Automatic Calendar Scheduler – USE CASES Experiment #208
Use Cases Experiment #208 – Automatic Calendar Scheduler
Use Case #4
Task development has an economic impact on the company, and therefore correct planning, not only in terms of time but also in terms of logistics, allows cost savings as well as improving development ratios among employees.
Let’s imagine a company that must attend to the entire national territory to resolve incidents.
An employee who is in the city of Madrid is assigned an incident to be resolved in the afternoon in Huelva, generating a significant economic cost to the company for the urgent transfer and a decrease in productivity by blocking working hours during the travel time, as well as stress to the employee for his sudden trip out of town.
In turn, this company has offices in Cadiz, much closer to Huelva, where two workers resolve incidents.
Only one of the two employees could solve the Huelva task, as it requires a minimum level of expertise that only he meets, but he has a full schedule.
The other employee, who could not solve the task, has the afternoon off.
The algorithm optimization must analyze not only the productivity and historical metrics but also the company’s cost to reduce it with the least possible impact on the company’s economy.
Therefore, it must understand that thanks to these metrics, the employee in Cadiz who has the afternoon off will be able to work on the afternoon tasks of the other employee with more expertise in Cadiz, freeing up his schedule.
This means that this employee can go to Huelva and solve the task in question because he meets the minimum level of expertise, and the employee in Madrid can stay in his city waiting for new urgent tasks that can be solved in his area.
With this algorithm optimization it will be possible to take into account that the cost generated by transfers, blockages, etc., can be evaluated to estimate the general cost of moving schedules and adjust the organization for greater economic profitability.
Use Case #5
Tasks have a deadline and an estimate of effort, which will depend on the time that has historically been dedicated to developing these tasks and the capacity of the team.
However, there may be employees who perform certain tasks with much greater agility and efficiency than the average and therefore can offer better performance in both time and form.
Imagine 3 tasks for 3 employees, who have the same deadlines and the same effort estimates. In turn, each task has 3 subtasks, which have the same estimates between them.
The goal of an algorithm optimization is to optimize the schedule allowing the tasks to be successfully delivered on time.
This algorithm must analyze how each employee performs with these tasks, and determine which ones would be appropriate for the performance of each one.
If a subtask has an estimated effort of 2 hours, but one of the 3 employees can develop it in 1 hour, it seems interesting that this employee solves it allowing greater productivity and efficiency in the resolution.
Therefore, if we have 3 employees and each one is an expert in a subtask and can optimize the effort, the idea of the algorithm optimization is that each employee works in his field of interest and efficiently completes the tasks.
Instead of each employee working on a task with 3 subtasks each, the idea is that each employee works on the appropriate subtask of each task… improving the effort ratio and minimizing the impact on the schedule for task resolution.
In this way the company has a lower economic impact since the agenda of each employee would be free earlier and the employee can afford to focus on the tasks in which his performance is very good.
