Industry 4.0 in Small and Medium-Sized Enterprises (SMEs)

Opportunities, Challenges, and Solutions

Ketan Kotecha, Satish Kumar, Arunkumar Bongale, R. Suresh, Ketan Kotecha, Satish Kumar, Arunkumar Bongale, R. Suresh

• Publisher: CRC Press
• Year: 2022
• Print ISBN: 9781032061313
• eBook ISBN: 9781000548969
• Edition: 1
• Topic: Business
• Subtopic: Workplace Culture
• Index: Business

Focusing on the broader areas of Industry 4.0 as it applies to small and medium-sized enterprises (SMEs), this book offers a smooth adoption of techniques and technologies and presents advances, challenges, and opportunities for implementation. It will also enhance the role of academia by training new engineers on Industry 4.0 and digital transformation.

Industry 4.0 in Small and Medium-Sized Enterprises (SMEs): Opportunities, Challenges, and Solutions presents concepts of predictive maintenance, digital factory, digital twin, additive manufacturing, and machining for sustainable development. It discusses the challenges faced by adopting Industry 4.0 including new security and privacy measures in the whole smart manufacturing setup while also explaining the impact of Industry 4.0 on Lean production systems. Implementation recommendations in the form of case studies, research studies, and the role academia can play are also provided.

Practitioners, research scholars, academicians, and those studying or working in the Industry 4.0 sector will find this book of interest.

1 Industry 4.0: An Introduction in the Context of SMEs

Priya Jadhav, Satish Kumar, and Arunkumar Bongale

DOI: 10.1201/9781003200857-1

Contents

1.1 Introduction

1.2 Digital Manufacturing

1.3 Components of Industry 4.0

1.3.1 Connectivity of Sensors

1.3.2 Data Storage and Cloud

1.3.3 Augmented Reality

1.3.4 Artificial Intelligence for Departments

1.3.5 Cybersecurity

1.3.6 Digital Twin in Smart Factories

1.4 IIoT in Industry 4.0

1.5 Smart Factories

1.5.1 Deep Learning

1.5.2 Radio-frequency Identification

1.5.3 Data Mining

1.5.4 Cloud Computing

1.6 Challenges and Opportunities

1.6.1 Implementation Challenges

1.6.1.1 Need for Technology Maturity

1.6.1.2 Insufficient Technology to Integrate with Manufacturing Systems

1.6.1.3 Difficulty in the Reconfiguration of Production Systems

1.6.2 Challenges for Data

1.6.2.1 Unstructured Format of Data

1.6.2.2 Management of Huge Data for Storage and Processing

1.6.2.3 Quality of Data

1.6.3 Skilled Workforce

1.6.4 Security Challenges

1.6.4.1 Sharing Data with Stockholders

1.6.4.2 Connectivity Protocols

1.6.5 Difficulty in Altering the Manufacturing Set-Up

1.6.6 Standardising the Process

1.6.7 Financial Challenges

1.6.8 Implementation Difficulties at Managerial Level

1.6.9 Environmental Challenges

1.7 Opportunities in Implementation of Industry 4.0

1.8 Conclusion

References

1.1 Introduction

The focus of the first Industrial Revolution was primarily on increasing mechanical productivity. The second Industrial Revolution in the 19th century was aided by the use of electricity to increase production speed while keeping costs low. The use of memory-driven computers and some automation led to the third revolution. With the introduction of 3D printing technology and intelligent, self-monitoring logistics systems, the Industrial Revolution has progressed significantly.

We are currently in the fourth Industrial Revolution with the digitisation of production systems improving with various artificial intelligence (AI) techniques such as machine learning and big data approaches. The various supporting smart devices, the industrial internet of things (IIoT), play a crucial role in integrating hardware or machines with the data and software systems. Hence, modern manufacturing systems are considered smart systems with real-time decision-making based on data analysis algorithms. Together with the use of robots and the automation of subprocesses, difficult tasks are made simple. Hence, Industry 4.0 makes manufacturing systems eco-friendly, sustainable, and economic.

This chapter focuses on the applications, methodologies, and associated data, communication, and security problems, as well as the opportunities that AI and big data bring to Industry 4.0. We examine a variety of AI and big data methodologies in depth, as well as the Industry 4.0 applications that have profited from AI and big data. The chapter also highlights and examines major technological, data-related, and security risks and problems that come with successful AI and big data deployment in Industry 4.0.

This chapter covers the following key concepts in Industry 4.0:

• Digital manufacturing
• Smart factories
• Challenges in Industry 4.0 adaptation
• Opportunities in implementation

1.2 Digital Manufacturing

Over time, generalised manufacturing systems have become customer requirement oriented, including the manufacture of parts and inventory management. Developing excellence in the field by creating a brand name and maintaining network systems with other manufacturing units is important for collaboration. This can be achieved using flexible manufacturing systems (FMS) or other techniques for adopting a greater number of changes in the schedules or set-ups of assembly lines. Still more high-end goals where automation is an integral part of the production system need more intelligence systems. Hence, manufacturing systems need to be more competitive to compete in the market (Bousdekis, Lepenioti, and Apostolou 2021; Pech and Vrchota 2021).

Product design, production, and maintenance management have been part of the traditional production method. Changes in design are the key step to improving quality and reducing costs in the initial stages of production. Supporting tools such as computer-aided design (CAD) and computer-aided manufacturing (CAM) benefit from virtual simulations that deliver faster and more reliable results. These simulations are an integral part of the design, quickly validating the shop floor management data for larger assembly lines.

FIGURE 1.1 Foundations of digital manufacturing.

In comparison to previous mass production techniques, lean manufacturing involves managing product development, production systems, and customers with less effort, less time, and better quality. The term “agile manufacturing” refers to the process of bringing a new product to market through modifying production systems. This fosters a culture of confronting issues and adapting to new approaches as market conditions dictate. On the other side, sustainable manufacturing integrates society, the environment, and the economy from an operational perspective, focusing on resource use, worker engagement, and community development (Chong, Ramakrishna, and Singh 2017). When these three pillars of lean, agile, and sustainability (Figure 1.1) are examined as a single system, lean emphasises a system’s stability, which can be referred to as autonomy, while agility adds the ability to adapt to new situations, focusing more on collaboration (Nylund and Andersson 2001).

Changes can also come from spontaneous suggestions that are perceived as improving the system’s competence. If the process has not yet been built, the current system must be analysed in order to create digital information and knowledge of what is now available. The new needs for the future model are formed by a synthesis of the existing system and possible adjustments. The solution principles are formed by a mixture of plausible new options and current capabilities. The end product is a set of integrated structures and abstract and conceptual formulations, which would include the future system’s goals and basic attributes. Possible technologies can be researched as the descriptions are more thoroughly examined, resulting in different solutions. The solution options can be modelled as virtual entities with their own operating rules, motion, and behaviour in addition to their digital description. A basic simulation model is created by combining existing and new virtual components.

Digital manufacturing relates to monitoring and controlling the production system using different sensors and actuators, and analysing the data obtained from them for monitoring and controlling purposes. Digital manufacturing starts with converting the digital data into measurable figures to perform an analysis using the various algorithms for monitoring. The digital systems can decode the data in the form of binary data and images as well as sound. The approach for the development of digital manufacturing is to develop computer-integrated programs that can improve the quality and quantity of production systems (Figure 1.2).

FIGURE 1.2 Steps in digital manufacturing.

The internet is a fusion of data and services between physical and digital machines. The drivers of innovation are the new factory model. One has to promote sustainable development, the efficient production of system resources, innovation, and a successful economy (Chong, Ramakrishna, and Singh 2017). A manufacturing system’s operation can be seen in three time dimensions: past, present, and future. The past depicts what has occurred, and it might be considered the system’s digital memory. The present time dimension, or what is going on right now, is used to run the current system by monitoring its state and comparing it to the desired state. The future dimension allows for future manufacturing planning. The data collected from a system’s activities as they occurred is displayed in the past. It can be used to investigate earlier manufacturing activity in order to determine what went wrong and why. The system can learn from its past and avert unpleasant events in the future by determining the root causes of problems. Regulations on the autonomy of manufacturing entities, as well as their collaboration, can be improved, and new rules can be devised. The term “present” refers to a time period in the near future when no major changes are expected.

1.3 Components of Industry 4.0

The machines, devices, and connecting interfaces have different layers for components of Industry 4.0. These components are grouped based on different principles.

On the shop floor, the controllers and smart IoT devices operate together with computerised numerical control (CNC) and other digital machinery. These devices are used to ...

Table of contents

• Cover
• Half-Title
• Title
• Copyright
• Contents
• Preface
• Editors
• Contributors
• Chapter 1 Industry 4.0: An Introduction in the Context of SMEs
• Chapter 2 Indian SMEs – Opportunities and Challenges: Assessing Industry 4.0 Readiness
• Chapter 3 Paradigm Shift in Construction Processes with Industry 4.0
• Chapter 4 Machinery Fault Detection using Artificial Intelligence in Industry 4.0
• Chapter 5 A Multi-Agent Reinforcement Learning Approach for Spatiotemporal Sensing Application in Precision Agriculture
• Chapter 6 Digital Twin of a Laboratory Gas Turbine Engine Using Deep Learning Framework
• Chapter 7 A Case Study of Additive Manufacturing in Prosthesis Development in Industry 4.0
• Chapter 8 Technology Gap Analysis with Respect to Mysore Printing Cluster: An Attractive Opportunity in Industry 4.0 Market
• Chapter 9 Intelligent Machining
• Chapter 10 Digital Market Scenario in India: A Case Study on “Unicorn” Indian Digital Start-Ups
• Chapter 11 Skill Requirement in Industry 4.0
• Chapter 12 The Changing Role of Academics from the Perspective of Educational Transformation in Education 4.0
• Index