Smart manufacturing

Smart manufacturing leverages big data analytics to optimize complex production processes and enhance supply chain management.{{Cite web|url = https://www.nist.gov/el/msid/syseng/upload/SMSDAFY2014.pdf|title = Smart Manufacturing Systems Design and Analysis|last = Rachuri|first = Sudarsan|date = February 4, 2014|website = National Institute of Standards and Technology |accessdate=February 16, 2016}} Big data analytics refers to a method for gathering and understanding large data sets in terms of what are known as the three V's, velocity, variety and volume. Velocity informs the frequency of data acquisition, which can be concurrent with the application of previous data. Variety describes the different types of data that may be handled. Volume represents the amount of data.{{Cite book|date = 2014-12-01|pages = 918–922|doi = 10.1109/IEEM.2014.7058772|first1 = J.|last1 = Leveling|first2 = M.|last2 = Edelbrock|first3 = B.|last3 = Otto| title=2014 IEEE International Conference on Industrial Engineering and Engineering Management | chapter=Big data analytics for supply chain management |isbn = 978-1-4799-6410-9|s2cid = 31872187}} Big data analytics allows an enterprise to use smart manufacturing to predict demand and the need for design changes rather than reacting to orders placed.

Some products have embedded sensors, which produce large amounts of data that can be used to understand consumer behavior and improve future versions of the product.{{cite journal |last1=Yang |first1=Chen |last2=Shen |first2=Weiming |last3=Wang |first3=Xianbin |title=The Internet of Things in Manufacturing: Key Issues and Potential Applications |journal=IEEE Systems, Man, and Cybernetics Magazine |date=January 2018 |volume=4 |issue=1 |pages=6–15 |doi=10.1109/MSMC.2017.2702391|s2cid=42651835 }}{{Cite journal|url=https://hbr.org/2014/11/how-smart-connected-products-are-transforming-competition|title=How Smart, Connected Products Are Transforming Competition|last=Porter|first=Michael E.|date=November 2014|journal=Harvard Business Review|publisher=April 2016|accessdate=}}{{Cite web|url=http://www.itworldcanada.com/assets/building-smarter-manufacturing-with-the-internet-of-things-iot|title=Building Smarter Manufacturing With The Internet of Things (IoT)|last=|first=|date=2014|website=IT World Canada|publisher=Lopez Research |accessdate=2020-04-12}}

One projected valuable element of big data processing is the introduction of and the ongoing progression to full supply chain autonomy. Supply chain autonomy is an emerging concept in operations and logistics that describes supply chains capable of functioning independently, with minimal to no human input, by leveraging data. Autonomous supply chains (ASCs) may be defined as systems that can self-manage across various functions, including planning, coordination, and execution, and the degree and inclusion of autonomy across these vectors defines the progression towards full autonomy.

These systems rely on technologies such as digital twins, AI-driven agents, and real-time data to achieve three core capabilities: self-configuration (adjusting operations dynamically), self-optimisation (continuously improving performance), and self-healing (responding to disruptions without manual intervention). Conceptual frameworks exist that illustrate how these elements interact—through sensing, processing, decision-making, and learning loops—to enable end-to-end autonomy,{{Cite journal |last1 = Xu |first1 = Liming |last2 = Mak |first2 = Stephen |last3 = Proselkov |first3 = Yaniv |last4 = Brintrup |first4 = Alexandra |title = Towards autonomous supply chains: Definition, characteristics, conceptual framework, and autonomy levels |journal = Journal of Industrial Information Integration |date = November 2024 |volume=42 |issue=100698 |doi=10.1016/j.jii.2024.100698 |arxiv = 2401.14183 }} which can provide a foundation for understanding how future supply chains can be made more resilient, efficient, and adaptive in complex and volatile environments.

Advanced industrial robots, also known as smart machines, operate autonomously and can communicate directly with manufacturing systems. In some advanced manufacturing contexts, they can work with humans for co-assembly tasks.{{Cite journal|first1=W. |last1=Wang |first2=R. |last2=Li |first3=Y. |last3=Chen |first4=Z. |last4=Diekel |first5=Y. |last5=Jia |title=Facilitating Human-Robot Collaborative Tasks by Teaching-Learning-Collaboration From Human Demonstrations|journal=IEEE Transactions on Automation Science and Engineering|language=en-US|volume=16|issue=2|pages=640–653|doi=10.1109/tase.2018.2840345|issn=1545-5955|year=2019|doi-access=free}} By evaluating sensory input and distinguishing between different product configurations, these machines are able to solve problems and make decisions independent of people. These robots are able to complete work beyond what they were initially programmed to do and have artificial intelligence that allows them to learn from experience. These machines have the flexibility to be reconfigured and re-purposed. This gives them the ability to respond rapidly to design changes and innovation, which is a competitive advantage over more traditional manufacturing processes.{{Cite web|url=https://www.nist.gov/el/isd/ms/rssm.cfm|title=Robotic Systems for Smart Manufacturing|publisher=US Department of Commerce|website=www.nist.gov|language=EN-US|accessdate=2016-03-04|date=October 2013}} An area of concern surrounding advanced robotics is the safety and well-being of the human workers who interact with robotic systems. Traditionally, measures have been taken to segregate robots from the human workforce, but advances in robotic cognitive ability have opened up opportunities, such as cobots, for robots to work collaboratively with people.{{Cite book|title=Springer Handbook of Robotics|last1=Bicchi|first1=Antonio|last2=Peshkin|first2=Michael A.|last3=Colgate|first3=J. Edward|chapter=Safety for Physical Human–Robot Interaction |date=2008-01-01|publisher=Springer Berlin Heidelberg|isbn=9783540239574|editor-last=Siciliano|editor-first=Bruno |pages=1335–1348|language=en|editor-last2=Khatib|editor-first2=Oussama Khatib|doi=10.1007/978-3-540-30301-5_58}}

Cloud computing allows large amounts of data storage or computational power to be rapidly applied to manufacturing, and allow a large amount of data on machine performance and output quality to be collected. This can improve machine configuration, predictive maintenance, and fault analysis. Better predictions can facilitate better strategies for ordering raw materials or scheduling production runs.

{{further|Industrial Internet of Things|Cyber-physical systems}}

As of 2019, 3D printing is mainly used in rapid prototyping, design iteration, and small-scale production. Improvements in speed, quality, and materials could make it useful in mass production{{Cite web |last=Zimmermann |first=Stefan |url=https://atos.net/en/blog/industry-4-0-3d-printing-manufacturing-industries |title=Industry 4.0 – 3D Printing in Manufacturing Industries |date=March 26, 2018 |work=Atos Blog |publisher=Atos SE |accessdate=2019-06-09}}{{Cite web |url=https://blog.lnsresearch.com/industry-4.0-is-more-than-data-3d-printing-in-manufacturing|title=Industry 4.0 is About More Than Data: 3D Printing in Manufacturing |last=Hughes |first=Andrew |date=Mar 23, 2017 |work=Digital Transformation and Operational Excellence Blog |publisher=LNS Research |accessdate=2019-06-09}} and mass customization.

However, 3D printing developed so much in recent years that it is no longer used just as technology for prototyping. 3D printing sector is moving beyond prototyping especially it is becoming increasingly widespread in supply chains. The industries where digital manufacturing with 3D printing is the most seen are automotive, industrial and medical. In the auto industry, 3D printing is used not only for prototyping but also for the full production of final parts and products. 3D printing has also been used by suppliers and digital manufacturers coming together to help fight COVID-19.{{Cite magazine |last=Wilson |first=Georgia |title=The evolution of 3D printing in manufacturing |url=https://manufacturingglobal.com/technology/evolution-3d-printing-manufacturing |date=May 16, 2020 |magazine=Manufacturing Global |publisher=BizClick Medial Limited |access-date=2021-06-04}}

3D printing allows to prototype more successfully, thus companies are saving time and money as significant volumes of parts can be produced in a short period. There is great potential for 3D printing to revolutionise supply chains, hence more companies are using it. The main challenge that 3D printing faces is the change of people's mindset. Moreover, some workers will need to re-learn a set of new skills to manage 3D printing technology.

Smart manufacturing can also be attributed to surveying workplace inefficiencies and assisting in worker safety. Efficiency optimization is a huge focus for adopters of "smart" systems, which is done through data research and intelligent learning automation. For instance operators can be given personal access cards with inbuilt Wi-Fi and Bluetooth, which can connect to the machines and a Cloud platform to determine which operator is working on which machine in real time.{{cite web|title=ThingTrax|url=http://www.thingtrax.com/devicenew/|website=ThingTrax Connected Manufacturing |location=London|archive-url=https://web.archive.org/web/20170412085445/http://www.thingtrax.com/devicenew/|archive-date=2017-04-12|url-status=dead |accessdate=2020-04-12}} An intelligent, interconnected 'smart' system can be established to set a performance target, determine if the target is obtainable, and identify inefficiencies through failed or delayed performance targets.{{Cite journal|last=Jung|first=Kiwook|date=2015-03-16|title=Mapping Strategic Goals and Operational Performance Metrics for Smart Manufacturing Systems|journal=Procedia Computer Science|volume=44|issue=44 p.184–193|pages=184–193|doi=10.1016/j.procs.2015.03.051|doi-access=free}} In general, automation may alleviate inefficiencies due to human error. And in general, evolving AI eliminates the inefficiencies of its predecessors.

As robots take on more of the physical tasks of manufacturing, workers no longer need to be present and are exposed to fewer hazards.{{Cite web|url=http://www.automationworld.com/sensors-discrete/smart-manufacturing-manufacturing-smart|title=From Smart Manufacturing to Manufacturing Smart|last=Louchez|first=Alain|date=January 6, 2014|website=www.automationworld.com|publisher=Automation World|accessdate=2016-03-04}}

Industry 4.0 is a project in the high-tech strategy of the German government that promotes the computerization of traditional industries such as manufacturing. The goal is the intelligent factory (Smart Factory) that is characterized by adaptability, resource efficiency, and ergonomics, as well as the integration of customers and business partners in business and value processes. Its technological foundation consists of cyber-physical systems and the Internet of Things.{{Cite web|url=http://www.totallyintegratedautomation.com/2014/07/smart-manufacturing-industry-4-0-whats/|title=Smart Manufacturing? Industry 4.0? What's It All About?|last=Jacinto|first=Joan|date=July 31, 2014|website=The Vault - Siemens Totally Integrated Automation|accessdate=}}

This kind of "intelligent manufacturing" makes a great use of:

• Wireless connections, both during product assembly and long-distance interactions with them;
• Last generation sensors, distributed along the supply chain and the same products (Internet of things);
• Elaboration of a great amount of data to control all phases of construction, distribution and usage of a good.

European Roadmap [http://ec.europa.eu/research/industrial_technologies/factories-of-the-future_en.html "Factories of the Future"] and German one [https://web.archive.org/web/20131014183202/http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf "Industrie 4.0″] illustrate several of the action lines to undertake and the related benefits. Some examples are:

• Advanced manufacturing processes and rapid prototyping will make possible for each customer to order one-of-a-kind product without significant cost increase.
• Collaborative Virtual Factory (VF) platforms will drastically reduce cost and time associated to new product design and engineering of the production process, by exploiting complete simulation and virtual testing throughout the Product Lifecycle.
• Advanced Human-Machine interaction (HMI) and augmented reality (AR) devices will help increasing safety in production plants and reducing physical demand to workers (whose age has an increasing trend).
• Machine learning will be fundamental to optimize the production processes, both for reducing lead times and reducing the energy consumption.
• Cyber-physical systems and machine-to-machine (M2M) communication will allow to gather and share real-time data from the shop floor in order to reduce downtime and idle time by conducting extremely effective predictive maintenance.

The Ministry of Economy, Trade and Industry in South Korea announced on 10 March 2016 that it had aided the construction of smart factories in 1,240 small and medium enterprises, which it said resulted in an average 27.6% decrease in defective products, 7.1% faster production of prototypes, and 29.2% lower cost.{{cite news |url=http://businesskorea.co.kr/english/news/industry/14073-smart-benefits-smart-factories-improving-productivity-smes|title=Smart Factories Improving Productivity of SMEs |author=Jung Min-hee |date=March 11, 2016}}

• Open manufacturing
• Fourth Industrial Revolution
• Urban manufacturing

{{Reflist}}

• [http://cesmii.org/ CESMII - US National Institute on Smart Manufacturing]
• [http://ec.europa.eu/research/industrial_technologies/factories-of-the-future_en.html Factories of the Future]
• [https://web.archive.org/web/20211221103330/https://academic.microsoft.com/author/2598638453 Agnieszka Radziwon], Arne Bilberg, Marcel Bogers, Erik Skov Madsen. [https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2013/157.pdf The Smart Factory: Exploring Adaptive and Flexible Manufacturing Solutions] – [https://daaam.info/24th-proceedings-2013/ Proceedings] of the 24th DAAAM International Symposium on Intelligent Manufacturing and Automation, 23–26 October 2013, Zadar, Croatia. – Elsevier, Procedia Engineering, ISSN 1877-7058, 69 (2014), {{nobr|1184–1190}}
• Agnieszka Radziwon, Marcel Bogers, Arne Bilberg. [https://ssrn.com/abstract=2503064 The Smart Factory: Exploring an Open Innovation Solution for Manufacturing Ecosystems] Date Written: May 28, 2014. Available at SSRN, 11 Pages. Posted: 1 Oct 2014
• [http://www.techrepublic.com/article/ge-launches-microfactory-to-co-create-the-future-of-manufacturing/#ftag=RSS56d97e7 GE launches 'microfactory' to co-create the future of manufacturing]
• [https://hello-pharma.com/pharma-news-and-events/smart-machines-future-factories/ Smart Machines & Future Factories]

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Category:Manufacturing