Program Overview
Logistics networks today face unpredictable demand, tighter delivery windows, rising fuel costs, and increasing pressure for accuracy. Traditional routing and planning methods cannot keep pace with these operational demands. This program equips professionals with practical, data-driven techniques to improve reliability, reduce empty miles, strengthen ETA accuracy, and streamline last-mile and linehaul operations. Through real-world cases, advanced optimisation methods, and hands-on routing simulations, participants learn how to diagnose route inefficiencies, apply predictive insights, utilise modern routing engines, and redesign delivery workflows for higher utilisation and lower cost. The program helps teams move from manual decision-making to a predictive, performance-driven logistics model.
Features
- Analyse route performance to identify cost leakage, delay drivers, and utilisation gaps
- Apply predictive models and routing techniques to improve ETA accuracy and delivery reliability
- Use data-driven methods to optimise multi-stop, last-mile, and linehaul routes
- Build a practical improvement plan to reduce kilometres, increase utilisation, and stabilise network performance
Target audiences
- Logistics, Supply Chain & Distribution Teams
- Fleet & Transport Operations
- Digital, Telematics & Analytics Roles
- Cross-Functional Stakeholders
Curriculum
- 4 Sections
- 24 Lessons
- 1 Day
- 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
Instructor
