Predictive Analytics in Industry 4.0
Usage of Predictive Analytics in the maintenance of industrial machines has evolved manifolds over the last decade in the USA, but with the conceptualization of Industry 4.0, implementation of Internet of things (IoT)/ Industrial Internet of things (IIoT) and artificial intelligence (AI), manufacturing businesses are saving millions of dollars through predictive maintenance.
For the uninitiated, predictive maintenance starts with linking the manufacturing equipment and machines to the IoT network, through which manufacturers can observe the conditions of their operations. In simple terms, IoT collects real-time data on the health and performance of the industrial machines and based on that data, advanced AI technology provides predictive analytics to
a) Prepare manufacturers to handle any wear and tear in the system in advance;
b) Schedule technician visits even before damages have set in;
c) Prevent/Minimize the likelihoods of downtime;
d) Ensure optimal utilization of resources and technician;
Imagine getting real-time performance reports from one of your house appliances, say your Air Conditioner, based on its cooling efficiency, fan vibrations, and health of condenser coils. You’ll be able to schedule a service visit for the AC before it stops functioning/gets damaged.
In an industrial set-up, predictive maintenance can save millions of dollars in a company’s annual infrastructural procurement and maintenance processes. Not only does it improve the longevity of the equipment or the machines, but it also means avoiding any downtime leading to errors or delays in the supply chain network. Once set up, predictive maintenance systems can enable machines to evaluate and predict any possibility of failures.
Although Industrial Maintenance Managers find it extremely challenging to link predictive maintenance automation with their existing machinery and Enterprise Resource Planning (ERP) systems, these challenges have begun to disappear after the advent of Industry 4.0. These challenges even existed in the first place because a predictive maintenance system needed seamless communication between machines, devices, sensors, and people involved. Interaction between humans and predictive maintenance technologies needed simplified user interfaces in the forms of Data Visualization Dashboards, Workflow Alerts, Action Items, Trigger Alarms, and Reports.
Rapid advancements in technology and data science have managed to navigate through these tricky territories. Now Maintenance Managers can easily ensure that
- Through investments on the state of the art IoT sensors and sensor nodes, they are able to gather highly reliable data in real-time; and
- Through the integration of Manufacturing Execution Systems (MES) with sophisticated ERP systems, the capability to extract value from the unstructured data from the IoT network is enhanced.
Manufacturing businesses now have a reasonable incentive to invest in predictive maintenance because of the lucrative results and returns. If companies can succeed in handling the issues related to the integration and automation of both MES and maintenance processes, they will quickly observe the colossal commercial advantages of having a cost-effective predictive maintenance system.