Labor shortages are no longer a regional problem. The baby boomers are retiring, and the birth rates are declining in most developed or very developing countries. This trend has accelerated during the Covid-19 pandemic. Manufacturers will soon have to deal with the double whammy of a shrinking pool of workers and rising wages. Manufacturing can only survive the impending crisis by getting smarter and moving to Industry 4.0, which includes the use of sensors, artificial intelligence analytics, automation, and robotics. Also, robots can be applied in more areas of manufacturing, optimizing more processes. Preventive maintenance Sudden equipment failures result in the unpredictability of the manufacturing process, increasing operational risks and costs. Malfunctions require repairs. While repairs are being made, production slows down or stops completely, causing a ripple effect on downstream operations that can be costly to manufacturers as well as customers. Preventive or predictive maintenance involves using connected sensors to monitor the condition of equipment and identify which machines are starting to have problems. Starting the repair process with sensors detecting problems means addressing problems before parts or machines fail. This discovery enables managers to plan ahead and make adjustments to ensure process continuity and avoid costly shutdowns while replacing parts or service machines. Digital twin data collection via IoT sensors, cloud data storage, AI data analytics, and decision making based on those analytics includes a digital twin. By placing multiple sensors on a product or prototype in the lab or field, product designers can collect large data sets. These data sets reveal how the product works in different settings. With this data, engineers can run simulations to improve product development, speeding up the design cycle with fewer prototyping and less testing. In addition to benefiting from testing and validation, digital twins can also help the team plan production in a timely manner and with the final production sequence of the prototype. The data collected by sensors can detect and solve production problems to ensure quality. Digital Twins also provide a collaborative platform for data scientists, product managers, and designers. With digital twins, managers can visualize data and trends to get a more comprehensive sense of machine operating and production processes. As a result, more successful designs can be produced, and operations can become more efficient to save time and resources. AI in Manufacturing Besides its role in scheduling proactive maintenance of machines to reduce downtime, AI can help make manufacturing more efficient. Lack of information sharing and coordination often leads to inefficiency. Errors and inconsistent implementation of policies can delay operations. For example, putting safety practices in place in some areas of the factory floor, but not in others, is a mistake that could lead to hazardous conditions. Lack of coordination may lead to situations where moving a piece of equipment, for example, is a convenience for some workers at the expense of others. Missed opportunities to coordinate can lead to work delays and even accidents. In addition, ineffective communication between different work stations may cause over or underproduction of certain parts, causing unbalanced workload and delays. Using IoT sensors, AI can help reduce errors and perform workload analysis for better balance and data integration to improve coordination between different departments in a plant or different work areas on the same floor. AI-generated analyzes that integrate data – for example, concentrations of toxic gases and temperature – help identify actions needed to prevent dangerous situations or shut down a system. Beyond robotics/analytics, software-based AI manufacturing improvements can be incorporated through hardware elements. For example, production lines can be improved with automated systems. Automation is usually carried out when producing individual component parts, where the required skills and product complexity are low. The sequence of operations is usually determined by the configuration of the equipment (stationary automation). On the other hand, bots are more popular in quality inspection, bots excel at performing high volume repetitive tasks with high accuracy and without any indication of fatigue. Robots will play an important role in making automation more flexible and programmable. This is changing. Assembly is traditionally performed by humans because humans, not machines, can solve various problems that appear in an unexpected frequency or sequence during the process. There are two potential strategies for making bots better at solving complex problems. A complex task can be broken down into smaller, more predictable and solvable parts for robots. For example, different work heads for specific tasks, such as screw turning, riveting or welding, can be installed at each station. Alternatively, robots can be used to help humans solve problems. Robots are better at identifying problems and dealing with repetitive, dangerous, or stressful jobs. They can leave the tangible and flexible work of humans, although robots are making progress in this field as well. Tracking assets within the store/warehouse floor A steady supply of parts is essential to ensure uninterrupted production. On the assembly line, the correct parts must be constantly transported to workstations. A faulty partial delivery will, at the very least, delay assembly and, in the worst case, result in defective equipment. In the back room, parts must be replenished immediately to prevent out-of-stock scenarios. When the tool breaks down, workers need to find a replacement quickly to avoid shutting down the entire assembly line. Working around one disorder per day may be possible. But if a part or tool is lost multiple times a day, the delays will add to storage, production, labor and even distribution costs. Also, searching for stolen, lost or misplaced equipment, even when the production line goes on, is a waste of time and a waste of human productivity. Tracking IoT assets can help reduce human errors and ensure that the right parts are delivered to the right business areas. Moreover, parts can be continually replenished, and lost or displaced equipment can be located quickly. IoT asset tracking also enables proactive maintenance of equipment, reducing unexpected shutdowns. Security inventory or information theft is a significant manufacturing cost. While increased connectivity offers benefits on the one hand, connecting everything also increases vulnerability for the entire system. Once a hacker breaks into one part of the manufacturing plant, he can easily penetrate other parts of the system. Hence, constant monitoring is necessary to ensure the physical and cyber security of the plant. AI can detect anomalies in how workers move around the factory as well as unusual patterns of access to different computer systems. Detecting these external patterns can help prevent potential attacks or weaken ongoing attacks. Design Case #1: MultiTech In addition to improving operational efficiency, AI can also help manufacturers respond to environmental conditions more effectively. For example, AI can integrate sensor measurements or weather forecasts of upcoming storms or heat waves into data analytics. This helps determine when to turn on or off a particular piece of equipment to avoid overburdening the entire system. However, the large number of wired position indicators or specific switches needed to integrate measurements and analyzes is very expensive to install. That is why Belgian company Aloxy has developed a safe and efficient Industrial Internet of Things (IIoT) solution to automate valve operations. Aloxy’s solution can not only determine if valves are positioned correctly but also enable real-time alerts. Aloxy needed a network solution with low data rate, high energy efficiency and long range transmission. The mesh must also be scalable and durable enough to withstand prolonged outdoor use. After much consideration, Aloxy selected the MultiTech Conduit IP67 Base Station, a powerful IoT gateway solution specifically for outdoor public or private LoRaWAN deployments. This gate is also highly scalable and able to withstand the harshest environmental factors, including moisture, dust, wind, rain, snow and extreme heat. Design Case #2: u-blox The integrity of the manufacturing plant can be greatly ensured by a secure IoT solution that consists of independent components, including hardware, software, and cloud services. A trusted IoT system needs to be carefully designed to protect the integrity of data stored locally on the device. However, the traditional way of owning dedicated hardware to store secrets, certificates, and keys can be costly, not to mention the lack of scalability and flexibility. u-blox’s SARA-R5 LTE-M modules provide an IoT security solution as a service that protects sensitive information on the device without the need for a specialized trusted chip. The design security package allows the customer to store sensitive information securely. Moreover, chip-to-chip security protects the device from external attacks such as bus sniffing and data injection. The solution can be adopted by other IoT applications. Conclusion: Achieving Real Productivity Industry 4.0 covers many aspects of manufacturing, including preventive maintenance, digital twins, artificial intelligence, robotics and analytics, asset tracking on the store floor and warehouse, and security. Real productivity in manufacturing can be achieved through increased transparency and information flow within the organization. Different teams must be able to access correct and appropriate information immediately to avoid confusion, errors or accidents. On the other hand, inside information must be protected to reduce the risk and cost of doing business. Through the continuous collection of data by IoT sensors, AI will be able to perform the necessary analyzes to achieve these two goals. Industry 4.0 is still in its infancy. In the future, new innovations related to the Internet of Things will continue to help future manufacturing increase productivity. .