**Meet the editors**

Tamás Bányai received his Masters degree in 1993 and his PhD degree in 1999 from the University of Miskolc, where he is currently working as associate professor. He has more than 25 years of teaching and research experience in the design and control of materials handling systems and supply chain management, with special emphasis in heuristic optimization of large-scale systems. He has pub-

lished over 150 research papers, book chapters, and conference proceedings. He has been a member and manager of more than 50 national and international research and development projects. Away from academia, his other interests include playing the piano and taking photographs.

Fabio De Felice is a professor at the University of Cassino and Southern Lazio, Italy, where he received his PhD in mechanical engineering. His current research focuses on multi-criteria decision-making analysis (with emphasis on AHP and ANP), digital manufacturing, and innovation management. He is the founder of the AHP Academy, which promotes the diffusion of the culture

and methodologies of decision making. He is a member of the editorial boards of several international organizations and journals and he has authored/co-authored more than 100 articles in the areas of decision science and business management.

Antonella Petrillo is a professor at the Department of Engineering of the University of Naples "Parthenope", Italy. She received her PhD in mechanical engineering from the University of Cassino. Her research interests include sustainability, multi-criteria decision analysis, industrial plant, manufacturing, and safety. She serves as an international reviewer for over 50 international journals and

she is a member of the Scientific Committee for PhD Programs, International Conferences and Proposal Evaluations.

Contents

**Preface VII**

Tamás Bányai

**Optimization 27** Xue-Ming Yuan

Claudio Pastrone

Chapter 7 **Digital Twin Technology 95** Zongyan Wang

**A Retrospective Review 1**

**Factories in Industry 4.0 11**

**Manufacturing System 39**

Chapter 1 **Introductory Chapter: Industry 4.0 and Its Impact on Logistics -**

**Based on Shop Floor Simulation: A Step towards Smart**

Salvador Cobos Guzman, Maria Dolores Cima Cabal, Francisco

Giuseppe Pacelli, Enrico Ferrera, Rosaria Rossini, Ilaria Bosi and

Chapter 2 **Advantages of Learning Factories for Production Planning**

Montoya Andrés, Guarín Álvaro and Mora Julián

Chapter 4 **Cyber-Physical System Architecture for Minimizing the Possibility of Producing Bad Products in a**

Chapter 5 **Leveraging Internet-of-Things to Support Circular Economy Paradigm in Manufacturing Industry 53**

Machio Regidor and Lucia Alonso Virgos

Chapter 6 **Parallel Genetic Algorithms with GPU Computing 69** John Runwei Cheng and Mitsuo Gen

Chapter 3 **Impact of Industry 4.0 on Inventory Systems and**

## Contents

#### **Preface XI**

#### Chapter 8 **The Role of Spin-Off Companies in the Technology Transfer and IS Management Potential in Developing a Sharing Economy 115** Mária Pomffyová, Mária Rostašová and Vladimír Krajčík

Preface

gies and impacts:

factory.

proposed.

This book offers a selection of chapters in the field of the fourth industrial revolution, pro‐ moting new research results in the field, building on other publications in the literature. Au‐ thors from ten countries (China, Colombia, Czech Republic, Hungary, Italy, Japan, Singapore, Slovakia, Spain, and USA) have published worked examples and case studies from their research in the field. Implementing Industry 4.0 technologies and solutions is a strategic objective of manufacturing and service companies to foster competitiveness on the global market. The Industry 4.0 as an industrial ecosystem is a potential glue that can hold interconnected logistics systems and value making chains together. In a brief description, the book has eight topics, determined by the application domain and Industry 4.0 technolo‐

*Industry 4.0 and its impact on logistics:* The globalization of economy led to an increased trans‐ portation and material handling intensity that is perceivable in purchasing, production, dis‐ tribution, and recycling. Within the frame of this chapter, the main research directions of

*Advantages of Learning Factories for Production Planning based on shop floor simulation:* In an In‐ dustry 4.0 industrial ecosystem, production planning and control strategies will be per‐ formed by humans backed by computational tools for decision making. In this chapter, shop floor simulation is identified as a tool for planning and controlling production and the trends in the construction of the Learning Factories are shown. This work also proposes the realization of a digital model in EAFIT University Learning Factory towards digital learning

*Impact of Industry 4.0 on Inventory Systems and Optimization:* Industry 4.0 opens up new hori‐ zons for inventory optimization and pushes the boundaries, especially in the automotive in‐ dustry. Within the frame of this chapter, the possible changes to factories, products, customers, and businesses in the environment of Industry 4.0 are discussed. The new inte‐ grative research and development framework for inventory systems and optimization is

*Cyber-physical System Architecture for minimizing the possibility of producing bad products in a manufacturing system*: The implementation of cyber-physical systems in an Industry 4.0 envi‐ ronment can increase the level of productivity in a manufacturing system. This chapter pro‐ poses a possible architecture of a generic manufacturing system including agile production,

*Leveraging Internet-of-Things to support Circular Economy paradigm:* New models based on In‐ dustry 4.0 technologies support the Circular Economy paradigm, through the development

Industry 4.0 are shown focusing on logistic applications.

lean manufacturing, and statistical approaches.

## Preface

Chapter 8 **The Role of Spin-Off Companies in the Technology Transfer and IS Management Potential in Developing a**

Mária Pomffyová, Mária Rostašová and Vladimír Krajčík

**Sharing Economy 115**

**VI** Contents

This book offers a selection of chapters in the field of the fourth industrial revolution, pro‐ moting new research results in the field, building on other publications in the literature. Au‐ thors from ten countries (China, Colombia, Czech Republic, Hungary, Italy, Japan, Singapore, Slovakia, Spain, and USA) have published worked examples and case studies from their research in the field. Implementing Industry 4.0 technologies and solutions is a strategic objective of manufacturing and service companies to foster competitiveness on the global market. The Industry 4.0 as an industrial ecosystem is a potential glue that can hold interconnected logistics systems and value making chains together. In a brief description, the book has eight topics, determined by the application domain and Industry 4.0 technolo‐ gies and impacts:

*Industry 4.0 and its impact on logistics:* The globalization of economy led to an increased trans‐ portation and material handling intensity that is perceivable in purchasing, production, dis‐ tribution, and recycling. Within the frame of this chapter, the main research directions of Industry 4.0 are shown focusing on logistic applications.

*Advantages of Learning Factories for Production Planning based on shop floor simulation:* In an In‐ dustry 4.0 industrial ecosystem, production planning and control strategies will be per‐ formed by humans backed by computational tools for decision making. In this chapter, shop floor simulation is identified as a tool for planning and controlling production and the trends in the construction of the Learning Factories are shown. This work also proposes the realization of a digital model in EAFIT University Learning Factory towards digital learning factory.

*Impact of Industry 4.0 on Inventory Systems and Optimization:* Industry 4.0 opens up new hori‐ zons for inventory optimization and pushes the boundaries, especially in the automotive in‐ dustry. Within the frame of this chapter, the possible changes to factories, products, customers, and businesses in the environment of Industry 4.0 are discussed. The new inte‐ grative research and development framework for inventory systems and optimization is proposed.

*Cyber-physical System Architecture for minimizing the possibility of producing bad products in a manufacturing system*: The implementation of cyber-physical systems in an Industry 4.0 envi‐ ronment can increase the level of productivity in a manufacturing system. This chapter pro‐ poses a possible architecture of a generic manufacturing system including agile production, lean manufacturing, and statistical approaches.

*Leveraging Internet-of-Things to support Circular Economy paradigm:* New models based on In‐ dustry 4.0 technologies support the Circular Economy paradigm, through the development of a marketplace for connecting buyers and sellers of manufacturing services, raw materials, and products towards building global supply chains. This approach is explained within the frame of this chapter.

*Parallel Genetic Algorithms with GPU Computing*: Parallelizing heuristic and metaheuristic al‐ gorithm is a key factor of big data problems in the Industry 4.0 environment. In this chapter, the authors give a concise overview of selective works of Parallel GAs on GPU from the per‐ spective of GPU architecture. Many optimization problems of cyber-physical systems will benefit from the described parallel GAs accelerated by GPU computing.

*Digital Twin Technology*: Digital twin technology is the core technology of realizing Cyber-Physical Systems. This chapter introduces the generation and characteristics of digital twin technologies, the relationship between Digital Twin and Digital Thread, and the implemen‐ tation of the product digital twin model.

*The role of spin-off companies in the technology transfer and IS management potential in developing a sharing economy*: Based on best practice results, the chapter recommends practices and rec‐ ommendations that should eliminate barriers to successful spin-offs and start-ups as well as SMEs doing business in competitive practice. Based on the synthesis of research outputs, the recommendations for technology transfer and regulatory treatment of sharing economy are summarized.

The aim of this book is to help students at the level of BSc, MSc, and PhD as well as manag‐ ers and researchers, to understand and appreciate the concept, design, and implementation of Industry 4.0 solutions. Based on these considerations, the book may have a large impact in the scientific community.

The editors thank the research authors of the chapters for their scientific contribution. The chapters were edited and published following a rigorous selection process. Also, it is a pleasure thanking and acknowledging the help of many individuals from the editorial proc‐ ess that made this book possible.

> **Tamás Bányai** University of Miskolc Department of Materials Handling and Logistics Hungary

> > **Antonella Petrillo** University of Napoli "Parthenope" Department of Engineering Italy

**Chapter 1**

**Provisional chapter**

**Introductory Chapter: Industry 4.0 and Its Impact on**

**Introductory Chapter: Industry 4.0 and Its Impact on**

The increased complexity of customers' demands led to the globalisation of economy. This caused increased transportation and material handling intensity which is perceivable in all four functional parts of logistics: purchasing, production, distribution and inverse processes. The globalisation, the demographical changes, the urbanisation and the sustainability influence the expected performance of manufacturing and service processes. The Industry 4.0 and its technologies offer new solutions to fulfil customers' demands, while economical, ecologi-

Within the frame of this short introductory chapter, the main research directions of Industry 4.0 are shown focusing on logistic applications. The following questions are answered with a short literature review: Who is doing what? Who first did it or published it? What are

Within the frame of this retrospective literature review, the systematic literature review (SLR) methodology of Cronin et al. [1] was used including the following aspects: define research questions, select sources from Web of Knowledge, reduce the number of articles, identify the main research directions, define a methodology to analyse the chosen articles, discuss the

The relevant terms were defined. The first search using the topic 'Industry 4.0' keyword resulted to 1066 articles, but a wide number of the articles have no relation with logistics;

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.89387

**Logistics - A Retrospective Review**

**Logistics - A Retrospective Review**

Additional information is available at the end of the chapter

cal, social and engineering impacts are taken into consideration.

**2. Conceptual framework and review methodology**

main scientific results and identify the scientific gaps and bottlenecks.

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.89387

Tamás Bányai

**1. Introduction**

research gaps?

Tamás Bányai

**Fabio De Felice** University of Cassino and Southern Lazio Department of Civil and Mechanical Engineering Italy

#### **Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review**

DOI: 10.5772/intechopen.89387

Tamás Bányai Tamás Bányai

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.89387

**1. Introduction**

The increased complexity of customers' demands led to the globalisation of economy. This caused increased transportation and material handling intensity which is perceivable in all four functional parts of logistics: purchasing, production, distribution and inverse processes. The globalisation, the demographical changes, the urbanisation and the sustainability influence the expected performance of manufacturing and service processes. The Industry 4.0 and its technologies offer new solutions to fulfil customers' demands, while economical, ecological, social and engineering impacts are taken into consideration.

Within the frame of this short introductory chapter, the main research directions of Industry 4.0 are shown focusing on logistic applications. The following questions are answered with a short literature review: Who is doing what? Who first did it or published it? What are research gaps?

### **2. Conceptual framework and review methodology**

Within the frame of this retrospective literature review, the systematic literature review (SLR) methodology of Cronin et al. [1] was used including the following aspects: define research questions, select sources from Web of Knowledge, reduce the number of articles, identify the main research directions, define a methodology to analyse the chosen articles, discuss the main scientific results and identify the scientific gaps and bottlenecks.

The relevant terms were defined. The first search using the topic 'Industry 4.0' keyword resulted to 1066 articles, but a wide number of the articles have no relation with logistics;

therefore, the following keywords were used to search in the Web of Knowledge database: the topic 'Industry 4.0' and topic 'logistics'. Initially, 59 articles were identified. The search was conducted in July 2019; therefore, new articles may have been published since then.

#### **3. Descriptive analysis**

The articles can be classified depending on the research area. **Figure 1** shows the classification of these 59 articles considering 10 subject areas. This classification shows the majority of engineering and management sciences, while the operational research and computer sciences define the importance of computational methods related to the design of logistics and supply chain solutions in Industry 4.0 environment. The classification shows that sustainability, energy efficiency and environmental impact of logistic solutions of cyber-physical systems get more and more importance.

As **Figure 2** demonstrates, Industry 4.0 and its effect on logistics and supply chain solutions have been researched in the past 5 years. The first article in this field was published in 2015 in the field of cloud computing [2], and it was focusing on the importance of cloud-assisted solutions of the emerging Industry 4.0 theory focusing on logistics and sales. The number of published papers has been increased; it shows the importance of this research field, especially in automotive industry.

As **Figure 3** demonstrates, most of the articles were published in journals with sustainability and manufacturing topics. The distribution of journals shows that the research of logistics and supply chain solutions in cyber-physical systems and Industry 4.0 environment is a multidisciplinary topic.

In the following step, the 59 articles were reduced after reading them. Articles whose topic cannot address the effect of Industry 4.0 on logistics were excluded, and some

articles were added from different research platforms. After this reduction and addition, we got 18 articles. The next section describes the content analysis of these articles and defines a framework of available sources related to the impact of Industry 4.0 in the field

**Figure 3.** Distribution of Industry 4.0 and logistic-related articles in journals, based on search in the Web of Knowledge.

**Figure 2.** Classification of articles by year of publication based on search in the Web of Knowledge.

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review

http://dx.doi.org/10.5772/intechopen.89387

3

The fourth industrial revolution is also known as 'Industry 4.0', 'Smart Manufacturing' or 'Integrated Industry', but all of these names cover the integration potential of value making chains from design, through production, to distributions and inverse processes. This potential is especially focused in just-in-time and just-in-sequence processes of automotive industry [3]. Industry 4.0 has a great impact on logistics of production [4] and service processes [5]. The lean paradigm (or lean manufacturing) is linked to the development and operation of cyber-physical systems, because it is widely regarded as an efficient improvement tool in Industry 4.0 environment [6]. Another aspect of the importance of lean manufacturing in

of logistics.

**4. Content analysis**

**Figure 1.** Classification of articles considering subject areas based on search in Web of Knowledge database using the topics 'Industry 4.0' and 'logistics'.

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review http://dx.doi.org/10.5772/intechopen.89387 3

**Figure 2.** Classification of articles by year of publication based on search in the Web of Knowledge.

**Figure 3.** Distribution of Industry 4.0 and logistic-related articles in journals, based on search in the Web of Knowledge.

articles were added from different research platforms. After this reduction and addition, we got 18 articles. The next section describes the content analysis of these articles and defines a framework of available sources related to the impact of Industry 4.0 in the field of logistics.

#### **4. Content analysis**

therefore, the following keywords were used to search in the Web of Knowledge database: the topic 'Industry 4.0' and topic 'logistics'. Initially, 59 articles were identified. The search was

The articles can be classified depending on the research area. **Figure 1** shows the classification of these 59 articles considering 10 subject areas. This classification shows the majority of engineering and management sciences, while the operational research and computer sciences define the importance of computational methods related to the design of logistics and supply chain solutions in Industry 4.0 environment. The classification shows that sustainability, energy efficiency and environmental impact of logistic solutions of cyber-physical systems

As **Figure 2** demonstrates, Industry 4.0 and its effect on logistics and supply chain solutions have been researched in the past 5 years. The first article in this field was published in 2015 in the field of cloud computing [2], and it was focusing on the importance of cloud-assisted solutions of the emerging Industry 4.0 theory focusing on logistics and sales. The number of published papers has been increased; it shows the importance of this research field, especially

As **Figure 3** demonstrates, most of the articles were published in journals with sustainability and manufacturing topics. The distribution of journals shows that the research of logistics and supply chain solutions in cyber-physical systems and Industry 4.0 environment is a multidis-

In the following step, the 59 articles were reduced after reading them. Articles whose topic cannot address the effect of Industry 4.0 on logistics were excluded, and some

**Figure 1.** Classification of articles considering subject areas based on search in Web of Knowledge database using the

conducted in July 2019; therefore, new articles may have been published since then.

**3. Descriptive analysis**

2 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

get more and more importance.

in automotive industry.

topics 'Industry 4.0' and 'logistics'.

ciplinary topic.

The fourth industrial revolution is also known as 'Industry 4.0', 'Smart Manufacturing' or 'Integrated Industry', but all of these names cover the integration potential of value making chains from design, through production, to distributions and inverse processes. This potential is especially focused in just-in-time and just-in-sequence processes of automotive industry [3]. Industry 4.0 has a great impact on logistics of production [4] and service processes [5]. The lean paradigm (or lean manufacturing) is linked to the development and operation of cyber-physical systems, because it is widely regarded as an efficient improvement tool in Industry 4.0 environment [6]. Another aspect of the importance of lean manufacturing in Industry 4.0 is that both cyber-physical systems and lean manufacturing can be characterized with decentralized control [7]. The globalisation of the economy led to the increased importance of sustainability and the minimization of environmental impact; therefore Industry 4.0 should include not only purchasing, manufacturing and distribution processes but also the inverse operations of the closed-loop economy [8]. The realisation of cyber-physical systems requires the integration of networking, organisational development, structural frame conditions and sustainability which in fact has a great impact on the logistic solutions in manufacturing and services [9]. The first-mile and last-mile operations of a supply chain solution represent complex optimization problems, because routing, assignment and scheduling problems should be taken into consideration. The Industry 4.0 technologies make it possible to make real-time decisions in the first-mile and last-mile processes in order to design a costefficient sustainable collection or distribution process. These design problems are NP-hard; therefore heuristic and metaheuristic solutions are required to solve them [10, 11]. Another revolutionary design method is the online optimization. Online optimization can support to solve real-time challenges [12] in complex supply chain processes or in-plant material handling systems.

Logistics and value chain will be influenced by the new solutions of Industry 4.0 technologies. Researches based on Porter's value chain model show that an increased economic sustainability can be achieved with the application of IoT tools, Big Data technologies and cyber-physical systems [13].

Bressanelli et al. [14] identified eight functionalities of Industry 4.0 technologies (improving product design, attracting target customers, monitoring and tracking product activity, providing technical support, providing preventive and predictive maintenance, optimising the product usage, upgrading the product, enhancing renovation and end-of-life activities) and analysed their impact on closed-loop value drivers, like resource efficiency, lifespan and closing the loop. These eight functionalities of Industry 4.0 technologies are important not only for manufacturing but also for logistic operations and supply chain processes. Case studies validate the impact of Industry 4.0 technologies on supply chain solutions. These case studies are based on manufacturing processes of Logistics Knapp AG, Nova Chemicals, BMW, Stratasys and Bosch [15].

The logistic operations include not only the in-plant supply processes but also the material handling operations of warehousing. The increased complexity of manufacturing processes led to increased complexity of component portfolios which led to complex warehousing processes. In the case of real-time data and contextual information, these complex warehouse processes can be improved through Industry 4.0 technologies [16]. The success of applied Industry 4.0 technologies depends on the characteristics of manufacturing environment [17]. The manufacturing environment can be described with the level of automation, level of product repetitiveness or type of products.

**Cloud**

**Supply**

**Lean**

**Closedloop**

**Organisation**

**Heuristics**

**Online**

**Real-time**

**Internet**

**Optimization**

**Service**

**Manufacturing**

**Case**

**studies**

**optimization**

**decision**

**of**

**Things**

**computing**

Yue et al. (2015)

X

Cloud-assisted

industrial

cyber-physical

systems [2]

Hofmann and

X

X

X

Rusch (2017)

Industry 4.0 and

the current status

as well … [3]

Sanders et al.

X

(2016) Industry

4.0 implies lean

manufacturing

… [6]

Jabbour et al.

X

X

(2018) Industry

4.0 and the

circular economy

[8]

Prause and

X

X

X

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review

Atari (2017)

On sustainable

production

networks for

… [9]

Banyai (2018)

X

X

X

X

X

http://dx.doi.org/10.5772/intechopen.89387

5

Real-time

decision making

in first mile and

… [10]

**chain**

**manufacturing**

**economy**

Researches show that the most important Industry 4.0 technologies applied in the field of logistics are focusing on the support of product identification and increase of traceability of products and resources [18]. **Table 1** shows the research framework represented by the reviewed articles from objective, methodology and case studies point of view.

Industry 4.0 is that both cyber-physical systems and lean manufacturing can be characterized with decentralized control [7]. The globalisation of the economy led to the increased importance of sustainability and the minimization of environmental impact; therefore Industry 4.0 should include not only purchasing, manufacturing and distribution processes but also the inverse operations of the closed-loop economy [8]. The realisation of cyber-physical systems requires the integration of networking, organisational development, structural frame conditions and sustainability which in fact has a great impact on the logistic solutions in manufacturing and services [9]. The first-mile and last-mile operations of a supply chain solution represent complex optimization problems, because routing, assignment and scheduling problems should be taken into consideration. The Industry 4.0 technologies make it possible to make real-time decisions in the first-mile and last-mile processes in order to design a costefficient sustainable collection or distribution process. These design problems are NP-hard; therefore heuristic and metaheuristic solutions are required to solve them [10, 11]. Another revolutionary design method is the online optimization. Online optimization can support to solve real-time challenges [12] in complex supply chain processes or in-plant material

4 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

Logistics and value chain will be influenced by the new solutions of Industry 4.0 technologies. Researches based on Porter's value chain model show that an increased economic sustainability can be achieved with the application of IoT tools, Big Data technologies and cyber-physical

Bressanelli et al. [14] identified eight functionalities of Industry 4.0 technologies (improving product design, attracting target customers, monitoring and tracking product activity, providing technical support, providing preventive and predictive maintenance, optimising the product usage, upgrading the product, enhancing renovation and end-of-life activities) and analysed their impact on closed-loop value drivers, like resource efficiency, lifespan and closing the loop. These eight functionalities of Industry 4.0 technologies are important not only for manufacturing but also for logistic operations and supply chain processes. Case studies validate the impact of Industry 4.0 technologies on supply chain solutions. These case studies are based on manufacturing processes of Logistics Knapp AG, Nova Chemicals, BMW,

The logistic operations include not only the in-plant supply processes but also the material handling operations of warehousing. The increased complexity of manufacturing processes led to increased complexity of component portfolios which led to complex warehousing processes. In the case of real-time data and contextual information, these complex warehouse processes can be improved through Industry 4.0 technologies [16]. The success of applied Industry 4.0 technologies depends on the characteristics of manufacturing environment [17]. The manufacturing environment can be described with the level of automation, level of prod-

Researches show that the most important Industry 4.0 technologies applied in the field of logistics are focusing on the support of product identification and increase of traceability of products and resources [18]. **Table 1** shows the research framework represented by the

reviewed articles from objective, methodology and case studies point of view.

handling systems.

systems [13].

Stratasys and Bosch [15].

uct repetitiveness or type of products.

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review http://dx.doi.org/10.5772/intechopen.89387 5

**Cloud**

**Supply**

**Lean**

**Closedloop**

**Organisation**

**Heuristics**

**Online**

**Real-time**

**Internet**

**Optimization**

**Service**

**Manufacturing**

**Case**

**studies**

**optimization**

**decision**

**of**

**Things**

X X

X

X

X

**computing**

Strandhagen

et

al. (2017) The

fit of Industry

4.0 applications

… [17]

Trappey et al.

(2017) IoT patent

roadmap for

smart logistic

… [18]

Dunke et al.

X

X

X

X

X

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review

http://dx.doi.org/10.5772/intechopen.89387

7

(2018) Time traps

in supply chains

… [12]

Tamás et al.

X

X

(2017) New

challenges for

quality assurance

… [5]

Nagy et al.

X

(2018) Impact

of Industry 4.0

on production

logistics [4]

**Table 1.**

Research framework and main topics of logistic-related research in Industry 4.0.

**chain**

**manufacturing**

**economy**

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review http://dx.doi.org/10.5772/intechopen.89387 7

**Cloud**

**Supply**

**Lean**

**Closedloop**

**Organisation**

**Heuristics**

**Online**

**Real-time**

**Internet**

**Optimization**

**Service**

**Manufacturing**

**Case**

**studies**

**optimization**

**decision**

**of**

**Things**

X

X

X

6 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

**computing**

Nagy et al.

X

(2018) The role

and impact of

Industry 4.0 and

the … [13]

Buer et al. (2018)

X

The link between

Industry 4.0 and

lean … [7]

Bressanelli et al.

X

X

(2018) Exploring

how usagefocused business

… [14]

Banyai et al.

X

X

X

X

X

(2018) Smart

scheduling: an

integrated first

mile … [11]

Szozda (2017)

X

X

X

X

Industry 4.0 and

its impact on

the functioning

… [15]

Lee et al. (2018)

X

X

X

X

Design and

application of

Internet of … [16]

**chain**

**manufacturing**

**economy**

X **Table 1.** Research framework and main topics of logistic-related research in Industry 4.0.

#### **5. Consequences of literature review**

The above described short literature review shows that the application of Industry 4.0 technologies offers innovative solutions for both manufacturing and logistic processes. Cloud and fog computing supports the solution of Big Data problems. Heuristic, metaheuristic and online optimization make it possible to solve NP-hard problems of complex logistic processes and supply chains. The adoption of bar codes and RFID technologies focuses on identification and traceability of products, resources and processes. In logistic processes, the importance of real-time decisions gets more and more importance, because real-time decisions can increase the efficiency of resources through an improved flexibility. Why is it necessary to put so much effort into the application of Industry 4.0 technologies? However, the application of Industry 4.0 technologies in logistics leads to new manufacturing technologies and logistic solutions, but the design and operation of these new processes and systems required sophisticated models and methods to build complex, efficient, sustainable solutions.

[7] Buer SV, Strandhagen JO, Chan FTS. The link between Industry 4.0 and lean manufacturing: Mapping current research and establishing a research agenda. International

Introductory Chapter: Industry 4.0 and Its Impact on Logistics - A Retrospective Review

http://dx.doi.org/10.5772/intechopen.89387

9

[8] Jabbour ABLD, Jabbour CJC, Godinho M, Roubaud D. Industry 4.0 and the circular economy: A proposed research agenda and original roadmap for sustainable operations.

[9] Prause G, Atari S. On sustainable production networks for Industry 4.0. Entrepreneurship

[10] Banyai T. Real-time decision making in first mile and last mile logistics: How smart scheduling affects energy efficiency of hyperconnected supply chain solutions. Energies.

[11] Banyai T, Illes B, Banyai A. Smart scheduling: An integrated first mile and last mile sup-

[12] Dunke F, Heckmann I, Nickel S, Saldanha-da-Gama F.Time traps in supply chains: Is optimal still good enough? European Journal of Operational Research. 2018;**264**(3):813-829

[13] Nagy J, Olah J, Erdei E, Mate D, Popp J. The role and impact of Industry 4.0 and the Internet of things on the business strategy of the value chain—The case of Hungary.

[14] Bressanelli G, Adrodegari F, Perona M, Saccani N. Exploring how usage-focused business models enable circular economy through digital technologies. Sustainability.

[15] Szozda N. Industry 4.0 and its impact on the functioning of supply chains. Log Forum.

[16] Lee CKM, Lv YQ, Ng KKH, Ho W, Choy KL. Design and application of Internet of things-based warehouse management system for smart logistics. International Journal

[17] Strandhagen JW, Alfnes E, Strandhagen JO, Vallandingham LR. The fit of Industry 4.0 applications in manufacturing logistics: A multiple case study. Advances in

[18] Trappey AJC, Trappey CV, Fan CY, Hsu APT, Li XK, Lee IJY. IoT patent roadmap for smart logistic service provision in the context of Industry 4.0. Journal of the Chinese

Journal of Production Research. 2018;**56**(8):2924-2940

Annals of Operations Research. 2018;**270**(1-2):273-286

and Sustainability Issues. 2017;**4**(4):421-431

ply approach. Complexity. 2018:5180156

of Production Research. 2018;**56**(8):2753-2768

Manufacturing. 2017;**5**(4):344-358

Institute of Engineers. 2017;**40**(7):593-602

Sustainability. 2018;**10**(10):3491

2018;**11**(7):1833

2018;**10**(3):639

2017;**13**(4):401-414

#### **Author details**

Tamás Bányai

Address all correspondence to<EMAIL_ADDRESS>
University of Miskolc, Hungary

#### **References**

[7] Buer SV, Strandhagen JO, Chan FTS. The link between Industry 4.0 and lean manufacturing: Mapping current research and establishing a research agenda. International Journal of Production Research. 2018;**56**(8):2924-2940

**5. Consequences of literature review**

8 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

**Author details**

University of Miskolc, Hungary

Tamás Bányai

**References**

els and methods to build complex, efficient, sustainable solutions.

Address all correspondence to<EMAIL_ADDRESS>
approach. British Journal of Nursing. 2008;**17**(1):38-43

Series: Materials Science and Engineering. 2018;**448**:012013

of Industrial Engineering and Management. 2016;**9**(3):811-833

logistics. Computers in Industry. 2017;**89**:23-34

The above described short literature review shows that the application of Industry 4.0 technologies offers innovative solutions for both manufacturing and logistic processes. Cloud and fog computing supports the solution of Big Data problems. Heuristic, metaheuristic and online optimization make it possible to solve NP-hard problems of complex logistic processes and supply chains. The adoption of bar codes and RFID technologies focuses on identification and traceability of products, resources and processes. In logistic processes, the importance of real-time decisions gets more and more importance, because real-time decisions can increase the efficiency of resources through an improved flexibility. Why is it necessary to put so much effort into the application of Industry 4.0 technologies? However, the application of Industry 4.0 technologies in logistics leads to new manufacturing technologies and logistic solutions, but the design and operation of these new processes and systems required sophisticated mod-

[1] Cronin P, Ryan F, Coughlan M. Undertaking a literature review: A step-by-step

[2] Yue XJ, Cai H, Yan HH, Zou CF, Zhou KL. Cloud-assisted industrial cyber-physical sys-

[3] Hofmann E, Rusch M. Industry 4.0 and the current status as well as future prospects on

[4] Nagy G, Illés B, Bányai Á. Impact of Industry 4.0 on production logistics. IOP Conference

[5] Tamás P, Illés B, Dobos P, Skapinyecz R. New challenges for quality assurance of manufacturing processes in Industry 4.0. Solid State Phenomena. 2017;**261**:481-486

[6] Sanders A, Elangeswaran C, Wulfsberg J. Industry 4.0 implies lean manufacturing: Research activities in Industry 4.0. Function as enablers for lean manufacturing. Journal

tems: An insight. Microprocessors and Microsystems. 2015;**39**(8):1262-1270

**Chapter 2**

Provisional chapter

**Advantages of Learning Factories for Production**

DOI: 10.5772/intechopen.79930

Advantages of Learning Factories for Production

**Planning Based on Shop Floor Simulation: A Step**

In future industry, defined as Industry 4.0, production planning and control strategies will be executed by human beings backed by computational tools for decision making; One of these tools is shop floor simulation, and a natural scenario to learn about how to use it for productive processes design and control are the Learning Factories. In this chapter, shop floor simulation is identified as a tool for planning and controlling production, also a state of the art about its implementation is exposed in academic and industrial environments. In addition, the trends in the construction of the Learning Factories are shown, and some aspects about how they can be used for shop floor simulation. This work also proposes the realization of a digital model in EAFIT University Learning Factory as a first step towards

Keywords: digital learning factory, shop floor simulation, plant planning and controlling,

When a production system is being designed, builded or even working, there are several behaviors that cannot be described by exact mathematical equations (analytical models); for this reason, plant designers commonly use Virtual Design (VD), Shop Floor Simulation (SFS), heuristics or metaheuristic methods to predict possible critical situations once system is perturbed by use of supply chain, machinery, workstations cells, buffering and response time, equipment capabilities, etc. Some heuristic approaches are, for example, CRAFT algorithm

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction: Learning Factories as simulated environments and

Planning Based on Shop Floor Simulation: A Step

**towards Smart Factories in Industry 4.0**

towards Smart Factories in Industry 4.0

Montoya Andrés, Guarín Álvaro and Mora Julián

Montoya Andrés, Guarín Álvaro and Mora Julián

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.79930

Abstract

digital learning factory.

innovation transfer actors

discrete event simulation, Industry 4.0

#### **Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart Factories in Industry 4.0** Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart Factories in Industry 4.0

DOI: 10.5772/intechopen.79930

Montoya Andrés, Guarín Álvaro and Mora Julián Montoya Andrés, Guarín Álvaro and Mora Julián

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.79930

#### Abstract

In future industry, defined as Industry 4.0, production planning and control strategies will be executed by human beings backed by computational tools for decision making; One of these tools is shop floor simulation, and a natural scenario to learn about how to use it for productive processes design and control are the Learning Factories. In this chapter, shop floor simulation is identified as a tool for planning and controlling production, also a state of the art about its implementation is exposed in academic and industrial environments. In addition, the trends in the construction of the Learning Factories are shown, and some aspects about how they can be used for shop floor simulation. This work also proposes the realization of a digital model in EAFIT University Learning Factory as a first step towards digital learning factory.

Keywords: digital learning factory, shop floor simulation, plant planning and controlling, discrete event simulation, Industry 4.0

#### 1. Introduction: Learning Factories as simulated environments and innovation transfer actors

When a production system is being designed, builded or even working, there are several behaviors that cannot be described by exact mathematical equations (analytical models); for this reason, plant designers commonly use Virtual Design (VD), Shop Floor Simulation (SFS), heuristics or metaheuristic methods to predict possible critical situations once system is perturbed by use of supply chain, machinery, workstations cells, buffering and response time, equipment capabilities, etc. Some heuristic approaches are, for example, CRAFT algorithm

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

used by Prasad et al. to optimize plant layout cost by machinery replacement [1] and Black Hole Algorithm used by Veres et al. to find optimal supply chains structure [2], which provide possible solutions in complex systems. However, SFS also allows to evaluate several system responses and to determinate optimal working conditions [3].

Simulation refers to Discrete Event Simulation (DES) which describe a model performance. According to Shanon [4], simulation is to design a real system model and carry out some experiences to learn its behavior and to evaluate strategies to understand how it works; it means that, in order to learn about system behavior, it is necessary an environment characterized by a high degree of fidelity regarding real system. That characteristic is typical of actionoriented learning methodologies such as role play [5], virtual reality [6], simulation [7] and Learning Factories (LF). Additionally, simulation has recently become a best valued teaching strategy by students [8], and LF are effective simulated environment tools for skills development through experiential learning [9]; its safe environments allow to face real industrial challenges with minimal abstractions [10] to replay to real situations in different knowledge fields during training process.

On the other hand, companies call for latest technical and technological knowledge and LF offer high potential for innovation transfer and application-oriented innovation platform of product and production processes research, and subsequent transmission of these innovations [11].

This article aims to provide a general state of the art of SFS based production planning in LF, seeks to define medium term research needs and proposes a research project to develop the EAFIT University LF digital model. It will be the starting point in the way to convert this LF in a digital Smart Factory towards Industry 4.0.

> Figure 1 shows an example of SFS based production control architecture [15]. There is a task generation software used to develop simulation model which obtain production master programs (for example parts orders) and process plans from a database. The database keeps track part orders and quantity. Simulation control manufacturing system sends and receives messages and uses communication links to task executor; it performs execution functions at floor level and monitor part status in the system. Executor receives instructions (messages) from simulation and, according to system status, sends messages to physical equipment controllers. Once a task message has been sent, both executor and simulation wait for a "completed ok" message from controller. When executor receives "completed ok" message, it sends a similar message to simulation, and simulation knows that current task was completed. Simulator and execution modules communicate through a Task Initiation Queue (TIQ) and a Task Completion Queue (TCQ). Simulation uses TIQ to order executor perform specific tasks and receives completion messages through the TCQ. Son et al., [16] and Rao et al. [17] shows others SFS

Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart…

http://dx.doi.org/10.5772/intechopen.79930

13

Figure 1. SFS based production control architecture in Industry 4.0. ([16] p. 382, 2005).

Digital models used in SFS are useful not only to control but also to design production plants layout. These models are usually stochastic-nature statistical distributions and are used to

software use for controlling production process.

#### 2. Shop floor simulation based production planning and control in Industry 4.0

SFS is commonly used to get information about production systems and is based on discrete events information related to resources flow and availability in the plant. In this context, a resource is everything needed for production (supplies and raw materials) and plant are the facilities used to transform those resources into a finished product (or service). Production system also involves other company areas not involved directly in product transformation but without whose existence production process would be much more complex and inefficient, for example: supply warehouse, packing, shipping, maintenance, etc. Interactions between all these areas are interesting for decision making and SFS lets to evaluate production capacity, inventory control, logistics, etc.

Currently, SFS role in Industry 4.0 context includes "real time" production programming and control. Some authors have suggested that using production system latest information and making simulations to predict events in the future it is possible to improve system performance by dynamically adjusting programming policies and control strategies [12, 13], it places SFS as an essential element in one of the four Industry 4.0 components, namely "smart factories" [14].

Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart… http://dx.doi.org/10.5772/intechopen.79930 13

Figure 1. SFS based production control architecture in Industry 4.0. ([16] p. 382, 2005).

used by Prasad et al. to optimize plant layout cost by machinery replacement [1] and Black Hole Algorithm used by Veres et al. to find optimal supply chains structure [2], which provide possible solutions in complex systems. However, SFS also allows to evaluate several system

Simulation refers to Discrete Event Simulation (DES) which describe a model performance. According to Shanon [4], simulation is to design a real system model and carry out some experiences to learn its behavior and to evaluate strategies to understand how it works; it means that, in order to learn about system behavior, it is necessary an environment characterized by a high degree of fidelity regarding real system. That characteristic is typical of actionoriented learning methodologies such as role play [5], virtual reality [6], simulation [7] and Learning Factories (LF). Additionally, simulation has recently become a best valued teaching strategy by students [8], and LF are effective simulated environment tools for skills development through experiential learning [9]; its safe environments allow to face real industrial challenges with minimal abstractions [10] to replay to real situations in different knowledge

On the other hand, companies call for latest technical and technological knowledge and LF offer high potential for innovation transfer and application-oriented innovation platform of product and production processes research, and subsequent transmission of these innova-

This article aims to provide a general state of the art of SFS based production planning in LF, seeks to define medium term research needs and proposes a research project to develop the EAFIT University LF digital model. It will be the starting point in the way to convert this LF in

SFS is commonly used to get information about production systems and is based on discrete events information related to resources flow and availability in the plant. In this context, a resource is everything needed for production (supplies and raw materials) and plant are the facilities used to transform those resources into a finished product (or service). Production system also involves other company areas not involved directly in product transformation but without whose existence production process would be much more complex and inefficient, for example: supply warehouse, packing, shipping, maintenance, etc. Interactions between all these areas are interesting for decision making and SFS lets to evaluate production capacity, inventory control, logistics, etc. Currently, SFS role in Industry 4.0 context includes "real time" production programming and control. Some authors have suggested that using production system latest information and making simulations to predict events in the future it is possible to improve system performance by dynamically adjusting programming policies and control strategies [12, 13], it places SFS as an essential element in one of the four Industry 4.0 components, namely "smart factories" [14].

2. Shop floor simulation based production planning and control in

responses and to determinate optimal working conditions [3].

12 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

fields during training process.

a digital Smart Factory towards Industry 4.0.

tions [11].

Industry 4.0

Figure 1 shows an example of SFS based production control architecture [15]. There is a task generation software used to develop simulation model which obtain production master programs (for example parts orders) and process plans from a database. The database keeps track part orders and quantity. Simulation control manufacturing system sends and receives messages and uses communication links to task executor; it performs execution functions at floor level and monitor part status in the system. Executor receives instructions (messages) from simulation and, according to system status, sends messages to physical equipment controllers. Once a task message has been sent, both executor and simulation wait for a "completed ok" message from controller. When executor receives "completed ok" message, it sends a similar message to simulation, and simulation knows that current task was completed. Simulator and execution modules communicate through a Task Initiation Queue (TIQ) and a Task Completion Queue (TCQ). Simulation uses TIQ to order executor perform specific tasks and receives completion messages through the TCQ. Son et al., [16] and Rao et al. [17] shows others SFS software use for controlling production process.

Digital models used in SFS are useful not only to control but also to design production plants layout. These models are usually stochastic-nature statistical distributions and are used to perform design variables analysis, control strategies and system performance estimation using commercial computer programs, for example: Arena™, AutoMod™, ProModel™, Tecnomatix Plant Simulation™, FlexSim™, Witness™, AnyLogic™, Process Model™, etc. Recently, these tools have been classified according to their popularity and tools [18].

Other interesting subject in Industry 4.0 is flexible production plants, it means "systems that allow changing product without high impact on costs either by sequence change programming" [19]. This flexibility can be understood as production lines flexibility or production processes flexibility. In general terms, if a good resources availability is achieved it is possible to maximize production process; This can be achieved adding new technology or using plant redistribution, and here SFS takes special interest because it let more easy decision making and it is much easier to achieve in a flexible LF where it is possible to reconfigure plant distribution. This is one of the greatest advantage in a LF for future research.

#### 3. Digital Learning Factories implementation trends

A digital LF is an integrated IT environment where all real LF resources, processes and products are tracked on a digital model. Otherwise, virtual LF provide visual software tools trough Virtual Reality (VR) or Augmented Reality (AR) technology [20] to improve Digital LFs. Some LFs have implemented virtual games to evaluate students learning after training [21]. With these technologies, it is possible to carry out digital simulation, simulate tasks or evaluate alternative designs before production start [22].

This literature review shows there is an important trend towards facilities and production processes digitalization. Digital [25] and virtual LFs have been developed [26], even equipped

Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart…

http://dx.doi.org/10.5772/intechopen.79930

15

Figure 2 interrelation between infrastructure and physical interface in digital and virtual LFs.

First Colombian LF was inaugurated last September 27th, 2017. This LF is located at EAFIT University in Medellín City, and its main goal is to perform experiential learning practices with university and industry students to enhance their skills facing up new local production processes challenges, and help to transform conventional companies into smart companies [28]. This LF is equipped with SFS such as Tecnomatix Plant Simulation and FlexSim for production processes improvement. Thus, next step towards Industry 4.0 once this LF was inaugurated is

1. High time consumption in digital modeling: Large time amount must be invested in the

with SFS software for material flow, tail and bottleneck analysis [27].

4. How to use Learning Factories in plant layout simulation?

Plant layout simulation difficult due to the following aspects:

In this context, the SFS brings LF infrastructure to digital level.

SFS implementation in its own Digital Model.

Figure 2. Physical, digital and virtual LF. ([24] p. 816).

digital modeling.

LFs have become widespread in recent years, particularly in Europe [23], and have adopted many installation ways varying in size, scope, function and sophistication. However, all of they have main objective to improve students and industrial users learning experience. Abele et al. [24] present a right sounding classifying LFs worldwide according to:

Learning Factories for production process enhancement: They deal with lean methods and principles, such as just-in-time, line balancing, problem solving or work optimization.

Learning Factories for reconfiguration, production and plant layout design: They deal with reconfigurability related to plant layout design.

Learning Factories for energy and resources use efficiency: They deal with the relationship between energy consumption, resources and production.

Learning Factories for Industry 4.0: They focus on production digitization and Internet of Things (IoT) researching and technology transfer.

Learning Factory applied concept: They deal with knowledge triangle vertices integration: education, research and innovation.

Learning Factories for other purposes: For logistic optimization. For management and organization, automation technologies and sustainability.

Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart… http://dx.doi.org/10.5772/intechopen.79930 15

Figure 2. Physical, digital and virtual LF. ([24] p. 816).

perform design variables analysis, control strategies and system performance estimation using commercial computer programs, for example: Arena™, AutoMod™, ProModel™, Tecnomatix Plant Simulation™, FlexSim™, Witness™, AnyLogic™, Process Model™, etc. Recently, these

Other interesting subject in Industry 4.0 is flexible production plants, it means "systems that allow changing product without high impact on costs either by sequence change programming" [19]. This flexibility can be understood as production lines flexibility or production processes flexibility. In general terms, if a good resources availability is achieved it is possible to maximize production process; This can be achieved adding new technology or using plant redistribution, and here SFS takes special interest because it let more easy decision making and it is much easier to achieve in a flexible LF where it is possible to reconfigure plant distribution.

A digital LF is an integrated IT environment where all real LF resources, processes and products are tracked on a digital model. Otherwise, virtual LF provide visual software tools trough Virtual Reality (VR) or Augmented Reality (AR) technology [20] to improve Digital LFs. Some LFs have implemented virtual games to evaluate students learning after training [21]. With these technologies, it is possible to carry out digital simulation, simulate tasks or

LFs have become widespread in recent years, particularly in Europe [23], and have adopted many installation ways varying in size, scope, function and sophistication. However, all of they have main objective to improve students and industrial users learning experience. Abele

Learning Factories for production process enhancement: They deal with lean methods and princi-

Learning Factories for reconfiguration, production and plant layout design: They deal with recon-

Learning Factories for energy and resources use efficiency: They deal with the relationship between

Learning Factories for Industry 4.0: They focus on production digitization and Internet of Things

Learning Factory applied concept: They deal with knowledge triangle vertices integration: edu-

Learning Factories for other purposes: For logistic optimization. For management and organiza-

et al. [24] present a right sounding classifying LFs worldwide according to:

ples, such as just-in-time, line balancing, problem solving or work optimization.

tools have been classified according to their popularity and tools [18].

14 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

This is one of the greatest advantage in a LF for future research.

3. Digital Learning Factories implementation trends

evaluate alternative designs before production start [22].

figurability related to plant layout design.

(IoT) researching and technology transfer.

cation, research and innovation.

energy consumption, resources and production.

tion, automation technologies and sustainability.

This literature review shows there is an important trend towards facilities and production processes digitalization. Digital [25] and virtual LFs have been developed [26], even equipped with SFS software for material flow, tail and bottleneck analysis [27].

Figure 2 interrelation between infrastructure and physical interface in digital and virtual LFs. In this context, the SFS brings LF infrastructure to digital level.

First Colombian LF was inaugurated last September 27th, 2017. This LF is located at EAFIT University in Medellín City, and its main goal is to perform experiential learning practices with university and industry students to enhance their skills facing up new local production processes challenges, and help to transform conventional companies into smart companies [28]. This LF is equipped with SFS such as Tecnomatix Plant Simulation and FlexSim for production processes improvement. Thus, next step towards Industry 4.0 once this LF was inaugurated is SFS implementation in its own Digital Model.

#### 4. How to use Learning Factories in plant layout simulation?

Plant layout simulation difficult due to the following aspects:

1. High time consumption in digital modeling: Large time amount must be invested in the digital modeling.

2. Digital modeling validation: Sometimes it is necessary to build expensive pilot plants or to carry out experiments in a section or all production line causing delays or stoppages. This is due to flexibility lack in the real plant to change its distribution.

Phase 1: Understanding the problem. Understanding involved variables in system behavior is essential to propose an adjusted model regarding real system. It is important to understand variable appearing and disappearing, impact on production process and variability, even when production strategies are changed. This let to define clear objectives. Analyst must consider possible interest variables like: production time, assembly time, product quantity per time unit (capacity and efficiency), inputs and raw materials consumed per time unit, buffering time, etc. in this phase, a LF is a "Test Bank" to analyze how those interesting variables impacts plant layout. Phase 2: Digital model building. Digital model main purpose is to represent adequately variables behavior. Sometimes it is necessary to make assumptions to simplify or complicate it, so there must have an adequate balance between easy use and high accuracy. Model must be detailed enough to capture as much information as possible according to study objectives. It is important to define detail level and construction method. LFs have limited size, and they allow build not very robust digital models with a high detail level and its construction does not demand excessive time. This aspect is an attractive alternative for future research because currently have low publications. Phase 3: Data Collection: Information quality, and not quantity, allows more accurate analysis. LFs flexibility let to analyst adapt plant layout to data acquisition needs, considering product assembly sequence. In other words, relationship between production model and its critical variables differ from one type of product to another, however, the LFs can be adapted to

Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart…

http://dx.doi.org/10.5772/intechopen.79930

17

Phase 4: Model Validation. Validate a model is to obtain high degree of confidence to ensure correct predictions about process being simulated. There is no way to know which is most correct process to validate a model, however, experts consider relevant some guidelines for

• Review simulation results to verify if they seem coherent. Here, again, LFs can be used. Given their resources redistribution availability, they are adequate to validate different simulated plant distribution models, becoming an interesting place for Engineers, Technicians and Managers, to evaluate decisions making impacts on productive practices. Phase 5: Experimentation: Main experiments objective is to check whether the model complies with the assumptions and simplifications considered for validation. A good Design of Experiments (DOE) is crucial at this stage. It consists in determining tests and methodologies should be performed to obtain data and provide objective statistically evidence to answer the questions raised in phase 1 and clarify those process uncertain aspects that are being simulation object [32]. Once again, the LFs resources redistribution availability can be used to execute

Phase 6: Results Analysis. Finally, simulation results must lead to an appropriate decision making, either analyst consider that model is reliable and can use it to estimate the real one productive process variations or to discard it. Last case implies a new approach, a new analysis, and

• Carry out sensitivity analyzes to determine most important factors in the model.

several experimental designs, expanding data collection opportunities portfolio.

certain types of products for data collection.

develop of a valid and credible model [31]:

therefore anew simulation and a new digital model.

• Use quantitative techniques to validate model components.

3. Planning and experimenting: Experiments sometimes requires redesign experiment itself to input or output not considered variables, which in many cases involves rethinking production strategies.

Thus, a LF is a right scenario to learn how to carry out plant layout planning based on SFS because it solves mentioned drawbacks: test model, validation and experimentation. This subject has been studied in digital LFs as noted in Section 2, however, although several digital LFs are equipped with SFS software, it is not common to find publications showing SFS use in LF layout planning.

So far, there is a worldwide trend to immerse plant layout designers on real, digital or virtual simulated environments to help them decision making. However, even for experienced staff, simulation can be a very long process, and transmitting that knowledge to undergraduate, postgraduate students or even business people usually takes a long time, mainly due to people focus more on simulation model construction than system behavior understanding.

In an LF, plant layout simulation can be carried out following methods described by Banks [29] and Tako [30]; their contributions, contextualized to plant layout are summarized in Figure 3. They suggest contemplating six phases to reproduce discrete events during productive system operation; therefore, a student, instructor or professional (henceforth called analyst) should focus his attention on:

Figure 3. Methodology proposed by Banks and Tako for simulation adapted to SFS. ([45] p. 3528).

Phase 1: Understanding the problem. Understanding involved variables in system behavior is essential to propose an adjusted model regarding real system. It is important to understand variable appearing and disappearing, impact on production process and variability, even when production strategies are changed. This let to define clear objectives. Analyst must consider possible interest variables like: production time, assembly time, product quantity per time unit (capacity and efficiency), inputs and raw materials consumed per time unit, buffering time, etc. in this phase, a LF is a "Test Bank" to analyze how those interesting variables impacts plant layout.

Phase 2: Digital model building. Digital model main purpose is to represent adequately variables behavior. Sometimes it is necessary to make assumptions to simplify or complicate it, so there must have an adequate balance between easy use and high accuracy. Model must be detailed enough to capture as much information as possible according to study objectives. It is important to define detail level and construction method. LFs have limited size, and they allow build not very robust digital models with a high detail level and its construction does not demand excessive time. This aspect is an attractive alternative for future research because currently have low publications.

Phase 3: Data Collection: Information quality, and not quantity, allows more accurate analysis. LFs flexibility let to analyst adapt plant layout to data acquisition needs, considering product assembly sequence. In other words, relationship between production model and its critical variables differ from one type of product to another, however, the LFs can be adapted to certain types of products for data collection.

Phase 4: Model Validation. Validate a model is to obtain high degree of confidence to ensure correct predictions about process being simulated. There is no way to know which is most correct process to validate a model, however, experts consider relevant some guidelines for develop of a valid and credible model [31]:

• Use quantitative techniques to validate model components.

2. Digital modeling validation: Sometimes it is necessary to build expensive pilot plants or to carry out experiments in a section or all production line causing delays or stoppages. This

3. Planning and experimenting: Experiments sometimes requires redesign experiment itself to input or output not considered variables, which in many cases involves rethinking

Thus, a LF is a right scenario to learn how to carry out plant layout planning based on SFS because it solves mentioned drawbacks: test model, validation and experimentation. This subject has been studied in digital LFs as noted in Section 2, however, although several digital LFs are equipped with SFS software, it is not common to find publications showing SFS use in

So far, there is a worldwide trend to immerse plant layout designers on real, digital or virtual simulated environments to help them decision making. However, even for experienced staff, simulation can be a very long process, and transmitting that knowledge to undergraduate, postgraduate students or even business people usually takes a long time, mainly due to people

In an LF, plant layout simulation can be carried out following methods described by Banks [29] and Tako [30]; their contributions, contextualized to plant layout are summarized in Figure 3. They suggest contemplating six phases to reproduce discrete events during productive system operation; therefore, a student, instructor or professional (henceforth called analyst) should

focus more on simulation model construction than system behavior understanding.

Figure 3. Methodology proposed by Banks and Tako for simulation adapted to SFS. ([45] p. 3528).

is due to flexibility lack in the real plant to change its distribution.

production strategies.

16 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

LF layout planning.

focus his attention on:

Phase 5: Experimentation: Main experiments objective is to check whether the model complies with the assumptions and simplifications considered for validation. A good Design of Experiments (DOE) is crucial at this stage. It consists in determining tests and methodologies should be performed to obtain data and provide objective statistically evidence to answer the questions raised in phase 1 and clarify those process uncertain aspects that are being simulation object [32]. Once again, the LFs resources redistribution availability can be used to execute several experimental designs, expanding data collection opportunities portfolio.

Phase 6: Results Analysis. Finally, simulation results must lead to an appropriate decision making, either analyst consider that model is reliable and can use it to estimate the real one productive process variations or to discard it. Last case implies a new approach, a new analysis, and therefore anew simulation and a new digital model.

#### 5. Shop floor simulation in industrial and academics scenarios

Layout planning can be categorized by static or dynamic environments. In the static layout approaches, material flow between machines is constant and an optimum layout is designed for a single time. On the contrary, if layout is evaluated and modified occasionally with respect to changes, research is categorized as dynamic. There are several plant simulation and discrete event simulation tools researching published, some of which have proposed design methodologies to structure, control and improve manufacturing processes [33, 34]. Below are some of them, both in industrial plants and training environments. The review of these cases cannot be considered complete but serves as a basis to demonstrate the capabilities of SFS in different scenarios.

i,j,k,l = Index of machines/locations.

Djl = Distance between machine i to l.

Atijl = Cost to shift machine i from location j to l in period t.

X N

Xtij ¼ 1, ∀i ¼ 1, 2,…N, ∀t ¼ 1, 2, …T (3)

http://dx.doi.org/10.5772/intechopen.79930

19

Xtij ¼ 1, ∀j ¼ 1, 2, …N, ∀t ¼ 1, 2, …T (4)

Ytijl ¼ Xð Þ <sup>t</sup>�<sup>1</sup> ij∗Xtil∀i, j, l ¼ 1, 2, …:N, ∀t ¼ 2, …T (5)

Constraint Eq. (4) ensures that each machine should be in one position and constraint Eq. (5)

Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart…

Function Eq. (2) was evaluated through simulation models where a possible finite set of layout scenarios were reached using Design of Experiments (DOE). In this case, a case study with five machines in a vehicle part production system with a planning horizon of two periods was considered. This allowed researchers to find by regression a Z2 optimal function (a minimal number of possible transporters accident). This work shows how numerical simulation, DOE and regression can be applied to evaluate a manufacturing system and recommends consider-

So, it is important to study if LFs can reach this situation, like the one described above, in which material flow between machines is constant (static design) or modified occasionally with respect to changes (dynamic design). Again, flexibility of LFs can be useful for this

On the other hand, there are several investigations using only SFS for plan layout planning to arrange and re arrange plant distribution. Filip et al. [37], for example, used Witness® software to optimize production systems in a printed circuit board (PCB) assembly plant. After completing initial simulation process and a series of subsequent simulations, researchers were able to identify equipment with highest workload, and part quantity produced. They also determined most economical method to increase production, which was to include new intermediate buffers. After including the new buffers, overloaded machine workload was reduced by 16.7%, resulting an increase of 1804 finished parts. This is an example about how understanding problem and setting clear objectives can lead to very successful results using SFS, an

Siderska [38] used Tecnomatix Plant Simulation® to simulate plant distribution and logistics in wire steel production process. Because of simulation, process statistics showed that only 10% of cutting machines capacity was being used due to a bottleneck generated by tip forming machine. An improvement was suggested simulating an additional tip forming machine in

i¼1

X N

i¼1

interesting aspect to be considering in LFs implementation.

ensures that in each position only one machine should be allocated.

ing dynamic layout problem where the flow between machines is stochastic.

Ftik = Total flow between machine i and k in period t.

t = Index for time periods.

purpose.

#### 5.1. Shop floor simulations in industrial production plants

Simulation based plant design and optimization techniques at industrial level have been implemented and developed several years ago. However, as mentioned in Section 1, there are many circumstances where uncertainty, interactions, objective functions or system constraints cannot be described using mathematical terms. In these cases, both, heuristic approach and SFS are often used to minimize some goals variables. For large dynamic plant layout problems, for example, some authors have suggested genetic algorithms-based heuristics solution methods [35].

Recently Pourhassan et al. [36] have presented mathematical models for relevant costs optimization during arrangement and re arrangement manufacturing facilities using genetic algorithms. A manufacturing system consisting of m machines processing n different products was considered. Products require m machines processing subsets causing material flow between they and eventually causing interference during material travel. Transporters are responsible to move materials between machines and there is plan divided into a several periods. Rate demand for each period is predicted and so material flow matrix is known in advance. Due to layout performance regarding to workflow is determined by number of possible transporters interference, researchers focused in to minimize material handling cost (MHC) and workflow interference as two inconsistent objective function:

$$\text{Min } Z\_1 = \sum\_{t=2}^{T} \sum\_{i=1}^{N} \sum\_{j=1}^{N} \sum\_{l=1}^{N} A\_{ijl} \* Y\_{i\bar{i}l} + \sum\_{t=1}^{T} \sum\_{i=1}^{N} \sum\_{j=1}^{N} \sum\_{k=1}^{N} \sum\_{l=1}^{N} F\_{\bar{t}k} \* D\_{\bar{j}l} \* C\_{i\bar{k}} \* X\_{i\bar{i}\bar{j}} \* X\_{i\bar{l}l} \tag{1}$$

Min Z<sup>2</sup> ¼ Number of possible transporters accident (2)

This model is subjected to decision variables:

Xtij <sup>¼</sup> <sup>1</sup>, if machine <sup>i</sup> is allocated to location <sup>j</sup> in period <sup>t</sup> <sup>0</sup>, otherwise �

and:

N = Number of machines/locations.

T = Number of periods in planning.

i,j,k,l = Index of machines/locations.

t = Index for time periods.

5. Shop floor simulation in industrial and academics scenarios

5.1. Shop floor simulations in industrial production plants

18 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

interference as two inconsistent objective function:

X N

X N

X N

Atijl∗Ytijl <sup>þ</sup><sup>X</sup>

T

X N

X N

X N

X N

Ftik∗Djl∗Cik∗Xtij∗Xtkl (1)

l¼1

k¼1

Min Z<sup>2</sup> ¼ Number of possible transporters accident (2)

j¼1

i¼1

t¼1

Xtij <sup>¼</sup> <sup>1</sup>, if machine <sup>i</sup> is allocated to location <sup>j</sup> in period <sup>t</sup>

l¼1

0, otherwise

j¼1

i¼1

T

t¼2

This model is subjected to decision variables:

�

Min Z<sup>1</sup> <sup>¼</sup> <sup>X</sup>

N = Number of machines/locations. T = Number of periods in planning.

and:

Layout planning can be categorized by static or dynamic environments. In the static layout approaches, material flow between machines is constant and an optimum layout is designed for a single time. On the contrary, if layout is evaluated and modified occasionally with respect to changes, research is categorized as dynamic. There are several plant simulation and discrete event simulation tools researching published, some of which have proposed design methodologies to structure, control and improve manufacturing processes [33, 34]. Below are some of them, both in industrial plants and training environments. The review of these cases cannot be considered complete but serves as a basis to demonstrate the capabilities of SFS in different scenarios.

Simulation based plant design and optimization techniques at industrial level have been implemented and developed several years ago. However, as mentioned in Section 1, there are many circumstances where uncertainty, interactions, objective functions or system constraints cannot be described using mathematical terms. In these cases, both, heuristic approach and SFS are often used to minimize some goals variables. For large dynamic plant layout problems, for example,

Recently Pourhassan et al. [36] have presented mathematical models for relevant costs optimization during arrangement and re arrangement manufacturing facilities using genetic algorithms. A manufacturing system consisting of m machines processing n different products was considered. Products require m machines processing subsets causing material flow between they and eventually causing interference during material travel. Transporters are responsible to move materials between machines and there is plan divided into a several periods. Rate demand for each period is predicted and so material flow matrix is known in advance. Due to layout performance regarding to workflow is determined by number of possible transporters interference, researchers focused in to minimize material handling cost (MHC) and workflow

some authors have suggested genetic algorithms-based heuristics solution methods [35].

Atijl = Cost to shift machine i from location j to l in period t.

Ftik = Total flow between machine i and k in period t.

Djl = Distance between machine i to l.

$$\sum\_{\mathbf{i}=1}^{N} \mathbf{X}\_{\mathbf{i}\mathbf{j}} = 1, \forall \mathbf{i} = 1, \mathbf{2}, \dots \\ \text{N}, \forall \mathbf{t} = 1, \mathbf{2}, \dots \\ \text{T} \tag{3}$$

$$\sum\_{\mathbf{i}=1}^{N} \mathbf{X}\_{\mathbf{i}\mathbf{j}} = 1, \forall \mathbf{j} = 1, 2, \dots \\ \mathbf{N}, \forall \mathbf{t} = 1, 2, \dots \\ \mathbf{T} \tag{4}$$

$$\mathbf{Y}\_{\text{tij}} = \mathbf{X}\_{(\mathbf{t}-1)\mathbf{i}\mathbf{j}} \* \mathbf{X}\_{\text{til}} \forall \mathbf{i}, \mathbf{j} \text{ 1} = \mathbf{1}, \mathbf{2}, \dots \text{N}, \forall \mathbf{t} = \mathbf{2}, \dots \text{T} \tag{5}$$

Constraint Eq. (4) ensures that each machine should be in one position and constraint Eq. (5) ensures that in each position only one machine should be allocated.

Function Eq. (2) was evaluated through simulation models where a possible finite set of layout scenarios were reached using Design of Experiments (DOE). In this case, a case study with five machines in a vehicle part production system with a planning horizon of two periods was considered. This allowed researchers to find by regression a Z2 optimal function (a minimal number of possible transporters accident). This work shows how numerical simulation, DOE and regression can be applied to evaluate a manufacturing system and recommends considering dynamic layout problem where the flow between machines is stochastic.

So, it is important to study if LFs can reach this situation, like the one described above, in which material flow between machines is constant (static design) or modified occasionally with respect to changes (dynamic design). Again, flexibility of LFs can be useful for this purpose.

On the other hand, there are several investigations using only SFS for plan layout planning to arrange and re arrange plant distribution. Filip et al. [37], for example, used Witness® software to optimize production systems in a printed circuit board (PCB) assembly plant. After completing initial simulation process and a series of subsequent simulations, researchers were able to identify equipment with highest workload, and part quantity produced. They also determined most economical method to increase production, which was to include new intermediate buffers. After including the new buffers, overloaded machine workload was reduced by 16.7%, resulting an increase of 1804 finished parts. This is an example about how understanding problem and setting clear objectives can lead to very successful results using SFS, an interesting aspect to be considering in LFs implementation.

Siderska [38] used Tecnomatix Plant Simulation® to simulate plant distribution and logistics in wire steel production process. Because of simulation, process statistics showed that only 10% of cutting machines capacity was being used due to a bottleneck generated by tip forming machine. An improvement was suggested simulating an additional tip forming machine in parallel with the initial one, increasing production. However, no subsequent work to was published by researchers to confirming if proposed change was implemented in the real plant, so simulated model could not be validated.

Due to this training, there is more people using SFS to test new product variants and production flows in their companies and these people are agree about this training methodologies should be facilitated by software tools that can support fast modeling and advanced experiments in a practical way. This experience is an interesting alternative to be replicated in LFs

Advantages of Learning Factories for Production Planning Based on Shop Floor Simulation: A Step towards Smart…

http://dx.doi.org/10.5772/intechopen.79930

21

Several authors have illustrated how SFS based production planning and control can be raised to business-level activities [46], therefore, decision making skills acquisition at all company levels becomes more important. Thus, EAFIT University Production Technologies Research Group (GITP) has started a LF implementation to become a focused digital LF towards Industry 4.0 development. In this LF, SFS will supports decision making in production planning and control; It intends become an innovation transmitter agent to students, technicians, engineers and managers. In this way, GITP proposes to establish a current state in SFS applied in LF to set medium term clear objectives, and as have been seen, information studied allows

1. SFS is an extensive research subject, and with enough technological maturity level to evaluate production systems behavior with digital validations. However, lack published

2. There are not validated LFs digital models linking cause/effect relationship between pro-

3. Advantages mentioned in present work let to LFs be a suitable scenario for research in SFS towards Industry 4.0 digital plant. Given their size, flexibility and resources redistribution availability, they allow digital models construction and experiments running, and can be used for SFS implementation as a decision-making tool for production control and

GITP has initiated experiential learning methodologies implementation in EAFIT University LF [47], and according to Guarín and Baena [48] "experiential learning is most used pedagogical development mechanism (…). However, not enough research has been done to systematically address experiential pedagogical structure in LF". Thus, and due to some authors have classified simulation as an experiential learning method [49], there is also a high interest in evaluating SFS role in teaching/learning process. This subject will be addressed in future

GITP considers EAFIT university LF digital model construction is needed. This digital model must be supported with good DOE to validate production system behavior, either by stochastic methods as described in Section 5.1 or SFS software. Initially, it is proposed to define a product and implement its assembly in LF production line. A data collection at process initial state will let define critical variables which have more impact in time, cost and production quality. Later, using DOE, alternatives for plant redistribution will be proposed to optimize

works using SFS in LFs comprises a knowledge gap needed to be addressed.

and is an accolade to SFS use for plant layout design.

6. Conclusions and further research

duction process and its critical variables.

research once LF digital model have been constructed.

concluding:

planning.

Hnát et al. [39] implemented Tecnomatix Factory CAD/Factory Flow in Zilina Intelligent Manufacturing System (ZIMS) laboratory, at Central European Technologies Institute (CEIT) for logistics process design and control. They described how these tools can be used for logistics systems conceptual design, control and monitoring. These researchers emphasize that using these technologies, companies can avoid failures due to inadequate design of material flows using simulation to verify flow before implementation.

Kikolski [40] applied simulation models to study different production scenarios. With Tecnomatix Plant Simulation, author simulated a real production plant using known process technological data and related material flow data to production. Their experiments analyzed how batch size influences system efficiency. The first simulated scenario consisted of the current conditions of the plant, whose lots comprise series of 30 items and each component in the system. For Kikolski, simulation model is an excellent tool to verify processes performance and give a clear visualization about assumptions. Analyzes require availability of one initial already system to design and optimize their virtual models. Therefore, it is observed that has been proposed start from initial distribution by other authors in other environments than the LFs one. Kikolski insists about continuing work on modeling and simulation methods, since "computer simulation can become a useful and reliable tool to design and study manufacturing processes and can provide a basis for other studies aimed at using digital models within of production engineering".

Kliment et al. [41, 42] have worked with Tecnomatix Plant Simulation to simulate logistics processes and production process on several production plants. Again, these works have focused on problems detection before physical plant building, and no evidence was found indicating proposed changes implementation during the simulation stage, so actual plant validation does could not be confirmed.

In Colombia, production plants simulation studies have been carried out in specific sectors such as mining [43] and biofuels [44], but do not in manufacturing plants, do not reach an appreciable reproducibility and scalability level and not involve entire value chain since raw materials supply to distribution chain. In conclusion, although there is an international trend, in Colombia there are no published works in which production plants simulation has been validated along value chain, or their knowledge has been transferred to industrial sector by academic institutions and much less by LFs.

#### 5.2. Shop floor simulations in educational environments

About educational environments, an experience to highlight is in Skövde University [45]. This institution has been able to demonstrate impact and importance of plants layout simulation on decision making. Its practical courses have competences focused on undergraduate students, engineers, technicians and managers, and contemplates strategies based on six simulation phases presented in Section 4.

Due to this training, there is more people using SFS to test new product variants and production flows in their companies and these people are agree about this training methodologies should be facilitated by software tools that can support fast modeling and advanced experiments in a practical way. This experience is an interesting alternative to be replicated in LFs and is an accolade to SFS use for plant layout design.

#### 6. Conclusions and further research

parallel with the initial one, increasing production. However, no subsequent work to was published by researchers to confirming if proposed change was implemented in the real plant,

Hnát et al. [39] implemented Tecnomatix Factory CAD/Factory Flow in Zilina Intelligent Manufacturing System (ZIMS) laboratory, at Central European Technologies Institute (CEIT) for logistics process design and control. They described how these tools can be used for logistics systems conceptual design, control and monitoring. These researchers emphasize that using these technologies, companies can avoid failures due to inadequate design of material

Kikolski [40] applied simulation models to study different production scenarios. With Tecnomatix Plant Simulation, author simulated a real production plant using known process technological data and related material flow data to production. Their experiments analyzed how batch size influences system efficiency. The first simulated scenario consisted of the current conditions of the plant, whose lots comprise series of 30 items and each component in the system. For Kikolski, simulation model is an excellent tool to verify processes performance and give a clear visualization about assumptions. Analyzes require availability of one initial already system to design and optimize their virtual models. Therefore, it is observed that has been proposed start from initial distribution by other authors in other environments than the LFs one. Kikolski insists about continuing work on modeling and simulation methods, since "computer simulation can become a useful and reliable tool to design and study manufacturing processes and can provide a basis for other studies aimed at using digital models within of

Kliment et al. [41, 42] have worked with Tecnomatix Plant Simulation to simulate logistics processes and production process on several production plants. Again, these works have focused on problems detection before physical plant building, and no evidence was found indicating proposed changes implementation during the simulation stage, so actual plant

In Colombia, production plants simulation studies have been carried out in specific sectors such as mining [43] and biofuels [44], but do not in manufacturing plants, do not reach an appreciable reproducibility and scalability level and not involve entire value chain since raw materials supply to distribution chain. In conclusion, although there is an international trend, in Colombia there are no published works in which production plants simulation has been validated along value chain, or their knowledge has been transferred to industrial sector by

About educational environments, an experience to highlight is in Skövde University [45]. This institution has been able to demonstrate impact and importance of plants layout simulation on decision making. Its practical courses have competences focused on undergraduate students, engineers, technicians and managers, and contemplates strategies based on six simulation

so simulated model could not be validated.

20 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

production engineering".

validation does could not be confirmed.

academic institutions and much less by LFs.

phases presented in Section 4.

5.2. Shop floor simulations in educational environments

flows using simulation to verify flow before implementation.

Several authors have illustrated how SFS based production planning and control can be raised to business-level activities [46], therefore, decision making skills acquisition at all company levels becomes more important. Thus, EAFIT University Production Technologies Research Group (GITP) has started a LF implementation to become a focused digital LF towards Industry 4.0 development. In this LF, SFS will supports decision making in production planning and control; It intends become an innovation transmitter agent to students, technicians, engineers and managers. In this way, GITP proposes to establish a current state in SFS applied in LF to set medium term clear objectives, and as have been seen, information studied allows concluding:

GITP has initiated experiential learning methodologies implementation in EAFIT University LF [47], and according to Guarín and Baena [48] "experiential learning is most used pedagogical development mechanism (…). However, not enough research has been done to systematically address experiential pedagogical structure in LF". Thus, and due to some authors have classified simulation as an experiential learning method [49], there is also a high interest in evaluating SFS role in teaching/learning process. This subject will be addressed in future research once LF digital model have been constructed.

GITP considers EAFIT university LF digital model construction is needed. This digital model must be supported with good DOE to validate production system behavior, either by stochastic methods as described in Section 5.1 or SFS software. Initially, it is proposed to define a product and implement its assembly in LF production line. A data collection at process initial state will let define critical variables which have more impact in time, cost and production quality. Later, using DOE, alternatives for plant redistribution will be proposed to optimize

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http://dx.doi.org/10.5772/intechopen.79930

23

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New Frontier. London: Springer London; 2008. ISBN: 978-1-84800-267-8

14(3):296-312. DOI: 10.1016/j.simpat.2005.07.001

cción. [thesis]. Universidad EAFIT; 2013

282 [Accessed: 28-March-2018]

DOI: 10.1016/j.sbspro.2012.09.608

2015;32(Clf):1-6. DOI: 10.1016/j.procir.2015.02.187

27(9):1603-1623. DOI: 10.1080/00207548908942642

28(54):127-158

promfg.2017.04.027

256563.256863

August 2015; 1994. pp. 962-969

SSP.261.503

Figure 4. Proposed methodology for based SFS digital model construction in EAFIT University LF.

critical variables and verify statistically system response. These alternatives and information collected will be fed to an SFS software. Results obtained will be implemented by redistributing LF to validate digital model. See Figure 4.

As can be seen, there are several ways to take Learning Factories advantage for production activities research towards Industry 4.0, and shop floor simulation use and teaching for decision-making is one of the most relevant aspects to be investigated in the medium term, this is main reason to prepare this chapter.

#### Author details

Montoya Andrés\*, Guarín Álvaro and Mora Julián \*Address all correspondence to<EMAIL_ADDRESS>EAFIT University, Medellín, Colombia

#### References

[1] Hari Prasad N, Rajyalakshmi G, Sreenivasulu Reddy A. A typical manufacturing plant layout design using CRAFT algorithm. Procedia Engineering. 2014;97:1808-1814. DOI: 10.1016/j.proeng.2014.12.334

critical variables and verify statistically system response. These alternatives and information collected will be fed to an SFS software. Results obtained will be implemented by redis-

Figure 4. Proposed methodology for based SFS digital model construction in EAFIT University LF.

As can be seen, there are several ways to take Learning Factories advantage for production activities research towards Industry 4.0, and shop floor simulation use and teaching for decision-making is one of the most relevant aspects to be investigated in the medium term,

[1] Hari Prasad N, Rajyalakshmi G, Sreenivasulu Reddy A. A typical manufacturing plant layout design using CRAFT algorithm. Procedia Engineering. 2014;97:1808-1814. DOI:

tributing LF to validate digital model. See Figure 4.

22 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

Montoya Andrés\*, Guarín Álvaro and Mora Julián

\*Address all correspondence to<EMAIL_ADDRESS>
this is main reason to prepare this chapter.

EAFIT University, Medellín, Colombia

10.1016/j.proeng.2014.12.334

Author details

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ing. CIRP Annals. 2017;66(2):803-826. DOI: 10.1016/j.cirp.2017.05.005

Congress and Exposition; 2013. pp. V005T05A037-V005T05A037

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**Chapter 3**

**Provisional chapter**

**Impact of Industry 4.0 on Inventory Systems and**

**Impact of Industry 4.0 on Inventory Systems and**

DOI: 10.5772/intechopen.90077

With evolution of Industry 4.0, how should we operate our production lines and factories, how should we manage and optimize inventory, how should we deploy our workers, how should we run our businesses, how should we manage our supply chains? This chapter aims to highlight the impact of Industry 4.0 on manufacturing systems and services, as well as supply chains, in particular, on inventory systems and optimization. An integrative R&D framework for inventory systems modeling and optimization is proposed, which directs our R&D effort in modeling and optimizing inventory systems

**Keywords:** Industry 4.0, cyber-physical systems, Internet of Things, Internet of Services,

The world is experiencing Industry 4.0, the fourth industrial revolution. The first industrial revolution took place in the eighteenth century with the introduction of mechanical production machines powered by water and steam. The second industrial revolution started at the beginning of the twentieth century with mass production powered by electric energy. The third industrial revolution came in 1970s with production automation using electronics, computers and information technology. The current industrial revolution began in the early of this millennium with autonomous production using Cyber-Physical Systems (CPS), Internet of Things (IoT) and Internet of Services (IoS). This digitization not only enables the integration of processes and systems across companies and industrial sectors, but also creates new busi-

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

inventory systems, inventory optimization

ness models and value generation opportunities.

http://dx.doi.org/10.5772/intechopen.90077

**Optimization**

**Optimization**

Xue-Ming Yuan

**Abstract**

with Industry 4.0.

**1. Introduction**

Xue-Ming Yuan

#### **Impact of Industry 4.0 on Inventory Systems and Optimization Impact of Industry 4.0 on Inventory Systems and Optimization**

DOI: 10.5772/intechopen.90077

#### Xue-Ming Yuan Xue-Ming Yuan

[42] Kliment M, Trebuňa P. Simulation as an appropiate way of verifying the efficiency of production variants in the design of production and non-production systems. Acta Logistica—

[43] Bustamante Rúa MO, Daza Aragón AJ, Bustamante Baena P. Barros Daza MJ. Simulación de plantas de procesamiento de minerales a través de MODSIM®. Revista Boletín Ciencias

[44] Rafael De la Rosa Ramos L, Henríquez Montero E, Sánchez Tuirán E, Angélica Ojeda Delgado K. Diseño y simulación de una planta para la producción de biodiésel a partir de Jatropha curcas L. en el departamento de Bolívar [Online]. 2014. Available: http://

[45] Ng AHC, Frantzen M. Production simulation education using rapid modeling and optimization: Successful studies. In: Proceedings of the 2015 Winter Simulation Conference;

[46] Lee S, Son Y-J, Wysk RA. Simulation-based planning and control: From shop floor to top floor. Journal of Manufacturing Systems. 2007;26(2):85-98. DOI: 10.1016/j.jmsy.2007.07.001

[47] Baena F. Integración de la Teoría de Aprendizaje Experiencial a la Fábrica de Aprendizaje

[48] Guarín Á, Restrepo FB. Fábrica de aprendizaje, una propuesta Didáctica. In: Tecnología, innovación e investigación en los procesos de enseñanza-aprendizaje. Barcelona, 2016. ISBN:

[49] Crawley E, Söre Östlund J, Brodeur D, Edström K. Rethinking Engineering Education.

2nd ed. Springer International; 2014. ISBN:<PHONE_NUMBER>926

de la Tierra, pp. 33-37, 2016. DOI: 10.15446/rbct.n39.50451. ISSN: 2357-3740

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International Scientific Journal. 2014;4(1):17-21

26 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

Vol. 2011. 2015. pp. 3526-3537

[thesis]. Universidad EAFIT; 2017

978-84-9921-848-9

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.90077

#### **Abstract**

With evolution of Industry 4.0, how should we operate our production lines and factories, how should we manage and optimize inventory, how should we deploy our workers, how should we run our businesses, how should we manage our supply chains? This chapter aims to highlight the impact of Industry 4.0 on manufacturing systems and services, as well as supply chains, in particular, on inventory systems and optimization. An integrative R&D framework for inventory systems modeling and optimization is proposed, which directs our R&D effort in modeling and optimizing inventory systems with Industry 4.0.

**Keywords:** Industry 4.0, cyber-physical systems, Internet of Things, Internet of Services, inventory systems, inventory optimization

#### **1. Introduction**

The world is experiencing Industry 4.0, the fourth industrial revolution. The first industrial revolution took place in the eighteenth century with the introduction of mechanical production machines powered by water and steam. The second industrial revolution started at the beginning of the twentieth century with mass production powered by electric energy. The third industrial revolution came in 1970s with production automation using electronics, computers and information technology. The current industrial revolution began in the early of this millennium with autonomous production using Cyber-Physical Systems (CPS), Internet of Things (IoT) and Internet of Services (IoS). This digitization not only enables the integration of processes and systems across companies and industrial sectors, but also creates new business models and value generation opportunities.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Industry 4.0 has been greatly influencing people's daily life in every aspect, from shopping to dining, from working to entertaining, etc. It is changing people's life styles and living behaviors, even thinking and mindset. Industry 4.0 has brought a revolutionary impact to manufacturing systems and services, as well as supply chains. In the environment of Industry 4.0, factories are smart, products are smart, and customers are demanding for being all round served with great satisfaction. Enterprises and businesses are digitalized, profitable and sustainable. Manufacturing systems and services are real time capable, interoperable, modular, decentralized, virtualized, and service oriented. Supply Chains are fully visible, connected and integrated.

production quality and services, reduce energy consumption, and improve efficiency in manufacturing. For example, data can be collected from various stages of the manufacturing process. The large amount of data can be analyzed in correlation with each other in order to identify the stages with redundant processes that need be streamlined. The data can be turned into actionable insights through early warning algorithms, predictive models, decision support, workflows and dashboards. In fact, the data have become the most valuable asset of an organization today. The data analytics is the essence to a success of enterprises and their

Impact of Industry 4.0 on Inventory Systems and Optimization

http://dx.doi.org/10.5772/intechopen.90077

29

*Internet of Things* (IoT): Embedded computing and networking connect sensors and devices. The Internet of Things connects more and more systems, devices, sensors, assets and people through networks ranging from wireless, low-power wide-area networks to wired high-

*Cloud Computing*: Many small enterprises cannot afford to own the storage for an increasing amount of data. Some of them lack the analytical and computational capabilities of processing the huge amount of data. However, the data and the capabilities of analytics and computation are even more important to them. Cloud service providers are offering a growing opportunity

*Cyber-Security*: Industrial systems are becoming increasingly vulnerable to threats as there are various devices and large amount of data sets. The cyber-security has to be put in place, which can recognize new vulnerabilities and challenges, and ensure industrial systems and

*Autonomous Robots*: The use of robots in manufacturing is no longer new. In Industry 4.0, robots are self-sufficient, autonomous, and interactive, which are no longer simply tools used

*Additive Manufacturing*: Additive manufacturing, in particular, 3D printing enables manufacturers to dramatically reduce the design time and cost, and increase the variety and customization of products. The benefits and values brought by additive manufacturing are even

*Augmented Reality*: Customers are now more and more demanding. Businesses have to provide customers all round services, from personalized product design, dedicated manufacturing process to individualized after-sales services. Augmented reality based systems are able to assist enterprises to gain their competitive edges and win more business opportunities in

**3. Factories, products, customers and businesses in the environment**

Industry 4.0 uses digital technologies to make manufacturing more agile, flexible and responsive to customers. It is able to create a smart factory where the Internet, wireless sensors,

to small enterprises through data storage and data processing with affordable costs.

businesses.

capacity networks.

businesses free of attacks.

such a competitive marketplace.

**of Industry 4.0**

by humans, but are the integral work units.

more evident in the high mix low volume (HMLV) manufacturing.

 With rapid growth of Industry 4.0 technologies, inventory systems and optimization are being transformed to a new state. This chapter explores what is the impact of Industry 4.0 on inventory systems and optimization, how Industry 4.0 enables the transformation of inventory systems and optimization to a new state and what are the benefits of such transformation to the industry.

We briefly review Industry 4.0 and enabling technologies in Section 2. In Section 3, we discuss the possible changes with factories, products, customers and businesses in the environment of Industry 4.0. Section 4 highlights the attributes of manufacturing systems and services with Industry 4.0. Section 5 discusses the benefits brought by Industry 4.0 to businesses. In Sections 6–8, we focus on inventory systems and optimization. Section 6 explores the impact of Industry 4.0 on inventory systems, and Section 7 discusses the impact of Industry 4.0 on inventory optimization. In Section 8, we propose a new integrative R&D framework for inventory systems modeling and optimization. Section 9 concludes the chapter.

#### **2. Industry 4.0 and enabling technologies**

Industry 4.0 is a transition to the digital transformation of industries, a merger of the physical and digital worlds. Industry 4.0 is also a fusion of technologies that clear the boundaries among the physical, digital, and biological spheres [1–3]. Those technologies include Artificial Intelligence, Robotics, Internet of Things, Autonomous Vehicles, 3-D Printing, Nanotechnology, Biotechnology, Materials Science, Energy Storage, and Quantum Computing [4–10]. In this chapter, we shall not detail the mentioned technologies, but briefly highlight how Industry 4.0 is enabled by the technologies.

Industry 4.0 is enabled by the technologies that integrate the digital and real worlds. As an illustration, the core technologies related to manufacturing systems and services, and supply chains, are elaborated as follows:

*Big Data Analytics and Business Intelligence*: Nowadays enterprises are embraced by huge amount of data from all kinds of sources. All decisions, either operational or managerial, have to rely on data analytics. All businesses cannot afford to ignore the data and data analytics. It has been proven that the data are very useful and much valuable when it comes to optimize production quality and services, reduce energy consumption, and improve efficiency in manufacturing. For example, data can be collected from various stages of the manufacturing process. The large amount of data can be analyzed in correlation with each other in order to identify the stages with redundant processes that need be streamlined. The data can be turned into actionable insights through early warning algorithms, predictive models, decision support, workflows and dashboards. In fact, the data have become the most valuable asset of an organization today. The data analytics is the essence to a success of enterprises and their businesses.

Industry 4.0 has been greatly influencing people's daily life in every aspect, from shopping to dining, from working to entertaining, etc. It is changing people's life styles and living behaviors, even thinking and mindset. Industry 4.0 has brought a revolutionary impact to manufacturing systems and services, as well as supply chains. In the environment of Industry 4.0, factories are smart, products are smart, and customers are demanding for being all round served with great satisfaction. Enterprises and businesses are digitalized, profitable and sustainable. Manufacturing systems and services are real time capable, interoperable, modular, decentralized, virtualized, and service oriented.  With rapid growth of Industry 4.0 technologies, inventory systems and optimization are being transformed to a new state. This chapter explores what is the impact of Industry 4.0 on inventory systems and optimization, how Industry 4.0 enables the transformation of inventory systems and optimization to a new state and what are the benefits of such transformation

We briefly review Industry 4.0 and enabling technologies in Section 2. In Section 3, we discuss the possible changes with factories, products, customers and businesses in the environment of Industry 4.0. Section 4 highlights the attributes of manufacturing systems and services with Industry 4.0. Section 5 discusses the benefits brought by Industry 4.0 to businesses. In Sections 6–8, we focus on inventory systems and optimization. Section 6 explores the impact of Industry 4.0 on inventory systems, and Section 7 discusses the impact of Industry 4.0 on inventory optimization. In Section 8, we propose a new integrative R&D framework for

Industry 4.0 is a transition to the digital transformation of industries, a merger of the physical and digital worlds. Industry 4.0 is also a fusion of technologies that clear the boundaries among the physical, digital, and biological spheres [1–3]. Those technologies include Artificial Intelligence, Robotics, Internet of Things, Autonomous Vehicles, 3-D Printing, Nanotechnology, Biotechnology, Materials Science, Energy Storage, and Quantum Computing [4–10]. In this chapter, we shall not detail the mentioned technologies, but briefly highlight how Industry

Industry 4.0 is enabled by the technologies that integrate the digital and real worlds. As an illustration, the core technologies related to manufacturing systems and services, and supply

*Big Data Analytics and Business Intelligence*: Nowadays enterprises are embraced by huge amount of data from all kinds of sources. All decisions, either operational or managerial, have to rely on data analytics. All businesses cannot afford to ignore the data and data analytics. It has been proven that the data are very useful and much valuable when it comes to optimize

inventory systems modeling and optimization. Section 9 concludes the chapter.

**2. Industry 4.0 and enabling technologies**

28 Industry 4.0 - Impact on Intelligent Logistics and Manufacturing

4.0 is enabled by the technologies.

chains, are elaborated as follows:

and integrated.

to the industry.

*Internet of Things* (IoT): Embedded computing and networking connect sensors and devices. The Internet of Things connects more and more systems, devices, sensors, assets and people through networks ranging from wireless, low-power wide-area networks to wired highcapacity networks.

*Cloud Computing*: Many small enterprises cannot afford to own the storage for an increasing amount of data. Some of them lack the analytical and computational capabilities of processing the huge amount of data. However, the data and the capabilities of analytics and computation are even more important to them. Cloud service providers are offering a growing opportunity to small enterprises through data storage and data processing with affordable costs.