Here’s the basics on how IIoT helps manufacturers create an automated ecosystem, driving optimization and continuous improvement.
IIoT initiatives can go a long way in enhancing the results of a manufacturer’s continuous improvement programs by enhancing real-time control and automation of manufacturing processes. This is achieved by providing an integrated, data-driven approach to operations that connects sensors, machines and devices to systems such as MES, PLM and ERP, among others.
As we’ve learned in this series of articles, IIoT involves the deployment of sensors and smart devices on machines and equipment throughout a manufacturing facility. These sensors continuously collect data such as temperature, pressure, speed, vibration, humidity and other critical performance metrics. This constant stream of real-time data allows operators to monitor machine health, detect performance deviations and measure production parameters instantly.
For example, a temperature sensor on an industrial oven can continuously monitor the baking process and relay that information to a central control system, allowing operators to adjust on the fly if the temperature deviates from the ideal range.
One of the key advantages of such a system in manufacturing is predictive maintenance. The real-time data from sensors not only help manufacturer predict when a machine or component is likely to fail before it breaks down, it helps develop a timeline of machine health to aid in long-term production scheduling. This is possible through techniques like vibration analysis, acoustic monitoring and thermal imaging, which detect signs of wear or malfunction.
By identifying potential issues early, manufacturers can perform maintenance only when needed—reducing unnecessary downtime and costly emergency repairs. This predictive capability enhances the automation of maintenance schedules, allowing for smoother, more continuous production runs.
The value of an IIoT regime isn’t limited to maintenance. When done properly, it enhances the automation of decision-making by integrating advanced data analytics with machine control systems. As data is collected in real time, algorithms analyze this information and automatically adjust equipment settings, production schedules or supply chain logistics.
For example, if a sensor detects a drop in the pressure of a hydraulic system, an IIoT-powered control system can automatically adjust the pressure to maintain optimal performance without human intervention, while signalling operators to warn them of a potential problem. This reduces human error, ensures consistent quality and increases the efficiency of manufacturing processes.
Integrating machines and systems
IIoT enables interconnectivity between different machines, production lines and even links multiple factories or plants. This integration creates seamless communication between devices and systems, enabling data to flow between them in real time. As a result, manufacturers can coordinate complicated operations across the entire production process, from raw material handling to final product assembly.
If a machine on one production line detects a fault and stops, IIoT can signal other parts of the system to adjust, switch tasks or even reroute resources to prevent bottlenecks or downtime elsewhere in the system.
Enhanced supply chain visibility and automation
The follow-on to this is that manufacturers gain real-time insights into their supply chain operations. By integrating data from inventory systems, suppliers and logistics providers, manufacturers can track raw materials, monitor stock levels and predict demand fluctuations. This improves dynamic scheduling and just-in-time production, ensuring that materials are available when needed and avoiding overproduction or stockouts.
IIoT can also help automate inventory management through systems that track and reorder supplies autonomously, reducing manual inputs and the risk of human error.
Energy and resource optimization
Using these systems, manufacturers can continuously monitor energy consumption and resource utilization in real time. Data on electricity use, water consumption, air pressure, and other utilities, help identify inefficiencies and optimize resource allocation.
For example, by tracking the energy usage of different machines and automatically adjusting power consumption, engineers can ensure equipment is operating at peak efficiency, reducing waste and operational costs. Smart systems can also suggest or adjust energy consumption during idle times or change temperature settings in response to production demands.
Real-time quality control
Just like many other aspects of manufacturing, IIoT can help turn quality control processes from reactive to proactive in real-time. Monitoring production parameters like temperature, speed, material composition and other factors during manufacturing, allows manufacturers to detect deviations that could eventually lead to quality defects.
Sensors would measure the consistency of materials or operations like material removal in machining, detect faults in real time and automatically adjust the production line to correct these issues before defects occur, ensuring products meet the required specifications.
Improved worker safety
IIoT can also enhance worker safety by automating dangerous tasks and monitoring environmental conditions in real time. These systems detect hazardous conditions such as toxic gases or abnormal machine vibrations, alerting workers to potential dangers. In more automated setups, IIoT allows for robots and machines to take over high-risk tasks, minimizing human exposure to dangerous environments.
In addition, IIoT-enabled wearables, such as safety vests or helmets with sensors, track workers’ vital signs and environmental conditions, ensuring their safety by triggering alarms or alerts if any dangerous situations arise.
The benefits significantly enhance real-time control and automation in manufacturing by enabling continuous monitoring, automated decision-making, predictive maintenance, and real-time optimization. Integrating data across machines, systems and supply chains makes manufacturing processes more intelligent, efficient and adaptable. By driving automation, improving quality control, reducing costs, and increasing productivity, IIoT is helping manufacturers stay competitive in a rapidly evolving industrial landscape.
Feedback loops
Data feedback loops are a critical concept in optimizing and adjusting processes dynamically, especially in the context of manufacturing. These loops involve continuously collecting data from various systems, processing it to generate insights and then using that information to manage the process. The objective is to maintain efficiency, improve quality, reduce waste and adapt to changing conditions.
The first step in a data feedback loop is the collection of real-time data. We have already learned this data can come from a variety of sensors on machinery, production equipment, supply chain systems, environmental sensors or even wearable devices used by workers.
Once data is collected, it’s sent to a central system where it is processed and analyzed. This may involve simple statistical analysis, machine learning algorithms or deep AI-driven analytics. For processes that require immediate adjustments, data is processed instantly at the edge, allowing for quick decision-making.
Some feedback loops use historical data and predictive algorithms to anticipate problems before they occur. A predictive maintenance system can use data on vibration levels and temperature changes to predict when a machine is likely to fail. Advanced analytics can identify patterns or trends in the data, such as recurring defects or inefficiencies. These insights allow companies to target specific areas for improvement.
Decision-making and adjustment
Based on the analysis, decisions are made on how to adjust or optimize the process in real-time. These adjustments can be made by human operators or automated control systems that directly change operational parameters without human intervention.
In an automated system, once a problem is detected or an optimization is identified, the system can adjust itself. For example, if a temperature sensor on a furnace shows that the temperature is too high, the control system can automatically reduce the heat. Similarly, in a factory, a machine might speed up or slow down based on real-time demand or product quality measurements.
In some cases, a feedback loop will alert a human operator about an issue, but the operator will make the decision on how to proceed, such as when a sensor detects a quality issue with a product.
The key advantage of data feedback loops is their ability to drive process optimization continuously. Over time—as the system collects more data—it improves its ability to make more accurate predictions, identify inefficiencies and adjust processes more effectively.
At a glance: a feedback loop in manufacturing
To explore the basic concept of data feedback loops in manufacturing, consider a smart factory scenario with a robotic assembly line, as described below:
Data Collection: Robots on the assembly line are equipped with sensors to monitor the position, speed, and performance of each part as it moves through the production process.
Data Processing: As the sensors collect data on each part, this information is fed into an analytics platform. The system compares the real-time data with pre-set performance targets, such as the desired part speed, quality standards, and cycle times.
Decision-Making and Adjustment: If the system detects that a part is not being assembled correctly (e.g., an incorrect part placement or missing component), the feedback loop triggers an automatic adjustment, such as slowing down the robot or stopping the line for a quality check. Alternatively, if the system notices that parts are moving too slowly, it can increase the robot speed to meet production goals.
Optimization: As the system continues to gather data and make adjustments, it identifies trends and optimizes the process. For example, it might recognize that certain parts are consistently experiencing defects and automatically adjust the settings to improve alignment or material flow.
Data feedback loops are particularly valuable because they provide real-time adaptability. Manufacturing environments are dynamic, and changes in demand, raw materials, or equipment conditions can occur quickly. A well-designed feedback loop ensures that the system can respond immediately to these changes, keeping production smooth and efficient.
Closing the loop
Once adjustments are made, the feedback loop continues by monitoring the results of those changes and further refining the process. If the adjustment improves performance, the loop continues to operate as usual. If it causes a problem or doesn’t improve performance, the loop learns from that data, adjusting its predictions and recommendations.
By enabling real-time data collection, analysis and automated adjustments, IIoT systems give manufacturers the information required to use feedback loops in a more nuanced, targeted manner to continuously improve performance, maintain high levels of quality, and adapt to changing conditions.
