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.

ai-powered-combined-delivery-transportation-scheduling

SITUATION

The existing scenario demanded smarter AI/ML tools to prepare for disruptions. They had to be ready with alternate plans and manage scheduling under multiple and diverse constraints.

CONTRIBUTING FACTORS

Analyze Reverse Scheduling, including time & cost accounting, for supply and demand-side perturbations. Provide easy integration with minimum to no downtime.

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.

HIGHLIGHTS

  • Easy-to-install, sat on top of existing software systems, gave 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 the workforce, while accounting for the human factor, personal preferences, preferred times, and locations.
  • Mitigated scheduling inefficiencies, eliminating drudgery.

BENEFITS

  • 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.