Best Practices in Manufacturing Processes

Jorge Luis García AlcarazLeonardo Rivera CadavidRosa Guadalupe González-RamírezGeorge Leal Jamil • Mario Gustavo Chong ChongEditors

Best Practicesin Manufacturing ProcessesExperiences from Latin America

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EditorsJorge Luis García AlcarazUniversidad Autónoma de Ciudad JuárezCiudad Juárez, Chihuahua, Mexico

Leonardo Rivera CadavidCiudad Universitaria MeléndezUniversidad del ValleValle del Cauca, Colombia

Rosa Guadalupe González-RamírezUniversidad de Los AndesSantiago, Chile

George Leal JamilInformações em Rede Consultoria eTreinamento Ltda

Belo Horizonte, Minas Gerais, Brazil

Mario Gustavo Chong ChongUniversidad del PacíficoLima—Jesús María, Peru

ISBN 978-3-319-99189-4 ISBN 978-3-319-99190-0 (eBook)https://doi.org/10.1007/978-3-319-99190-0

Library of Congress Control Number: 2018951699

© Springer Nature Switzerland AG 2019This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Nowadays, the globalization environment and fierce competition have forcedcompanies worldwide to place significant efforts to improve their performance andbe more competitive to stay in the market. Since the Industrial Revolution, severalmethodologies and concepts have been introduced to support manufacturingcompanies to reduce costs and increase their productivity and efficiency in theiroperations. As an example of such methodologies, we can mention mass cus-tomization, just in time, lean manufacturing, additive manufacture, and concurrentengineering, among others. Another important trend is related to the automationof the manufacturing processes and industrial robotics. According to theInternational Federation for Robotics, since 2010, the technical improvement inindustrial robots and their demand has been considerably accelerated.

Furthermore, the emergence of new technologies such as Internet of Things andSensors, Cloud Computing, Big Data, Blockchain, etc., presents challenges andopportunities for manufacturing companies on how to adopt such technologies tosupport their decision-making and improve their performance.

Latin America and the Caribbean is a region recognized by a strong manufac-turing sector, being Mexico one of the main manufacturing countries. According tothe Global Manufacturing Index (GMI) computed by Deloite, in 2016, Mexico waspositioned among the top 10 countries in the ranking (out of 40), with the 8th place.Other countries from the region were Brazil, Colombia, and Argentina that occu-pied the 29th, 36th, and 39th positions, respectively. As indicated in the GMIreport, there are three dominant regional clusters: North America, Europe, and AsiaPacific, with Mexico being part of the first one.

In this book, we aim to report the best practices that companies established inLatin America and the Caribbean are implementing in their manufacturing pro-cesses to generate high-quality products and stay in the market, as well as newtechnologies and operational and managerial philosophies that are being proposedby researchers and implemented by practitioners. The book has 24 chapters, fromBrazil (2), Chile (1), Colombia (3), Ecuador (1), Spain (1), Mexico (12), Peru (3),and Trinidad and Tobago (1).

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The content of this book is structured into four parts: (I) Techniques, Tools andMethodologies, (II) Production Management, (III) Manufacturing and Technology,and (IV) Human Factors.

Part I: Techniques, Tools and Methodologies comprises eight chapters, whichare further described below:

Chapter 1 titled “Digital Transformation: Digital Maturity Applied to StudyBrazilian Perspective for Industry 4.0” is carried out by Ferreira-Braga et al. andprovides an analysis of the digital transformation efforts in Brazilian companies,aiming to identify opportunities and limitations.

Chapter 2 titled “Modelling and Analysis of the Apples Export Supply ChainBusiness Processes: Experiences from Chile” presented by López-Campos et al.presents an analysis of the fresh apple export supply chain in Chile, analyzing theshipment business process and providing a redesign proposal that considers theimplementation of electronic data interchange procedures.

Chapter 3 titled “Considerations for the Latin American and Caribbean Regionin Light of the Global Move Towards Low Carbon Shipping” is proposed by Singhand Rambarath-Parasram. The chapter presents some realities and developments onclimate action in the international maritime industry that may impact the economiesof the region and highlights the opportunities to overcome the barriers for theimplementation and adoption of maritime technological advances to mitigatingclimate change.

Chapter 4 titled “Risk-based Strategic Inventory Supply Model for NewProducts” is carried out by Sánchez-Vega et al. They propose a risk-based strategicinventory supply model to make more efficient the supply chain of new products.For this, they perform a comprehensive study to determine the most importantfactors that may affect the supply of new products.

Chapter 5 titled “Fuzzy QFD and TOPSIS for Dispatching Prioritization inMaritime Transportation Considering Operational Risk” presented byOsorio-Gómez et al. presents a multi-criteria approach to prioritize containershipments dispatching according to the availability of resources (vessels) and theassociated risks, considering a case study, the Port of Buenaventura in Colombia.

Chapter 6 titled “Logistics Planning for the Synchronization of Key FunctionalAreas of a Latin American Bottling Company” is carried out by Sánchez-Partidaet al. and, based on a case study real bottling company in Mexico, proposes aforecast and inventory policy with the aim of reducing their forecast variability andensuring the availability of materials for production lines.

Chapter 7 titled “Demand Management to Optimize the Supply Chain of anAgribusiness Company” is presented by Pérez-Pérez et al., which analyzes thequinoa supply chain of the agribusiness company BAMSA and proposes a redesignof its supply chain strategy with the aim to reduce lost sales and improve theirinventory levels (safety stock).

Chapter 8 titled “Courier Logistics Strategy to Create Commercial Impact inPeru. Application of Correlation and Regression Analysis, Lean Manufacturing”presents a research carried out by Núñez-Pauca et al. They analyze the Express

vi Preface

services in Peru with the aim to determine if imports of international expressshipments explain the external trade volume of the country, to which extent they doit and which is the future field of action of projected foreign trade based on Expressshipment growth.

Part II: Production Management consists of six chapters regarding techniquesand method used in industry for support decision-making; those charters aredescribed as follows:

Chapter 9 titled “Implementation of the S&OP Process in Textile Company CaseStudy: Ecuadorian Textile ABA” by Bofill-Altamirano and Avilés-Sacoto reports acase study from textile industry for improving communication and coordinationamong different departments involved in a production order, and they propose anS&OP as a solution that gives results fast and with high reliability, making easy theproduction control.

Chapter 10 is entitled “Reflecting on Industrial Business Models: A History ofTradition, Challenges, and Potential Innovations” by Jamil et al. They analyze andevaluate market options for business models to be adopted by industrial agents andafter a deep literature review, and they propose a methodological approach alignedto production strategy. Their report is interesting because they integrate the expe-rience of several successful companies.

Chapter 11 is entitled “The Constrained Joint Replenishment Problem UsingDirect and Indirect Grouping Strategies with Genetic Algorithms” by Zapata-Corteset al.; a model for inventory optimization for solving the joint replenishmentproblem (JRP) when ordering multiple products is required. The model allowsreducing total inventory costs compared to the traditional practice of performingindividual optimization of each product.

Chapter 12 is entitled “Improvement of the Demand Planning of Imported Seedsin the Company Agro Perú SA” by Coba et al. They analyze the current structureof the demand planning in a Peruvian enterprise and the potential of rethinking thedemand planning structure and decisions on the whole supply chain performance.In their case study, they combine the CSAR framework for rethinking supply chainstrategies and a multi-criteria method.

Chapter 13 is entitled “Numerical Analysis in a Beverage Can Utilizing TubeHydroforming Process” and is presented by León-Anaya et al. They are reporting anumerical analysis for Tube Hydroforming (THF) and predict the behavior ofextruded aluminum tube in a forming die for beverage can applications. They areusing simulation and experimental determination of the mechanical properties ofaluminum 6061-T5 for test specimens.

Chapter 14 is entitled “The E-Strategy for Lean-Sigma Solutions, LatinAmerican Case Study in a New Product Validation Process” by Estrada-Oranteset al. They integrate lean manufacturing and six sigma concepts for proposing anE-strategy for solving problems in production systems and report a case study orproof of the new methodology.

Preface vii

Part III: Manufacturing and Technology presents studies that relate traditional,classical, and innovative applied research for industrial contexts, such as finiteelements analysis, fused deposition modeling, and manufacturing/operations plan-ning and systems implementations, among others, resulting in a coherent evaluationon how theoretical contents can decisively be considered to implement real solu-tions, the new environment of “best practices” in times of fast-changing tech-nologies. With these researches, authors from several countries aim to improve thediffusion of consistent scientific methodological principles, resulting in a morereliable level of comprehension of critical aspects regarding emerging technologiesand scenarios for industrial complexes.

Chapter 15 entitled “Manufacturing’s Strategic Role and Management Practices:Evidence from Colombian Companies” by Vivares and Sarache is aimed toimprove the understanding of manufacturing’s strategic role (MSR) applying asurvey in a group of Colombian companies of the coffee sector, where theyexplored MSR’s potential as one fundamental component for manufacturing/operations system (MOS), taken as an integrative part of the organizational oper-ational strategy (OS). For this, a consistent literature review and modeling rela-tionship were provided by the authors. With their contribution, practices inserted inthe MSR context are related to corporative strategies, as elements of the OS andtheir study also helps to configure the basics of MSR research variables to apply infurther studies and designing practical, applicable solution in real organizationalsystems, with strategic evaluation.

Chapter 16 is entitled “Additive Manufacturing: Fused Deposition ModelingAdvances”; through an application of systematic literature review, Aguilar-Duqueet al. approached the advances of fused deposition modeling (FDM) as one of themost used technologies for additive manufacturing (AM) in the last years in thischapter. FDM was considered as an alternative for limitations of the classicalmanufacturing methods. For instance, those shown by environmental issues, costsand overall economic impacts, as well as the production of complex shapes in newassemblies. As the modern integration of manufacturing systems is reached everyday, additive methodologies, such as FDM, can become more applied, with ver-satility and flexibility, resulting in appreciable opportunity to customize design andproduction, implicating in a space for new set of best practices for industrialcomplexes and associated productive arrays.

Chapter 17 entitled “Analysis of the Productivity of a Shoe Production Line—Application of Queueing Theory and Lean Manufacturing” by Hernández-Gonzálezet al. studies one of the most competitive and complex production lines with itsassociated systems and processes, reporting the case of footwear industry. They arefocused to propose decision-making to allow future planning and strategic imple-mentation, nowadays expressively demanded by this type of industrial complex. Toaccomplish their intent, the authors applied a two-step methodology: First, aphysical analysis was conducted through a Factory Physics approach, and then, theresults were studied from lean manufacturing principles. As an outcome of the caseapproached, it was eventually reached a significant cost of production cost.

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Chapter 18 is entitled “Performance Evaluation of a Commercial 3D Printer thatUses Fused Filament Deposition Technology” by Ramos-Lozano et al., whichpromotes a study on an opportune market demand: the performance evaluation of3D printing resources. Through a design of experiment approach, Ramos-Lozanoet al. applied an FDM—filament deposition technique—procedure to determineparameters for this analysis, such as those related to dimensional accuracy, resultingin a first proposition for an analytical criteria. As 3D printing is expanding con-tinuously, eventually posing as an element of industrial operational planning, thisstudy contributes to initiate a comprehension toward best practices not only for itsimmediate usage but also for its implementation as parts of optimal new industrialarrangements.

Chapter 19 is entitled “Organizational Systems Convergence with the Industry 4.0Challenge” by Pérez-Lara et al. They evaluated the issue of Industry 4.0 conceptu-alization, and although largely announced, debated, and treated by commercialplayers, Industry 4.0 still needs a stable conceptualization, as this trend is supportedby many different technological fronts, emerging resources and solutions, under themarket pressures to answer economical competitive demands. In their work, authors,inspired by supply chain principles, propose guidance toward a measurement system,as a fundamental to understand the 4.0 market evidence, conducting to a more pre-dictable comprehension for these phenomena. This recommendation, proposed bythe study, was supported by a methodological approach from a literature review,measurement proposition, and a final comparison with supply chain alternatives,allowing it to be developed for application.

Chapter 20 is entitled “Modeling by Finite Element of a Turning Process withChip Detachment” by Lucero-Velázquez et al. It concludes the third section of ourbook reporting the application of finite element modeling techniques. After amodeling proposition and correspondent simulation, the results obtained by authorswere validated through a real case study, producing a solid case study about finiteelement analysis, improving its comprehension as a best practice for both man-agement, trend analysis, design, and implementation, with clear applicationimplications.

Part IV: Human Factors, as its name declares, reports four chapter regarding thehuman factors in production and manufacturing process, a concept or factor for-gotten usually, and that can make the difference between the success and failureof the company.

Chapter 21 is entitled “The Role of Knowledge Transfer in Supply ChainFlexibility and Performance” by Díaz-Reza et al. They propose a structural equationmodel to analyze four latent variables: internal knowledge transfer, externalknowledge transfer, supply chain flexibility, and supply chain performance. Thosevariables are interrelated through three research hypotheses. The model is validatedwith information from 269 Mexican manufacturing companies. The results indicatethat internal knowledge transfer has the most important direct effect on supply chainflexibility and the highest indirect effect on supply chain performance.

Preface ix

Chapter 22 is entitled “Magnitude of Low Back Pain, Occupation, Education,and Economic Level in Mexican Workers” by Prado-León and Rosales-Cinco.They report an investigation to describe the magnitude of low back pain in workerswith lumbar spondyloarthrosis and determine occupational and sociodemographicfactors related to the disorder. They compare two groups using informationobtained by the Nordic Questionnaire. The cases demonstrated that the disorder hadpresented itself at a young age.

Chapter 23 is entitled “The Knowledge-Based Maintenance: An Approach forReusing Experiences in Industrial Systems” by López-Ramos et al. They report theimportance of maintenance in any production process. They indicate that fre-quently, the common vision about the maintenance process takes only into accountthe probability of an event and the risk associated with some expected effects withthe goal to reduce the effective cost. They proposed a learning process in themaintenance process for reducing the loss of knowledge and increased thedependency on some experts and presented a knowledge-based maintenanceapproach that aims to create a framework to manage a maintenance program butalso to preserve the valuable experience deployed in the maintenance activities.

Chapter 24 is entitled “Industry 4.0 and Engineering Education: An Analysis ofNine Technological Pillars Inclusion in Higher Educational Curriculum” byHernández-Muñoz et al. They present an analysis of engineering curriculum and therelation with nine technological pillars of the Industry 4.0, considering the case offour universities in the state of Nuevo Leon, in Mexico. Their results show that theengineering curriculum is contributing directly to those pillars, but some are notentirely defined or missed, such as: Big data, Cloud, Augmented Virtual Reality,Internet of Things and Additive Manufacturing. Hence, they provide some rec-ommendations for public policies and incentives that enhance the revision andactualization of the engineering curriculum to prepare professionals that meet therequirements of the digital era.

Ciudad Juárez, Mexico Jorge Luis García AlcarazCali, Colombia Leonardo Rivera CadavidSantiago, Chile Rosa Guadalupe González-RamírezRio de Janeiro, Brazil George Leal JamilLima, Peru Mario Gustavo Chong Chong

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Acknowledgements

This project is the sum of efforts from many colleagues, friends, and institutions,and therefore, the invited editors wish to thank to:

• The talented technical reviewers who helped review and improve every chapterin this book. The knowledge and enthusiasm they brought to the project wassimply amazing.

• The contributors or authors that during one-year support with their researchworks and suggestions.

• We equally and especially wish to thank Springer International Publishing AGand associate editors for granting us the opportunity to edit this book andproviding valuable comments to improve the selection of research works.

• Guest editors are grateful to the Autonomous University of Ciudad Juárez(Mexico), University of El Valle (Colombia), University of Los Andes (Chile),University of El Pacifico (Peru), and Fundação Dom Cabral (Brazil), our affil-iation institutions.

• We want to express our gratitude to the Mexican National Council of Scienceand Technology (CONACYT) for supporting the project named ThematicNetwork in Industrial Process Optimization, as well as by the Public EducationSecretary (SEP).

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Contents

Part I Techniques, Tools and Methodologies

1 Digital Transformation: Digital Maturity Applied to StudyBrazilian Perspective for Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . 3Hugo Ferreira Braga Tadeu, André Luis de Castro Moura Duarte,Cezar Taurion and George Leal Jamil

2 Modelling and Analysis of the Apples Export Supply ChainBusiness Processes: Experiences from Chile . . . . . . . . . . . . . . . . . . 29Mónica López-Campos, Rosa Guadalupe González-Ramírez,Lorena Bearzotti and Salvatore Cannella

3 Considerations for the Latin American and Caribbean Regionin Light of the Global Move Towards Low Carbon Shipping . . . . . 53Sukhjit Singh and Vivian Rambarath-Parasram

4 Risk-based Strategic Inventory Supply Modelfor New Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75María-del-Rosario Sánchez-Vega, Santiago-Omar Caballero-Morales,Diana Sánchez-Partida and José-Luís Martínez-Flores

5 Fuzzy QFD and TOPSIS for Dispatching Prioritizationin Maritime Transportation Considering Operational Risk . . . . . . 97Juan Carlos Osorio-Gómez and Diego Fernando Manotas-Duque

6 Logistics Planning for the Synchronization of Key FunctionalAreas of a Latin American Bottling Company . . . . . . . . . . . . . . . . 117Diana Sánchez-Partida, Dulce Arroyo-García,José-Luis Martínez-Flores and Emmanuel Juárez-García

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7 Demand Management to Optimize the Supply Chainof an Agribusiness Company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Alicia Pérez-Pérez, José Claudio Villamonte-Cornejo,César López-Rosas, Roberto Pérez-Franco andMario Gustavo Chong Chong

8 Courier Logistics Strategy to Create Commercial Impact in Peru.Application of Correlation and Regression Analysis, LeanManufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159Daniel Núñez-Pauca, Gisela Landa-Gálvez andMario Gustavo Chong Chong

Part II Production Management

9 Implementation of the S&OP Process in Textile Company CaseStudy: Ecuadorian Textile ABA . . . . . . . . . . . . . . . . . . . . . . . . . . . 183María Angeline Bofill-Altamirano and Sonia Valeria Avilés-Sacoto

10 Reflecting on Industrial Business Models: A History of Tradition,Challenges, and Potential Innovations . . . . . . . . . . . . . . . . . . . . . . . 211George Leal Jamil, Antonio Juan Brioñes-Peñalverand Domingo García-Perez de Lema

11 The Constrained Joint Replenishment Problem Using Directand Indirect Grouping Strategies with Genetic Algorithms . . . . . . . 239Julian Andres Zapata-Cortes, Martín Darío Arango-Sernaand Víctor Jaime Saldarriaga-Romero

12 Improvement of the Demand Planning of Imported Seedsin the Company Agro Perú SA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261Leonilda Coba, Cesar Shimabuku and Jesús González-Feliu

13 Numerical Analysis in a Beverage Can Utilizing TubeHydroforming Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283Jorge Carlos León-Anaya, Juan Carlos Cisneros-Ortega,Eloy Dimas-Celestino and José Antonio Juanico-Lorán

14 The E-Strategy for Lean-Sigma Solutions, Latin American CaseStudy in a New Product Validation Process . . . . . . . . . . . . . . . . . . 297Francisco J. Estrada-Orantes, Abimael H. García-Pérezand Noé G. Alba-Baena

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Part III Manufacturing and Technology

15 Manufacturing’s Strategic Role and Management Practices:Evidence from Colombian Companies . . . . . . . . . . . . . . . . . . . . . . 325Jorge A. Vivares and William Sarache

16 Additive Manufacturing: Fused DepositionModeling Advances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347Julián Israel Aguilar-Duque, Juan Luis Hernández-Arellano,Liliana Avelar-Sosa, Guillermo Amaya-Parraand Ulises Jesús Tamayo-Pérez

17 Analysis of the Productivity of a Shoe Production Line––Application of Queueing Theory and Lean Manufacturing . . . . . . . 367Salvador Hernández-González, Ricardo Ramírez-Tapiaand José Alfredo Jiménez-García

18 Performance Evaluation of a Commercial 3D Printer thatUses Fused Filament Deposition Technology . . . . . . . . . . . . . . . . . . 389Secundino Ramos-Lozano, Javier Molina-Salazar, Lázaro Rico-Pérezand David Atayde-Campos

19 Organizational Systems Convergence with the Industry 4.0Challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411Magdiel Pérez-Lara, Jania Astrid Saucedo-Martínez,José Antonio Marmolejo-Saucedo and Tomás Eloy Salais-Fierro

20 Modeling by Finite Element of a Turning Processwith Chip Detachment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433Baldomero Lucero-Velázquez, Juan José Delfín-Vázquez,José Efrén Ruelas-Ruíz, Eusebio Jiménez-Lópezand Mario Acosta-Flores

Part IV Human Factors

21 The Role of Knowledge Transfer in Supply Chain Flexibilityand Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465José Roberto Díaz-Reza, Valeria Martínez-Loya,Jorge Luis García Alcaraz and Ismael Canales-Valdiviezo

22 Magnitude of Low Back Pain, Occupation, Education,and Economic Level in Mexican Workers . . . . . . . . . . . . . . . . . . . . 487Lilia Roselia Prado-León and Rosa Amelia Rosales-Cinco

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23 The Knowledge-Based Maintenance: An Approach for ReusingExperiences in Industrial Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 505Luis Alberto López-Ramos, Guillermo Cortés-Robles,Eduardo Roldán-Reyes, Giner Alor-Hernándezand Cuauhtémoc Sánchez-Ramírez

24 Industry 4.0 and Engineering Education: An Analysis of NineTechnological Pillars Inclusion in Higher EducationalCurriculum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 525Guadalupe Maribel Hernández-Muñoz, Lizbeth Habib-Mireles,Flor Araceli García-Castillo and Fernando Montemayor-Ibarra

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List of Figures

Fig. 1.1 Structure of dimensions for digital maturity analysisFDC (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Fig. 1.2 What do you understand by digitalization? FDC (2015) . . . . . 16Fig. 1.3 From the following expressions, what is the most

representative for digitalization concept? FDC (2015) . . . . . . . 17Fig. 1.4 Is there a digital strategy in your organization?

FDC (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Fig. 1.5 What are the organizational areas which have a strategy to

develop digitalization? FDC (2015) . . . . . . . . . . . . . . . . . . . . . 18Fig. 1.6 What expression represents the best description for your

digitalization strategy? FDC (2015) . . . . . . . . . . . . . . . . . . . . . 18Fig. 1.7 Has a process analysis or a feasibility study been already

performed? FDC (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Fig. 1.8 What could be done, or is needed to advance digitalization

in your organization? FDC (2015) . . . . . . . . . . . . . . . . . . . . . . 19Fig. 1.9 Choice, from these technological trends, the most important

that could be applied in your business environmentFDC (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Fig. 1.10 What are the main digital transformation benefits for yourorganization? FDC (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Fig. 1.11 What are the internal barriers for increased usage of digitaltechnologies and processes in your organization?FDC (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Fig. 1.12 What are the main external barriers to improve the applicationof digital technologies and digital processes in yourorganization? FDC (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Fig. 1.13 What sector, in your organization, is the main responsible fortopics related to digital strategy decisions? FDC (2015) . . . . . 22

Fig. 1.14 Are you interested on participate in an “alpha test”(pilot project) of digitalization? FDC (2015) . . . . . . . . . . . . . . 23

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Fig. 1.15 Are you interested on creating pilot projects aboutdigitalization? FDC (2015). . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Fig. 2.1 Macro-processes of the international supply chain.Source “Business Process Analysis Guide to Simplify Tradeprocedures”, 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Fig. 2.2 Types of apples produced in Chile.Source www.chileanfreshfruit.com . . . . . . . . . . . . . . . . . . . . . . 41

Fig. 2.3 UML uses case diagram for ship processes and actors. . . . . . . 44Fig. 2.4 BPMN diagram for ship process . . . . . . . . . . . . . . . . . . . . . . . 44Fig. 2.5 BPMN diagram AS-IS arrange transport process . . . . . . . . . . . 45Fig. 2.6 BPMN diagram arrange transport process indicating the

different documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Fig. 2.7 BPMN TO-BE diagram arrange transport process . . . . . . . . . . 49Fig. 3.1 Approach towards reducing port generated

GHG emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Fig. 3.2 Classification of barriers to energy efficiency

(Singh 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Fig. 4.1 Opportunity forces and challenges in the integration of NPs

and the SC (Khan et al. 2016) . . . . . . . . . . . . . . . . . . . . . . . . . 77Fig. 4.2 Independent base models of a Periodic Review (P) and

b Continuous Review (Q, R) . . . . . . . . . . . . . . . . . . . . . . . . . . 84Fig. 4.3 Volume and trend of the estimated monthly demand for the

NP (multi-flavor lollipops) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Fig. 4.4 Visualization of results: Demand with Seasonality

(multi-flavor lollipops) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Fig. 4.5 Visualization of results: Constant Demand (multi-flavor

lollipops) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Fig. 4.6 Visualization of results: Demand with Trend (multi-flavor

lollipops) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Fig. 5.1 TOPSIS matrix (Wachowicz 2011) . . . . . . . . . . . . . . . . . . . . . 103Fig. 5.2 Methodological approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Fig. 5.3 Normalized decision matrix (Wachowicz 2011) . . . . . . . . . . . . 108Fig. 5.4 Weighted normalized decision matrix (Wachowicz 2011) . . . . 108Fig. 5.5 Whats-Hows correlation matrix . . . . . . . . . . . . . . . . . . . . . . . . 112Fig. 6.1 Monthly forecast chart with MAPE of 49%. . . . . . . . . . . . . . . 127Fig. 6.2 Quarterly forecast chart with MAPE of 7.79%. . . . . . . . . . . . . 127Fig. 6.3 Comparison between the forecast in AOP, the real demand

and the smoothed forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128Fig. 7.1 Summary of the methodological process . . . . . . . . . . . . . . . . . 141Fig. 7.2 Working framework of the supply chain strategy of a

business unit (Pérez-Franco et al. 2018). . . . . . . . . . . . . . . . . . 142Fig. 7.3 The internal and external elements what the organization

controls. Model for rethinking supply chain strategy . . . . . . . . 143Fig. 7.4 Five Porter’s forces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146

xviii List of Figures

Fig. 7.5 Value Stream Map: map of the processes of the supply chainof the company . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Fig. 7.6 The initial capture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149Fig. 7.7 Optimized strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150Fig. 7.8 Evaluation of coherence of functional strategies. . . . . . . . . . . . 151Fig. 7.9 Selection of products for forecast. . . . . . . . . . . . . . . . . . . . . . . 152Fig. 7.10 Recommended future strategy . . . . . . . . . . . . . . . . . . . . . . . . . 155Fig. 8.1 Courier process diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161Fig. 8.2 Research procedural framework . . . . . . . . . . . . . . . . . . . . . . . . 164Fig. 8.3 Dispersion map, variables: “Express Imports” and “Total

Commercial Exchange” (Jan 2000–Mar 2017) . . . . . . . . . . . . . 165Fig. 8.4 Dispersion map and simple regression line, variables:

“Express Imports” and “Total Commercial Exchange”(Jan 2000–Mar 2017). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

Fig. 8.5 Commercial exchange projection based on Express shipments(pessimistic and optimistic scenarios, performance gap ofimpact from the improvements) . . . . . . . . . . . . . . . . . . . . . . . . 170

Fig. 8.6 Value stream mapping to supply chain of express shipments—current process. Source Elaborated by the authors . . . . . . . . 174

Fig. 8.7 Value stream mapping to supply chain of express shipments—proposed process. Source Elaborated by the authors . . . . . . 175

Fig. 11.1 Solving process of a genetic algorithms. Source Nagasawaet al. (2015) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

Fig. 11.2 Chromosome representation for the IGS strategy.Source Own Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

Fig. 11.3 Representation of the chromosome for DGS strategy.Source Own Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250

Fig. 11.4 Two-point crossover operator for the IGS strategy. . . . . . . . . . 251Fig. 11.5 Two-point crossover operator for the DGS strategy.

Source Own Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251Fig. 11.6 Mutation operator for the IGS strategy.

Source Own Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252Fig. 11.7 Mutation operator for the DGS strategy.

Source Own Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252Fig. 11.8 Different individuals representation for the same solution.

Source Adapted from Olsen (2005) . . . . . . . . . . . . . . . . . . . . . 252Fig. 11.9 Best individual fitness value evolution for the IGS and DGS

strategies. Source Own Source . . . . . . . . . . . . . . . . . . . . . . . . . 256Fig. 12.1 Imported corn sales budgets versus sales results 2017 . . . . . . . 263Fig. 12.2 Introduction to supply chain. . . . . . . . . . . . . . . . . . . . . . . . . . . 266Fig. 12.3 Improved conceptual map . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270Fig. 12.4 Inventory behavior during 2016 . . . . . . . . . . . . . . . . . . . . . . . . 272Fig. 12.5 Inventory management pillar detail . . . . . . . . . . . . . . . . . . . . . 274Fig. 12.6 Ideal process map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275

List of Figures xix

Fig. 12.7 New operations and logistics structure . . . . . . . . . . . . . . . . . . . 276Fig. 12.8 S&OP process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277Fig. 13.1 Tube hydroforming process. Source León et al. (2014) . . . . . . 284Fig. 13.2 Surface stress in tube subject to internal pressure (Source

Author) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286Fig. 13.3 Test specimens according to ASTM E8/E8 M-09 Standard.

Source Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286Fig. 13.4 Tension test for mechanical characterization . . . . . . . . . . . . . . 287Fig. 13.5 100� magnification photography of the internal face of the

specimen test before and after the tension test.Source Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 288

Fig. 13.6 Rendered model of one of the dies . . . . . . . . . . . . . . . . . . . . . 288Fig. 13.7 Geometric model and meshed model of the tube

(Source Author) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289Fig. 13.8 Radial deformation, friction of µ = 0.05 and µ = 0.10. . . . . . . 290Fig. 13.9 Von Mises stress, µ = 0.10 . . . . . . . . . . . . . . . . . . . . . . . . . . . 290Fig. 13.10 Radial deformation versus time . . . . . . . . . . . . . . . . . . . . . . . . 291Fig. 13.11 Longitudinal deformation versus time . . . . . . . . . . . . . . . . . . . 291Fig. 14.1 Image of a roadmap of DMAIC phases and the potential tools

for each phase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303Fig. 14.2 Simplified diagram of the Lean-Sigma methodology steps. . . . 304Fig. 14.3 Schematic diagram of the E-Strategy Diagnostic Phase . . . . . . 306Fig. 14.4 Schematic diagram of the E-Strategy Solution Phase . . . . . . . . 307Fig. 14.5 a Partial drawing of the crimping machine, b Pulling testing

equipment used for these experiments . . . . . . . . . . . . . . . . . . . 309Fig. 14.6 Control chart of data after pulling force testing . . . . . . . . . . . . 311Fig. 14.7 a Normality test after pulling force testing, b Initial capability

analysis after pulling force testing . . . . . . . . . . . . . . . . . . . . . . 312Fig. 14.8 a Capability analysis for tube diameter, b Capability analysis

for tube length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313Fig. 14.9 Capability analysis for the end cap diameter . . . . . . . . . . . . . . 314Fig. 14.10 I-MR chart comparing the before versus after implementation

data for the pulling force testing . . . . . . . . . . . . . . . . . . . . . . . 317Fig. 14.11 Capability analysis for pull force after changes . . . . . . . . . . . . 318Fig. 14.12 Before versus after comparison of the capability analysis for

the pulling force testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318Fig. 14.13 Pulling-force testing data for the confirmation run using the

hypothesis test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Fig. 14.14 Pulling-force testing data comparing before versus after

implementation using the hypothesis test . . . . . . . . . . . . . . . . . 319Fig. 15.1 MOS’ performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334Fig. 15.2 MP implementation levels (evaluated on a six-point scale

[0—not used, 1—very deficient, 2—deficient, 3—medium,4—high, 5—very high]). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335

xx List of Figures

Fig. 15.3 Descriptive statistics for MSR in Company A . . . . . . . . . . . . . 337Fig. 15.4 Implementation level of management practices

in Company A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337Fig. 15.5 New analytical framework proposed for OS . . . . . . . . . . . . . . 338Fig. 16.1 Alternatives of the process to develop a product

layer by layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351Fig. 16.2 Timeline of additive manufacturing . . . . . . . . . . . . . . . . . . . . . 353Fig. 16.3 Surface defects over spherical part . . . . . . . . . . . . . . . . . . . . . . 358Fig. 16.4 Surface defects in a cavity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358Fig. 16.5 Process parameters for dimensional accuracy . . . . . . . . . . . . . . 359Fig. 16.6 Impacts of 3D printing on manufacturing systems . . . . . . . . . . 361Fig. 17.1 Production chain of leather and footwear products in Mexico

(INAES 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368Fig. 17.2 Shoe manufacturing process (INAES 2017) . . . . . . . . . . . . . . . 369Fig. 17.3 A scientific approach to decision-making. . . . . . . . . . . . . . . . . 370Fig. 17.4 Queueing systems with a single station . . . . . . . . . . . . . . . . . . 372Fig. 17.5 Cycle time in a station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373Fig. 17.6 Kendall’s notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Fig. 17.7 Open Jackson network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376Fig. 17.8 System in series with a buffer between stages . . . . . . . . . . . . . 377Fig. 17.9 Process flow diagram for the selected shoe model . . . . . . . . . . 379Fig. 17.10 Results of the goodness-of-fit test for the stations. a Station 1,

b station 2, c station 3, d station 4, e station 5, f station 6, andg station 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380

Fig. 18.1 3D printer technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390Fig. 18.2 Generalized additive manufacturing process. . . . . . . . . . . . . . . 391Fig. 18.3 Sample design with an internal square feature

used for DOE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395Fig. 18.4 Model drawing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395Fig. 18.5 BIAS. Source MSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397Fig. 18.6 Surface profile showing Ra and Rmean . . . . . . . . . . . . . . . . . . . 399Fig. 18.7 Method diagram. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399Fig. 18.8 Pareto chart of the factors effects of external volume. . . . . . . . 402Fig. 18.9 Optimal values for external volume using optimizer

Minitab tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402Fig. 18.10 Pareto chart of the factor effects of internal volume. . . . . . . . . 403Fig. 18.11 Optimal values using optimizer Minitab tool for internal

dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403Fig. 18.12 Pareto charts and optimum levels for lateral surface

roughness using Minitab optimizer . . . . . . . . . . . . . . . . . . . . . 403Fig. 18.13 Optimum levels for upper surface roughness using Minitab

optimizer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405Fig. 19.1 The industry evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413Fig. 19.2 Group technologies in Industry 4.0 . . . . . . . . . . . . . . . . . . . . . 414

List of Figures xxi

Fig. 19.3 Value creation modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419Fig. 19.4 Supply chain integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419Fig. 19.5 Level scoreboard of Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . 420Fig. 19.6 Selected models on Industry 4.0 . . . . . . . . . . . . . . . . . . . . . . . 422Fig. 20.1 Physical model of the process of orthogonal cut in three

dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438Fig. 20.2 Orthogonal 3D cutting process where the tool and the

workpiece are shown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443Fig. 20.3 The influence of hardness coefficient (b) with the maximum

shear stress (Ƭ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447Fig. 20.4 Temperature distribution to different hardnesses:

a temperature distribution, to C = 220, P = 5, b = 0 andb temperature distribution, to C = 220, P = 5, b = 0.5 . . . . . . 449

Fig. 20.5 Interaction of ABC factors, in relation to the maximumtemperature in the cut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452

Fig. 20.6 Graph of correlation of data of maximum temperature ofcutting of experimental tests . . . . . . . . . . . . . . . . . . . . . . . . . . 453

Fig. 20.7 Oblique cut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456Fig. 21.1 Proposed model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471Fig. 21.2 Evaluated model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479Fig. 22.1 Sequence of degeneration by spondylosis. a Normal

functional unit: A1 = vertebral body; A2 = intervertebraldisc; A3 = anterior common vertebral ligament;A4 = posterior common vertebral ligament; A5 = pedicle;A6 = nerve; A7 = conjunctive hole; A8 = facet and A9 =cartilage. b The disc (1) degenerates; the common vertebralligament (2) loosens and separates from the vertebral body;the conjunction hole (3) narrows; the cartilaginous terminalplates (4) show sclerotic changes; posterior or osteophyticspurs (5) begin to form; cationic degenerative changes(6) occur in the facets; conjuctive hole stenosis appears(7) leading to nerve root compression . . . . . . . . . . . . . . . . . . . 490

Fig. 22.2 Activities which involve manual materials handling . . . . . . . . 496Fig. 22.3 Prevalence rate comparison, according to gender . . . . . . . . . . . 498Fig. 22.4 When manual lifting or transporting of objects is necessary

and the objects are heavy, or the task must be performedfrequently, it is safer to do so between two people . . . . . . . . . 499

Fig. 22.5 Patient handling is also a heavy burden that must be carriedout between two people and/or with mechanical aids . . . . . . . 499

Fig. 22.6 Handle height lower than the user’s bent elbow,tiny wheels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500

Fig. 22.7 Adequate handle height (for flexed elbows) and large wheels,which require less effort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500

xxii List of Figures

Fig. 22.8 (1) Handles above shoulder height. (2) Ergonomic adaptationto the height of the flexed elbow . . . . . . . . . . . . . . . . . . . . . . . 501

Fig. 23.1 The case-based reasoning process . . . . . . . . . . . . . . . . . . . . . . 508Fig. 23.2 Paper making process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511Fig. 23.3 The architecture of the KBM system . . . . . . . . . . . . . . . . . . . . 517Fig. 23.4 The alternatives in the user interface . . . . . . . . . . . . . . . . . . . . 518Fig. 23.5 The alternatives in the user interface . . . . . . . . . . . . . . . . . . . . 519Fig. 23.6 The user interface of the KBM system . . . . . . . . . . . . . . . . . . 519Fig. 24.1 The industry evolution. Source Informe

Tendencias (2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528Fig. 24.2 The nine pillars of technological . . . . . . . . . . . . . . . . . . . . . . . 529Fig. 24.3 Skills for the 21st century (Soffel 2016) . . . . . . . . . . . . . . . . . 533Fig. 24.4 Pathways to Bachelor of Engineering, Bachelor of

Technology toward Master of Engineering in Manufacturingfor Professional License (Justason et al. 2018). . . . . . . . . . . . . 534

Fig. 24.5 Student population national level-bachelor’s degree andbachelor’s degree manufacturing and process area . . . . . . . . . . 535

Fig. 24.6 Students studying at bachelor’s degrees in Mexico focused onIndustry 4.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536

List of Figures xxiii

List of Tables

Table 2.1 Macro process ship and it processes of the apple exportlogistic chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Table 2.2 Times involved in the ship process . . . . . . . . . . . . . . . . . . . . 46Table 2.3 Opportunity areas per processes of the apple export logistic

chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Table 3.1 Member states and associate members of ECLAC

(ECLAC 2018) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Table 3.2 Summary of type of ship charter . . . . . . . . . . . . . . . . . . . . . . 64Table 3.3 MTCC’s regional membership. . . . . . . . . . . . . . . . . . . . . . . . 69Table 4.1 Contribution of each individual risk to the inventory cost of

NPs based on their unit cost . . . . . . . . . . . . . . . . . . . . . . . . . 79Table 4.2 Relationship of life cycle and type of demand with the

adapted tool for NP planning . . . . . . . . . . . . . . . . . . . . . . . . 81Table 4.3 Percentage of impact of the risks (%Risk) on NP inventory

costs in relation to life cycle and type of demand . . . . . . . . . 82Table 4.4 Estimated monthly demand for the NP (millions of

multi-flavor lollipops) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Table 4.5 Contribution of each individual risk to the inventory costs

of the NP based on its unit cost (multi-flavor lollipops) . . . . 89Table 4.6 Estimated weekly demand for each month (millions of

multi-flavor lollipops) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90Table 4.7 Estimated weekly average demand and standard deviation

for each quarter (millions of multi-flavor lollipops). . . . . . . . 90Table 4.8 Estimated cumulative demand per quarter according to the

RSISNP model (millions of multi-flavor lollipops) . . . . . . . . 90Table 4.9 Inventory supply strategies for each quarter based on the

%Risk associated to the NP’s cycle time andtype of demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Table 5.1 Papers related to QFD and FQFD applications . . . . . . . . . . . 101Table 5.2 Main applications of the QFD - TOPSIS. . . . . . . . . . . . . . . . 103Table 5.3 Linguistic scale for the FQFD . . . . . . . . . . . . . . . . . . . . . . . . 106

xxv

Table 5.4 Qualification of the What’s . . . . . . . . . . . . . . . . . . . . . . . . . . 110Table 5.5 Importance of the What’s . . . . . . . . . . . . . . . . . . . . . . . . . . . 111Table 5.6 Weighting of the criteria (How’s) . . . . . . . . . . . . . . . . . . . . . 112Table 5.7 Data for the case study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112Table 5.8 Weighted normalized matrix and ideal positive and negative

solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113Table 5.9 Priority order result of the applied methodology . . . . . . . . . . 113Table 6.1 A classic and effective approach to inventory

management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124Table 6.2 Forecasting method selected for quarterly smoothing

demand of the finished product . . . . . . . . . . . . . . . . . . . . . . . 126Table 6.3 Comparison of error between the AOP forecast and the

smoothed forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128Table 6.4 Difference between the real demands, AOP forecast, and

smoothed forecast by sales channel . . . . . . . . . . . . . . . . . . . . 129Table 6.5 Summary of ABC classification . . . . . . . . . . . . . . . . . . . . . . 130Table 6.6 Total monthly inventory cost in January 2017 according to

the proposed inventory models . . . . . . . . . . . . . . . . . . . . . . . 133Table 6.7 Current inventory cost in January 2017. . . . . . . . . . . . . . . . . 133Table 6.8 Comparison of the purchasing cost of raw materials

proposed versus current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Table 6.9 Final comparison of results . . . . . . . . . . . . . . . . . . . . . . . . . . 134Table 7.1 Compatibility between strategic pillars (Matrix 1) . . . . . . . . . 144Table 7.2 Synergies between strategic pillars and main objectives

(Matrix 2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144Table 7.3 Synergies matrix between main objectives (Matrix 3) . . . . . . 144Table 7.4 Synergies matrix between operational practices and main

objectives (Matrix 4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144Table 7.5 Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Table 7.6 Calculation of the safety stock . . . . . . . . . . . . . . . . . . . . . . . 153Table 8.1 Annual growth of e-commerce, data collected. . . . . . . . . . . . 166Table 8.2 Model summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167Table 8.3 Regression model coefficients . . . . . . . . . . . . . . . . . . . . . . . . 168Table 8.4 Express import projection, foreign trade impact . . . . . . . . . . 169Table 8.5 Qualitative comparison of options . . . . . . . . . . . . . . . . . . . . . 171Table 9.1 Results of (Q, R) model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199Table 10.1 Collected cases from the literature review . . . . . . . . . . . . . . . 228Table 11.1 Data for the joint inventory policy definition . . . . . . . . . . . . 254Table 11.2 Amounts to be ordered and total cost using the

EOQ method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255Table 11.3 Best individual for IGS with and without constraints . . . . . . 255Table 11.4 Best individual for DGS with and without constraints. . . . . . 255Table 12.1 Inventory report of imported corn 2017 . . . . . . . . . . . . . . . . 264Table 12.2 Cost without demand planning in 2016. . . . . . . . . . . . . . . . . 265

xxvi List of Tables

Table 12.3 Scale criteria and description. . . . . . . . . . . . . . . . . . . . . . . . . 268Table 12.4 Oliver wight checklist in Agro Perú . . . . . . . . . . . . . . . . . . . 269Table 12.5 Matrix to manage supply planning . . . . . . . . . . . . . . . . . . . . 271Table 12.6 Standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271Table 12.7 Seed line ABC classification . . . . . . . . . . . . . . . . . . . . . . . . . 272Table 12.8 Activities established for S&OP meetings . . . . . . . . . . . . . . . 279Table 13.1 Comparison of experimental versus literature mechanical

properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Table 13.2 Models of linear regression for frictionless conditions. . . . . . 292Table 13.3 ANOVAa table for the frictionless model . . . . . . . . . . . . . . . 293Table 13.4 Regression coefficients for the frictionless model . . . . . . . . . 293Table 13.5 Models of linear regression, µ = 0.05 . . . . . . . . . . . . . . . . . . 293Table 13.6 ANOVAa table for the model with µ = 0.05 . . . . . . . . . . . . . 294Table 14.1 Data for samples after crimping and pulling testing . . . . . . . 310Table 14.2 Experimental identification of affecting variables divided as

controllable and non-controllable. . . . . . . . . . . . . . . . . . . . . . 314Table 14.3 Controllable factors and levels proposed for the

experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314Table 14.4 Levels proposed for the non-controllable factors . . . . . . . . . . 314Table 14.5 L9 array for controllable factors . . . . . . . . . . . . . . . . . . . . . . 315Table 14.6 L4 array combinations for non-controllable factors . . . . . . . . 315Table 14.7 Results of the experiment for the pulling force testing

(values in Newtons, N) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315Table 14.8 Signal-to-noise ratio (S/N) values . . . . . . . . . . . . . . . . . . . . . 316Table 14.9 Selected values for controllable factors . . . . . . . . . . . . . . . . . 316Table 14.10 Data of the confirmation run after the pulling

force testing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316Table 15.1 Some MP reported in the literature . . . . . . . . . . . . . . . . . . . . 331Table 15.2 MSR operationalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332Table 15.3 Descriptive statistics for MSR . . . . . . . . . . . . . . . . . . . . . . . . 334Table 15.4 Regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334Table 15.5 MSR differences in accordance with MP

implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336Table 15.6 Performance differences in accordance with MP

implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336Table 16.1 Historical development of the manufacturing process . . . . . . 349Table 16.2 Resume of documents found with the syntaxes “fused

deposition modeling advances” . . . . . . . . . . . . . . . . . . . . . . . 354Table 16.3 Resume of documents found with the syntaxes “fused

deposition modeling advances”and “additive manufacturing” . . . . . . . . . . . . . . . . . . . . . . . . 355

Table 16.4 Resume of documents found with the syntaxes title: “fuseddeposition modeling advances” and body “advances” . . . . . . 355

List of Tables xxvii

Table 17.1 Economic data for shoe manufacturing in Mexico(INEGI 2014) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368

Table 17.2 Symbols for probability distributionsin Kendall’s notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375

Table 17.3 Data of the stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379Table 17.4 Process path . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383Table 17.5 Results of the analytical model . . . . . . . . . . . . . . . . . . . . . . . 384Table 17.6 Performance of the production line . . . . . . . . . . . . . . . . . . . . 384Table 17.7 Cost data and cost per station (Mexican pesos, MXN) . . . . . 385Table 17.8 Economic assessment of increasing the capacity of stations

2, 6, and 7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385Table 18.1 Selected factors and its corresponding values . . . . . . . . . . . . 393Table 18.2 Full factorial 25 array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 394Table 18.3 Nominal and measured values . . . . . . . . . . . . . . . . . . . . . . . . 401Table 18.4 Optimal factor values for volume and roughness. . . . . . . . . . 404Table 18.5 Results of dimensional analysis . . . . . . . . . . . . . . . . . . . . . . . 406Table 18.6 Percentage of variation in relation to reference value . . . . . . 407Table 18.7 Geometrical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408Table 19.1 Sequence description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413Table 19.2 Relationship between supply chain processes and horizontal

integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418Table 19.3 Value creation modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418Table 19.4 Evaluation models in Industry 4.0 . . . . . . . . . . . . . . . . . . . . . 421Table 20.1 Conversion: operation of turning against

orthogonal cutting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438Table 20.2 Results of the maximum shear stresses and maximum

cutting temperatures at different cutting conditions . . . . . . . . 439Table 21.1 Survey scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472Table 21.2 Surveyed industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475Table 21.3 Surveyed job positions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475Table 21.4 Descriptive analysis of the items . . . . . . . . . . . . . . . . . . . . . . 476Table 21.5 Latent variable coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . 478Table 21.6 Total effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480Table 22.1 Characteristics of lumbar symptoms in the case group . . . . . 492Table 22.2 Characteristics of lumbar symptoms in the case group . . . . . 492Table 22.3 Distribution of occupation data between cases

and controls. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493Table 22.4 Description of socio-demographic data in cases

and controls. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494Table 22.5 OR (odds ratio) and confidence intervals for

socio-demographic variables . . . . . . . . . . . . . . . . . . . . . . . . . 495Table 22.6 Percentage reported a backache by sector and gender

(Eurofound 2007 cited by Norasteh 2012). . . . . . . . . . . . . . . 497Table 23.1 Distribution of time in the production line no. 1 . . . . . . . . . . 512

xxviii List of Tables

Table 23.2 Unproductive time composition. . . . . . . . . . . . . . . . . . . . . . . 512Table 23.3 Corrective maintenance by speciality. . . . . . . . . . . . . . . . . . . 512Table 23.4 Failure mode in the GRIC and the risk priority number . . . . 514Table 23.5 Failure mode in the GRIC and the risk priority number . . . . 515Table 23.6 Variables that compose the problem description . . . . . . . . . . 515Table 23.7 Variables that compose the problem description

in the roller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516Table 23.8 Variables that compose the solution . . . . . . . . . . . . . . . . . . . 516Table 24.1 Questionnaire proposed by Sackey and Bester for

universities (Sackey and Bester 2016) . . . . . . . . . . . . . . . . . . 533Table 24.2 Educational offer in local universities . . . . . . . . . . . . . . . . . . 537Table 24.3 Example of relation between the pillars and some the

engineering careers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 538Table 24.4 Example of relation between the pillars and the subject

engineering careers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540

List of Tables xxix

Contributors

Mario Acosta-Flores Faculty of Chemical Sciences and Engineering,Autonomous University of the State of Morelos, Cuernavaca, Morelos, Mexico

Julián Israel Aguilar-Duque Faculty of Engineering, Architecture and Design,Autonomous University of Baja California, Ensenada, Baja California, Mexico

Noé G. Alba-Baena Department of Industrial Engineering and Manufacturing,Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua, Mexico

Giner Alor-Hernández Division of Research and Postgraduate Studies,Tecnologico Nacional de México/Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

Guillermo Amaya-Parra Faculty of Engineering, Architecture and Design,Autonomous University of Baja California, Ensenada, Baja California, Mexico

Martín Darío Arango-Serna Departamento de Ingeniería de la Organización,Universidad Nacional de Colombia, Medellín, Antioquia, Colombia

Dulce Arroyo-García Department of Logistics and Supply Chain Management,Universidad Popular Autónoma del Estado de Puebla, Puebla, Puebla, Mexico

David Atayde-Campos Department of Industrial Engineering and Manufacturing,Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua, Mexico

Liliana Avelar-Sosa Department of Industrial Engineering and Manufacturing,Autonomous University of Ciudad Juarez, Ciudad Juárez, Chihuahua, Mexico

Sonia Valeria Avilés-Sacoto Department of Industrial Engineering, UniversidadSan Francisco de Quito, Quito, Ecuador

Lorena Bearzotti School of Transport Engineering, Pontificia UniversidadCatólica de Valparaíso, Valparaíso, Chile

María Angeline Bofill-Altamirano Department of Industrial Engineering,Universidad San Francisco de Quito, Quito, Ecuador

xxxi

Hugo Ferreira Braga Tadeu Fundação Dom Cabral, Belo Horizonte, Brazil

Antonio Juan Brioñes-Peñalver Department of Economy and Business,Universidad Politecnica de Cartagena, Cartagena, Spain

Santiago-Omar Caballero-Morales Universidad Popular Autónoma del Estadode Puebla A.C., Puebla, Mexico

Ismael Canales-Valdiviezo Department of Electrical Engineering andComputational, Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua,Mexico

Salvatore Cannella Università Degli Studi Di Catania, Catania, Italy

Mario Gustavo Chong Chong Graduate School, Universidad del Pacífico,Jesús María, Lima, Peru

Juan Carlos Cisneros-Ortega Universidad Popular Autónoma del Estadode Puebla, Puebla, Mexico

Leonilda Coba Department of Supply Chain Management, UniversidadDel Pacifico, Lima, Peru

Guillermo Cortés-Robles Division of Research and Postgraduate Studies,Tecnologico Nacional de México/Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

André Luis de Castro Moura Duarte Insper Institute, São Paulo, Brazil

Juan José Delfín-Vázquez Department of Mechanical Engineering, InstitutoTecnológico Superior de Cajeme, Ciudad Obregón, Sonora, Mexico

José Roberto Díaz-Reza Department of Electrical Engineering andComputational, Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua,Mexico

Eloy Dimas-Celestino División de Ingeniería Industrial, Universidad Politécnicade Valle de México, Tultitlán, Estado de México, Mexico

Francisco J. Estrada-Orantes Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua,Mexico

Jorge Luis García Alcaraz Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua,Mexico

Flor Araceli García-Castillo Universidad Autónoma de Nuevo León, Facultad deCiencias Físico Matemáticas, San Nicolás Los Garza, Nuevo León, Mexico

xxxii Contributors

Abimael H. García-Pérez Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua,Mexico

Domingo García-Perez de Lema Financial Economics and Accounting,Universidad Politécnica de Cartagena, Cartagena, Spain

Jesús González-Feliu Institut Henri Fayol, Saint-Etienne Cedex, France

Rosa Guadalupe González-Ramírez Faculty of Engineering and AppliedSciences, Universidad de Los Andes, Chile, Santiago, Chile

Lizbeth Habib-Mireles Universidad Autónoma de Nuevo León, Facultad deIngeniería Mecánica Y Eléctrica. Centro de Investigación E Innovación EnIngeniería Aeronáutica, San Nicolás de Los Garza, Nuevo León, Mexico

Juan Luis Hernández-Arellano Department of Design, Autonomous Universityof Ciudad Juarez, Ciudad Juárez, Mexico

Salvador Hernández-González Department of Industrial Engineering,Tecnológico Nacional de México en Celaya, Celaya, Guanajuato, Mexico

Guadalupe Maribel Hernández-Muñoz Universidad Autónoma de Nuevo León,Facultad de Ingeniería Mecánica Y Eléctrica. Centro de Investigación E InnovaciónEn Ingeniería Aeronáutica, San Nicolás de Los Garza, Nuevo León, Mexico

George Leal Jamil Fundação Dom Cabral, Belo Horizonte, Brazil

José Alfredo Jiménez-García Department of Industrial Engineering, TecnológicoNacional de México en Celaya, Celaya, Guanajuato, Mexico

Eusebio Jiménez-López Department of Research and Development,Technological University of Southern Sonora-ULSA NOROESTE-IIMM, CiudadObregón, Sonora, Mexico

José Antonio Juanico-Lorán División de Ingeniería Industrial, UniversidadPolitécnica de Valle de México, Tultitlán, Estado de México, Mexico

Emmanuel Juárez-García Department of Logistics and Supply ChainManagement, Universidad Popular Autónoma del Estado de Puebla, Puebla,Puebla, Mexico

Gisela Landa-Gálvez Escuela de Post Grado, Universidad del Pacífico, JesúsMaría, Lima, Peru

Jorge Carlos León-Anaya División de Ingeniería Industrial, UniversidadPolitécnica de Valle de México, Tultitlán, Estado de México, Mexico

Mónica López-Campos Industrial Engineering Department, UniversidadTecnológica Federico Santa María, Valparaíso, Chile

Contributors xxxiii

Luis Alberto López-Ramos Division of Research and Postgraduate Studies,Tecnologico Nacional de México/Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

César López-Rosas Graduate School, Universidad del Pacífico, Jesús María,Lima, Peru

Baldomero Lucero-Velázquez Department of Mechanical Engineering, InstitutoTecnológico Superior de Cajeme, Ciudad Obregón, Sonora, Mexico

Diego Fernando Manotas-Duque Grupo de Investigación en Logística yProducción, Universidad del Valle, Colombia, Cali, Valle, Colombia

José Antonio Marmolejo-Saucedo Facultad de Ingeniería, UniversidadPanamericana, Ciudad de México, Mexico

José-Luis Martínez-Flores Department of Logistics and Supply ChainManagement, Universidad Popular Autónoma del Estado de Puebla, Puebla,Puebla, Mexico

Valeria Martínez-Loya Department of Industrial Engineering and Manufacturing,Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua, Mexico

Javier Molina-Salazar Department of Industrial Engineering and Manufacturing,Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua, Mexico

Fernando Montemayor-Ibarra Universidad Autónoma de Nuevo León, Facultadde Ingeniería Mecánica Y Eléctrica. Centro de Investigación E Innovación EnIngeniería Aeronáutica, San Nicolás de Los Garza, Nuevo León, Mexico

Daniel Núñez-Pauca Escuela de Post Grado, Universidad del Pacífico,Jesús María, Lima, Peru

Juan Carlos Osorio-Gómez Grupo de Investigación en Logística y Producción,Universidad del Valle, Colombia, Cali, Valle, Colombia

Roberto Pérez-Franco Centre for Supply Chain and Logistics, Deakin University,Melbourne, Australia

Magdiel Pérez-Lara Department of Logistic and Supply Chain, UniversidadAutónoma de Nuevo León, San Nicolás de Los Garza, Nuevo León, Mexico

Alicia Pérez-Pérez Graduate School, Universidad del Pacífico, Jesús María, Lima,Peru

Lilia Roselia Prado-León University of Guadalajara, Guadalajara, Jalisco,Mexico

Vivian Rambarath-Parasram Centre for Maritime and Ocean Studies, TheUniversity of Trinidad and Tobago, Chaguaramas, Trinidad and Tobago

xxxiv Contributors

Ricardo Ramírez-Tapia Department of Industrial Engineering, TecnológicoNacional de México en Celaya, Celaya, Guanajuato, Mexico

Secundino Ramos-Lozano Department of Industrial Engineering andManufacturing, Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua,Mexico

Lázaro Rico-Pérez Department of Industrial Engineering and Manufacturing,Universidad Autónoma de Ciudad Juárez, Juárez, Chihuahua, Mexico

Eduardo Roldán-Reyes Division of Research and Postgraduate Studies,Tecnologico Nacional de México/Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

Rosa Amelia Rosales-Cinco University of Guadalajara, Guadalajara, Jalisco,Mexico

José Efrén Ruelas-Ruíz Department of Mechanical Engineering, InstitutoTecnológico Superior de Cajeme, Ciudad Obregón, Sonora, Mexico

Tomás Eloy Salais-Fierro Department of Logistic and Supply Chain, UniversidadAutónoma de Nuevo León, San Nicolás de Los Garza, Nuevo León, Mexico

Víctor Jaime Saldarriaga-Romero Escuela de Negocios, InstituciónUniversitaria CEIPA, Sabaneta, Antioquia, Colombia

Diana Sánchez-Partida Department of Logistics and Supply Chain Management,Universidad Popular Autónoma del Estado de Puebla, Puebla, Puebla, Mexico

Cuauhtémoc Sánchez-Ramírez Division of Research and Postgraduate Studies,Tecnologico Nacional de México/Instituto Tecnológico de Orizaba, Orizaba,Veracruz, Mexico

María-del-Rosario Sánchez-Vega Universidad Popular Autónoma del Estado dePuebla A.C., Puebla, Mexico

William Sarache Facultad de Ingeniería y Arquitectura, Departamento deIngeniería Industrial, Universidad Nacional de Colombia, Manizales, Colombia

Jania Astrid Saucedo-Martínez Department of Logistic and Supply Chain,Universidad Autónoma de Nuevo León, San Nicolás de Los Garza, Nuevo León,Mexico

Cesar Shimabuku Department of Supply Chain Management, Universidad DelPacifico, Lima, Peru

Sukhjit Singh Centre for Maritime and Ocean Studies, The University of Trinidadand Tobago, Chaguaramas, Trinidad and Tobago

Ulises Jesús Tamayo-Pérez Faculty of Engineering, Architecture and Design,Autonomous University of Baja California, Ensenada, Baja California, Mexico

Contributors xxxv

Cezar Taurion I2a2 (Applied Artificial Intelligence Institute), Brasilia, Brazil;IBM, Rio de Janeiro, Brazil; PWC, Rio de Janeiro, Brazil

José Claudio Villamonte-Cornejo Graduate School, Universidad del Pacífico,Jesús María, Lima, Peru

Jorge A. Vivares Facultad de Ingeniería y Arquitectura, Departamento deIngeniería Industrial, Universidad Nacional de Colombia, Manizales, Colombia

Julian Andres Zapata-Cortes Escuela de Negocios, Institución UniversitariaCEIPA, Sabaneta, Antioquia, Colombia

xxxvi Contributors