Predictive maintenance in production
Production is the pillar upon which many companies are built. The great challenge for these entities, regardless of their sector, is to increase productive time and minimize process failures, as both milestones ultimately translate into money. Clear examples of this can be found in the food, retail, chemical, and automotive industries.
In these and many other sectors, production line uptime is gold, which requires achieving maximum performance from every machine and optimizing processes. Maintenance is, therefore, a vital task since unexpected stops or downtime exceeding estimates increase production costs and directly impact either corporate profits or product pricing—the latter potentially affecting sales.
If a bicycle manufacturing company suffers a major breakdown in the carbon frame production section that halts the line for 12 hours, the price per unit for a determined period will increase by X%. This will either be absorbed by the organization as a reduction in profits or passed on to the product cost, affecting wholesalers or the end customer. At this point, we must consider that it is not only the non-manufacturing time that is monetized, but also the material and personnel employed to return the line to estimated production parameters.
No machine is exempt from suffering failures, breakdowns, deterioration, or capacity losses… moreover, no machine is exempt from experiencing any of these circumstances without prior warning or indicators that could provide clues to maintenance teams to carry out corrective actions, which are also costly, but to a lesser extent.
Solution: predictive maintenance
Today, Big Data, Artificial Intelligence, and Internet of Things (IoT) devices allow us to take maintenance to its highest levels of efficiency, anticipating problems that industrial machinery may have. Knowing what is going to happen before it occurs may seem like science fiction, but it is the present. How do we achieve it? With predictive maintenance tools.
Predictive maintenance is a technique based on data exploitation and its conversion into useful information, which allows for simplified decision-making and anticipating problems before they occur. Predictions improve with more data and higher quality, a point where the Internet of Things is of vital importance.
The development of sensor technology, which is becoming more advanced and affordable every day, allows for the full exploitation of Internet of Things possibilities. It enables constant, real-time monitoring of production lines to detect anomalies in machine performance, thereby anticipating possible breakages or failures.
To obtain the predictions mentioned, values such as vibration, temperature, displacement, and speed are measured. Any deviation in the standard parameters of these references, or in several of them, can signal a more significant problem that can be addressed prematurely, minimizing downtime and maintenance costs.
In other words, predictive maintenance is based on a sequence of detection, analysis, and intervention. Through this, it is possible to eliminate scheduled corrective actions that are disconnected from the actual needs of the machines and, simultaneously, detect potential failures early to act upon them, avoiding more significant breakdowns that are costlier in terms of both time and money.
Consequently, machine uptime is increased; the useful life of parts is extended, as they are only replaced when they show damage or alterations from their optimal state; and planning levels are improved (analyzing the best time for a shutdown, assessing material and human resource needs, etc.), reducing team stress and increasing confidence through simplified and more accurate decision-making.
Greater productivity, quality, and profitability
In summary, the shift from preventive maintenance to predictive maintenance reduces machinery interventions, which will only occur when necessary; minimizes production line downtime; reduces expenses on parts and spares; decreases rejection rates; and increases foresight. This allows for work to be planned during off-peak times when a shutdown is less costly. And what do these benefits translate into? Greater productivity, higher quality, and increased profitability.