Maintenance management is an aspect that affects many sectors: from the production industry, mining, but also logistics, transport, fashion and luxury, hospitality, etc., all organizations need to be able to rely on reliable and performing machinery and infrastructure.
The object of maintenance, then, is not only the production machinery, but all those physical assets that allow a company to manage production and business optimally (computers, equipment, industrial machinery, etc.) and which must be efficient and in good working order.
It should be added that each sector has its own peculiarities: if for a logistics company a fundamental asset is given by the transport fleet, for a manufacturing company production plants are the fundamental component.
Malfunctions, machine downtime failures represent, in fact, a loss not only in terms of cost to be incurred, but also in terms of production failure, unexpected interruption of operation, failure to comply with orders and so on. For this reason, companies are more and more careful to contain and foresee the damage then act retrospectively:
Predicting failure helps prevent it and prolong the machinery lifetime, with a positive impact on cost incidence and resource optimization.
What is predictive maintenance
Predictive maintenance is an advanced form of preventive maintenance that allows to identify the remaining time before the occurrence of the fault and then to prevent it, establishing the most convenient time to program the maintenance actions on the machinery.
This type of maintenance is enabled by IoT technologies that allow the continuous real-time monitoring of machines and the collection of a large amount of data. All this information is transformed through mathematical models into precise indicators that allow to evaluate the state of actual deterioration of the machinery and, therefore, to establish the most advantageous moment to program the maintenance actions.
For all these reasons, predictive maintenance has become one of the pillars of Industry 4.0.
We see in detail the differences between reactive, preventive and predictive maintenance, in terms of benefits, costs and opportunities.
Reactive Maintenance (RM)
This form of maintenance is carried out with corrective actions through a “run to failure” approach, i.e., it involves the intervention of maintenance only at the occurrence of the fault or unexpected problems. Today this model is mostly anachronistic because it is the less efficient, predictable and potentially more expensive than ever. If the failure of sellers’ devices can be an inconvenient impasse that leads to malfunctions and damage that can be avoided within a short time and with contained investments, imagine what the unexpected failure can mean for a mining company operating in the Oil & Gas sector, both in terms of cost and time for component procurement and repair (or replacement).
Preventive Maintenance (PM)
Similar to scheduled maintenance, preventive maintenance is based on statistical and historical data on faults: according to certain wear and time conditions, manufacturers can stable average times within which to intervene to prevent certain failures and malfunctions. The limit of this approach is to rely on past statistical data taking as the only indicator time, in reality there are a number of other factors that can shorten or lengthen the life of a machine (atmospheric agents, conditions of use, stress, past failures, lubrication conditions, wear, misalignment of bearings, etc.).
This approach is the most widespread, based on periodic inspections and programmed interventions, which allow to establish the most convenient moment for a machine downtime. Although statistically reliable, it is now inadequate in the most competitive sectors, where time, resources and cost incidence can mean a double-digit % deviation from a system based on “condition monitoring”.
Predictive Maintenance (pdm)
Predictive Maintenance is based on technologies and tools that allow real-time condition monitoring using a data-driven approach.
The data on machinery, faults and past events and the data acquired in real-time by monitoring the machines through sensors and IoT components are acquired and made to be delivered in an AI (Artificial Intelligence) platform, where they are processed, analyzed and used to instruct a predictive model: an algorithm able to predict the occurrence of certain events based on specific indicators. Such events may be the possibility or not of the occurrence of a failure and the determination of a risk level.
The result is a new maintenance model, based on real-time monitoring, capable of generating countless benefits.
Benefits of predictive maintenance
First of all, there is a paradigm shift: from the intervention implemented to solve a problem, to an efficient planning of maintenance activities aimed at reducing costs and extending the life of the plants themselves.
The main advantages of preventive maintenance can be summarized in:
- Prevent the occurrence of failure and unexpected downtimes (identifying anomalies allows you to act before the occurrence of the malfunction avoiding the failure itself);
- Reduction of costs (maintenance, non-production, inactivity, purchase of new assets, missed orders, etc.);
- Increased productivity and safety at work;
- High quality and production performance thanks to optimal asset conditions.
How to structure a predictive maintenance system?
There are conditions necessary to implement predictive maintenance:
- Data collection system through IoT and sensor technologies;
- Software CMMS (Computerized Maintenance Management System), that is a program that allows to manage the assets and to plan the maintenance through dashboard;
- AI Machine Learning tool platforms for the implementation of predictive models and data analysis algorithms.
When is it convenient?
The convenience of a predictive maintenance system in the long term is so obvious that it has already been identified as a highway for many industries (manufacturing, mining, petrochemical, pharmaceutical, logistics and transport, etc.).
The investments needed to implement a predictive approach discourage especially small companies that have not yet enabled data-driven business models and that risk to accumulate a gap more and more compared to more structured companies, able to invest and therefore to contain costs, increase revenues and obtain higher profits already in the short term.
The Business Intelligence & Predictive Analytics result today the indispensable tools for those companies operating in hyper-competitive markets, which are not by chance the first to invest in this regard.
Here is a case study of Predictive Maintenance in the Fashion & Luxury sector.
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