Dynamic dispatching for telecommunications can be extremely effective in increasing the total amount of field service work accomplished daily, however it requires certain circumstances. With dynamic dispatching, each worker is given only the first job of the day's information when starting. The first worker to complete his work is given the next work assignment immediately, so there is no 'down time', workers compete to complete the most tasks. This can result in a total productivity work increase of 15%+ per day, day after day.
The circumstances that need to be present for true Dynamic Dispatching to work are a number of workers in a small geographic area so that any travel time between workers and jobs is small enough to allow the first worker finished to be a logical choice for the next job, and that the workers have the same skill sets so they can do each other's jobs, i.e. they are fungible. Another requirement for dynamic dispatching is that two way data transmission must be available between the central source of work and the technicians in the field. As soon as the first worker finishes, this information has to be sent back so the next piece of work can be given out.
Intelligent dynamic dispatching combines all of the above together with algorithm calculations that involve additional variables, such as the likelihood that the closest worker to the next job will finish before the first worker can get to the job, 'rush hour' and directionality. A worker that has to travel two miles in rush hour traffic living in a city in the afternoon is likely to get to the job after one that has to travel four miles but is going in the opposite direction of the rush hour.
A modified form of a neural network (computer learning) is used within FieldPower telecom software to accomplish the intelligent telco dynamic dispatching. The computer performs all these calculations, and then presents its suggestions to a telecommunications dispatcher, along with additional choices. Choices and results are remembered by the computer, and used to make better choices when similar situations arise in the future. |
