Predictive maintenance is a technique used in companies and factories that employ IoT sensors and systems to collect real-time data from their machines and devices. This allows them to remotely monitor operational processes or make predictions about future events, thus contributing to optimization and efficiency. Opposite to this are the not data-driven companies and factories that follow a predefined maintenance schedule (not knowing when it is actually needed) in the hopes of preventing malfunctions. This leaves them at a disadvantage compared to their counterparts who will not suffer from inefficiencies and costly equipment failures.
Because of the lack of digitalization today, most of the available data embedded in industrial operations is lost and hence not used to make important operational decisions. Nonetheless, to achieve high productivity and prevent sudden failure or breakdown, machine operations must be maintained for prolonged times and across several periods. Adopting IoT technology provides stakeholders with real-time insights to practice predictive analytics (recognizing patterns and predicting failures) and thus predictive maintenance on their equipment. For example, knowing when a machine or parts of it require attention, allows engineers and technicians to adjust and manage their resources.
When a predictive maintenance strategy works effectively, maintenance is only performed on machines when required, thus reducing the operations and labor costs associated with replacements. Experience in the field has shown that switching to predictive maintenance results in significant cost reductions, increased work safety, reduced maintenance costs, less downtime, and an extension of the operating life of the assets.
Furthermore, using IoT technology opens possibilities such as the creation of a replica in the digital format of a machine, product, process, or service (Digital Twin). With a Digital Twin of, for example, a machine in the field, you can remotely detect potential problems, test new settings, and simulate scenarios, thus, aiding the predictive maintenance goals of avoiding downtime and unnecessary costs.
IoT systems (which include sensors, gateways, cloud environments, and more) used together with artificial intelligence (AI) allow companies to manage and control their data better and easier. AI algorithms enable companies to spot patterns, identify abnormal behavior in variable industrial settings, understand long-term trends, and avoid undesired events.
In addition, software robots can automate diverse tasks and processes across several industries (e.g., manufacturing, supply chains, agriculture, and management systems. For example, automation can be applied in administrative tasks such as filling in forms or writing reports or it can be applied to measure, monitor, and change climate conditions in a greenhouse.
Wherever it is applied, automation using IoT offers many advantages including increased uptime (independently from workers’ hours), improved worker safety, increased operational and functionality efficiency, enhanced insight on customer or product behavior, and increase product quality and flexibility, among others.
IoT can help streamline inventory management and improve every process within warehouses or retail stores. Through IoT sensors, the movement and use of material and assets inside facilities can be monitored in real-time, thus keeping inventory information always updated. Sensors can, furthermore, measure and report on critical factors such as location, temperature, light, humidity, handling speed, and storage conditions of products. This helps businesses ensure quality, speed, and efficiency throughout their supply chains.
In addition, shelves can be made “smart” with IoT to measure the freshness of products as well as their storage period and conditions. By measuring the evolution of early warning parameters of perishable goods, for example, actions can be taken to reduce their shelf-life and prevent spoilage and waste. Along with the use of smart shelves, IoT devices can help identify usage patterns.
Furthermore, continuous data-driven insight helps streamline coordination between warehouse operations and various logistics providers. This includes improved traceability of goods during transportation, for which IoT can help answer questions like: due to transportation circumstances, are there products that require better (un)loading or more efficient storing? All without the need for time-consuming, imprecise, and costly manual data collection.