The service and maintenance area of the ERP is responsible for keeping up customer satisfaction minimising their downtime. Also, it assures the standard level of functionality by performing maintenance plans.

On the other hand, it involves order management, logistic, personnel organisation, contract management and customer care. Let's see how Artificial Intelligence can help here.

On-site service optimisation

The service on-site requires intelligent coordination of times, resources and locations. This planning has to consider the duration of a service is often unpredictable. The urgency related to the SLA's conditions plays a significant role independently from the area, and people working time shifts always bring an elevation of complexity.

For the AI, each of the factors mentioned above is a parameter in calculating the optimisation. They are all weighted. AI considers all the aspects like, for example, listing at the bottom the postpone-able appointments in case of unpredicted elongation of the previous ones. The automated calculation formulates the daily plan proposal for each person. On each manager's confirmation, the algorithm improves understanding the preferences in the metrics.

Maintenance management

The process of preserving the customer's systems well-performing has, in some cases, specific conditions like, for example, particular periodic certifications. Furthermore, in some cases, they might need the participation of external organisations that require coordination.

In these occurrences, Artificial Intelligence could help automate communication with the customer and the external participants to establish a convenient appointment for the company resources. The algorithm takes into consideration all the factors like deadlines and shifts.

Monitoring system alarms

When a customer requires a strict SLA for low downtimes, it is worth installing a monitoring system that provides prompt alarms if some parameters exit their established valid range.

The monitoring systems usually allow sending even informational data, not strictly related to a system failure. These additional parameters help predict possible malfunctions. Here the ML's algorithm takes into consideration all the metrics and predicts the defect.

Conclusions

The Artificial Intelligence here helped to provide background calculations that would facilitate the manager's daily choices. The essential value is the adaptation to the conditions.