Scheduling of the appropriate medical staff to meet the needs of patients is critical. While individual staff members have intuitive knowledge about the fluctuation of cases over time, such institutional knowledge isn't captured and leveraged in a consistent fashion. Machine learning can coordinate staff scheduling with the number and type of medical appointments. Intelligent scheduling optimizes the servicing of patients in light of variable operating conditions at a healthcare facility.
Machine learning can be used to address some of the biggest scheduling challenges for manufacturers. Raw materials can be ordered according to a just-in-time schedule. Production schedules can be tailored to ensure that products ship to the end customer on time. Scheduling solutions allow for the efficient coordination of equipment, materials, and employees at every step of the process.
Machine learning can be used to optimize vehicle routes for a group of geographically dispersed customers with known demands and defined delivery expectations. Solutions can also inform the optimal management of a given vehicle fleet, from cost containment to driver time window constraints.