Abstract: The present subject matter relates to techniques of assigning a mobility factor to the user based on the spatial movement data. In one example, the technique may include four phases for assigning the mobility factors to the users. In a first phase, spatial movement data from a plurality of user devices assigned to a plurality of users may be received. In a second phase, spatial movement behavior for a set of user devices may be assessed using the received spatial movement data of the members of the set using machine learning techniques. In a third phase, user devices may be selected based on the assessed spatial movement behavior using machine learning techniques for reconfiguration. Finally, at a four phase, the selected user devices may be reconfigured.

Abstract: The present subject matter relates to techniques of assigning a mobility factor to the user based on the spatial movement data. In one example, the technique may include four phases for assigning the mobility factors to the users. In a first phase, spatial movement data from a plurality of user devices assigned to a plurality of users may be received. In a second phase, spatial movement behavior for a set of user devices may be assessed using the received spatial movement data of the members of the set using machine learning techniques. In a third phase, user devices may be selected based on the assessed spatial movement behavior using machine learning techniques for reconfiguration. Finally, at a four phase, the selected user devices may be reconfigured.

Abstract: This disclosure relates generally to doorstep delivery of services and products, and more particularly to a system and a method for dynamic fleet management for order delivery are provided. Initially, a primary route is assigned to a vehicle from a fleet of vehicles, based on at least one of a known order and a forecasted order. Further, when the vehicle is in transit along the primary route, in response to an input with respect to at least one of a route alteration parameter, one of an alternate route or a corrective action, is determined, and one or more corresponding actions are triggered, which helps with the order delivery.

Abstract: Method and system for enabling dynamic capacity planning are disclosed. In an embodiment, a plurality of operational inputs are configured to forecast a demand volume and generate a dynamic capacity planning schedule. For example, said plurality of operational inputs include raw data received from a server. Further, historical data is processed based on said plurality of operational inputs to create a plurality of contextual data elements. Furthermore, said demand volume is forecasted and said dynamic capacity planning schedule is generated using at least a part of the historical data and a current capacity planning requirement, said current capacity planning requirement is being generated dynamically by said server. In addition, said dynamic capacity planning schedule is rendered based on said forecasted demand volume and said rendered dynamic capacity planning schedule is clustered into a plurality of work reports associated with said plurality of contextual data elements.

Abstract: This disclosure relates generally to doorstep delivery of services and products, and more particularly to a system and a method for dynamic fleet management for order delivery are provided. Initially, a primary route is assigned to a vehicle from a fleet of vehicles, based on at least one of a known order and a forecasted order. Further, when the vehicle is in transit along the primary route, in response to an input with respect to at least one of a route alteration parameter, one of an alternate route or a corrective action, is determined, and one or more corresponding actions are triggered, which helps with the order delivery.

Abstract: Method and system for enabling dynamic capacity planning are disclosed. In an embodiment, a plurality of operational inputs are configured to forecast a demand volume and generate a dynamic capacity planning schedule. For example, said plurality of operational inputs include raw data received from a server. Further, historical data is processed based on said plurality of operational inputs to create a plurality of contextual data elements. Furthermore, said demand volume is forecasted and said dynamic capacity planning schedule is generated using at least a part of the historical data and a current capacity planning requirement, said current, capacity planning requirement is being generated dynamically by said server. In addition, said dynamic capacity planning schedule is rendered based on said forecasted demand volume and said rendered dynamic capacity planning schedule is clustered into a plurality of work reports associated with said plurality of contextual data elements.