In this article, we tell you how state-of-the-art models and algorithms help to optimize maintenance planning for electrical installations.
To guarantee the proper functioning of the electricity system, distribution companies carry out routine maintenance, revision, and inspection work on electrical installations throughout the distribution network. The aim is to ensure continuity of supply in all areas in which they operate and to improve the safety of installations. Many energy trading companies also have an installation maintenance service for individuals or companies that have contracted their services with them.
There are many types of maintenance of the electricity grid, ranging from work with forest stands (cleaning and care of the areas through which the electricity grid runs to reduce the risk of fire) to exhaustive checks of the high, medium, and low voltage lines, distribution centers or substations. Not to mention troubleshooting tasks.
In order to be able to carry out maintenance in an optimal manner, distribution and marketing companies must:
- Have the necessary capacities to carry out maintenance tasks. This means having a sufficient fleet, equipment, and qualified staff to provide the service.
- Optimally allocate the available employees (with different skills and knowledge) to the different types of maintenance and tasks to be performed. Taking into account multiple variables and constraints such as the skills of each employee, the available fleet, labor, and safety compliance, etc.
Capacity planning for maintenance tasks
When it comes to capacity planning for maintenance tasks, some companies use experience and intuition to size equipment or calculate the material or number of vehicles they will need for the job. This practice is inefficient, as increasingly in this changing and uncertain world, what worked yesterday will not work tomorrow.
Leading companies have already realized this and rely on different models and algorithms to help them plan capacities in an optimal way. For example, predictive models use historical data to understand what the future service needs will be. Predictive maintenance applies this type of model by collecting data from equipment and facilities and analyzing their condition and operation, thus anticipating possible failures that help to reduce corrective interventions and their associated costs.
Based on these predictions, constraints, business rules, and the availability of manpower, optimization algorithms calculate the best sizing for planning to ensure service coverage.
In addition, these models provide a complete picture of costs, activity, employee workloads, and future recruitment needs. Using simulation capabilities, they allow you to play with multiple scenarios, changing workforce availability, demand, labor standards, or other factors.
Optimal staff planning for maintenance tasks
Once capacities are sized, companies must allocate personnel with the appropriate equipment and vehicles to the different maintenance tasks. Again, many companies do this planning manually with a spreadsheet or similar software, which is inefficient.
Although it may seem like a simple task, the number of variables and constraints that come into play in the planning process make it very complicated. Mathematical optimization algorithms must be used to make this allocation and to generate the optimal shifts. They take into account the availability of the employees (sick leave, holidays, weekly hours worked, maximum daily/weekly hours, rest hours/days, etc.), their location, their skills and knowledge, their preferences, etc. It also takes into account the availability of other assets such as the fleet or materials, and regulatory compliance in different areas (labor, safety at work, etc.). All of this is done with the aim of achieving the most beneficial planning for the company on the basis of established objectives. In this way, energy companies can ensure that they use as few resources as possible while guaranteeing coverage and service levels.
Planning and allocation are fully automatic and optimized, but planners can reject any suggestions from the system. They can also see in real-time what impact the planning will have and, if necessary, suggest alternative solutions.
Energy companies need to invest in new technologies to help them operate more efficiently in the maintenance of electrical installations.
At decide4AI we help energy distribution and marketing companies to optimize their operations and processes to improve productivity and efficiency and minimize costs and risks.
If you are interested in finding out how we can help you, please contact us.
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