Benefits of predictive maintenance in logistics

Logistics is a fundamental part of the supply chain. The success of the preceding links depends on it functioning properly, and it is no easy task to ensure that the whole mechanism works perfectly and responds optimally to predefined requirements. In operations as complex as distribution, it is common for events to arise that disrupt the planned schedule, so any measures taken to prevent abnormal incidents in the process are never enough.

In these lines, as we already indicated in the title, we will focus on the benefits of predictive maintenance in warehousing, order picking, and order transport tasks. Warehouses are increasingly equipped with more mechanised elements to carry out repetitive, heavy, and dangerous tasks. On this basis, maintenance of machinery, as in other areas of activity, is a highly relevant task to keep systems operational for as long as possible, avoiding failures that force unexpected stoppages that may affect product delivery times. This is where predictive maintenance comes into play as a tool to optimise machine care activities.

Logistics today

Let us get specific. Nowadays, in addition to more traditional machinery such as forklifts, electric pallet trucks, or hydraulic stackers, many warehouses are robotised and feature loading or packing arms, smart conveyor belts, electrified tracks, reference readers, automated guided vehicles, and a long list of other automation solutions. However, although it is the first thing that comes to mind, not all products can be stored in boxes. Liquids and gases, for example, require direct loading systems into fleets, which also have to perform at their maximum capacity, since a major breakdown may mean that a certain number of trucks cannot depart for their destination, resulting in missed deliveries.

In all these cases, both the most traditional and the most advanced, implementing predictive maintenance tools allows us to anticipate failures or deviations from optimal performance that may occur in machines. These systems, which combine the capabilities of advanced analytics, artificial intelligence, the Internet of Things (IoT), and the now widespread Industrial Internet of Things (IIoT), monitor equipment operation in real time to use the extracted data to show the true performance of the assets.

Benefits of predictive maintenance

In this way, with second-by-second tracking of parameters such as speed, vibration, displacement, or temperature, among others, anomalous machine behaviour can be predicted by anticipating breakdowns. Consequently, intervention takes place when equipment truly needs it rather than according to the preset schedules of preventive maintenance, which in most cases cause unnecessary stoppages and the replacement of parts that still have useful life.

Therefore, the shift is from scheduled maintenance to condition-based maintenance, which acts with reference to deviations in predefined machine parameters. In other words, it monitors correct operation at all times and, upon detecting any change from the conditions set as optimal, issues alerts so maintenance teams can assess whether intervention is necessary and determine the best time to carry it out. This enables early mitigation of issues that could lead to more serious breakdowns, with longer intervention times and higher costs.

In summary, the main benefits of predictive maintenance in logistics departments are:

• Increased productive uptime of machines, ensuring on-time deliveries.
• Extends the service life of parts, which are only replaced when they show damage or deviations from their optimal condition.
• Avoids unnecessary stoppages that compromise distribution.
• Detects potential failures early so they can be repaired, minimising the impact of major unforeseen events.
• As a result of the four advantages above, it delivers significant cost savings.
• Improves planning capabilities (analysis of the best time for a stoppage based on volumes and delivery deadlines, assessment of material needs and human resources…).
• Reduces stress for teams and increases their confidence, thanks to simplified decision-making that will also be more accurate.

Conclusion

In logistics, as in all business sectors, process mechanisation is increasingly evident. Strenuous, repetitive, or risky tasks are assigned to machines, which help us with warehousing and order picking. In this scenario, to ensure optimal logistics performance, predictive maintenance tools are required, which, thanks to big data, artificial intelligence, and the Internet of Things (IoT – IIoT), ensure maximum machine performance. Real-time monitoring makes it possible to detect deviations from typical operating patterns, enabling early interventions and avoiding major failures—very costly in time and money—that can lead to missed deliveries.