Route optimization is an important process for many businesses operating a fleet of vehicles. It helps reduce transportation costs and increase efficiency by optimizing the routes used by drivers. One way to further optimize routing is to consider the different types of customers served.

COREDINAT Factory is a software and hardware solution designed by considering the requirements of industrial customers. Routing optimization has developed to take into account the needs of factory customers.

Personnel services management in factories requires hard work in order not to disrupt production processes. It is also highly dependent on human resources. This addiction requires that the drivers who use the buses know the stops.

Geographic information systems (GIS) and artificial intelligence are used to optimize the routing for the staff's home or fixed stops, to map the staff's positions, and the best routes to follow. AI can also take into account variables such as traffic congestion, weather conditions, and closed roads.

By using the COREDINAT factory, businesses can create separate routing plans for each service type, taking into account the specific needs and requirements of each group. This increases efficiency and reduces service costs by 35% by minimizing travel time, reducing fuel consumption, and improving delivery accuracy.

Another way to optimize routing for variable address and staffing types is to use machine learning algorithms that can analyze customer behavior data and preferences. This data is used to predict future delivery patterns and optimize routing plans accordingly. The COREDINAT Factory has flexible algorithms and trained artificial intelligence according to the customer's staffing pattern (fixed stops or pick-up from home).

As a result, the efficiency and effectiveness of service operations increase according to variable customer types. Using machine learning algorithms, COREDINAT Factory can create custom routing plans for each type of customer, taking into account unique needs and preferences. Thus, the workload of the personnel responsible for the service operation, such as the chief driver and human resources, is reduced. With fewer vehicles and fewer drivers, there is a decrease in overtime costs.