Smart manufacturing is an advanced methodology that makes use of the Internet-connected infrastructure to oversee and regulate the production process. The objective of SM is to discern prospects for automating processes and use comprehensive data analytics to enhance manufacturing performance.
Smart manufacturing is a distinct implementation of the Industrial Internet of Things (IIoT). Deployments include the installation of sensors into industrial equipment to gather data on their operating state and performance. Historically, such information was often stored in local databases on separate devices and only used for analyzing the root causes of equipment malfunctions post-incident. Through the analysis of data generated by a whole set of machines at a factory or across different facilities, manufacturing engineers and data analysts may identify indications of potential component failures. This allows for proactive maintenance to reduce unexpected device downtime.
Smart Manufacturing – the way to ascertain the most optimal methods of execution
Manufacturers through smart manufacturing technologies may also examine the data to identify patterns and detect bottlenecks or inefficiencies in their manufacturing processes and material use. Furthermore, data scientists and other analysts might use the data to conduct simulations of various processes in order to ascertain the most optimal methods of execution. With the increasing prevalence of digital manufacturing and smart factories as well as the growing interconnectedness of machines via the Internet of Things, their ability to interact and facilitate higher degrees of automation will be significantly enhanced.
The primary obstacles impeding wider acceptance of smart manufacturing solutions
For instance, smart manufacturing solutions can automatically procure more raw materials when supplies are running low, assign additional equipment to manufacturing tasks as necessary to fulfill orders, and organize distribution networks when orders have been fulfilled. The primary obstacles impeding the wider acceptance of smart manufacturing solutions are the absence of established norms and the lack of interoperability. The lack of widespread adoption of technical standards for sensor data hinders the ability of various devices to efficiently share and communicate data with one another.
The National Institute of Standards and Technology (NIST) in the United States is now exploring possibilities for creating and advocating for standards in collaboration with different industry players, such as technology businesses and manufacturers. The procedure is currently in progress. Additional obstacles include the financial burden of deploying sensors on a large scale and the intricacy involved in constructing prognostic models.
Historical context of smart manufacturing technologies
The initial Industrial Revolution, believed to have started in about 1760, occurred over 264 years ago. In the United States, the most recent version of this procedure, referred to as the fourth industrial revolution, is termed “smart manufacturing,” but in Europe, it is recognized as “Industry 4.0.” The first industrial revolution was distinguished by the use of steam power and the introduction of the power loom. The assembly line debuted in the Second Industrial Revolution. In the 1970s, the third industrial revolution brought automation and data-enhanced automation. Well-connected automated systems that combine physical, digital, and biological domains define the fourth industrial revolution.
Related technologies at a glance
Besides the Internet of Things, additional technologies will also enable smart manufacturing:
- Industrial enterprises collect massive amounts of data that AI and machine learning use to automate decision-making. AI/machine learning can analyze massive data sets and make smart decisions
- Drones and driverless automobiles may boost productivity by reducing the need for repetitive tasks like vehicle transfer
- Blockchain technology’s immutability, traceability, and disintermediation make data storage and recording fast and efficient
- Edge computing helps companies make better decisions by analyzing massive amounts of machine-generated data. It uses network-connected alarms and temperature sensors to do data analytics at the data source
- Predictive analytics lets companies analyze massive amounts of data from several sources to predict problems and improve projections
- Digital twins are virtual representations that smart manufacturing companies may use to simulate their processes, networks, and equipment. By doing so, they can anticipate and prevent issues in advance, while also enhancing efficiency and production.
Advantages and disadvantages of smart manufacturing and Industry 4.0
Smart manufacturing and Industry 4.0 provide several advantages, such as enhanced efficiency, heightened production, and enduring cost reductions. Productivity in a smart plant is consistently improved. If a machine is causing a decrease in production speed, the data will bring attention to this problem, and artificial intelligence systems will take action to address it. These versatile systems provide more flexibility.
Smart additive manufacturing comes with remote sensors and diagnostics
Regarding efficiency, a significant cost decrease is achieved by minimizing the amount of time that production is halted. Smart factories and smart manufacturing often come with remote sensors and diagnostics that promptly notify operators of occurring issues. Anticipatory AI technology can identify potential issues in advance and implement measures to minimize the associated financial expenses. An optimally built smart factory incorporates both automation and human-machine cooperation, which facilitates operational efficiency.
An inherent drawback of smart manufacturing is the initial expenditure required for its deployment. Consequently, several small to medium enterprises will be unable to bear the substantial cost of the technology, especially if they embrace a short-term mindset. Nevertheless, considering that the long-term savings will surpass the initial expenses, manufacturing companies must strategize for the future, even if they are unable to promptly adopt smart factories. Furthermore, the intricate nature of the technology is a drawback, since poorly built or inadequate systems may impede profitability.
The distinguishing characteristics of SM in comparison to conventional production methods
Conventional manufacturing techniques, established during the era of large-scale production, prioritize cost efficiency and the optimal use of machinery. The rationale for this approach was that machines were deemed unprofitable while not in use. Hence, companies ensured their constant operation. In order to attain customer satisfaction, conventional manufacturing organizations maintain substantial inventories to ensure the fulfillment of possible orders.
As a result, these organizations must maintain their equipment with precise configurations. It is necessary to minimize the expenses associated with manufacturing the components. This is referred to as batch-and-queue processing, which is a method of operations characterized by mass manufacturing. In this technique, the components are processed and sent to the next stage, regardless of whether they are required or not. They then wait in a queue until further processing is possible.
Nevertheless, this strategy lacks efficiency due to several causes, such as:
- Increased machine set-up time results in a higher amount of production time wasted, since no output is generated during machine downtime
- The product’s quality is compromised due to the possibility that incorrectly manufactured pieces within a batch may be unnoticed until the subsequent operation. Consequently, this necessitates the repetition of the task, resulting in significant costs and the allocation of precious resources
- Smart manufacturing technologies are a cooperative and comprehensive production system. It promptly adapts to dynamic situations and requirements within the plant, supplier network, and consumer requests.
Concluding Remarks
The objective of smart manufacturing is to enhance the manufacturing process by using a technology-driven strategy. SM leverages Internet-connected machines to oversee the production process. Smart manufacturing allows firms to find potential for automating activities. And, it does it using data analytics to enhance manufacturing performance.
