The first IT case

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Such a system is suitable for technologically simple enterprises, where the process of equipment wear is easily predictable, and maintenance actions are standard and not costly for the enterprise.

At this stage, valuable data also begins to accumulate, which is necessary for the implementation of the following. At a large and complex enterprise, maintenance becomes a non-trivial task. For certain types of equipment, specialists are required, whose visit to the site must be planned and paid for separately. The focus of diagnostic analytics systems is on identifying not just a general decline in production, but a specific factor that has a detrimental effect on productivity.

Once an enterprise has completed the content writing service first two stages, it has already accumulated enough data to begin predicting with high accuracy which factors, when and to what extent will have a negative effect on production processes. Only at this stage does it begin to talk about predictive analytics.

A common mistake is to try to jump straight to this stage, bypassing the first two. Now, when a specialist arrives at the site, he clearly sees which systems are at risk and to what extent. Instead of organizing an additional visit for scheduled maintenance, some work can be done in advance, "at the same time" as performing the main task of the current visit.




At the final stage, the enterprise can implement prescriptive analytics, which is a logical continuation of predictive analytics.

This system not only generates forecasts, but also takes on the decision-making (or generation of recommendations) for specific actions. At this stage, it is possible to partially replace the specialist who carries out technical maintenance, since to solve minor problems it is now enough to look at the recommendations offered by the system.

PROSPECTS IN RUSSIA

Not every enterprise needs to go through all the stages of implementing such a system and implement the predictive analytics system itself. Moreover, it may not be economically feasible.

When calculating the cost of implementing such a system, it is not enough to look at the cost of implementation work and sensors. It is important to evaluate the effect of false alarms of the system: because of them, in the worst case, it is necessary to interrupt production, call a team of specialists who will discover that it was a false alarm.

In practice, many focus on ensuring that the system accurately predicts failures, but forget to target the reduction of false predictions.

But it is precisely they who ultimately make the implemented systems simply unprofitable for the enterprise.

At the same time, failures and inaccuracies of the system operation at the first two stages do not incur costs for production, and the benefit from its correct operation will gradually pay off the cost of implementation. That is why it is recommended to begin implementation gradually and specifically from these stages.

When talking about the risks and barriers to implementation, we must not forget about the risks of cybersecurity. Implementing such a system opens up additional loopholes (for example, through deliberately provoking false positives of the system) for intruders. This is confirmed by a Gartner survey, according to which the largest number of industrial enterprise managers (66%) will increase their costs of ensuring information security.

Currently, relatively few domestic enterprises, with the exception of the largest ones, use predictive analytics and IIoT. These technologies require a set of cloud services, such as PaaS and SaaS products, while

Russian enterprises are characterized by patchwork automation – the simultaneous use of several independent systems

This complicates the implementation of predictive analytics and IIoT systems.

At the same time, the potential for using intelligent maintenance systems in Russia is really big. For example, in the mining industry, the cost of excavators is estimated in the range from 250 million to 900 million rubles, while their use level is only 70% of the calendar time. According to McKinsey estimates, the implementation of predictive analytics can reduce equipment downtime by 30%.