In essence, a smart factory is a highly digitized production facility that relies on fully integrated, collaborative manufacturing systems that can respond in real-time to changing and dynamic conditions. Additionally, the actual manufacturing processes are enhanced, and the product significantly improved through the adoption of emerging technologies and manufacturing science.
A smart factory works by integrating production systems, machines, workers, and real-time data into a single, digitally connected ecosystem. A smart factory not only accesses and analyzes data, but it can also learn from experience. It interprets and gains insights from data sets to forecast trends and events and to recommend and implement smart manufacturing workflows and automated processes. These cognitive manufacturing ecosystems rely on the access of real-time operational information that drive predictive and prescriptive analytics. Today, the implementation of a digital twin is often an integral component of a smart factory system.
It is becoming increasingly apparent to business leaders that digital transformation is an urgent priority for supply chains and manufacturing operations that hope to be competitive and resilient well into the 2020s. The pandemic further exposed global supply chain weaknesses and industry vulnerabilities. Manufacturers are realizing that traditional supply chains and current manufacturing systems are inadequate in today’s global commerce, and there is a real need to shift to more adaptable, agile, and intelligent production systems that are fully digitally enabled.
In the context of today’s manufacturing environment where the Industrial Internet of Things (IIoT) and Manufacturing 4.0 have mandated that factories must be intelligent and connected, the definition of a Smart Factory would also include established manufacturing methods, such as Overall Equipment Efficiency (OEE) improvement, quality cost reduction, inventory reduction, speed-to-market time reduction, operating cost reduction, and energy efficiency. Each of these areas represent candidates for process improvement that the technology and methodology of a smart factory can directly address and more.
Benefits of a Smart Factory
Having a factory that uses smart, connected production equipment and devices allows for data-driven decision-making that enables efficiency and productivity throughput. Further, by introducing the use of advanced analytics powered by Artificial Intelligence/Machine learning (AI/ML) allows the factory systems’ maturity levels to progress from connected and intelligent to self-awareness, and ultimately to autonomous self-healing systems.
There are four levels that can be used to assess the journey through the improvement process to become a smart manufacturer. The levels begin with factories that have data/information that is available but not readily accessible. The next level would be manufacturers with more accessible and structured data in more usable forms. Progressing to AI/ML powered real-time operational data that enables predictive analytics would be the next stage. And finally, the use of action-oriented data to create prescriptive solutions that improve the process and determine best practices.
Level One: Basic data availability. At this level a factory or facility is not really “smart” at all. There is data available, but it is not easily accessed or analyzed. A certain percentage of manufacturers have not progressed beyond this level.
Level Two: Proactive data analysis. At this level data can be accessed in a more structured and usable form. Usually, this data is centrally available and organized with degrees of visualization through displays and dashboards allowing for more proactive data analysis. A significant percentage of manufacturers are currently operating at this level.
Level Three: Active/Predictive data. At this level operational data can be analyzed using AI/ML. The system is more automated than level two and can predict situations. Some manufacturers are beginning to reach early stages of this level.
Level Four: Action-oriented data. At this level the system determines prescriptive solutions based on the active data of level three, and takes action to alleviate an issue, improve a process, or determine best practices. This is done with minimal human intervention and can enable autonomous systems. Fully autonomous factory systems are the exception in the current manufacturing environment, but it is likely that certain aspects of the production process will evolve to purely autonomous operations.
Emerging Technologies Enable Smart Manufacturing
Discrete manufacturers in industries like transportation and mobility, aerospace, and defense (A&D), heavy equipment, medical devices, and other manufacturing areas are leading the charge in the adoption of technologies and science that enable them to implement smart manufacturing methods.
For example, to meet the significant challenges of manufacturing the next generation of mobility like electric vehicles, car makers are embracing technologies that will be essential to the production of vehicles that completely depart from current manufacturing methods and processes. Next generation intelligent automation along with the implementation of cognitive manufacturing powered by AI, advanced analytics, and digital twin implementation will optimize production processes, cut costs, and help automotive manufacturers reach pre-pandemic production rates faster and more efficiently. Additionally, the emergence of material science is spawning a range of new materials needed for everything from composite bodies to new battery technology, along with additive manufacturing, and human/robot collaboration that will significantly change the face of automotive manufacturing.
The Aerospace and Defense industry has a history of early adoption of disruptive technologies to advance both product development and manufacturing processes. There are currently several key technologies that the industry is using to meet new manufacturing challenges and deal with the pre-pandemic order backlog of aircraft. These technologies include widescale use of additive manufacturing, advanced composite manufacturing methods, implementation of the digital twin, advanced analytics using AI/ML, all of which are permeating many areas of the production process.
Digital Twin Maturity Driving Smart Manufacturing
It is becoming clear that digital twins will be used throughout the product and manufacturing process lifecycle to simulate, predict, optimize, and maintain products, assets, and production systems. Because of the maturity of the technology and architecture, a significant percentage of companies and organizations are planning to use some form of a digital twin as an integral component of a predictive/prescriptive analytics strategy.
While the digital twin is relatively well-defined today, when it is time to implement a pilot project, companies continue to struggle with finding the right fit for their products and manufacturing use cases. An important initial step when developing and implementing a digital twin is to identify the exact operational configuration of the product, asset, or production equipment that represent the physical components. That is, defining the operational and asset data required to manage and optimize the performance of the asset or production system.
In manufacturing, the digital twin is a virtual representation of the as-designed, as-built, and as-maintained physical product; augmented by real-time process data and analytics based on accurate configurations of the physical product, production systems, or equipment. This is, in essence, the operational context of the digital twin needed to support performance optimization. While virtual models are conceptual in nature, the real-time and operational data is a digital representation of real physical events. CAD models represent the virtual fit, form, and function of the digital twin’s physical counterpart. However, real-time operational and asset data are required to execute analytics applications that define the state and behavior of the performance-based digital twin and allow optimization and process improvement.
Author: Dick Slansky
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