scholarly journals Research and Finding Technical Enablers using Ism for Industry 4.0 in Indian Agricultural Industries

2019 ◽  
Vol 8 (11S) ◽  
pp. 137-141 ◽  
Keyword(s):  
Literature Review ◽  
Industry 4.0 ◽  
Learning Technologies ◽  
Smart Manufacturing ◽  
Automation System ◽  
Agricultural Field ◽  
International Benchmarking ◽  
Individual Industry ◽  
Agricultural Industries ◽  
Indian Industries

At the present scenario, agriculture industries are working hard to produce farmer satisfied products at affordable cost. The globalization and heavy worldwide competition stress them to precise and sustain in the market. The existing system are to be modified for smart manufacturing to cop up international benchmarking. The modifications consist of modern machine tools, automation system, machine learning technologies and systematic approach. The existing system and path for every individual industry are unique. Here the skill needed is to fit suitable enablers to the factors. The enactment of Industry 4.0 appropriately to industry is a task, because different industries lie at different sectors. In this context a study is carried to identify the important technological enablers for the enactment of Industry 4.0 in Indian agricultural industries. Various enablers essential for implementing Industry 4.0 has been identified from literature review. The Interpretive structural modelling(ISM) is employed for finding the mutual relationship among the enablers. Data collected to rank the enablers in the agricultural field. The technological enablers are further being classified as dependent and driving factors. Thus a hypothetical model is created based on literature review. A proper acknowledgement of interactions among enablers will help organization to rank the factors and manage these factors with more efficiency to produce advantages of implementing Industry 4.0. This paper is aiming to identify the various enablers to implement Industry4.0 in Indian industries.

scholarly journals Reference Architectures for Industry 4.0: Literature Review

2020 ◽  
Author(s):  
Alexandre Helmann ◽  
Fernando Deschamps ◽  
Eduardo de Freitas Rocha Loures
Keyword(s):  
Literature Review ◽  
System Architecture ◽  
Industry 4.0 ◽  
New Technologies ◽  
Production Systems ◽  
Smart Manufacturing ◽  
Automation System ◽  
Manufacturing Processes ◽  
Direct Integration

Currently, production systems are receiving the application of more advanced, integrated and connected technologies to optimize the performance of their manufacturing processes. The new technological solutions demand architectures that support intelligent solutions for a new digitalized industry. However, production systems already in operation have difficulty in implementing these technologies. The existing barriers limit the availability of the direct integration of different systems contemplated in an automation system architecture. This article systematically reviews the existing literature to portray the characteristics of each architecture and that can guide the adoption of new technologies. Through this review, emerging reference architectures were identified, such as RAMI4.0, IIRA, IBM Industry 4.0 and NIST Smart Manufacturing. In conclusion, the article presents a framework for considering which model best fits with the new technological solutions.

scholarly journals A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

2021 ◽  
Vol 13 (10) ◽  
pp. 264
Author(s):  
Tuuli Katarina Lepasepp ◽  
William Hurst
Keyword(s):  
Literature Review ◽  
Medical Device ◽  
Systematic Literature Review ◽  
Digital Libraries ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
New Technologies ◽  
Quality Analysis ◽  
Smart Manufacturing ◽  
Synonymous Term

Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.

scholarly journals Systematic Literature Review Predictive Maintenance Solutions for SMEs from the Last Decade

Machines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 191
Author(s):  
Sepideh Hassankhani Dolatabadi ◽  
Ivana Budinska
Keyword(s):  
Environmental Factors ◽  
Literature Review ◽  
Rapid Growth ◽  
Industry 4.0 ◽  
Predictive Maintenance ◽  
Smart Manufacturing ◽  
Equipment Failure ◽  
Specific Category ◽  
Low Costs ◽  
Manufacturing Technologies

Today, small- and medium-sized enterprises (SMEs) play an important role in the economy of societies. Although environmental factors, such as COVID-19, as well as non-environmental factors, such as equipment failure, make these industries more vulnerable, they can be minimized by better understanding the concerns and threats these industries face. Only a few SMEs have the capacity to implement the innovative manufacturing technologies of Industry 4.0. The system must be highly adaptable to any equipment, have low costs, avoid the need of doing complex integrations and setups, and have future reliability due to the rapid growth of technology. The goal of this study was to provide an overview of past articles (2010–2020), highlighting the major expectations, requirements, and challenges for SMEs regarding the implementation of predictive maintenance (PdM). The proposed solutions to meet these expectations, requirements, and challenges are discussed. In general, in this study, we attempted to overcome the challenges and limitations of using smart manufacturing—PdM, in particular—in small- and medium-sized enterprises by summarizing the solutions offered in different industries and with various conditions. Moreover, this literature review enables managers and stakeholders of organizations to find solutions from previous studies for a specific category, with consideration for their expectations and needs. This can be significantly helpful for small- and medium-sized organizations to save time due to time-consuming maintenance processes.

scholarly journals Exploring the Interrelationship between Additive Manufacturing and Industry 4.0

Designs ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 13 ◽  
Author(s):  
Javaid Butt
Keyword(s):  
Additive Manufacturing ◽  
Literature Review ◽  
Industry 4.0 ◽  
Data Exchange ◽  
Smart Manufacturing ◽  
Cognitive Computing ◽  
Digital Twins ◽  
Technological Developments ◽  
Manufacturing Techniques ◽  
Manufacturing Technologies

Innovative technologies allow organizations to remain competitive in the market and increase their profitability. These driving factors have led to the adoption of several emerging technologies and no other trend has created more of an impact than Industry 4.0 in recent years. This is an umbrella term that encompasses several digital technologies that are geared toward automation and data exchange in manufacturing technologies and processes. These include but are not limited to several latest technological developments such as cyber-physical systems, digital twins, Internet of Things, cloud computing, cognitive computing, and artificial intelligence. Within the context of Industry 4.0, additive manufacturing (AM) is a crucial element. AM is also an umbrella term for several manufacturing techniques capable of manufacturing products by adding layers on top of each other. These technologies have been widely researched and implemented to produce homogeneous and heterogeneous products with complex geometries. This paper focuses on the interrelationship between AM and other elements of Industry 4.0. A comprehensive AM-centric literature review discussing the interaction between AM and Industry 4.0 elements whether directly (used for AM) or indirectly (used with AM) has been presented. Furthermore, a conceptual digital thread integrating AM and Industry 4.0 technologies has been proposed. The need for such interconnectedness and its benefits have been explored through the content-centric literature review. Development of such a digital thread for AM will provide significant benefits, allow companies to respond to customer requirements more efficiently, and will accelerate the shift toward smart manufacturing.

Industry 4.0 transition: a systematic literature review combining the absorptive capacity theory and the data–information–knowledge hierarchy

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lorenzo Ardito ◽  
Roberto Cerchione ◽  
Erica Mazzola ◽  
Elisabetta Raguseo
Keyword(s):  
Literature Review ◽  
Systematic Literature Review ◽  
Absorptive Capacity ◽  
Industry 4.0 ◽  
Digital Technologies ◽  
Clear Picture ◽  
Smart Manufacturing ◽  
Future Research ◽  
Content Type

Purpose The effect of the transition toward digital technologies on today’s businesses (i.e. Industry 4.0 transition) is becoming increasingly relevant, and the number of studies that have examined this phenomenon has grown rapidly. However, systematizing the existing findings is still a challenge, from both a theoretical and a managerial point of view. In such a setting, the knowledge management (KM) discipline can provide guidance to address such a gap. Indeed, the implementation of fundamental digital technologies is reshaping how firms manage knowledge. Thus, this study aims to critically review the existing literature on Industry 4.0 from a KM perspective. Design/methodology/approach First, the authors defined a structuring framework to highlight the role of Industry 4.0 transition along with absorptive capacity (ACAP) processes (acquisition, assimilation, transformation and exploitation), while specifying what is being managed, that is data, information and/or (actual) knowledge, according to the data-information-knowledge (DIK) hierarchy. The authors then followed the systematic literature review methodology, which involves the use of explicit criteria to select publications to review and outline the stages a process has to follow to provide a transparent and replicable review and to analyze the existing literature according to the theoretical framework. This procedure yielded a final list of 150 papers. Findings By providing a clear picture of what scholars have studied so far on Industry 4.0 transition, in terms of KM, this literature review highlights that among all the studied digital technologies, the big data analytics technology is the one that has been explored the most in each phase of the ACAP process. A constructive body of research has also emerged in recent years around the role played by the internet of things, especially to explain the acquisition of data. On the other hand, some digital technologies, such as cyber security and smart manufacturing, have largely remained unaddressed. An explanation of the role of these technologies has been provided, from a KM perspective, together with the business implications. Originality/value This study is one of the first attempts to revise the literature on Industry 4.0 transition from a KM perspective, and it proposes a novel framework to read existing studies and on which to base new ones. Furthermore, the synthesis makes two main contributions. First, it provides a clear picture of the different digital technologies that support the four ACAP phases in relation to the DIK hierarchy. Accordingly, these results can emphasize what the literature has looked at so far, as well as which digital technologies have gained the most attention and their impacts in terms of KM. Second, the synthesis provides prescriptive considerations on the development of future research avenues, according to the proposed research framework.

scholarly journals Sustainable Cyber-Physical Production Systems in Big Data-Driven Smart Urban Economy: A Systematic Literature Review

2021 ◽  
Vol 13 (2) ◽  
pp. 751
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Mariana Iatagan ◽  
Iulian Hurloiu ◽  
Irina Dijmărescu
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Literature Review ◽  
Operations Management ◽  
Industry 4.0 ◽  
Production Systems ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Industrial Big Data ◽  
Cyber Physical Production Systems

In this article, we cumulate previous research findings indicating that cyber-physical production systems bring about operations shaping social sustainability performance technologically. We contribute to the literature on sustainable cyber-physical production systems by showing that the technological and operations management features of cyber-physical systems constitute the components of data-driven sustainable smart manufacturing. Throughout September 2020, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “sustainable industrial value creation”, “cyber-physical production systems”, “sustainable smart manufacturing”, “smart economy”, “industrial big data analytics”, “sustainable Internet of Things”, and “sustainable Industry 4.0”. As we inspected research published only in 2019 and 2020, only 323 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 119, generally empirical, sources. Future research should investigate whether Industry 4.0-based manufacturing technologies can ensure the sustainability of big data-driven production systems by use of Internet of Things sensing networks and deep learning-assisted smart process planning.

scholarly journals Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In-Line Metrology System

2021 ◽  
Vol 11 (3) ◽  
pp. 1312
Author(s):  
Ana Pamela Castro-Martin ◽  
Horacio Ahuett-Garza ◽  
Darío Guamán-Lozada ◽  
Maria F. Márquez-Alderete ◽  
Pedro D. Urbina Coronado ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Design Feature ◽  
Production Equipment ◽  
Smart Manufacturing ◽  
Process Data ◽  
Industrial Sectors ◽  
Advanced Manufacturing Technologies ◽  
Manufacturing Technologies ◽  
Equipment Application ◽  
The Impact

Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.

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