Enabling Technologies for the Successful Deployment of Industry 4.0

Antonio Sartal, Diego Carou, J. Paulo Davim

• Publisher: CRC Press
• Year: 2020
• ISBN:
  9780429619144
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

This book offers the latest research advances in the field of Industry 4.0, focusing on enabling technologies for its deployment in a comprehensive way. This book offers successful implementation of technologies such as artificial intelligence, augmented and virtual reality, autonomous and collaborative robots, cloud computing, and up-to-date guidelines. It investigates how the technologies and principles surrounding Industry 4.0 (e.g., interoperability, decentralized decisions, information transparency, etc.) serve as support for organizational routines and workers (and vice versa). Included are applications of technologies for different sectors and environments as well as for the supply chain management. It also offers a domestic and international mix of case studies that spotlight successes and failures.

Features

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• Provides a historical review of Industry 4.0 and its roots

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• Discusses the applications of technologies in different sectors and environments (e.g., public vs. private)

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• Presents key enabling technologies for successful implementation in any industrial and service environment

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• Offers case studies of successes and failures to illustrate how to put theory into practice

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• Investigates how technologies serve as support for organizational routines and workers

1 Understanding Digital Transformation

Ercan Oztemel

Contents

1.1 Introduction

1.2 Transformation and the Basic Challenges

1.2.1 Smart Factories

1.2.2 Swarm Robotics

1.2.3 Smart and Secure Networks

1.2.4 Simulation and Augmented Reality

1.2.5 Glass as a Vision

1.2.6 Big Data and Internet of Things

1.2.7 Cloud Network

1.2.8 Mobile Systems

1.2.9 Additive Manufacturing and 3D Printing

1.2.10 Cyber-Physical System

1.2.11 Preventive Maintenance

1.2.12 Green Technology

1.2.13 Smart City and Related Applications

1.3 Facts about the Digital Transformation

1.4 How to Cope With the Transformation

1.4.1 Setting Up a Clear Vision and a Strategy

1.4.2 Generating and Implementing a Training Programme

1.4.3 Creating an Action Plan to Implement the Strategy

1.4.4 Sharing the Best Practices

1.5 Conclusion

References

1.1 Introduction

Digital transformation is defined as the changes associated with digital technology applications and integration of that to all aspects of human life. This transformation is in fact to move from physically empowered life to the digital one. Everyone should accept the fact that everything in the world is changing. The only thing that doesn’t change is the change itself. This has been realized throughout history. When it began, human life was based on hunting animals. As a result of use of the seed, life was empowered by agriculture and land owners became powerful social actors. The change did not stop, and machines were invented just before the last century. This consciously created the so-called “first industrial society” and landowners had to leave their social impacts and positions to bosses. Industrial revolutions are followed one after the other. Each transformation also led to new countries developing superiority over weak followers.

During the end of last century scientific progress experienced the term “information” as the key to success in business. Information management systems and methodologies completely changed the way of life and had a remarkable impact on nearly all aspects of human life. People became unable to live without the support of computers. Computer engineering and programming were considered the most important drivers of the society. Enterprises had to change their operating platforms from traditional machines to software-driven automated ones. It was no surprise to see the political powers of digital investors. Being able to manage information and progress along with the respective technologies, such as artificial intelligence over time, made it possible to deal with and well manage the “knowledge”. This naturally led to the generation of intelligent systems. In recent years, people started to utilize knowledge-based systems. This in turn encouraged the transformation towards a knowledge-driven society.

It is now very obvious that the change will continue and the wisdom society will emerge. Wisdom managers will be the key power in future societies. Progress in the same vein should be followed very carefully, in order to cope with possible challenges in the most effective way. Not only should the nations take necessary actions, but the enterprises should also develop proper action plans and follow a systematic process in order to be aligned with possible transformation. This process should be continuously updated in accordance with the basic requirements of the change. This chapter intends to provide guidelines for those who have an interest in digital transformation and wish to comply with it.

1.2 Transformation and the Basic Challenges

As it is clearly stated above, everything is changing. There is no way to stop this. The change appears from various aspects including the technological, methodological, environmental, managerial and customer related changes as well as those in manufacturing systems, etc. Oztemel (2010) analyzed each of these changes and provided some recommendations. It is wise to understand the directions of the change alongside these aspects and try to develop respective actions to sustain compatibility. Analyzing these aspects may also help support generating clear and implementable roadmaps. The changes and transformation of manufacturing systems, for example, are well characterized as shown in Figure 1.1. Similar analysis for other aspects listed above is given in the reference.

FIGURE 1.1 Transformation of manufacturing systems.

The digital transformation process requires special attention on some basic issues as they present challenges to the practitioners. It is important to consider these in any transformation process. Some of the challenges are reviewed below.

1.2.1 Smart Factories

Creating intelligent operations in manufacturing functions to support the creation of so-called smart factories (unmanned factories or dark factories) should be the basic concern of practitioners. It is now very certain that the digital transformation cannot be complete without having intelligent and smart systems especially in manufacturing areas. Running unmanned systems will definitely require a great amount of knowledge and experience in related processes. This will in turn necessitate new skills and generate various supporting jobs. This is to say that the smart factories will in fact generate new jobs rather than leading to unemployment. Companies should be encouraged to spend time and effort to generate required autonomy as fast as possible.

So much is written about smart factories. This book will also provide some information; but taking humans out of the loop in a manufacturing environment and having a smooth-running system is not easy. This requires generating not only the machines, but also human behaviour that is able to cooperate with machines. With this understanding smart factories are considered to be the road to the digital factories of the future. See Grunov (2016) for more information. There are various challenges that have to be sorted out to run a smart factory. The challenges are twofold: in developing the smart systems and in running the smart factories.

Some of the challenges for developing smart systems are the following.

• Integration: Machines are not small equipment which can be changed easily. Keeping them running all the time without any problems is important. The digital designers should place a lot of effort in finding solutions to the problems without stopping the manufacturing lines.
• Connectivity: The machines in a smart factory are connected to each other and communicate one way or another. Sustaining this is important as the connection also serves as an information network carrying the information from one machine to others.
• Skills or behaviour: Machines are expected to perform the operator’s behaviour. There are two issues here. One is having the skills to generate the required intelligence to the machines, the other is to model the behaviour of the machine on human-like performance.
• Fragmentation: Generating a smart machine requires extensive knowledge. Turning this into a smart factory makes it even harder. Knowledge from various scientific disciplines is required. Fragmentation is important to simplify the selection and planning process. It gives a chance for people from different knowledge sets to work together.
• Security: It is important to keep the information network isolated to prevent machines from being accessed and hacked. This is also essential to keep machines running in harmony with others in the manufacturing suites. Security issues may also create a problem with customers if trust is lost. Digital designers should make an effort to sustain the required level of trust by running the system safely.
• Uncertainty: When generating smart factories, the highly complex nature of technologies makes it hard to assess potential benefits due to uncertainty about the particular adaptation of processes and respective smart capabilities. The cost of setting up a smart factory is too high and the possible benefits are uncertain until realized. The investors should be made aware of the fact that the transformation is inevitable and be given support during the transformation.

Some of the challenges that are related to running the smart system as listed below.

• Process management: Manufacturing companies will always face difficulties in changing traditional routines and processes to those running digitally. As there is no systematically proven approach for this, there will always be a tendency to keep traditional practices. This rigid culture is difficult to change. The designers should spend a great deal of effort to generate applicable business transformation models both to enable the transformation and to attract the people with the competencies to support it.
• Supply chain: The ability to simplify a complex supply chain requires time and innovative ideas in order not to lose a quick and fast response to the market. Since global operations expand due to the impact of digital transformation, the supply chain must also extend to a growing scope of geographical areas.
• Reduced lead-times: Today, the business world is experiencing very fast delivery of products and services. It is therefore very important to reduce lead-times. Tailored inventory programmes are essential. This may help reduce development cost and enables fast lead-times for customer-specific devices. Achieving this still requires research and a disruptive technology.
• Product lifetime: Ensuring long-term availability of technology helps keep products in production for as long as possible. This, naturally, maximizes the investment value. Efficient management of failures and predictive maintenance contribute to continuity of the production line. Receiving customer requirements and aligning the production line accordingly is another important aspect of sustaining product longevity. The designer should consider the functionality of the products as well as keep those in the market for longer times.
• Robustness and consolidation: This is as important as product longevity. Having high-quality and robust products, offering resistance to water, dust, impact, vibration and a range of other harsh conditions will be a key success to competition. This issue still remains open to new ideas.
• Demand management: Since smart factories allow fast response to customer requirements and customized products will seem to be available in the market, this may necessitate an active, efficient and effective demand management system. It would be wise enough to use this capability for marketing and the revenue generation process. In this respect the demand management system should have very specific capabilities which is yet to be developed.

Note that these and some other challenges not listed here introduce complexity to the transformation process. It would be nice if structured step-wise approaches to manage the large-scale organizational transformation of people, processes and technologies were developed and implemented. The framework proposed at the end of this chapter intends to provide this.

1.2.2 Swarm Robotics

Swarm robotics is the study of how to design groups of distributed robots that operate without relying on any external infrastructure or on any form of centralized control. A collective behaviour (Garnier et al., 2005) emerges from the interactions between the robots and interactions of robots with the environment in which they are operating. Swarm intelligence and biological studies of insects, ants, bees and others are the main source of swarm behaviour. In swarm robotics, automatic design has been mostly performed using the evolutionary robotics approach (Nolfi and Floreano, 2000). Evolutionary robotics has been used to develop several collective behaviours including collective transport (Groß and Dorigo, 2008) and development of communication networks (Huaert et al., 2008). A swarm robot team is fault tolerant, scalable and flexible. The robots in a swarm environment are able to perform different activities concurrently. More importantly, swarm robotics promotes the development of systems that are able to cope well with the failure of one or more of their constituent robots. That is to say that the failure of an individual robots does not imply the failure of the whole swarm (Fault tolerance) as the swarm does not rely on any centralized control entity, leaders or any individual robot playing a predefined role.

Swarm robotics should also have a flexible structure where...

Table of contents

• Cover
• Half-Title
• Series
• Title
• Copyright
• Contents
• Preface
• Editors
• Contributors
• Chapter 1 Understanding Digital Transformation
• Chapter 2 Digitalization in Industry: IoT and Industry 4.0
• Chapter 3 Autonomous Robots and CoBots: Applications in Manufacturing
• Chapter 4 Augmented Reality and Virtual Reality: From the Industrial Field to Other Areas
• Chapter 5 Cloud Computing: Virtualization, Simulation and Cybersecurity – Cloud Manufacturing Issue
• Chapter 6 Industry 4.0 and Lean Supply Chain Management: Impact on Responsiveness
• Index