scholarly journals Smart SMEs 4.0 Maturity Model to Evaluate the Readiness of SMEs Implementing Industry 4.0

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
Nilubon Chonsawat ◽  
Apichat Sopadang
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Advanced Technology ◽  
Smart Manufacturing ◽  
Advanced Manufacturing ◽  
Maturity Model ◽  
Five Dimensions ◽  
Physical Systems ◽  
Increase Productivity ◽  
The Internet Of Things

In the Industry 4.0 revolution, advanced manufacturing capabilities integrate technology and data to create intelligent production systems, such as automation, cloud computing, the Internet of Things and cyber-physical systems. Small and medium-sized enterprises, which are the backbone of economic growth, especially must apply the advanced technology in their business and operations so as to increase productivity. This paper empirically proposes the Smart SMEs 4.0 maturity model and its implementation for assessing the readiness of an organisation to enter the realm of smart manufacturing. The model is categorised into five dimensions as well as 43 sub-dimensions for evaluating SMEs 4.0 maturity. These dimensions are mainly composed of “manufacturing and operations”, “people capability”, “technology-driven process”, “digital support” and “business and organisation strategies”. Moreover, the model is implemented in two case studies for two companies in Thailand. The results imply that the model can evaluate an organisation’s readiness and also can guide companies to implement the Smart SMEs 4.0 efficiently.

scholarly journals Smart manufacturing: Characteristics, technologies and enabling factors

2017 ◽  
Vol 233 (5) ◽  
pp. 1342-1361 ◽  
Author(s):  
Sameer Mittal ◽  
Muztoba Ahmad Khan ◽  
David Romero ◽  
Thorsten Wuest
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
Enabling Factors ◽  
Physical Systems ◽  
Industrial Community ◽  
Body Of Knowledge ◽  
Cyber Physical Production Systems ◽  
Selection Of

The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

scholarly journals Artificial Intelligence and the Internet of Things in Industry 4.0

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Razvan Nicolescu ◽  
Michael Huth ◽  
Omar Santos
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Industry 4.0 ◽  
Cyber Physical Systems ◽  
Physical Systems ◽  
Leading Industry ◽  
Key Technologies ◽  
The Internet Of Things ◽  
State Of Art ◽  
Social Level

AbstractThis paper presents a new design for artificial intelligence in cyber-physical systems. We present a survey of principles, policies, design actions and key technologies for CPS, and discusses the state of art of the technology in a qualitative perspective. First, literature published between 2010 and 2021 is reviewed, and compared with the results of a qualitative empirical study that correlates world leading Industry 4.0 frameworks. Second, the study establishes the present and future techniques for increased automation in cyber-physical systems. We present the cybersecurity requirements as they are changing with the integration of artificial intelligence and internet of things in cyber-physical systems. The grounded theory methodology is applied for analysis and modelling the connections and interdependencies between edge components and automation in cyber-physical systems. In addition, the hierarchical cascading methodology is used in combination with the taxonomic classifications, to design a new integrated framework for future cyber-physical systems. The study looks at increased automation in cyber-physical systems from a technical and social level.

scholarly journals Automation technology as a key component of the Industry 4.0 production development path

2021 ◽  
Author(s):  
Christian Brecher ◽  
Aleksandra Müller ◽  
Yannick Dassen ◽  
Simon Storms
Keyword(s):  
Industry 4.0 ◽  
Information Technologies ◽  
Production Systems ◽  
Product Variety ◽  
Transformation Process ◽  
Smart Manufacturing ◽  
Production Development ◽  
Flexible Production Systems ◽  
Current Production ◽  
Automation Technology

AbstractSince 2011, the Industry 4.0 initiative is a key research and development direction towards flexible production systems in Germany. The objective of the initiative is to deal with the challenge of an increased production complexity caused by various factors such as increasing global competition between companies, product variety, and individualization to meet customer needs. For this, Industry 4.0 envisions an overarching connection of information technologies with the production process, enabling smart manufacturing. Bringing current production systems to this objective will be a long transformation process, which requires a coherent migration path. The aim of this paper is to represent an exemplary production development way towards Industry 4.0 using eminent formalization approaches and standardized automation technologies.

scholarly journals Innovation Trajectories for a Society 5.0

Data ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 115
Author(s):  
Fabio De Felice ◽  
Marta Travaglioni ◽  
Antonella Petrillo
Keyword(s):  
Industry 4.0 ◽  
Well Being ◽  
Enabling Factors ◽  
Interpretive Structural Modeling ◽  
Main Driver ◽  
Increase Productivity ◽  
New Perspective ◽  
Hierarchy Process ◽  
The Internet Of Things

Big Data, the Internet of Things, and robotic and augmented realities are just some of the technologies that belong to Industry 4.0. These technologies improve working conditions and increase productivity and the quality of industry production. However, they can also improve life and society as a whole. A new perspective is oriented towards social well-being and it is called Society 5.0. Industry 4.0 supports the transition to the new society, but other drivers are also needed. To guide the transition, it is necessary to identify the enabling factors that integrate Industry 4.0. A conceptual framework was developed in which these factors were identified through a literature review and the analytical hierarchy process (AHP) methodology. Furthermore, the way in which they relate was evaluated with the help of the interpretive structural modeling (ISM) methodology. The proposed framework fills a research gap, which has not yet consolidated a strategy that includes all aspects of Society 5.0. As a result, the main driver, in addition to technology, is international politics.

scholarly journals How connectivity and search for producers impact production in Industry 4.0 networks

2018 ◽  
Vol 15 (4) ◽  
pp. 528-534
Author(s):  
Adriano Pereira ◽  
Eugênio De Oliveira Simonetto ◽  
Goran Putnik ◽  
Helio Cristiano Gomes Alves de Castro
Keyword(s):  
Industry 4.0 ◽  
Industrial Revolution ◽  
Cyber Physical Systems ◽  
Average Degree ◽  
Collaborative Networks ◽  
Production Network ◽  
Simulation Environment ◽  
Physical Systems ◽  
Study Results ◽  
The Internet Of Things

Technological evolutions lead to changes in production processes; the Fourth Industrial Revolution has been called Industry 4.0, as it integrates Cyber-Physical Systems and the Internet of Things into supply chains. Large complex networks are the core structure of Industry 4.0: any node in a network can demand a task, which can be answered by one node or a set of them, collaboratively, when they are connected. In this paper, the aim is to verify how (i) network's connectivity (average degree) and (ii) the number of levels covered in nodes search impacts the total of production tasks completely performed in the network. To achieve the goal of this paper, two hypotheses were formulated and tested in a computer simulation environment developed based on a modeling and simulation study. Results showed that the higher the network's average degree is (their nodes are more connected), the greater are the number of tasks performed; in addition, generally, the greater are the levels defined in the search for nodes, the more tasks are completely executed. This paper's main limitations are related to the simulation process, which led to a simplification of production process. The results found can be applied in several Industry 4.0 networks, such as additive manufacturing and collaborative networks, and this paper is original due to the use of simulation to test this kind of hypotheses in an Industry 4.0 production network.

Smart Manufacturing and Industry 4.0: A Preliminary Approach in Structuring a Conceptual Framework

2021 ◽  
Vol 18 ◽  
pp. 27-36
Author(s):  
Eleni Didaskalou ◽  
Petros Manesiotis ◽  
Dimitrios Georgakellos
Keyword(s):  
Conceptual Framework ◽  
Concept Mapping ◽  
Industry 4.0 ◽  
Concept Maps ◽  
Production Systems ◽  
Software Tool ◽  
Smart Manufacturing ◽  
Future Research ◽  
Key Concepts ◽  
Comprehensive Framework

Engineering concepts usually, are complex concepts, thus many times are difficult for infusing into curriculums or to be comprehensive for practitioners. A concept that still now is not fully understandable is that of Industry 4.0, an approach that increases the complexity of production systems. Nowadays production systems are facing new challenges, as physical productions systems and internet technologies are directly linked, hence increasing the complexity but also the productivity of the systems. The paper introduces an approach of visualizing the concept of smart manufacturing in the context of Industry 4.0, as the term is not clearly specified, although has attracted attention both academicians and businesses. Concept mapping is a method of capturing and visualizing complex ideas. Concept maps are graphical tools for organizing, representing and communicating complex ideas by breaking them into more key concepts. As Industry 4.0 is a factor that can boost innovation and competitiveness of business, all parties involved in shaping the strategy of an organization, should perceive the issues to be covered. Furthermore, learners must be prepared to meet these challenges and knowledgebuilding activities may enhance their process of learning. The paper makes an interesting and valuable contribution, by identifying key concepts within the subject of smart manufacturing and Industry 4.0, using the method of concept mapping. Taking into consideration these concepts a conceptual framework will be introduced, by using the software tool CmapTools. The map can be used as a basis for future research in constructing a more comprehensive framework and identifying the concepts that describe smart manufacturing in the context of Industry 4.0, in a more thorough manner.

The Implementation of Industry 4.0 by Using Industrial and Service Robots in the Production Processes

2020 ◽  
pp. 1-30 ◽  
Author(s):  
Isak Karabegović ◽  
Edina Karabegović ◽  
Mehmed Mahmic ◽  
Ermin Husak
Keyword(s):  
Radio Frequency Identification ◽  
Industry 4.0 ◽  
Production Systems ◽  
Service Robots ◽  
Production Processes ◽  
New Business ◽  
3D Printers ◽  
Frequency Identification ◽  
Smart Factories ◽  
The Internet Of Things

From the very knowledge of Industry 4.0, its implementation is carried out in all segments of society, but we still do not fully understand the breadth and speed of its implementation. We are currently witnessing major changes in all industries, so new business methods are emerging. There is a transformation of production systems, a new form of consumption, delivery, and transportation, all thanks to the implementation of new technological discoveries that cover robotics and automation, the internet of things (IoT), 3D printers, smart sensors, radio frequency identification (RFID), etc. Robotic technology is one of the most important technologies in Industry 4.0, so that the robot application in the automation of production processes with the support of information technology brings us to smart automation (i.e., smart factories). The changes are so deep that, from the perspective of human history, there has never been a time of greater promise or potential danger.

Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

2021 ◽  
pp. 144-172
Author(s):  
Luis Alberto Estrada-Jimenez ◽  
Terrin Pulikottil ◽  
Nguyen Ngoc Hien ◽  
Agajan Torayev ◽  
Hamood Ur Rehman ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Heterogeneous Systems ◽  
Smart Manufacturing ◽  
Use Case ◽  
Integrative Framework ◽  
Data Formats ◽  
Exchange Information ◽  
Cyber Physical Production Systems ◽  
Physical Components

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference models and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks.

scholarly journals A Lightweight Slice-Based Quality of Service Manager for IoT

IoT ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 49-75
Author(s):  
Antonio Oliveira-Jr ◽  
Kleber Cardoso ◽  
Filipe Sousa ◽  
Waldir Moreira
Keyword(s):  
Quality Of Service ◽  
Industry 4.0 ◽  
Farming Systems ◽  
Smart Manufacturing ◽  
Network Slicing ◽  
Wide Range ◽  
The Internet Of Things ◽  
Service Manager ◽  
Digital Farming

Industry 4.0 and digital farming rely on modern communication and computation technologies such as the Internet of Things (IoT) to provide smart manufacturing and farming systems. Having in mind a scenario with a high number of heterogeneous connected devices, with varying technologies and characteristics, the deployment of Industry 4.0 and digital farming solutions faces innovative challenges in different domains (e.g., communications, security, quality of service). Concepts such as network slicing and Software-Defined Networking (SDN) provide the means for faster, simpler, scalable and flexible solutions in order to serve a wide range of applications with different Quality-of-Service (QoS) requirements. Hence, this paper proposes a lightweight slice-based QoS manager for non-3GPP IoT focusing on different use cases and their varying requirements and characteristics. Our focus in this work is on non-3GPP IoT unlicensed wireless technologies and not specifically the end-to-end network slice perspective as described in 5G standards. We implemented and evaluated different QoS models in distinct scenarios in a real experimental environment in order to illustrate the potential of the proposed solution.

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