Smart factory—or smart manufacturing—Is a relatively new concept in manufacturing, and a core part of the trending “Industry 4.0” ideology.
In a nutshell, the smart factory can be defined as a system within a manufacturing plant/factory that is completely integrated to enable a real-time response.
A smart factory implementation allows the business to be faster and more flexible in answering customers’ demands and the ever-changing market situation. It can benefit a factory in virtually all aspects, from planning, manufacturing speed, end-product quality, and product development.
Let’s learn more about smart factory and how it can benefit your manufacturing plant.
What Is a Smart Factory
We can’t properly understand the concept and definition of the smart factory without first discussing Industry 4.0.
Simply put, ‘Industry 4.0’ is a jargon used to describe the fourth industrial revolution, which is already happening now.
At the core of this fourth industrial revolution are the automation of machines/equipment (smart machines) and even the automation of whole factories (the smart factories.)
Thus, we can say that a smart factory is an automated factory: a manufacturing plant that can operate on its own without any human intervention. However, automation is not the only feature that makes a smart factory smart.
Instead, a factory can be categorized as smart when it shows the following characteristics:
Data collection and analysis
A smart factory should be able to automatically collect and analyze data via the installation of sensors and interconnected IoT devices.
With this data analytics, the factory can analyze historical trends, identify patterns, and use the information to make better decisions.
Decisions can be taken manually—for example, by operators—or the result of data analytics can be fed to IIoT (Industrial IoT) devices and machines to enable automation.
Customization and personalization of products
Another key characteristic of a smart factory is its ability to produce customized goods to meet individual customers’ requirements rather than mass-produced units.
With the aid of advanced technologies, such as simulation software, 3D printing, and others, it’s now viable to manufacture personalized products in small quantities. Before smart factories and industry 4.0, mass production is the only cost-effective way to manufacture products, but that’s no longer the case.
Smart factories (and smart machines) are all about interconnectivity and high-speed data interchange between sensors, devices, and industrial machines.
This creates a phenomenon we call IT/OT convergence: the merging between IT (Information Technology) and OT (Operational Technology,) allowing real-world OT machines to communicate with a digital network.
The application of this IT/OT convergence is practically limitless. For a company with multiple plants, for example, a machine in plant A can be utilized by plant B via cloud communications in real-time, effectively increasing the yield of each plant.
Data-driven supply chain
Smart factories can send their production data to vendors in real time to facilitate a more efficient delivery schedule. For example, the supplier can know immediately if the factory is consuming materials faster than usual (i.e., due to increased demands) and may decide to send the material one day in advance. On the other hand, when a plant is experiencing disruptions, the vendor may reroute the delivery to prevent wasted time.
On the other side of the supply chain, the factory can also use data analytics—for example, traffic and weather data—to plan the most optimal time to send finished goods, allowing them to save money and time.
At the moment, various Blockchain-based technologies are also being developed to further improve supply chain transparency in smart factories.
What technologies are powering smart factories?
To achieve the ‘smart’ characteristics discussed above, a smart factory should leverage the use of several technologies:
1. Industrial Internet of Things (IIoT)
Industrial IoT, or IIoT, is the implementation of IoT in manufacturing, including factories.
The Internet of Things, or IoT, is essentially about connecting devices that aren’t traditionally connected to the internet, including machines on the factory floor.
The basic implementation of IIoT in smart factories is equipping machines with sensors that are connected to the cloud, allowing the sensor to feed the machine’s performance data to the network.
IIoT makes it possible for factories to collect and analyze a massive amount of production data in real time, as well as facilitating automation.
2. Artificial Intelligence
AI and its extensions, machine learning and deep learning, allow manufacturing plants to leverage the large amount of data collected by the IoT sensors.
For example, AI can use the data collected from the machines to analyze when is the most optimal time for the materials to be delivered by suppliers or to perform preventative maintenance to prevent breakdown.
AI can facilitate automation and improve the visibility of operations, ultimately resulting in higher efficiency and productivity in the manufacturing process.
3. Cloud computing
Cloud computing is about connectivity, and there’s simply no smart factory without connectivity and integration.
To be more exact, a smart factory requires cloud computing to facilitate the integration of engineering, manufacturing process, supply chain, distribution, and customer support.
A fully-fledged smart factory involves a large amount of data being stored, transported, and analyzed, and cloud computing can help facilitate processing this data in a faster and more cost-effective way.
For factories with limited capital, cloud computing can also help reduce the costs otherwise spent on hardware investment and other expenses.
4. Digital twin
The term “digital twin” refers to a virtual replica ( a “twin”) of manufacturing processes, production lines, and supply chains.
A digital twin is created from the data collected from IoT sensors, devices, and other connected objects with the help of AI.
The actual implementation of digital twins can vary depending on the smart factory’s unique needs. For example, factories can create an exact twin of their production lines and use this twin to test new applications and identify bottlenecks.
Cybercriminals are virtually everywhere nowadays, and blindly implementing a smart factory without first establishing a strong cybersecurity practice is simply suicidal.
As discussed, a key characteristic of a smart factory is to connect operational equipment to the cloud. However, while connectivity enables automation and more efficient operations, it also opens up new vulnerabilities for cyber-attacks and malware.
When designing and implementing a smart factory, it’s crucial to consider a holistic cybersecurity approach to protect both the IT and OT aspects of the smart factory.
6. Virtual and augmented reality
Not every smart factory has implemented AR and VR, but the adoption of the technology has been rapidly increasing in recent years.
VR (Virtual Reality) is a digital technology that allows its users to experience an immersive virtual world through the use of special glasses. AR (Augmented Reality,) on the other hand, is the overlaying of digital information across reality over a digital camera (commonly via a smartphone’s camera.)
Smart factories can integrate AR and VR to provide signage (visual factory), organize products, and aid in the maintenance and repair of equipment, among other applications.
Designing a smart factory: 5 key principles
Smart factories can be designed in various sizes and shapes across many different industries.
However, all smart factory implementations should follow these principles:
The term ‘modularity’ refers to the ability of a system’s components (in this case, machines, tools, and solutions in a smart factory) to be separated, recombined, and reconfigured as needed in a fast and accessible plug-and-play method.
A smart factory must possess an adequate level of modularity to facilitate integration between components and fast reconfiguration whenever required. In practice, modularity enables the smart factory to change its manufacturing process quickly to answer the rapidly changing customer demands.
Modularity implementation in a smart factory must also take redundancy into account. That is, modularity shouldn’t be provided by a single vendor but should be provided by multiple other vendors to prevent dependency.
Interoperability in the context of smart manufacturing refers to the ability of a system to share information between components. In a wider context, it can also refer to the ability to share business information between a manufacturing plant and its customers.
At the core of interoperability in smart factories is standardization, especially in mechanical, electrical, and information systems. Standardized systems can seamlessly connect and communicate with each other to facilitate interoperability
An example of interoperability in a smart factory is how a machine is connected via the cloud to two different factories and can perform different tasks as requested by the two plants. This is made possible with technologies such as OPC (Open Platform Communications) Unified Architecture, a platform developed by the OPC foundation that facilitates open and standardized communications between machines, as well as between a computer and a machine.
Decentralization refers to how different elements and components in the smart factory (i.e., modules, software, etc.) can make decisions by themselves in an automated way based on the data collected from sensors and user inputs without requiring control from human users or controlling hardware/software.
In a truly decentralized smart factory system, a machine can make independent decisions in real-time, but if necessary, human operators can intervene to make a different decision (and action) depending on strategic or tactical needs.
An example of decentralization in smart factories is the usage of sensors and actuators that are connected to a Cyber-Physical System (CPS). A CPS is an autonomous computational platform that can make decisions and send outputs based on data collected from sensors connected to it. An autopilot system in an airplane is an example of CPS, and with today’s technologies, we can
implement a similar autopilot system in a manufacturing plant.
Virtualization in a smart manufacturing context is the practice of creating a virtual environment with the help of a CPS to monitor and simulate the physical processes of a manufacturing process.
As discussed, in a CPS system, sensors collect the performance data of a machine. Virtualization can then use this data to create a virtual environment. The more comprehensive the collected data, the closer the virtual process is to the actual manufacturing process virtualized.
In practice, virtual systems can be implemented to monitor and control physical equipment within the smart factory environment. The factory can also use virtualization to create digital prototypes of the manufacturing process, allowing them to check and modify the process model without disrupting the actual process. This can help the company in diagnosing the production process to predict and prevent errors.
Factories can also leverage virtualization to train employees and operators, allowing newly hired operators to learn the manufacturing process of the plant by studying and experiencing the virtual model.
A smart factory must shift its paradigm from the traditional manufacturing industry that is solely focused on making and selling products into one that sells and provides services.
In fact, there’s an ongoing trend for smart manufacturing companies to sell their products with zero margins or even at a loss to make their money on selling subscription services, training, maintenance, and other service-oriented strategies.
The implementation of smart factory practices, especially cloud manufacturing, is very significant in facilitating this business model transformation.
6. Real-time response
The factory’s ability to respond to changes—whether changes in customer demands or changes due to malfunctions or system failure—in a fast and timely manner.
To be able to respond to customers’ demands, the smart factory’s information flow must be accessible and available for analysis in real-time. Then, the smart factory’s system can automatically decide whether it’s possible to answer this demand at the moment after considering resource availability and other factors.
To facilitate real-time response, the smart factory must also have a high level of modularity so that fast reconfiguration can be achieved when it’s needed to answer a unique customer’s demands. The factory must be able to detect and fix any system failures and errors as fast as possible, so it can bounce back from any downtime quickly.
Data structure and smart factory: levels of implementations
Now that we’ve learned about the key principles of smart factory design, we can see that data accessibility is the most critical factor in smart factory implementation.
At the core of transforming a traditional factory into a smart factory is automating the collection of data from machines, production lines, and applications and leveraging this data to gain valuable insights. This is made possible by adopting Industrial IoT (IIoT) technologies like LineView Smart Factory software.
A factory that has yet to implement IIoT technologies for data collection and analytics typically has its data locked in a siloed system—or worse, it doesn’t have any data available at all—that is very difficult to analyze and leverage into insights.
It typically requires manual and difficult work to translate and integrate any available data into useful information, often from multiple disconnected applications or systems. We can call this condition level zero, and if a company stays at this level, it will result in a significant waste of time, resources, and money.
Below, we will discuss the four levels of data structure transformation in a smart factory implementation. Knowing these levels can help you assess where you are at the moment and what you’ll need to do to get to the next level.
Level 1: Connected and accessible data
The first level of smart factory transformation is to connect all useful data by integrating different (currently disconnected) data sources into a centralized system that continuously collects data and tracks data production.
At this level, data is beginning to be structurally organized in one location that is always available, enabling real-time monitoring and remote monitoring of the factory floor. With easy access to valuable data, both the factory and its operators can gain increased productivity and agility that can better answer to the changing demands.
However, at this stage, it will still take the factory a significant amount of time and manual labor before it can perform a predictive analysis, a proactive approach to analyze data into valuable insights that enable the company to predict and prevent issues before they occur.
Meaning, while data analysis is available to some degree, in most cases, the factory is still relying on reactive problem-solving.
To move on from this level into the next, the factory must start integrating AI and machine learning technologies that enable faster and more accurate predictive analysis.
Level 2: Active data and predictive analytics
At this second level, the smart factory has integrated AI and machine learning to perform predictive analytics, enabling the operators to perform preventative maintenance or take other proactive actions to avoid quality failures or downtime.
The main benefit of predictive analytics is the elimination of time-consuming, manual data analysis, so operators can focus their time and efforts on other productive things. Automated systems can detect anomalies and potential issues to accurately predict future failures and take the necessary actions to prevent them.
Combined with a connected data structure that centralizes all your factory’s production data, with machine learning, you now have an intelligent system that can quickly pinpoint and prevent issues more accurately.
To move on to the next level, you’ll need machine learning technology to mature itself by “consuming” more data (at least 3-6 months’ worth of data) to enable accurate prescriptive analytics.
Level 3: Action-oriented prescriptive analytics
Prescriptive analytics takes optimization one step further by suggesting recommendations on how to optimize your manufacturing process and even automate some of your processes based on:
Your factory’s most efficient runs, the AI will attempt to replicate it to achieve more consistency
Analyzing historical data of other factories, allowing you to use their optimization settings
In practice, prescriptive analytics identifies variables and configurations that contribute to your most efficient and least efficient runs and will send recommendations to your operators and engineers. The engineers can then review the insights and perform the changes as they see fit.
Following recommendations from the prescriptive analytics results allow factories to eliminate inefficiencies, improve productivity, and, ultimately, improve the company’s profitability.
Level 4: Automation
At this most advanced level, the machine learning technology automatically deploys the prescriptive analytics’ recommendation by analyzing manufacturing results.
A machine learning model will identify the possibility of optimization, then send the recommended settings in real-time to the machine to be optimized. The machine will automatically execute this recommended setting, and the result of this production instance will be automatically sent back to the machine learning model for further analysis.
To achieve this level four automation, the machine learning model requires a large enough dataset with adequate validated results to enable accurate and reliable automation. The amount of time needed for a smart factory to move from level 3 to level 4 will vary depending on the time required to gather these datasets.
Benefits of smart factory implementation in industry 4.0
What are the benefits of implementing smart manufacturing for companies?
Faster manufacturing process and reduced operational costs
With decentralization and modularity, a smart factory implementation allows its components to have multiple functions (and sometimes, multiple forms.)
With one smart manufacturing solution or machine having two or more functions, the operational process can be executed in a much faster and more efficient way. In larger factories that manufacture thousands of units every day, this incremental increase in productivity can add up to be significant.
Besides allowing faster operations, the utilization of smart technologies can also help minimize errors, which in turn, will result in improved efficiency and reduced operational costs.
Automated and more efficient maintenance
No matter how expensive and seemingly perfect a machine is, it will always require maintenance sooner or later.
Smart manufacturing implementations allow factories to shift from manual, time-consuming maintenance into automated, preventative, and more efficient maintenance. Maintenance can be performed before machine failure to prevent downtime, but at the same time not performed too often so that it will result in efficiency losses.
More effective training and improved human resources
Although one of the key pillars in smart factory is automation, human resources will still be an integral part of most factories, at least for the near future.
Virtualization enables advanced training programs with the help of simulation, which is also more cost-effective than real-world training. Besides cost-savings, virtual training can also offer other benefits like a more flexible schedule, lower risks of accidents and hazards, more accurate and faster training results, and so on.
While smart factories or smart manufacturing is still a relatively new concept in manufacturing, the benefits of smart factory implementation are already very obvious.
Not only will smart factory will allow manufacturing processes to be more productive and efficient, they can also help manufacturing companies in shifting their business model from a solely product-oriented model to more profitable and sustainable service-oriented models.
Yet, the actual implementation of a smart factory can be easier said than done. You’ll still need careful evaluation of your current process and meticulous planning. However, despite the potentially challenging implementation, the benefits it will bring will be worth it in the long run.