AI-powered Combined Delivery & Transportation Scheduling with ‘What-If’ Analysis
Mitigated scheduling inefficiencies eliminating drudgery. Maximized profit by optimizing resource, time & cost models simultaneously.
Achieved 15% savings on OpEx costs.
Situation
Existing scenario demanded smarter AI/ML enabled tools to prepare for disruptions. They had to be ready with alternate plans, and manage scheduling under multiple and diverse constraints.
Challenges
Real life variables (last minute cancellations, reroutes, product recalls, diverse workforce, and their time & location preferences) were under-represented in end-to-end software optimization tools. Lack of a flexible add-on AI module to augment greenfield and brownfield solutions.
Additional Requirements
Analyse Reverse Scheduling, including time & cost accounting, for supply and demand-side perturbations. Provide easy integration with minimum to no down-time.
- Easy-to-install, sits on top of existing software systems, gives a single dashboard view for all systems.
- No disruption of existing systems during setup.
- Connected thousands of employees & nodes in a supply chain. Tied all schedules together. Identified the best possible allocation of workforce, while accounting for human factor, personal preferences, preferred times and locations.
- Mitigated scheduling inefficiencies, eliminating drudgery.
- Route optimization under multiple constraints for weeks and months in advance, with a provision for contingencies.
- Efficient scheduling to save resources. Ensured happy employees (by considering personal preferences) and successful fulfillment.
- Achieved parallel optimization across tens of specified activities.
- Proactive scheduling to avoid small issues from becoming critical.