Smart manufacturing

Smart manufacturing is a broad category of manufacturing with the goal of optimizing concept generation, production, and product transaction. While manufacturing can be defined as the multi-phase process of creating a product out of raw materials, smart manufacturing is a subset that employs computer control and high levels of adaptability. Smart manufacturing aims to take advantage of advanced information and manufacturing technologies to enable flexibility in physical processes to address a dynamic and global market. There is increased workforce training for such flexibility and use of the technology rather than specific tasks as is customary in traditional manufacturing.[1]

General Description

In recent years, manufacturing has been conceptualized as a system that goes beyond the factory floor, and paradigms of "manufacturing as an ecosystem" have emerged. The term "smart" encompasses enterprises that create and use data and information throughout the product life cycle with the goal of creating flexible manufacturing processes that respond rapidly to changes in demand at low cost to the firm without damage to the environment. The concept necessitates a life-cycle view, where products are designed for efficient production and recyclability.[2]

Smart Manufacturing enables all information about the manufacturing process to be available when it is needed, where it is needed, and in the form that it is needed across entire manufacturing supply chains, complete product lifecycles, multiple industries, and small, medium and large enterprises.The Smart Manufacturing Leadership Coalition (SMLC) is building the technical and business infrastructure that facilitates the development and deployment of Smart Manufacturing systems across the entire manufacturing ecosystem.[3]

One previous definition of an advanced manufacturing enterprise is the "intensified application of advanced intelligence systems to enable rapid manufacturing of new products, dynamic response to product demand, and real-time optimization of manufacturing production and supply-chain networks (SMLC 2011)." This idea is represented by a Smart Factory that relies on interoperable systems; multi-scale dynamic modelling and simulation; intelligent automation; scalable, multilevel cyber security; and networked sensors. Such enterprises utilize data and information throughout the entire product life cycle with the goal of creating flexible manufacturing processes that respond rapidly to changes in demand at low cost to the firm, as well as to the environment. These processes facilitate the flow of information across all business functions inside the enterprise and manage the connections to suppliers, customers, and other stakeholders outside the enterprise.

Current technology

The broad definition of smart manufacturing covers many different technologies. Some of the key technologies in the smart manufacturing movement include big data processing capabilities, industrial connectivity devices and services, and advanced robotics.[4]

[5] Graphic of a sample manufacturing control system showing the interconnectivity of data analysis, computing and automation.
Advanced robotics used in automotive production

Big data processing

Smart manufacturing utilizes big data analytics, to refine complicated processes and manage supply chains.[6] 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.[7] Big data analytics allows an enterprise to use smart manufacturing to shift from reactionary practices to predictive ones, a change that targets improved efficiency of the process and performance of the product.[1]

Advanced robotics

Advanced robots, also known as smart machines operate autonomously and can communicate directly with manufacturing systems. 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.[4] 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.[8] 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.[9]

Industrial connectivity devices and services

Leveraging the capabilities of the internet, manufacturers are able to increase integration and data storage. Employing cloud software allows companies access to highly configurable computing resources. This allows for servers, networks and other storage applications to be created and released at a rapid pace. Enterprise integration platforms allow the manufacturer to collect data broadcast from its machines, which can track metrics such as work flow and machine history. Open communication between manufacturing devices and networks can also be achieved through internet connectivity. This encompasses everything from tablets to machine automation sensors and allows for machines to adjust their processes based on input from external devices.[4]

Benefits and aims

Smart Manufacturing aims to be an idealized practice in manufacturing. It involves the integration in all steps of the product fabrication process. The aim being a more harmonious development process utilizing data to develop intelligent technology to expedite new and higher quality goods.

Emerging business practices

If adopted, intelligent networks of manufacturing will see results in influencing business both domestically and worldwide. Business models can be more easily conceptualized around the integration of every step of the development process; invention, manufacturing, transportation and retailing. The eventual goal being a more flexible, adaptive, and reactive approach to participating in competitive markets. Companies may be forced to adapt or adopt the practice to compete, further stirring up the market.[10]

A large expectation of the premise also resides on the collaboration between technicians, intermediaries, and consumers alike. Establishing a network, also referred to as Internet of Things, of multidisciplinary professionals including scientists, engineers, statisticians, economists etc. is a fundamental resource for 'smart' business ventures.[11]

Eliminating workplace inefficiencies and hazards

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. 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.[12] In general, automation may alleviate inefficiencies due to human error. And in general, evolving AI eliminates the inefficiencies of its predecessors.

Worker safety can be augmented by safe, innovative design and increasing integrated networks of automation. This is under the notion that Technicians are exposed less to hazardous environments as automation matures. If successful, less human supervision and user instruction for automation will devitalize workplace safety concerns.[11]

Impact of Industry 4.0

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.[13]

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

European Roadmap "Factories of the Future" and German one "Industrie 4.0″ illustrate several of the action lines to undertake and the related benefits. Some examples are:

Interesting results have already been proven in real situations. Korean government has just announced the raise of a 57.5 billion $ for building smart factories this year after that 1240 Korean smart SMEs have showed "a 27.6% decrease in fraction defective, a cost reduction of 29.2% and a 7.1% reduction in the length of time taken for prototype production".

According to a research carried out by the German company Roland Berger Strategy Consultants, the full realization of this production model in Europe will require 90 billion of investment annually, reaching full maturity in 2030, when the Smart Manufacturing will be able to generate a turnover of to EUR 500 billion and providing jobs, only in the Old World, to six million people.[14]

Action and Effects

The basis for any significant deployment of cyber physical systems is a seamless data connection across every stage of the value-adding process. For each product, alongside its actual physical depiction, a virtual depiction continues to undergo further development. Consequently, Smart Manufacturing development and implementation focus on the optimum integration between the real and the virtual world.

A key component of Smart Manufacturing is decentralizing control: Intelligent components operate in each stage of the assembly system through which a part moves. In this type of assembly process, communication occurs at each step to determine what pieces to add or assembly steps to implement. Decentralized control makes it easier to add or change out parts as needed, making it smoother to meet the increasing demand for mass customization.

More than $4 billion has been invested in software companies since 2007, with the aim of enabling digital depiction of the value chain. It is only through complete integration of the individual, value-adding steps that it will be possible to achieve all conceivable advances in productivity.

From Siemens' perspective,[13] there are three core elements to this evolution:

  1. Manufacturing execution. Manufacturing execution will play an even more important role. The degree of connectivity between the automation level and the manufacturing execution system (MES) will increase significantly, also across the borders of companies and locations. The integration of Enterprise Resource Planning (ERP) and MES levels will also advance to achieve complete transparency as well as connectivity to business data. That means that all necessary information is available in real time.
  2. The merging of the product and production life cycle. The second core element is the merging of product and production life cycle based on a common data model. This will allow manufacturers to meet the challenges that result through ever-shorter product life cycles, both technically and in business.
  3. Cyber physical systems. Cyber physical systems are a basis for the increase in manufacturing flexibility that results in shorter time to market. These production units can be flexibly integrated into existing production processes. Cyber physical systems combine communications, IT, data, and physical elements using core technologies, including sensor networks; Internet communication infrastructure; intelligent, real-time processing and event management; big data and data provisioning; embedded software for logic; and automated operations and management of systemic activities across enterprises.

Bits vs. Atoms [15]

An interesting analysis of enabling technologies for smart factories and manufacturing relies on a taxonomy that defines the Bits’ world, made of information goods, data, cloud technologies, and the Atoms’ one, related to physical aspects of production and human-machine interaction.

The world of bits

This domain refers to all those industrial cloud technologies that allow to collect, store, manage and analyse data. The aim is to gain structured knowledge and share it in a distributed and collaborative platform inside the factory smart environment (e.g. supply chain management and industrial logistics).Such technologies could be: big data and computing capability, industrial wireless networks (IWN), machine2machine (M2M), machine learning, auto ID technologies (RFID, NFC used as data collectors), augmented reality (AR).

The world of atoms

This is the field of application of technologies, such as robotic systems and smart devices, that are closely related to the physical realization of an industrial product. In this case, bits are not aiming to replace atoms, but rather to change the way in which we produce them.Such technologies could be: robotics, advanced human-machine interaction, advanced manufacturing processes, augmented reality (AR), machine learning.

Inside this taxonomy, machine learning and augmented reality (AR) could actually fit in both worlds, ensuring their possibility of interaction. Machine learning uses sets of information to enable an automatic and efficient robot training. Augmented reality (AR) implies the use of physical sensors to enrich data collection.

Company examples

Several companies have already developed technologies that belong to one of these two classes:

A coordinated usage of smart technologies of the two classes could guide a rapid revolution in the manufacturing sector, with the following creation of an interconnected and efficiently global industrial ecosystem.

Role of Big Data, IoT and other technologies [16][17]

Following the ‘third wave of IT’, the mechanism of transmitting information is radically changing the nature of "things": IT is now becoming a fundamental part of the product. Product functionality is improving thanks to embedded sensors and processors in products and to a product cloud, designed to store and analyse product data.

This system allows the exchange of information between the product, the maker, the final user, its operating environment and other products or systems.

The development and adoption of connectivity technologies is a crucial element of smarter manufacturing. The new smart, connected products, along with the massive usage of unstructured information coming from IoT and Big Data, wants a whole new technology infrastructure within a company.

Industry structure has to be reshaped, redefining its boundaries, and new skills are required, e.g. software development, systems engineering, data analytic, online security enterprise.

The aim is to create intelligent networks along the value chain, connecting people, processes and data and generating new best practices.

Change in Value Chain [18]

Generally speaking, value chain is a full set of primary and support activities – from product design to its distribution – through which a firm delivers valuable products or services to the market. The value chain considers all the activities from raw materials to the product delivery to the customer, so everything that is added to it before the product is sold.

The added value of a product from the value chain is higher than a simple sum of the added value of each activity.

Smart Manufacturing can reshape the traditional definition of Value Chain.

Enabled by new ICT technology, activities could undergo some changes concerning mainly production innovation, infrastructure management and customer relationship management. They could experience increasing in accuracy, reductions in production time, improvements in productivity, or to be taken over by intelligent machines.

Considering technologies that facilitate information exchange, such as industrial wireless network and machine2machine systems, they could help in increasing accuracy and reducing transaction times in receiving raw materials, in manufacturing and in invoicing.

Regarding advanced manufacturing technologies, they could be used to reduce production time, especially in manufacturing, packaging and finishing goods.

Concerning data processing technologies, for example big data and cloud computing, they could bring structured information and analyse patterns behind data which is valuable in sales analysis, market research and demand forecast.

There can also be several changes in the phase of product innovation. Thanks to the technologies that provide rich information related to customer needs, companies can easily develop tailored products to meet the urgent needs of customization that the market is requiring.

Open Manufacturing [19]

The importance of collaborative solutions can not only be applied to innovation – mostly R&D – in products and services, but also for the manufacturing functions in a company.

Therefore, a more complete open innovation framework also within innovation ecosystem needs to take the more downstream innovation activities, such as manufacturing, into account (Chesbrough and Bogers, 2014). As such, collaborative efforts in manufacturing play an important part of any collaborative manufacturing platform.

Intelligent factories require an incredible effort in acquiring the resources and competences to be built upon, which can’t always be economically sustainable and this type of investments can end up in being unsuccessful. Thus, the new way is to look outside the walls of the factory to find the missing know-how and techniques, together with the most profitable way to take advantage of them. But not only: the technology transferral towards the outside can convert into a new profitable revenue creator for the business.

In the context of manufacturing ecosystems, a smart factory could relate to a way to enhance the collective and individual capabilities of manufacturing companies to foster growth and competitiveness by providing integration and alliance between partners and systems when they collaborate in an open innovation ecosystem where competences and technologies are mutually shared. The organization and management of a smart factory should be based on in-depth technological understanding as well as clear rules and procedures regarding knowledge sharing and potential disclosure in this open innovation environment (Bogers et al. 2012). This could be either related to open networks of wireless connected devices operating without direct human interaction, or to an organization where interconnection between different actors chosen to collaborate is more physical.

The creation of such an innovation community should generate more opportunities for knowledge sharing where the innovation process is facilitated for each member (Iansiti and Levien 2004; Moore 1996).

A prerequisite for such collaboration is an alignment of business models between the participating companies (Chesbrough and Bogers, 2014; West and Bogers, 2014).

https://thesmartcollars.wordpress.com

The Smart Collars proposes itself as a reference blog page for creating an innovative and clearer view of the smart manufacturing topic. It’s part of an academic work made by students of the XI cycle of the Alta Scuola Politecnica programme, restricted to 150 young and exceptionally talented students from Politecnico di Milano and Politecnico di Torino. The mission of ASP is to provide society with high-profile graduates combining in-depth (vertical) disciplinary knowledge from their Laurea Magistrale programmes with interdisciplinary (horizontal) skills that are needed to work in a truly multidisciplinary environment. (www.polimi.it)

http://smartmanufacturingT.com

Article and Essay

No Clear Path for Prospective Cybersecurity Specialists: http://theinstitute.ieee.org/career-and-education/career-guidance/no-clear-path-for-prospective-cybersecurity-specialists

Logistics Viewpoints - The Smart Factory, Industrie 4.0, and Supply Chain Optimization: https://logisticsviewpoints.com/2015/05/14/the-smart-factory-industrie-4-0-and-supply-chain-optimization/

What Supply Chain Leaders Need to Know about Industrie 4.0: http://www.gartner.com/smarterwithgartner/what-supply-chain-leaders-need-to-know-about-industrie-4-0/

Factories of the Future: http://ec.europa.eu/research/industrial_technologies/factories-of-the-future_en.html

Business Korea - Smart Factories Improving Productivity of SME: http://businesskorea.co.kr/english/news/industry/14073-smart-benefits-smart-factories-improving-productivity-smes

The Smart Factory: Exploring an Open Innovation Solution for Manufacturing Ecosystems: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2503064

GE launches 'microfactory' to co-create the future of manufacturing: http://www.techrepublic.com/article/ge-launches-microfactory-to-co-create-the-future-of-manufacturing/#ftag=RSS56d97e7

FCA - Our Businesses: http://www.fcagroup.com/en-US/group/our_businesses/Pages/default.aspx

SIGFOX: http://www.sigfox.com

Amazon Robotics: https://www.amazonrobotics.com

References

  1. 1 2 Davis, Jim; Edgar, Thomas; Porter, James; Bernaden, John; Sarli, Michael (2012-12-20). "Smart manufacturing, manufacturing intelligence and demand-dynamic performance". Computers & Chemical Engineering. FOCAPO 2012. 47: 145–156. doi:10.1016/j.compchemeng.2012.06.037.
  2. ↑ Shipp, Stephanie S. (March 2012). "Emerging Global Trends in Advanced Manufacturing" (PDF). Emerging Global Trends in Advanced Manufacturing. Insititute for Defense Analysis. Retrieved April 2016. Check date values in: |access-date= (help)
  3. ↑ "Automation conference and Expo".
  4. 1 2 3 "On the Journey to a Smart Manufacturing Revolution". www.industryweek.com. Retrieved 2016-02-17.
  5. ↑ 20899, James S. Albus, National Institute of Standards and Technology, Gaithersburg, Maryland (1995-01-01), English: Architecture for the NBS Automated Manufacturing Research Facility (AMRF)., retrieved 2016-03-04
  6. ↑ Rachuri, Dr. Sudarsan (February 4, 2014). "Smart Manufacturing Systems Design and Analysis" (PDF). National Institute of Standards and Technology. Retrieved February 16, 2016.
  7. ↑ Leveling, J.; Edelbrock, M.; Otto, B. (2014-12-01). "Big data analytics for supply chain management". 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): 918–922. doi:10.1109/IEEM.2014.7058772.
  8. ↑ NIST, US Department of Commerce,. "Robotic Systems for Smart Manufacturing". www.nist.gov. Retrieved 2016-03-04.
  9. ↑ Bicchi, Antonio; Peshkin, Michael A.; Colgate, J. Edward (2008-01-01). Prof, Bruno Siciliano; Prof, Oussama Khatib, eds. Safety for Physical Human–Robot Interaction. Springer Berlin Heidelberg. pp. 1335–1348. ISBN 9783540239574.
  10. ↑ Mckewen, Ellen (2015-07-28). "What is Smart Manufacturing". CMTC Manufacturing Blog. CMTC. Retrieved 2016-02-17.
  11. 1 2 Louchez, Alain (January 6, 2014). "From Smart Manufacturing to Manufacturing Smart". www.automationworld.com. Automation World. Retrieved 2016-03-04.
  12. ↑ Jung, Kiwook (2015-03-16). "Mapping Strategic Goals and Operational Performance Metrics for Smart Manufacturing Systems". Procedia Computer Science. Elsevier (44 p.184-193). doi:10.1016/j.procs.2015.03.051. Retrieved 2016-02-17.
  13. 1 2 Jacinto, Joan (July 31, 2014). "Smart Manufacturing? Industry 4.0? What's It All About?". The Vault - Siemens Totally Integrated Automation.
  14. ↑ "Smart Manufacturing: è in arrivo la "quarta rivoluzione industriale"". Telecom Italia TIM. 18 September 2015.
  15. ↑ "maNEWfacturing: bits & atoms". Blog - The Smart Collars. March 2016. Retrieved April 2016. Check date values in: |access-date= (help)
  16. ↑ Porter, Michael E. (November 2014). "How Smart, Connected Products Are Transforming Competition". Harvard Business Review. April 2016.
  17. ↑ "Building Smarter Manufacturing With The Internet of Things (IoT)". IT World Canada. Lopez Research. 2014. Retrieved April 2016. Check date values in: |access-date= (help)
  18. ↑ "Value Cha(i)nge". Blog - The Smart Collars. March 2016. Retrieved April 2016. Check date values in: |access-date= (help)
  19. ↑ "#OpenManufacturing". Blog - The Smart Collars. March 2016. Retrieved April 2016. Check date values in: |access-date= (help)
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