Today, plant management in manufacturing is still governed, in most cases, by traditional preventive manifestation systems. The initial assumption is that by regularly maintaining machinery, the risks of malfunctions are reduced. Predictive maintenance, for its part, overcomes this paradigm in favor of a system in which maintenance is managed according to the actual state of equipment, supplies and machinery through a system of monitoring, analysis and prediction.

The main features of preventive maintenance

In business, preventive maintenance takes a number of names, for example, planned maintenance, but it remains a concept linked mainly to two simple variables: the chronological one and the one related to cycles of use and wear. The initial assumption is that a component, machinery or apparatus degrades after a certain number of cycles of use, or after a certain time, that are typical for that specific component.

Before reaching that threshold, parts or the system are replaced, reconditioned, or otherwise inspected.

The main disadvantages of preventive maintenance

The main weakness of preventive maintenance is that it relies on general data: for example, the fact that an electric motor typically stops working after 10,000 hours of operation does not mean that a failure cannot occur at 7,000, because that particular part is out of specification. In such cases, preventive maintenance proves ineffective.

Likewise, since maintenance is a considerable cost, performing it too frequently or with too much margin of caution risks becoming an overwhelming expense item for the company.

Finally, just as there are equipment that are less durable than average, there are others that could still be used beyond the average threshold set by the characteristics.

Predictive maintenance: managing equipment through data

The principle behind predictive maintenance is simple: instead of performing operations based on standard parameters, maintenance is performed based on the actual condition of machinery, spare parts and consumables. As the name suggests, this management method strives to predict possible failures and malfunctions before they occur.

To achieve this, systems are constantly monitored, and the data thus collected converge into a series of algorithms and mathematical models that predict impending failures and malfunctions. Today’s analytical models ensure that maintenance work can be planned with realistic advance margins while minimizing the impact on production. In addition, advanced solutions based on machine learning and predictive algorithms make it possible to make the prediction system increasingly advanced and performant as the quantity and quality of data collected progresses.

It must be said that adopting a predictive maintenance system can be challenging, since it requires the adoption of advanced technological solutions, but the benefits are numerous.

Predictive and preventive maintenance compared

Since it is a more advanced system, predictive maintenance has many features that make it attractive, particularly in manufacturing-related sectors where good condition and efficiency of equipment is a priority asset.

In the face of a larger initial investment and the need to train staff to use the new tools, predictive maintenance offers several advantages over preventive maintenance. Here are the main ones:

  • Reduces downtime and improves machine uptime
  • Increases the life cycle of machinery and components, which are used according to their true durability
  • Improves product quality: machinery is always in better condition
  • Reduces the risk of catastrophic damage and related accidents
  • Prevents unforeseeable failures and malfunctions through preventive maintenance

Authoritative research says it reduces maintenance cost by 8 to 12 percent

Predictive maintenance algorithms are constantly improving

We have already mentioned how data collection and management systems used today are able to improve over time by leveraging artificial intelligence and machine learning solutions. Another aspect that should not be underestimated, particularly in cloud-based implementations, is that the predictive models used are constantly being updated and improved, making predictive maintenance systems increasingly effective over time. A company that today has maintenance among its major cost items should undoubtedly consider switching to a predictive system, which will ensure greater efficiency and cost containment.