LogiNext’s field workforce optimization is machine learning-enabled planning engine which pulls in all the constraints such as preferred time slot visits, avoiding repeat visits, avoiding overlap or mirroring, and also to optimize the service time or time spent at each outlet
With effective schedules and permanent journey plans for the delivery and field agents, resource movement cost can be reduced while increasing overall resource utilization. Shorter distances traveled with lesser detention leads to higher number of deliveries fulfilled and visits accomplished.
Remember the viral video of Travis Kalanick from earlier this year where he is berating an Uber driver to take ownership of his own problem. Well we all know that video as the start of the derailment of Uber’s public relations. It’s easy to now imagine the company as being self-centered and culturally egoistic.
White Paper: The Assumption Within AIDAS is Unsaid but Profound. This idea that the expectation stage of consumers can be generalized across industries and timelines, is prone to error. How does this generalization affect the decision flow of consumers? To answer this, I will help you look closely at these assumptions.