Data has been integral to factories since the rise of assembly line automation in the 1970s. Historically, factory data was siloed, specific to individual machines or assembly lines, often originating from programable logic controllers (PLCs) and aggregated into software like Wonderware historians. With machine and product data, manufacturers could generate insights into past events, such as quantity of products produced and environmental variables. However, most factory insight stopped there.
The current paradigm shift includes a more comprehensive approach to manufacturing data and analytics, measuring operations across products and factories to generate holistic insights for manufacturers at every stage.
This is enabled by a growing trend toward smart manufacturing, which refers to production through smart – typically IIoT-enabled and highly automated – factories. Smart manufacturing aims to offer cost-effective and often customized products through automation and analytics efficiencies. And as opposed to the factories of the past, these smart factories generate data on an unprecedented scale, all of which can be used to improve products and operations in a way that impacts manufacturers’ bottom lines.
Smart factories allow manufacturers to leverage sensors, data, AI, and cloud technologies to tap into new capabilities that weren’t available in the past. This offers a more in-depth perspective, emphasizing the importance of interconnected data for a more efficient and innovative manufacturing landscape.
The rise of smart manufacturing and availability of vast volumes of production data gives manufacturers new opportunities to turn their data into tangible business value. For example, with massive amounts of data at their fingertips, manufacturers can build digital twins – precise virtual replicas – to accelerate efficiency and innovation. With the right approach, manufacturers can use digital twins across a variety of applications to enhance profitability by improving decision-making, reducing downtime, lowering costs, and increasing agility in response to market demands.
Let’s explore the transformative potential of smart manufacturing and digital twins, understanding how they optimize operations, drive efficiency, and spur innovation in today’s industrial landscape.
The extensive data smart factories product enables the creation of digital twins of their equipment, the products they create, or the factory. For instance, a smart factory with a cutting-edge CNC machine that’s connected to the industrial internet of things (IIoT) can leverage the CNC machine’s data to create a digital replica of the machine. With this virtual model, engineers can simulate various performance scenarios, optimizing efficiency and predicting maintenance needs.
The significance of this lies in the ability to simulate scenarios with a digital asset, factory, or process that mirrors its ongoing activities and operations. Instead of physically implementing changes that require significant investment, the digital twin allows the manufacturer to simulate and evaluate adjustments to the assembly line or the introduction of new products before they happen.
This simulation, enabled by digital twin, allows you to explore “what if” scenarios, offering insights into likely costs and outcomes. Thus, the digital twin has massive potential as a crucial tool within the broader context of smart manufacturing.
Manufacturers are using digital twins to increase profitability by simulating three critical areas of production: factory layout, machine maintenance, and product design.
Digital twins offer manufacturers a powerful tool for optimizing factory layouts and boosting production output to reduce costs. By creating virtual replicas of their facilities, manufacturers can simulate various layout scenarios, identify bottlenecks, and fine-tune processes for maximum efficiency.
Leveraging data analytics and predictive modeling, digital twins enable informed decision-making regarding resource allocation, equipment positioning, and workflow optimization. This strategic approach results in heightened productivity, lower expenses, and increased production rates, helping manufacturers achieve higher throughput at a lower cost and higher speed.
Manufacturers can harness digital twins for predictive maintenance by creating virtual models of critical machinery that mirror their physical assets. These digital replicas use real-time data from sensors embedded in equipment, enabling manufacturers to anticipate and fix potential failures before they occur through predictive analytics.
Machine learning and AI use machine data to provide insights and identify when equipment is most likely to need maintenance. A predictive maintenance strategy can mitigate the impact of wear on certain parts of the machinery, minimize the likelihood of machines producing out-of-spec parts, and greatly reduce the impact of unexpected downtime on production lines. This proactive approach ensures optimal equipment functionality, enhances operational efficiency, and prolongs asset lifespan, ultimately driving productivity and reducing unforeseen expenses.
Digital twins help streamline production by creating virtual replicas of production processes and systems. These digital twins enable manufacturers to simulate different design configurations, test production scenarios, and optimize workflows before implementing changes in the physical factory environment. It also saves costs by providing a virtual testing environment to assess efficacy and make necessary adjustments before investing in physical production.
By analyzing data and performance metrics from 3D CAD designs, ERP systems, and sensor data within a digital twin environment, manufacturers can identify potential bottlenecks, improve resource allocation, and foster continuous improvement in production design and operations.
Manufactures face substantial challenges in adopting smart factory initiatives. One of the primary hurdles is the substantial infrastructure investment required, especially for factories lacking sensors and IIoT capabilities. Identifying which machines warrant the initial hardware investment is a crucial decision. Even with the right investment, creating an effective data architecture to collect and process the necessary data can be challenging.
Many manufacturers need the help of additional expertise in data architecture, ingestion, and governance to execute their data initiatives and realize tangible business value. Seeking outside help for data-related challenges is crucial, as experts with specialized skills to efficiently manage data complexities and drive value can accelerate timelines and improve outcomes.
At Wavicle, our expertise lies not only in technical data ingestion but also bridging the gap between data initiatives and business objectives. We excel in identifying the most impactful manufacturing analytics and AI use cases on a business level and helping companies build the data and analytics infrastructure necessary to reach their goals. We guide clients through the complexities of data acquisition, creating data models, and executing data science projects using machine learning AI.
Get in touch with us to start turning your data into business value.