How IoT Data Powers Maintenance Management Functions

IoT data provides immense value to maintenance management functions, but the entire quality of the value depends on the amount of data that you have. That means the value of the data depends on the impact of the factors such as source, timelines and accuracy. When we look at developments that are designing the future of maintenance; for example, how large volumes of IoT data are now being created can be managed by suitable utilization of artificial intelligence techniques. A white paper, ‘The future of Maintenance’ declared by Infosys proposes that a data-based perspective will take maintenance to the next level and ‘Maintenance as a Service’ will rather become the standard.

Recognize the data type needed to accomplish your objective and the data you can accumulate from machines or in the field. Here, you might realize the gap between these two data points. Minimizing this gap is a long-term objective that could be attained as the sensor and network technology develops in the future.

Here we will look at how these maintenance improvements can provide machine manufacturers the trust to provide users a ‘Maintenance as a service’ business model in which they can provide not just a machine, but also assures a better level of productivity for an agreed operating period.
Verify the data available with you on the parameters of timeliness, accuracy and reliability to strain out the suitable data. Design a CMMS software architecture that can convert the suitable data into information. Let’s take a look at how companies in asset-determined industries are utilizing IoT to modify their maintenance management functions.

Adoption of Predictive Maintenance

The main aim of applying IoT to handle your assets is predictive maintenance. Instead of conducting routine calendar-based inspections and component substitution, predictive techniques analyze equipment for uncertain failures and inform you when a replacement of the part is needed. Sensors set in equipment check for unusual conditions and set off work orders when safe operating limits are breached.

When a predictive maintenance strategy is operating efficiently, maintenance is only executed on machines when it is needed, thus minimizing the parts and labor costs linked with replacements. First, IoT data enables you to forecast maintenance requirements and asset failures. With sufficient time to schedule the best field service technicians to depend on availability and skill set, the process is efficient.

Secondly, the data-driven capability to execute maintenance scheduling on an ad-hoc basis reserves your time and money and improves the first-visit effectiveness.

Precise Performance Measurement

By precisely recognizing user behavior, rectifying failure patterns OEMs will be enabled to design out failures, increase their product quality and guarantee uptime. Even monitoring and tracking of team and assets allow the management team to regulate KPIs and track progress.
Based on the data insights, you can arrange training and skill development initiatives for field service technicians lagging. In the same way, you can work on restoring the asset that is constantly causing threat and minimize downtime.

Availability, reliability and other key performance metrics likewise mean time between failures (MTBF) and mean time to repair (MTTR) can be measured automatically with the help of the system and added to the reporting dashboards. This process eliminates the human involvement in capturing all downtime, assuring the data is as precise as possible. Additionally, reliability metrics from various customer sites can be monitored to recognize best practices for performance around the world. 

Data-Enabled Inventory Management

Data-driven inventory management concerns the collection and utilization of data and algorithms in real-time, to handle and optimize inventory levels. Inventory is essential to the maintenance function. However utilizing an IoT solution, information about customer patterns, real-time customer reviews about a specific product as well as data from factory ERP and MES systems, can be incorporated so that the amount of this product in the inventory is maintained at precise levels at all times. Such processes root is common inventory management errors such as:

Inaccurate data entry: Manual entry of data tends to misguided information.

Mismanaged warehouse: After the processes are manual, there is no mechanism to examine the standard of data.

Weak Communication: Weak communication within the industries, especially between office executives and warehouse staff, can also lead to inaccurate data entry.

In order to eliminate these mistakes, companies have already started to depend on computerized maintenance management software. The software can record and process the IoT data to offer companies visibility into inventory levels. End-users are instructed to look at various IoT platforms that can help them with data-driven inventory optimization.
Early facilitators of IoT in maintenance have described exceptional advantages of visibility, transparency as well as efficiency in the process.

The IoT will become the norm in increasing asset integrity and driving cost takeout by providing real-time, intelligent and actionable data insights to connected systems or the end-user.