Run smarter and lower your costs through predictive maintenance


High quality products against an attractive price level, short term delivery and customized products wherever possible. The rising expectations of customers bring manufacturers to the challenge to gain productivity improvements and enhance manufacturing operations in order to remain competitive. Digital transformation through the use of IoT technology can help overcome these challenges. It can help organizations become more competitive and save costs by enabling predictive maintenance practices. This article explains how.

A definition of Industrial IOT

With Industrial IOT (IIOT) solutions, you can create a digital replica of your physical manufacturing environment and processes. This is commonly referred to as a Digital Twin. An IIOT solution employs a network of sensors to collect real-time data on a wide range of parameters and uses intelligent cloud technology to turn this data into valuable insights that can be used to increase the operational efficiency. Because this data is (near) real-time in nature, IIOT solutions offer a live view on operations, whereas traditional ERP and Manufacturing Execution Systems (MES) usually report on events from the past only. IIOT can help optimize operations by supporting the following use-cases:

Equipment utilization

IoT solutions that monitor equipment utilization provide organizations with real-time utilization metrics, offering a detailed view of what is occurring at every step of the manufacturing process. These solutions are processing real-time data about machine operating parameters like operating speed, product output and run-time from SCADA, DCS systems or physical sensors. This data is captured real-time and transmitted to the cloud for processing. Aggregated data can be visualized on the web or a mobile device through a set of intelligent dashboards on equipment utilization KPIs (i.e. Total Effective Equipment Performance (TEEP), Overall Equipment Effectiveness (OEE), setup time, idle time). These dashboards may lead to suggestions to increase equipment utilization, resulting in a higher production without additional investments in new machinery.

Conditions monitoring

Monitoring manufacturing conditions can contribute to an improved quality level of the produced goods by detecting potential bottlenecks in the manufacturing processes, identifying badly tuned or poor performing equipment and timely preventing machine damages. To do so, several parameters need to be monitored. These can be related to the manufacturing equipment (i.e. calibration, vibration, speed, energy consumption and temperature), but also to external conditions (i.e. inside/outside temperature, humidity). For each parameter, thresholds will be defined. If sensor readings are exceeding a threshold, this may indicate to a potential product defect or machine malfunction. The system will alert the operations manager and recommend a mitigating action to repair the machine and minimize impact.

Asset and inventory tracking

The increasing demand for customized products will generally lead to a growth in inventory objects for any give manufacturing company. It will also lead to frequent changes in manufacturing configurations, where specific elements may be added to or removed from a production line. Keeping track on both assets and inventory can be a time consuming and therefore costly process, as it often depends on manual data entry. Automated tracking solutions based on RFID and IoT technology will help in optimizing the tracking process. They provide real-time data about enterprise’s assets, inventories, their statuses, locations and movements.

Predictive maintenance

The use cases described earlier will result in lots of relevant data that can be used for other purposes. Predictive maintenance relies on the insights gained with equipment utilization and condition monitoring. If we combine this data with metadata from the assets used in the manufacturing environment and historic maintenance data from ERP or other maintenance systems, a more coherent picture arises about the state of the machinery. The smart use of machine learning algorithms can help to pinpoint abnormal patterns that may lead to equipment failures. Companies that succeed in doing this, will be able to make a move from reactive (based on plans or malfunctioning) to proactive maintenance (based on preventing failures) practices, leading to significant cost savings.

Heroes IOT Insights

Heroes has developed IOT Insights, an easy to architecture framework, based on Azure technology. With IOT Insights, it is fairly easy to start your journey with IOT. Simply start by connecting a subset of your sensors and applications to the gateway and build smart dashboards on your data. No heavy investments needed up front. You will be up and running in a number of days. Start small, learn, evaluate and expand.

IOT Insights: Heroes’ reference architecture for IoTSolutions



Industrial IoT solutions can help manufacturing companies to maximize productivity, improve quality and save costs by turning real-time data into valuable insights.

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