Smart manufacturing (SM)—the use of advanced, highly integrated technologies in manufacturing processes—is revolutionizing how companies operate. Evolving technologies and an increasingly globalized and digitalized marketplace have driven manufacturers to adopt smart manufacturing technologies to maintain competitiveness and profitability.
An innovative application of the Industrial Internet of Things (IIoT), SM systems rely on the use of high-tech sensors to collect vital performance and health data from an organization’s critical assets.
Smart manufacturing, as part of the digital transformation of Industry 4.0, deploys a combination of emerging technologies and diagnostic tools (e.g., artificial intelligence (AI) applications, the Internet of Things (IoT), robotics and augmented reality, among others) to optimize enterprise resource planning (ERP), making companies more agile and adaptable.
Smart manufacturing (SM) is a sophisticated process, dependent on a network of new technologies working collaboratively to streamline the entire production ecosystem.
Key SM tools include the following:
Industrial Internet of Things (IIoT)
The IIoT is a network of interconnected machinery, tools and sensors that communicate with each other and the cloud to collect and share data. IIoT-connected assets help industrial manufacturing facilities manage and maintain equipment by utilizing cloud computing and facilitating communication between enabled machinery. These features use data from multiple machines simultaneously, automate processes and provide manufacturers more sophisticated analyses.
In smart factories, IIoT devices are used to enhance machine vision, track inventory levels and analyze data to optimize the mass production process.
The IIoT not only allows internet-connected smart assets to communicate and share diagnostic data, enabling instantaneous system and asset comparisons, but it also helps manufacturers make more informed decisions about the entire mass production operation.
Artificial intelligence (AI)
One of the most significant benefits of AI technology in smart manufacturing is its ability to conduct real-time data analysis efficiently. With IoT devices and sensors collecting data from machines, equipment and assembly lines, AI-powered algorithms can quickly process and analyze inputs to identify patterns and trends, helping manufacturers understand how production processes are performing.
Companies can also use AI systems to identify anomalies and equipment defects. Machine learning algorithms and neural networks, for instance, can help identify data patterns and make decisions based on those patterns, allowing manufacturers to catch quality control issues early in the production process.
Furthermore, utilizing AI solutions as a part of smart maintenance programs can help manufacturers:
Robotic process automation (RPA) has been a key driver of smart manufacturing, with robots taking on repetitive and/or dangerous tasks like assembly, welding and material handling. Robotics technology can perform repetitive tasks faster and with a much higher degree of accuracy and precision than human workers, improving product quality and reducing defects.
Robotics are also extremely versatile and can be programmed to perform a wide range of tasks, making them ideal for manufacturing processes that require high flexibility and adaptability. At a Phillips plant in the Netherlands, for example, robots are making the brand’s electric razors. And a Japanese Fanuc plant uses industrial robots to manufacture industrial robots, reducing personnel requirements to only four supervisors per shift.
Perhaps most significantly, manufacturers interested in an SM approach can integrate robotics with IIoT sensors and data analytics to create a more flexible and responsive production environment.
Cloud and edge computing
Cloud computing and edge computing play a significant role in how smart manufacturing plants operate. Cloud computing helps organizations manage data collection and storage remotely, eliminating the need for on-premises software and hardware and increasing data visibility in the supply chain. With cloud-based solutions, manufacturers can leverage IIoT applications and other forward-thinking technologies (like edge computing) to monitor real-time equipment data and scale their operations more easily.
Edge computing, on the other hand, is a distributed computing paradigm that brings computation and data storage closer to manufacturing operations, rather than storing it in a central cloud-based data center. In the context of smart manufacturing, edge computing deploys computing resources and data storage at the edge of the network—closer to the devices and machines generating the data—enabling faster processing with higher volumes of equipment data.
Edge computing in smart manufacturing also helps manufacturers do the following:
Reduce the network bandwidth requirements, latency issues and costs associated with long-distance big data transmission.
Ensure that sensitive data remains within their own network, improving security and compliance.
Improve operational reliability and resilience by keeping critical systems working during central data center downtime and/or network disruptions.
Optimize workflows by analyzing data from multiple sources (e.g., inventory levels, machine performance and customer demand) to find areas for improvement and increase asset interoperability.
Together, edge computing and cloud computing allow organizations to utilize software as a service (SaaS), expanding technology accessibility to a wider range of manufacturing operations.
In manufacturing environments, where delays in decision-making can have significant impacts on production outcomes, cloud computing and edge computing help manufacturing companies quickly identify and respond to equipment failures, quality defects, production line bottlenecks, etc.
Blockchain is a shared ledger that helps companies record transactions, track assets and improve cybersecurity within a business network. In a smart manufacturing execution system (MES), blockchain creates an immutable record of every step in the supply chain, from raw materials to the finished product. By using blockchain to track the movement of goods and materials, manufacturers can ensure that every step in the production process is transparent and secure, reducing the risk of fraud and improving accountability.
Blockchain can also be used to improve supply chain efficiency by automating many of the processes involved in tracking and verifying transactions. For instance, an organization can utilize smart contracts—self-executing contracts with the terms of the agreement written directly into lines of code—to verify the authenticity of products, track shipments and make payments. This can help reduce the time and cost associated with manual processes, while also improving accuracy and reducing the risk of errors.
Manufacturers can also utilize blockchain technologies to protect intellectual property by creating a record of ownership and improve sustainability practices by tracking the environmental impact of production processes.
Digital twins have become an increasingly popular concept in the world of smart manufacturing. A digital twin is a virtual replica of a physical object or system that is equipped with sensors and connected to the internet, allowing it to collect data and provide real-time performance insights. Digital twins are used to monitor and optimize the performance of manufacturing processes, machines and equipment.
By collecting sensor data from equipment, digital twins can detect anomalies, identify potential problems, and provide insights on how to optimize production processes. Manufacturers can also use digital twins to simulate scenarios and test configurations before implementing them and to facilitate remote maintenance and support.
3D printing, also known as additive manufacturing, is a rapidly growing technology that has changed the way companies design, prototype and produce products. Smart factories primarily use 3D printing to manufacture complex parts and components quickly and precisely.
Traditional manufacturing processes like injection molding can be limited by the complexity of a prototype’s part geometry, and they may require multiple steps and operations to produce. With 3D printing, manufacturers can produce complex geometries in a single step, reducing manufacturing time and costs.
3D printing can also help companies:
Develop customized products and components by using digital design files.
Build and test prototypes right on the shop floor.
Enable on-demand manufacturing to streamline inventory management processes.
Smart manufacturing relies heavily on data analytics to collect, process and analyze data from various sources, including IIoT sensors, production systems and supply chain management systems. Using advanced data analytics techniques, predictive analytics can help identify inefficiencies, bottlenecks and quality issues proactively.
The primary benefit of predictive analytics in the manufacturing sector is their ability to enhance defect detection, allowing manufacturers to take preemptive measures to prevent downtime and equipment failures. Predictive analysis also enables organizations to optimize maintenance schedules to determine the best time for maintenance and repairs.
Benefits of smart manufacturing
Smart manufacturing solutions, like IBM Maximo Application Suite, offer a number of benefits compared to more traditional manufacturing approaches, including the following:
Increased efficiency: Smart manufacturing can improve organizational efficiency by optimizing production processes and facilitating data convergence initiatives. By leveraging new information technologies, manufacturers can minimize production errors, reduce waste, lower costs and improve overall equipment effectiveness.
Improved product quality: Smart manufacturing helps companies produce higher-quality products by improving process control and product testing. Using IIoT sensors and data analytics, manufacturers can monitor and control production throughputs in real time, identifying and correcting issues before they impact product quality.
Increased flexibility: Smart manufacturing improves production flexibility by enabling manufacturers to adapt quickly to changing market demands and maximizing the benefits of demand forecasting. By deploying robotics and AI tools, manufacturers can quickly reconfigure production lines throughout the lifecycle to accommodate changes in product design or production volume, effectively optimizing the value chain.
Smart manufacturing and IBM Maximo Application Suite
IBM Maximo Application Suite is a comprehensive enterprise asset management system that helps organizations optimize asset performance, extend asset lifespan and reduce unplanned downtime. IBM Maximo provides users an integrated AI-powered, cloud-based platform with comprehensive CMMS capabilities that produce advanced data analytics and help maintenance managers make smarter, more data-driven decisions.