How to reduce and eliminate the 6 major industrial losses thanks to Industrial Internet of Things (IIoT)

In the 2026 manufacturing landscape, it has become essential for companies to invest in their manufacturing processes in order to optimize operations and stay competitive. Political uncertainty makes external factors hard to predict: so you have to focus, more than anything, on improving the elements within our control. Several methods make it possible to maximize operational efficiency, but you must first start by identifying the biggest losses in your plant.

The 6 major TRS losses (Overall Synthetic Yield Rate)

Traditionally, these losses are grouped into three main categories:

Availability : Unplanned downtime (breakdowns) and planned downtime but poorly managed (changeovers that are too long).

Performance : Micro-stops and idling, which slow down the overall pace.

Quality : Production rejects (defective parts), as well as losses and rejects related to the start-up and commissioning phases.

Each of these losses represents a direct and invisible cost to the company.

Why choose IIoT in an industrial environment?

Visibility is the key factor of IIoT (Industrial Internet of Things). It enables you to radically transform the way you capture and calculate your performance indicators (KPIs). Collecting data directly at the source helps break down information silos that dilute real data. By eliminating human subjectivity, IIoT captures the “invisible leak” of productivity. You then move from a simple estimate of a shift’s productivity to precise knowledge of its effectiveness, comparable over several months.

The demographic challenge is one of the biggest drivers of IIoT adoption. Nearly 30% of factory employees are 57 or older and are preparing to retire. When IIoT is properly mastered and integrated into decision-making processes, it helps capture the plant’s processes and standards in order to train and support the next generation. In addition, young talent view the use of digital tools as a major attractiveness factor. Finally, the transition to high-value roles is essential for retaining staff: operators want to run production lines—not play firefighters or repeatedly endure breakdowns and recurring supply problems.

Put IIoT to work for machine availability

Machine availability depends on many factors. Unplanned stops, micro-stops, and changeovers that are too long are all situations that eat into your profits. IIoT and industrial digital solutions, such as Artemis Nexus, make it possible to gather valuable insights into these productivity problems and highlight causal links.

Thanks to IIoT, you can instantly detect when a machine stops when it is supposed to be producing. Micro-stops, often overlooked or impossible for operators to note manually, are captured by sensors and visualized instantly on your dashboards. By cross-referencing these automatic stops with the causes entered by operators on their interface, you can act in a thoughtful and calculated way, rather than navigating blindly.

Beyond detection, IIoT paves the way for predictive maintenance. By continuously analyzing equipment data (temperature, vibrations, pressure, motor amp draw), the system detects anomalies before a breakdown. A planned repair costs infinitely less than an unexpected production stop in the middle of a critical order.

Use IIoT to maximize performance

The main performance challenge lies in the gap between the equipment’s actual speed and its maximum theoretical speed. Without data captured continuously, it is impossible to obtain an end-to-end 24/7 view of each piece of equipment on the production floor.

With a robust system like Artemis Edge, coupled with the dashboards of Artemis Link, management follows in real time the efficiency and performance of each machine, for each shift. Speed losses (or slowdowns) often occur when parts are worn, poorly lubricated, or when the operator “intentionally brakes” the machine to avoid quality defects. IIoT makes it possible to identify the root cause in order to apply a permanent solution rather than tolerate a financial loss minute by minute.

IIoT to optimize production quality

Manufacturing defects often result from poor machine parameter settings. By integrating pressure sensors, temperature sensors, or even cameras connected to artificial intelligence (AI), the system can suggest adjustments to the operator through their interface before defective parts are produced. This drastically reduces rejects, which represent wasted time, raw materials, and energy.

In addition, changeover or start-up phases generally cause non-quality rejects. IIoT minimizes this waste by automating start-up checklists and validating that optimal conditions (temperatures, speeds, pressures) are perfectly reached before launching series production.

Conclusion

Integrating industrial data from IIoT into everyday continuous improvement and maintenance processes has become an imperative. In the era of Industry 4.0, equipping your teams with these tools is the only way to give them the means to be effective and competitive.

Reducing downtime, achieving real-time visibility of the plant, and precisely tracking TRS (Overall Synthetic Yield Rate) and TRG inevitably go through the Industrial Internet of Things. This project must become the number-one priority for your plant to secure your margins and ensure the long-term sustainability of your operations.