How IoT Data Powers Maintenance Management Functions

The Wealth of IoT Data

IoT data offers tremendous value to maintenance management functions, but the quality of the value depends on the data you have. This means that source, timeliness, and accuracy can greatly impact the overall value data can offer. If you wish to create IoT data that helps you materialize your business objectives:

• Identify the data type required to fulfill your objective and the data you can collect from machines or in the field. Here, you might find the gap between these two data points. Reducing this gap is a long-term goal that could be achieved as the sensor and network technology advances in the future.
• Validate the data available with you on the dimensions of timeliness, accuracy, and reliability to filter out the relevant data.
• Build a CMMS software architecture that can translate the relevant data into information.

Take a look at how companies in asset-intensive industries are using IoT to transform their maintenance management functions.

Predictive Maintenance

No benefit of IoT data is as powerful as predictive maintenance, for two key reasons.

First, IoT data allows you to predict maintenance requirements and asset failures. With enough time to schedule the best field service technicians based on availability and skill set, the process is streamlined.

Second, the data-driven ability to perform maintenance scheduling on an ad-hoc basis saves your time and money and increases the first-visit effectiveness.

For example, temperature sensors in HVAC equipment can monitor the airflow efficiency and can react to low airflow conditions by sending alerts to the system for filter maintenance or replacement. Similarly, sensors in solar panels connected through IoT can generate work orders as and when required.

Data-Driven Inventory Management

Inventory is integral to the maintenance function. Many companies rely on a spreadsheet or other paper-based processes for inventory control and management even today. Such processes cause common inventory management mistakes such as:

• Incorrect data entry: manual entry of data leads to misleading information.
• Mismanaged warehouse: often, it is not the data entry methods but what type of data being recorded. Since the processes are manual, there is no mechanism to check the quality of data.
• Poor Communication: Poor communication within the organization, especially between office executives and warehouse staff, can also lead to erroneous data entry.

In an attempt to avoid these mistakes, companies have begun to rely on computerized maintenance management software. The software can capture and process the IoT data to provide companies with visibility into inventory levels.

Using IoT data to predict the inventory levels, including stock-in and stock-out of spare parts in different locations, you can optimize the spare parts stock and control the expenditures on new purchases. Just like you only schedule a visit when necessary, you only order the new stock when required.

Performance Measurement

IoT data is useful in making decisions related to asset and team performance. Regular monitoring and tracking of your team and assets enable the management team to set KPIs (Key Performance Indicators) and track progress.

For example, you can see who is the best performer in the team and the team members’ usual average performance. Based on the data, you can plan training and skill development programs for field service technicians lagging. Also, you can develop a reward, recognition, and compensation program for star performers. Similarly, you can plan on replacing the asset that is continuously causing menace and reduce downtime.

The Bottom-Line

Better planning at the initial stage ensures better data. With better data, you get reliable information, which in turn results in enhanced decision-making capabilities. Early implementers of IoT in maintenance have reported extraordinary benefits of visibility, transparency, and efficiency in the process. You, too, can revisit your processes and check to see how IoT can transform your maintenance management function.

• Automation
• Big Data
• Data Analytics
• Inventory Management
• Predictive Maintenance

• Automation
• Big Data
• Data Analytics
• Inventory Management
• Predictive Maintenance