The SpaceTime Difference
We live in a customer-driven, on-demand economy. Shipments of goods have become smaller, more frequent, and widely distributed. Demand is more volatile with wider seasonal swings, creating uncertainty for logistics providers. Customer tolerance for services downtime is lower than ever before, compressing the time between planning and execution of maintenance operations from months to minutes. Businesses have to do more, faster, with fewer resources, and SpaceTime’s machine learning Operations Analytics can make it happen.
Optimize Throughput and Capacity Utilization
SpaceTime uses reinforcement learning for Hub Operations Analytics to optimize throughput continuously and align capacity with demand. Hub operations software optimizes for multiple constraints such as service level by product, available workforce, and system capacity. Arrivals and departures are tracked in real time, spatio-temporal queueing of products throughout the hub is maintained, and goods are dynamically reassigned to transport.
Optimize Transport Networks
Optimized and more predictable operations are essential to transport networks. Rail velocity must be maintained, dangerous conditions predicted, cargo condition tracked, real-time inventory known, and hub and loading dock throughput maximized.
If assets are down, business is lost. SpaceTime Asset Analytics goes beyond predictive maintenance with real-time optimization of maintenance and repair schedules to extend the life of assets and minimize downtime.
Operations Analytics optimizes operations for:
Third Party Logistics
and any business with Field Maintenance Operations