 1 – FMCG Regional Distribution
- 3.2Case Study 2 – E-commerce Last-Mile Delivery
- 3.3AI-based demand prediction for fleet allocation
- 3.4Predictive ETAs using live conditions + historical route signatures
- 3.5Dynamic routing for multi-stop, urban, and hub-to-hub deliveries
- 3.6Load optimisation: palletisation, cube utilisation, weight balancing
- 3.7Cross-dock & relay optimisation for long-haul reliability
- 3.8Fuel and driving pattern optimisation using telematics
- 3.9Technology stack: routing engines, control towers, TMS, telematics APIs
- Predictive Planning & Logistics Improvement4
- 4.1Predictive Route Optimization: Create efficient routes using order patterns, service windows, and vehicle constraints
- 4.2Data Analysis: Review route logs to pinpoint delays, deviations, and performance gaps
- 4.3Route Redesign: Improve utilization, reduce distance, and meet tighter ETAs through optimized routing
- 4.4Dashboard Review: Use key metrics to recommend actions that lower delivery cost and enhance reliability
Core drivers: Demand visibility, ETA reliability, responsiveness to disruptions
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