Most logistics companies say that efficiency is a number one priority. But what actually helps them become more efficient?
The answer is knowledge. More specifically, the knowledge that you gain from your own data. And nothing is more powerful now than data collected through the Internet of Things (IoT).
So, it’s no secret that predictive maintenance is one of the most popular uses of IoT in logistics. But what is it, and how does it work? What are the actual benefits for companies?
Predictive maintenance programs use data (including IoT-collected data) to monitor equipment, focusing on detecting errors. This enables warehouse managers, fleet owners, and workers to act proactively and fix equipment failures before they happen.
Predictive maintenance differs significantly from other types, such as reactive and preventive maintenance. Reactive maintenance means taking action after something breaks down. Preventive means scheduling maintenance at a specific frequency. Both come with risks such as losing money, time, resources and even health hazards.
In comparison, predictive maintenance is a just-in-time method of dealing with repairs or replacements.
IoT solutions enable predictive maintenance solutions by delivering data in real-time. So you can monitor your equipment no matter its location.
Take, for example, our client, a provider of transport solutions for commercial vehicles in industries such as mining, material handling and environmental services. They also offer intelligent waste management solutions, which wouldn’t be possible without IoT. Here is a quick breakdown of their process:
Sensors are placed on static and mobile compactors, tipping trucks and other types of vehicles. They detect parameters such as location, trip duration, events and payload and send this information to a gateway.
The data is centralised in the cloud. Once processed, clients can see valuable information such as events. For example, there’s a risk of tipping over when vehicles are overloaded. However, they can also see when and where an event occurs so they can take action.
The software is one of the most essential parts since it displays widgets and generates periodic reports so clients can make decisions about maintenance. Most importantly, the platform uses predictive analytics based on specific health status parameters. For example, they can see which vehicles have minor defects, and they can plan maintenance before they malfunction.
As we can see, IoT has a significant role in collecting data and the type of data you can manage. But what are the benefits for companies?
The benefits of predictive maintenance through IoT include improved productivity and lower costs. Here is how:
Predictive maintenance lowers maintenance costs by 25%.
Failing equipment can derail the entire production process, significantly losing resources, time and money. Instead, predictive maintenance is cost-effective.
Total replacements, if not necessary, are more expensive than repairing components. This is especially true if it happens suddenly.
Predictive maintenance based on IoT technologies notifies you when something is wrong ahead of time. Depending on your solution, you can even schedule maintenance time to avoid the headache of figuring out the best times for maintenance.
Predicting failures via advanced analytics can increase equipment uptime by up to 20%.
You can significantly reduce the likelihood of downtime via predictive maintenance.
Simultaneously, service crews also have an easier time locating malfunctioning equipment and arriving just on time to fix things. In this way, they can set a more efficient schedule and plan maintenance activities. This is true no matter how much equipment or locations you have.
Predictive maintenance reduces breakdowns by 70%.
IoT allows you to monitor equipment 24/7 and see changes happening in real-time. This also means that you’ll be able to be proactive and see precisely what needs to be done.
Predictive maintenance is an advantage since minor, frequent repairs keep the equipment functioning but also make it last longer. As a consequence, companies save money since paying upfront for new equipment is costly. Also, broken equipment can damage stock and pose safety risks.
Foresight studies (e.g. by EU-OSHA) have shown that such a technological change can help improve working conditions, for example, by taking over heavy, dangerous or routine work (automation, robotisation, exoskeletons) or by better communication and remote control via ICT tools.
As we’ve seen in our client’s example, equipment malfunction is immediately flagged.
Since malfunctioning equipment comes with health hazards, this can actually prevent major injuries in the workplace. According to the European Agency for Safety & Health at Work (OSHA), better technical and organisational prevention contributes to a substantial reduction in accidents.
First, it helps companies avoid non-compliance with safety regulations. Second, defective equipment can lead to serious health problems. For example, when using loading vehicles, crane accidents and electrical equipment.
On average, predictive maintenance increases productivity by 25%.
Overall productivity increases as companies can organise production and maintenance time most efficiently.
This is all because maintenance is not guesswork or a schedule that you must strictly follow. Predictive maintenance tells you what is happening and even how much time to spend on maintenance to fix errors.
Additionally, you can prioritise which piece of equipment should be dealt with first - especially if you have a large operation and can’t do everything simultaneously.
Making more of your resources leads to a competitive advantage. IoT helps companies monitor and detect anomalies early. You don’t have to wait for broken equipment or workplace accidents before taking action.
As IoT becomes more available to logistics companies (including competitors), it’s a good idea to consider adopting it.
As a leading software development company, we know how important your data is. We also know how to help you make the most out of it, including building predictive maintenance features. Learn more about what we do: