Smart manufacturing, or the application of cutting-edge technology to improve the effectiveness of conventional manufacturing processes, fosters the development of a more flexible and effective industrial base.
An Internet-connected piece of equipment is used in smart manufacturing (SM) to monitor production. SM aims to find ways to automate processes and use data analytics to boost industrial efficiency.
A particular use of the Industrial Internet of Things is SM (IoT). Sensors are embedded in manufacturing equipment during deployments to gather information about the machines’ performance and operational state. Previously, the data was often stored locally on individual devices and only used to determine the root cause of equipment problems after they had already occurred.
Manufacturers and data analysts can now look for indications that specific parts may break by evaluating the data coming off an entire factory’s worth of equipment or even across different sites. This enables preventive maintenance to eliminate unscheduled device downtime.
Manufacturers can also look for patterns in the data to see where manufacturing is slowing down or using resources inefficiently in their operations. Data scientists and other analysts can also use the data to simulate several procedures to determine the most effective.
Machines will be better equipped to communicate with one another as smart manufacturing companies spread. More of them are networked through the Internet of Things, potentially allowing higher degrees of automation.
For instance, SM systems can automatically place orders for more raw materials as needed, assign additional machinery to production tasks to complete orders, and set up distribution networks after orders are finished.
The main obstacles to the broader use of innovative manufacturing are interoperability and a need for standards. The lack of widespread adoption of technical standards for sensor data prevents various machine types from successfully exchanging data and interacting with one another.
The National Institute of Standards and Technology (NIST) in the US is looking into ways to promote and establish standards with different industry players, such as manufacturers and technology firms. The procedure continues. The expense of widely implementing sensors and the difficulty of creating prediction models are two additional difficulties.
History & Background of Smart Manufacturing
The initial Industrial Revolution, believed to have begun around 1760, has continued for almost 260 years. The fourth industrial revolution is the most recent version of this process. In the United States, it is referred to as “smart manufacturing,” while in Europe, it is referred to as “Industry 4.0.”
The assembly line and steam power dominated the first and second industrial revolutions. The third industrial revolution, which began in the 1970s, was marked by the introduction of automation and data-enhanced automation.
An array of interconnected, automated systems that combine the physical, digital, and biological worlds are what this fourth industrial revolution is known for.
A multitude of technologies, in addition to the Internet of Things, will enable smart manufacturing, such as;
- Machine learning and artificial intelligence (AI) allow for automatic decision-making based on the vast amounts of data that manufacturing companies gather. AI/machine learning may analyze all this data, which can then use the inputted data to make intelligent decisions.
- By lowering the number of personnel required to complete routine tasks, including driving cars around a facility, drones, and driverless vehicles can enhance production.
- Blockchain can offer a quick and effective means to collect and retain data because of its advantages, such as immutability, traceability, and disintermediation.
- Edge computing – edge computing aids manufacturers in transforming enormous amounts of machine-generated data into usable information so they can get insights and make better decisions. This is achieved by using network-connected resources, such as thermometers or alarms, to enable data analytics at the data provider.
- Using predictive analytics, businesses may examine the vast volumes of data they gather from all of their data sources to foresee issues and enhance predictions.
- Digital twins are virtual representations of a company’s processes, networks, and equipment that can be used to predict issues before they arise and increase productivity and efficiency.
Smart manufacturing’s advantages & disadvantages
Intelligence manufacturing has various advantages, such as greater productivity, improved efficiency, and long-term cost savings. Productivity is continuously improved in a smart factory. The data will indicate any issues, such as a machine causing production lag, and the artificial intelligence algorithms will try to fix them. Greater flexibility is possible thanks to these highly flexible systems.
The elimination of production downtime results in one of the most significant cost benefits in terms of efficiency. Modern machines frequently have remote sensors and analyzers installed to notify operators of issues as they develop. Predictive AI technology can identify problems and take action to reduce costs. Automation and human-machine collaboration are elements of a well-designed smart factory.
The initial cost of deployment is a significant drawback to intelligent manufacturing. As a result, many small to medium businesses will only be able to afford the technology’s high cost if they adopt a short-term mindset.
However, even if enterprises can’t instantly integrate smart factories, they must plan for the future because long-term savings will surpass the starting expenditures.
Due to the complexity of the technology, it is also a drawback that systems that are inadequate for a specific activity or poorly built could reduce earnings.
What sets smart manufacturing apart from conventional manufacturing techniques?
Traditional manufacturing techniques, created during the mass production era, emphasize machine usage and economies of scale. Companies kept machines running continually because it was believed that if a machine was not in use, it was losing money.
Traditional manufacturing businesses maintain extensive inventories to fill future orders and maintain customer satisfaction. In addition, to lower the cost of producing the parts, these businesses must run their machines with specified settings for as long as possible.
Batch-and-queue processing is a method of mass manufacturing where parts are processed, moved on to the next step, and then queued up regardless of whether they are required (queue).
This strategy, however, is ineffective for several reasons, including:
- Because no work is done when a machine is down, a longer machine setup time results in more significant lost production time.
- If pieces in a batch aren’t created correctly, it’s possible that no one will discover the issue until the next operation, which lowers the quality of the final product. Because of this, the task will need to be redone, costing money and consuming essential resources.
On the other hand, smart manufacturing is a cooperative, a completely integrated production system that reacts in real-time to changing environments and demands in production, supply chain, and the needs of the consumers.
The objective of “smart manufacturing” is to optimize the production process via a technologically advanced methodology that uses Internet-connected gear to track the production process. Using data analytics to enhance manufacturing performance, smart manufacturing helps businesses to spot the potential for automating processes.