Achieve better manufacturing outcomes with real-time condition monitoring

Despite the rapid advancements in Industrial IoT (IIoT) over the past decade, many manufacturers still struggle to turn the vast amounts of potential data into actionable insights. One key area where data can drive real value is machine health and performance. By closely monitoring production machinery, manufacturers can make smarter business decisions, improve efficiency, and increase operating income.

From data-driven machine insight to condition-based maintenance

In the past, only large manufacturers with significant resources leveraged condition-based monitoring. Now, with platforms like innius, mid-sized and smaller manufacturers can also gain real-time insights to improve machine reliability and efficiency.
There are real benefits to monitoring the condition of your machinery and equipment. Most importantly, it can enable Condition-Based Maintenance, which in turn can help improve your and help you improve your Overall Equipment Effectiveness (OEE). This comprises assessments of the performance, availability and quality produced by your machinery into a single compound value.

To do this, you need to find a way to extract intelligence from the masses of data generated in your operation. Platforms like innius help manufacturers bridge this gap by structuring machine data into actionable insights, enabling real-time monitoring and predictive decision-making.

With innius, OEE monitoring is not just about tracking a single number—it provides deeper insights into downtime causes, production bottlenecks, and quality trends, ensuring that every improvement step is data-backed.

Once you know the OEE value for your operations, you can identify actions to improve it, keeping an eye on the data evidence to verify that your actions create the results you want.

In OEE, lean manufacturing meets the IoT

OEE was originally developed in the discipline of lean manufacturing, and lean practitioners have created best practices to take effective action to improve your OEE results and avoid distraction from data overload.

When considering a production line, it is important to identify the bottleneck – the machine or process action that determines the output more than any other element – and then treat this as a priority. You determine what quality faults, performance losses, or breakdowns occur, identify the steps to correct them, and capture the results.
Then you refine your approach or move on to the next-urgent IIoT finding that highlights a need for improvement.
You can find on industry websites various descriptions of this common-sense method, often referred to as IDA (Information, Decision, Action). When you follow it to review and act promptly on real-time data, you can boost OEE.

Several desirable outcomes can then happen. For instance, you might run at full capacity, avoid unplanned breakdowns, minimize waste, and maintain a steady flow of production. Those, in turn, have a direct impact on the quality you provide to customers, the timeliness of your delivery, and your ability to run a profitable operation.

Large payoffs in condition-based monitoring

Improving OEE by as little as 10% can improve a manufacturer’s bottom line by 20% or more. On average, machinery runs at between 35% and 45% OEE, which means many companies could do much better and are not currently enjoying the benefit of immediately actionable improvement opportunities.
Frequently, part of the reason is that they are not looking at all the data they should consider or do so with a delay, when poor quality or slow production have already harmed the customer experience.

With real-time condition-based monitoring via innius, manufacturers can shift from reactive to proactive maintenance, preventing costly breakdowns and optimizing machine utilization. While OEE monitoring is an important part of improving machine performance, condition-based monitoring offers additional benefits such as predictive maintenance and real-time alerts.

Keep in mind that OEE improvements are not the sole reason for condition-based monitoring. You can use innius IoT data to review how well machines withstand their workloads, anticipating and avoiding possible machine slowdowns and breakdowns before they impact your production.
Alerts sent to the right people when IoT sensor data crosses certain thresholds help them jump into action by getting a technician to the machine to replace a part, for instance, or adjusting the flow of raw materials.