Between 2021 and 2022, the adoption of smart factories accelerated by an astonishing 50%. It is one of the most defining technology trends of our time, particularly in the context of a post-pandemic awareness of automation needs. In fact, 83% of manufacturers believe smart factories will be essential for their success. How do these next-gen manufacturing systems work? To understand that better, let us first unpack the meaning of smart factories.
What is a Smart Factory?
Connecting the digital and physical worlds, smart factories monitor the comprehensive production process, spanning supply chain management, right through to manufacturing equipment and even the activity of individuals on the shop floor.
Its completely integrated, collaborative production systems provide a variety of advantages for operators, including the ability to adapt and optimize processes rapidly.
A smart factory is defined as a digitally-enabled manufacturing facility that employs linked devices, machines, and production systems to collect and exchange data continually. This information is then utilized to make choices on process improvement and problem resolution.
This method is now further enhanced, thanks to technologies like artificial intelligence (AI), big data analytics, cloud services, and industrial IoT (the Internet of Things).
Understanding Smart Factories in the Context of Industry 4.0
Smart manufacturing is the most efficient application of technologies inspired by Industry 4.0, often referred to as the fourth industrial revolution. (For context, the first revolution introduced machines instead of hand production, the second relied on electrification, and the third was digital.)
The smart factory isn’t about implementing a single program throughout the entire shop floor and achieving instantaneous production process enhancements. Instead, smart manufacturing is optimized by the integration of multiple Industry 4.0 technologies. The following are the most significant:
- The cloud: Cloud computing enables smart factories to save, analyze, and distribute data more flexibly and affordably than conventional on-premise solutions. Large volumes of data may be uploaded by interconnected devices and equipment and will be accessible anywhere, anytime.
- Sensors: Attached sensors on equipment, machines, and sites provide the collection of discrete data points at certain phases of the production process. Temperature sensors, for instance, may detect and monitor the environment in a laboratory. The data may subsequently be utilized for AI-based self-correction (Artificial Intelligence).
- IoT: The internet of things is composed of networked devices, equipment, and/or processes that promote the interchange and use of data between machines and humans. Typically, such devices have sensors; this is how industrial IoT enables visibility and control.
- Big data: Over time, the development of big data (fresh, unprocessed information) may reveal insights about the manufacturing process’s efficacy, the essential indicators to prioritize, as well as which systems are failing.
All of these Industry 4.0 systems together power the core functionalities of a smart factory.
How Does a Smart Factory Work? Explained in 8 Steps
Let’s go into the internal dynamics of a smart factory or smart manufacturing site to fully comprehend its functioning:
1. Sensors help create an integrated landscape
Connected factories monitor the real-time position of personnel, commodities, machinery, and mobile assets. IIoT digitizes the development environment by connecting embedded devices and process instruments with the Manufacturing Execution System (MES) as well as Enterprise Resource Planning (ERP) systems for real-time communication.
The network of interconnected systems, monitors/sensors, and controllers generates vast quantities of valuable data in a wide range of formats. This moves us to the next stage.
2. Big (raw) data flows across systems using the integrated landscape
The manufacturing process is unified by the seamless flow of data between equipment and enterprise platforms. Real-time information from the user, system-to-system, and human communication systems enhance the quality and dependability of manufacturing via prompt interventions.
The just-in-time delivery of materials in the bin is enabled by the synchronizing of production schedules as well as supply chain ops using real-time IoT device data. However, raw data has limited utility. Complex analytic models are required to monetize the data and predict demand – and that’s the next step in the working of a smart factory.
3. Data travels from the factory floor to the cloud
Computing on the cloud allows smart factories to create, analyze, and store enormous data volumes at a minimal cost. The extensible and secure cloud infrastructure fulfills the requirements of interdependent ecosystems.
4. The cloud applies advanced analytics
Using powerful analytical tools and cognitive models, big data analysis techniques are smartly used to build a reactive and self-healing environment. Predictive analytics minimizes equipment retooling and asset maintenance downtimes. Simulation eliminates new product failure. Furthermore, analytics helps manufacturers increase revenues from after-sales services by correctly predicting the product’s lifetime and maintenance requirements.
5. Data from event triggers enable automated actions
The agility of a smart factory is ensured via automated servicing, purchasing, shipping, assembling, delivery, and after-sales services. For instance, in autonomous bins, sensors may prompt the rearrangement of the manufacturing line’s sequencing to increase efficiency and save costs.
6. Data also helps the factory to make automatic decisions and self-correct
The four basic tech components of Industry 4.0 enables the manufacturing environment to autonomously assess existing scenarios, adapt to its restrictions, and react. Big data analytics leads to fact-based decision-making and can be conducted regularly by bots or automatons.
7. The smart factory is self-monitoring, freeing up human workers
Using sensor-based technology, computerized tracking of physical processes is achievable. Humans, released from monotonous activities by automation, can find ways to focus their newly acquired availability toward activities that can be individual-focused, like the invention of valuable new products.
8. Finally, the smart factory achieves better outcomes than regular manufacturing plants
The networked smart factory produces high-quality products in tighter production cycles, responds to consumer demand for product diversity, and minimizes waste across operations. The interaction of monitors, data, and analytics is the driving force behind this revolution.
Transitioning to Smart Manufacturing Practices
Smart factories are rarely set up from scratch – in reality, they mature from Industry 3.0 manufacturing plants that progressively became more technologically advanced. The maturity roadmap typically looks like this:
1. A lot of data generation, amid silos
Today, many organizations have hit the first phase of smart-factory maturity. They have sufficiently digitized and automated processes to generate a vast amount of operational data, but this information is often siloed and difficult to act upon.
2. Mobilizing data through analytics, but acting on them remains tough
The second stage is shifting from having data available to make that data accessible. In this setting, monitors and visualization tools provide access to data from disparate systems. To be proactive with data, however, usually requires addressing several hurdles.
3. The introduction of forecasting, automation, integration, and AI, albeit in parts
The third stage of the maturity model requires operations to be proactive. This entails applying preemptive approaches to problem-solving. In many situations, attaining this goal involves not just extensive data integration across all operational domains, but also AI-powered solutions that make it simpler to recognize trends and predict possible pitfalls.
4. The interlinking of machines (operational technology) and data systems (information technology)
The fourth stage of maturity is denoted by machine-driven action. Here, machine learning algorithms not only recognize patterns but also interface with equipment and software to make automatic modifications.
Why Shift to a Smart Factory?
Adopting smart factories as part of your industrial operating model has several benefits. By improving the capacities of both manufacturing machines and humans, they increase efficiency and productivity. Through data collection, the emphasis is on developing an agile, iterative manufacturing process.
By continually enhancing the efficiency of production processes, they are able to cut manufacturing costs, downtime, and waste. Using more reliable data, these factories of the future may also enhance human decision-making.
Indeed, this “future” is not too far away. Companies like General Electric, Siemens, and Cisco are already investing in Industry 4.0 products that are all about connecting and supporting smart factories to revolutionize how goods are designed, produced, packaged, and delivered. The benefits of shifting to a smart factory model are many, but one must first mature from a siloed data landscape.
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