In the manufacturing sector, companies are facing pressure on multiple fronts.

On the one hand, manufacturers must continuously find ways to drive down costs and increase profit margins. On the other hand, businesses have to maintain worker safety without compromising productivity. Workers are at risk when performing exhausting and dangerous tasks such as moving heavy machinery. Humans also tend to get bored with repetitive work, causing a lack of attention that can lead to making mistakes or becoming less sensitive to them. Enterprises also want to get more out of their machines and reduce the cost of maintenance.

All these challenges, according to McKinsey & Company, mean that while manufacturing is already the most automated sector, there is still high potential for even more gains from automation in this market.

While humans will get fatigued or become distracted, robots will not. Therefore, robots are perfect for high-volume, repetitive tasks. Today, most manufacturing robots are deployed to handle materials, process parts, assemble or perform inspections.

As manufacturing tasks become more complex, the control and coordination of robots needs to become more nuanced, real-time and open to fine-tuning. The Industrial Internet of Things (IIoT) is the industrial version of the Internet of Things (IoT). It provides the set of tools needed to implement this kind of sophisticated control and coordination.

The IIoT consists of data-collecting sensors, data integration and analytics, and artificial intelligence (AI) algorithms. Sensors installed on machines or assets collect data continuously. AI analyzes the integrated data sets to inform decisions made about factory operations.

With the data collection, integration and analytics, a digital replica, or digital twin, of the factory floor can be created. The digital twin enables different teams within the factory plant to visualize, analyze and optimize varying aspects of production, such as workflow, the movements of machines or people, or hazardous zones.

So far, IoT has been used in various manufacturing settings to improve operational efficiency, schedule predictive maintenance, increase workplace safety and even improve supply chain management.

Operational Efficiency

Manufacturers can analyze the flow of raw material and movement of personnel across the factory floor with the AI component of IIoT. As a result, they can monitor production flow to reduce traffic congestion, avoid collisions, balance the workload and decrease the amount of non-value added work.  Supervisors also are relying on IoT to make monitoring remote sites easier and more efficient.

To reduce the mistakes that can occur during the assembly of complex products, Airbus has launched its “Factory of the Future,” which aims to streamline operations and increase productivity. Airbus has integrated sensors into handtools as well as large machines on the shop floor. Additionally, workers wear connected virtual reality (VR) headsets that collect data during tasks such as drilling, measuring, tightening, clamping and data-logging. Through the use of data analytics, the tools and machines can adjust themselves around the worker and specific task. The data gathered during assembly is analyzed to ensure the execution has been correct. So far, the wearables have made a significant impact, such as making the marking of cabin seats almost error-free.

Magna Steyr, an Austrian automaker, is using wearable IoT technologies to help guide its employees in the production of bespoke vehicles. 

In Volvo plants, robots are tasked to handle dangerous, monotonous jobs that demand precision, such as welding. Robots are even trained to navigate the factory floor on their own. At the same time Volvo’s human workers handle tactile work that requires flexibility, creativity and problem-solving, such as fitting assembled parts together.

Komatsu, a Japanese heavy-equipment maker, has linked all of its robots to the company’s central cloud to enable managers to monitor international operations in real-time.

Kuka, a specialty robotics company based in Germany, has connected all its assembly robots to its private cloud for analytics and decision-making. As a result, the factory using such robots can complete a car body every 77 seconds and produce more than 800 vehicles daily.

On the other hand, Toyota is working with Hitachi’s AI and that company’s big data analysis platform to improve efficiency on the factory floor. By feeding real-time analytics into the decision loop, workers can identify and solve new issues.

Lastly, by applying IoT to its production process, Hyundai has made it possible for workers to detect anomalies and prevent machine malfunctioning. Hyundai is tagging materials and products at every production stage, recording various kinds of information such as the model number, target market country and production timeline. This way, workers can identify and eliminate inefficiencies and potential problems.

Predictive Maintenance

Reactive repairs are unpredictable, variable and costly.  By keeping track of machine conditions with sensors, manufacturers can proactively predict and schedule maintenance sessions and eliminate a lot of uncertainty.

Gehring Technologies, Fanuc, John Deere and Kaeser Kompressoren are all using cloud-based data analytics to track machine conditions and reduce downtime. Toyota and Hitachi have also developed IoT-reliant solutions to prevent unexpected facility failures and increase the efficiency of maintenance work.

Workplace Safety

Workplace accidents carry high financial, operational and human costs. Traditional factories have to hire safety officers to patrol the floor to identify and defuse dangerous situations. This process is slow, labor-intensive and inefficient. Analyzing data collected from strategically installed sensors can ensure workplace safety more effectively.

IoT data collection and analytics can identify inappropriate personnel or irrelevant materials in restricted areas. Gathering and analyzing information also helps identify workers who are too tired, intoxicated or distressed to work safely. Artificial intelligence can also ensure everybody has dressed appropriately in safety helmets and footwear, goggles and earmuffs.

For example, steelmaker North Star BlueScope Steel has inserted sensors into the helmets and wristbands that workers wear so that managers can track employee safety and prevent hazardous scenarios. The wearables also monitor biometrics such as a worker’s body temperature, pulse and activity levels so that supervisors can balance the workload within a group of workers. The company also is using sensors to monitor the temperature and levels of radiation and toxic gases in the factory.

By installing sensors into handtools and machines, and giving workers connected virtual-reality glasses, Airbus can help workers reduce errors and increase workplace safety. Similarly, by attaching sensors to tools and equipment, Hyundai enables its workers to detect signs that machines are not operating properly and prevent sudden breakdowns.

Supply Chain Management

IoT can also help manufacturers improve their supply chain by reducing the cost of inventory, increasing inter-team communications, and enabling rapid diagnoses and problem-solving.

For example, Magna Steyr uses IoT to track its inventory, such as tools and vehicle parts, so that the system will automatically replenish supplies.

Maersk, a Danish shipping company, has used IoT to track its assets and optimize fuel consumption and routes of its ships. Maersk is also using sensors and data analytics to analyze how it transports and stores empty shipping containers.

Looking Ahead and Remaining Challenges

As manufacturers like Volvo are finding out, automation is not always the answer. Sometimes, automation works best when it works beside humans. Robots are best in handling heavy-duty and repetitive work while people are best at tasks that involve complex manipulations and require some on-the-spot problem-solving. While robots can be trained to identify certain problems, humans are still much better at solving them.

Airbus is already using VR to help its workers become more efficient and less error-prone. VR and augmented reality (AR) are likely to become more prevalent as the cost of headsets goes down.

The automation of low-skill tasks has become fairly prevalent. As manufacturers begin to implement automation for more complex tasks, they will require more robust support from IoT hardware and software. Seamless alignment among sensors, machines and algorithms is necessary to achieve more nuanced and optimal automation of complex processes.

Communication between the data collection, data analytics and decision-making arm within the IoT must take place with minimal latency. This will help ensure successful, accurate and precise execution. The timely and synchronized delivery of information to all the devices within a network is also essential. The current 4G network cannot provide robust and consistent support for such processes.

The expansion of the 5G network will provide significant speed, coverage, stability and latency improvements. With performance 10 to 20 times better than 4G, depending on the application, 5G promises ultra-reliable low latency of less than 1 millisecond and availability of more than 99.9 percent. 5G will be ideal for mission-critical, massive, multi-component manufacturing processes. The industry has high hopes for 5G. Unfortunately, it looks like it will be a few years away for 5G to be commonly applied in factories, but the day will come.