DIPARTIMENTO DI STUDI LINGUISTICI E CULTURALI
CORSO DI LAUREA MAGISTRALE IN
LANGUAGES FOR COMMUNICATION IN INTERNATIONAL ENTERPRISES AND
ORGANIZATIONS
Case studies on the adoption of Industry 4.0 in the automotive Mexican companies.
A qualitative analysis
Prova finale di:
Roberta Avasto
Relatore:
Prof. Giovanni Bonifati
Correlatore
Prof.ssa Margherita Russo
Anno Accademico 2017-2018
ABSTRACT
The objective of this paper is to analyze the digital transformation and its impact on Mexican companies.
The idea started from the general plan of collaboration to a research on the diffusion of digital technologies in the automotive sector in Mexico.
New technologies have changed our lives and habits forever. History teaches us that evolution has never stopped and has now reached unthinkable levels of automation and digitization.
The aim of the thesis is to analyze the opportunities provided by the innovations that characterize the industry 4.0, the main enabling technologies and to analyze its impact on the world of work.
Industry 4.0 is considered a new industrial stage in which vertical and horizontal manufacturing processes integration, and product connectivity can help companies to achieve higher industrial performance.
After providing a clear definition of the 4.0 industry, the first chapter will focus on the analysis of the determining factors of this revolution, the impact that these changes will have on employment, the new skills required, the human role in the new production process an all the key concepts related to industry 4.0.
The research team composed of Professor Margherita Russo, Professor Anna Simonazzi, and Professor Jorge Carreto Sanginés, has personally conducted interviews in Mexican automotive companies in Mexico during the period from January 15th to February 15th. The material obtained in the form of a recorded interview, and then audio file, was used to test and verify the functionality of new speech-to-text transcription software.
In fact, in the second chapter, I will analyze in detail the tools used to transcribe automatically the audio interviews and note down all the features that emerged.
The revision of the transcripts of the interviews has a double purpose:
• Checking the functionality of the software used;
• Withdrawing the topics of the interviews concerning industry 4.0 especially related to Mexican companies’ state of art.
This way, I become involved in a project that already existed and that was even the subject of analysis on CAQDAS1, especially on ATLAS.ti8.
The third chapter will concern qualitative research analysis methods applied to new CAQDAS. I will show which tools are suitable to carry out a qualitative analysis of the transcriptions obtained.
The fourth and last chapter will describe the steps taken to process the contents: the processing and analysis of data, in fact, will be used to describe the phenomena detected.
Within this research, interviews conducted will be the leitmotif of this thesis.
The topics covered will concern the use of software for automatic transcription and analysis of qualitative data.
The objectives of this thesis are:
• Verifying the validity of the automatic transcription software;
• Verifying the usefulness of the functionalities of the qualitative analysis software;
• Providing an analysis of the subject, the adoption of Industry 4.0 in the automotive Mexican companies, highlighting its peculiarities.
1 CAQDAS, computer-assisted qualitative data analysis software
ABSTRACT FR
L'objectif de cette thèse est d'analyser la transformation numérique et son poids dans les entreprises mexicaines.
L'idée est partie du plan général de collaboration à une recherche sur la diffusion des technologies numériques dans le secteur automobile au Mexique.
Les nouvelles technologies ont changé nos vies et nos habitudes pour toujours. L'histoire nous enseigne que l'évolution n'a jamais cessé et a atteint des niveaux impensables d'automatisation et de numérisation.
L'objectif de la thèse est d'analyser les opportunités offertes par les innovations qui caractérisent l'industrie 4.0, les principales technologies habilitantes et leur impact sur le monde du travail.
Industry 4.0 est considéré comme une nouvelle phase industrielle au cours de laquelle l'intégration verticale et horizontale des processus de production et la connectivité des produits peuvent aider les entreprises à améliorer leur performance industrielle.
Après une définition claire de l'industrie 4.0, le premier chapitre se concentrera sur le thème de l'analyse des déterminants de cette révolution, l'impact que ces changements auront sur l'emploi, les nouvelles compétences requises, le rôle humain dans le nouveau processus de production.
L'équipe de recherche, composée des professeurs Margherita Russo, Anna Simonazzi et Jorge Carreto Sanginés, a personnellement mené des entretiens au Mexique du 15 janvier au 15 février.
Le matériel obtenu sous forme d'interview enregistrée, puis de fichiers audio, a été utilisé pour tester et vérifier la fonctionnalité du nouveau logiciel de transcription audio en texte.
En fait, dans le deuxième chapitre, j'analyserai en détail les outils utilisés pour transcrire automatiquement les entrevues audio et noter toutes les caractéristiques qui ont émergé.
La révision subséquente des transcriptions des entrevues a un double objectif :
• Vérifiez la fonctionnalité du logiciel utilisé ;
• Prenez les thèmes des interviews concernant l'industrie 4.0, liés notamment à l'état de l'art des entreprises mexicaines.
De cette façon, j'ai été impliqué dans un projet déjà existant qui a déjà été analysé sur CAQDAS2, notamment sur ATLAS.ti8.
Le troisième chapitre traitera des méthodes traditionnelles d'analyse de la recherche qualitative appliquées au nouveau CAQDAS. Je vous montrerai quels sont les outils adaptés pour effectuer une analyse qualitative des transcriptions obtenues.
Le quatrième et dernier chapitre décrira les étapes du traitement du contenu : le traitement et l'analyse des données serviront, en effet, à décrire les phénomènes détectés.
Dans le cadre de cette recherche, les entretiens menés seront les fils rouges de cette thèse.
Les sujets abordés concerneront l'utilisation de logiciels de transcription automatique et l'utilisation de logiciels d'analyse semi-automatique de données qualitatives.
Les objectifs de cette thèse sont :
• Vérification de la validité du logiciel de transcription automatique ;
• Vérification de l'utilité des fonctionnalités du logiciel d'analyse qualitative logiciel
;
• Analyse du sujet, l'adoption de Industry 4.0 dans le secteur automobile au Mexique, en soulignant ses particularités.
2 logiciel d'analyse de données qualitatives assistées par ordinateur
ABSTRACT IT
L'obiettivo di questa tesi è analizzare la trasformazione digitale e il suo peso nelle aziende messicane.
L'idea è partita dal piano generale di collaborazione ad una ricerca sulla diffusione di tecnologie digitali nel settore automobilistico in Messico.
Le nuove tecnologie hanno cambiato per sempre la nostra vita e le nostre abitudini. La storia ci insegna che l'evoluzione non si è mai fermata e ha raggiunto livelli impensabili di automazione e digitalizzazione.
Lo scopo è di analizzare le opportunità offerte dalle innovazioni che caratterizzano l'industria 4.0, le principali tecnologie abilitanti e di analizzarne l'impatto sul mondo del lavoro.
L'industria 4.0 è considerata una nuova fase industriale in cui l’integrazie verticale e orizzontale dei processi produttivi e la connettività dei prodotti può aiutare le aziende a ottenere prestazioni industriali più elevate.
Dopo aver fornito una chiara definizione dell'industria 4.0, il primo capitolo si concentrerà sul tema della l'analisi dei fattori determinanti di questa rivoluzione, l'impatto che questi cambiamenti avranno sull'occupazione, le nuove competenze richieste, il ruolo umano nel nuovo processo produttivo.
Il team di ricerca, composto dalla professoressa Margherita Russo, dalla professoressa Anna Simonazzi e dal professor Jorge Carreto Sanginés, ha condotto personalmente delle interviste in Messico nel periodo dal 15 gennaio al 15 febbraio.
Il materiale ottenuto sotto forma di intervista registrata, e quindi di file audio, è stato utilizzato per testare e verificare la funzionalità del nuovo software di trascrizione audio in testo.
Infatti, nel secondo capitolo, analizzerò nel dettaglio gli strumenti utilizzati per trascrivere automaticamente le interviste audio e annotare tutte le caratteristiche emerse.
La successiva revisione delle trascrizioni delle interviste ha un duplice scopo:
• Verificare le funzionalità del software utilizzato;
• Prelevare gli argomenti delle interviste riguardanti l'industria 4.0 legati in particolare allo stato dell'arte delle compagnie messicane.
In questo modo, sono stata coinvolta in un progetto già esistente e già oggetto di analisi su CAQDAS3, soprattutto su ATLAS.ti8.
Il terzo capitolo riguarderà i metodi tradizionali di analisi della ricerca qualitativa applicati ai nuovi CAQDAS. Mostrerò quali sono gli strumenti adatti per effettuare un'analisi qualitativa della trascrizioni ottenute.
Il quarto ed ultimo capitolo descriverà i passi compiuti per elaborare i contenuti: la l'elaborazione e l'analisi dei dati, infatti, servirà a descrivere i fenomeni rilevati.
All'interno di questa ricerca, le interviste condotte saranno il filo conduttore di questa tesi.
Gli argomenti trattati riguarderanno l'utilizzo di software per la trascrizione automatica e l'utilizzo di software per l’analisi semi-automatica di dati qualitativi.
Gli obiettivi di questa tesi sono:
• Verifica della validità del software di trascrizione automatica;
• Verifica dell'utilità delle funzionalità del software di analisi qualitativa
• software;
• Analisi dell'argomento, l'adozione di Industry 4.0 nel settore automotive in Messico, evidenziando le sue peculiarità.
3 software per l'analisi qualitativa dei dati assistita da computer
INDEX
INTRODUCTION ... 1
1 INDUSTRY 4.0 ... 3
1.1 Smart factory ... 3
1.2 Industrial revolutions: the 4 main revolutions in the industrial world ... 4
1.3 Industry 4.0: an overview ... 6
1.4 Optimization and digitization of production ... 7
1.5 Machine learning (ML) ... 8
1.5.1 Definitions ... 8
1.6 Artificial intelligence (AI) ... 9
1.6.1 Definition ... 9
1.7 Machine learning: progress in artificial intelligence ... 9
1.8 Key concepts of industry 4.0 ... 10
1.8.1 Big data ... 11
1.8.2 Internet of things (IOT) ... 12
1.8.3 Cloud computing ... 13
1.8.4 Additive manufacturing ... 14
1.8.5 Augmented reality (AR)... 14
1.8.6 Autonomous robots ... 15
1.9 Impact on employment... 15
1.10 Digital innovation across sectors ... 17
1.10.1 Agri-food sector ... 17
1.10.2 Tourism sector ... 19
1.10.3 Retail sector ... 19
1.10.4 Smart home ... 20
1.10.5 Automotive industry ... 22
1.11 Policies - Preparing tomorrow's workforce ... 23
1.11.1 Demand of skills ... 25
1.12 Structure and content of the thesis ... 26
2 SPEECH TO TEXT RECOGNITION ... 27
2.1 What is a transcription? ... 28
2.2 Speech Recognition ... 29
2.3 Why choose automatic over manual transcription... 30
2.4 State of the art ... 30
2.4.1 Speechmatics ... 30
2.4.2 IBM Watson ... 31
2.4.3 AssemblyAI ... 32
2.5 Call to an ASR Service ... 33
2.5.1 Autoedit 2.0 ... 34
2.5.2 Transcript Editor ... 37
2.6 Results of Automatic transcription ... 39
3 QUALITATIVE DATA ANALYSIS: FROM THE MANUAL WAY TO THE USE OF CAQDAS ... 42
3.1 What is qualitative research? ... 42
3.2 How to conduct an accurate qualitative research? ... 42
3.2.1 Prepare the research ... 42
3.2.2 Collect and analyze data ... 43
3.3 Grounded theory as method of the qualitative research ... 45
3.4 Grounded theory at the base of CAQDAS ... 46
3.5 NCT method ... 47
3.6 ATLAS.ti ... 48
3.6.1 ATLAS.ti current versions ... 49
3.7 The VISE Principle ... 49
3.7.1 Visualization ... 49
3.7.2 Integration ... 50
3.7.3 Serendipity ... 50
3.7.4 Exploration ... 50
3.8 Teamwork... 50
3.9 General steps when working with ATLAS.ti ... 51
3.9.1 Creating a new project ... 52
3.9.2 Importing an existing project ... 52
3.9.3 Adding documents to the projects and commenting the documents ... 53
3.9.4 Grouping the documents and commenting the document groups ... 53
3.9.5 Assigning existing codes or create new codes ... 53
3.9.6 Coloring and grouping the codes and commenting the code groups ... 53
3.9.7 Exporting the project ... 54
3.10 Navigation Area ... 54
3.10.1 Home ... 55
3.10.2 Search Project ... 55
3.10.3 Analyze tab ... 55
3.10.4 Import / Export ... 56
3.10.5 Tools & Support ... 56
3.11 Main concepts and features... 57
3.11.1 Documents ... 57
3.11.2 Quotations ... 57
3.11.3 Coding ... 58
3.11.4 Memo ... 59
3.12 ATLAS.ti: aspects to be improved ... 60
4 CONCEPTUAL RECONSTRUCTION OF THE ANALYSIS ... 61
4.1 General steps ... 61
4.2 Working method ... 62
4.3 Main results ... 63
4.4 United States–Mexico–Canada Agreement (USMCA): an overview ... 64
4.5 Content analysis ... 66
4.5.1 Peasa ... 66
4.5.2 MTA Mexico ... 71
4.5.3 Eurotranciatura Mexico... 74
4.5.4 Rassini ... 77
4.6 Post-analysis reflections ... 81
CONCLUSION... 83
BIBLIOGRAPHY ... 85
FIGURE INDEX ... 89
TABLES INDEX ... 90
APPENDIX ... 91
1
INTRODUCTION
The objective of this research is to analyze the digital transformation and its impact on companies, humans and daily habits.
New technologies have become part of our lives in a very short time, drastically changing our habits: through the mobile network we are constantly connected with the rest of the world, thanks to our smartphone we can access information everywhere and share thoughts and information with friends and anyone interested.
Consumer demands are increasing, and this has inevitably led to a new industrial and technological era.
Solutions such as IoT, cloud, augmented reality, data analytics are just some of the innovations that have transformed the way of doing business in recent years and represent only the beginning of a technological and cultural revolution within organizations and with customers.
The digital economy, born thanks to the Internet, is entering a new phase: the new challenge is how to conceive, produce and distribute products and services on the market.
The use of new technologies allows for structural changes in the way processes are organized. The availability of data and information is able to influence the production process, business models and the creation of products.
The current revolution is not by chance called the fourth industrial revolution because looking back at other industrial revolutions we can see that the word “production” often come with by discoveries that have allowed the development of new production techniques and new strategies thus allowing improvements in productivity. Starting from this consideration it is possible to identify an analogy between the revolutions of the past and what is happening in today's market, the identification of the set of phenomena that are characterizing and shaping the production sector in recent years, going so far as to profoundly transform the processes of those companies interested in maintaining high production standards and ensuring the satisfaction of the needs of its customers.
After identifying the main causes and effects of this revolution, we will discuss the difficulties related to personnel management, the difficulties in finding skilled workforce for new job position and how the company structures will depend on and the impact on employment.
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The aim of understanding the situation in Mexico is particularly challenging because it was in the middle of a big change for a technology transformation with the adoption of digital technologies and the new trade agreements with U.S. and Canada. So, it is interesting and useful to find out to which extent Mexico was changing the technologies and to which extent the relationships among companies are changing because of these technological advancement and trade agreement changes. It is an opportunity to have a deep investigation to see where Mexico in this position is.
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1 INDUSTRY 4.0
Scientific and technological progress has reached a remarkable development today.
History teaches us that man has never stopped his technological-scientific advance.
At the beginning it had none of the comforts we know today, but humans, instinctively, try to make life as comfortable as possible. Through his own intelligence, man has created from nothing what was missing by nature.
Compared to the recent past, characterized by well-defined production cycles, the requests for customization are constantly increasing and it is necessary to manage a wide range of different products in a market where the customer increasingly tries to modify the product in order to achieve a higher degree of customization.
We begin to talk about Industry 4.0, in relation to the need to implement new technologies within industrial production systems.
1.1 Smart factory
The term Industry 4.0 is synonymous with Factory of the future, intelligent factory or smart factory and serves to indicate precisely the Fourth Industrial Revolution, which began at the dawn of the 21st century. During the past years, we have moved from mass production to one characterized by the greatest possible customization of the product.
The smart factories adopt an innovative approach to production as they allow a strong customization of products based on customer needs, traceability throughout the supply chain, greater energy and economic efficiency, given by the ability of the machines to self-regulate, and an acceleration of decision-making times thanks to the availability of information and the ability of the machines to make autonomous and predictive decisions.
The objective is the adoption of production processes aimed at reducing waste through the continuous improvement of processes, reducing costs and waiting times in the various phases of product processing.
Particular mention should be made of the possibility offered by the Internet to directly connect equipment such as robots and machinery, but also tools not directly involved in
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a production process, using the "Internet of Things", which refers exactly to a network that allows the transfer of information between inanimate objects.
There has also been a progressive increase in the amount of data to be managed, which will then have to be analyzed in order to obtain useful information from them. This led to the need to adopt more advanced systems capable of managing what are called "big data".
1.2 Industrial revolutions: the 4 main revolutions in the industrial world
To better understand the reasons and causes that led to the birth of the industry 4.0 is useful to make brief references to the historical reasons of the previous industrial revolutions.
The First Industrial Revolution took place in the second half of the 18th century in England and concerned mainly the textile and metallurgical sectors. This transformed an economic system that was historically composed of agriculture, crafts and commerce into one based on industry characterized by the use of machines first driven by mechanical force (chassis) and then thanks to the invention of the steam engine (steam engine). There are no specific causes that triggered this event, but it is useful to refer to the sum of several factors. Some of the reasons are: an agricultural class not very strong as in other countries, a consolidated bourgeoisie enriched through trade and the strength of the country due to the fact of being a commercial and military power, the increase in population and therefore in demand. The consequences were the creation of capitalism and the working class. (Wikipedia, 2019a)
The Second one took place in Western Europe during the second half of the 1800s. Its causes are mainly due to scientific progress in that historical period that led to various inventions applicable to industry and, in common life, generated better living conditions.
In particular, the discoveries that most influenced this revolution were oil and electricity, since which many other things were created.
In addition, medicine undertook many discoveries and treatments for ancient diseases such as tuberculosis, plague, leprosy, and malaria; hygiene and health conditions were improved; the discovery of steel led to a great development of railways and ships, telecommunications had the birth of the telegraph and then the telephone.
We have witnessed the creation of real great industries.(Wikipedia, 2019b)
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Then came the Third Industrial Revolution: it took place in the second half of the 1900s (second post-war period) in the countries of the first world. It was caused by the growth of scientific and technological knowledge born during the world wars for war purposes and the exploitation of atomic energy. For the United States and the Soviet Union, the space race began, opening new knowledge in a field that was still little known. The inventions that most determined the revolution were those in computer science, electronics and telematics: microprocessors, radio, Internet and then the Web have generated a progressive evolution over time.
We had the creation of multinationals and the phenomenon of globalization.
The third industrial revolution led us to the fourth industrial revolution. (SENTRYO, 2017)
The German initiative that launched the “Industrie 4.0 programme” in 2011, coding the number 4 as a symbol of the current industrial phase, marked the transition from the third to the fourth industrial revolution, or otherwise identified as industry 4.0, and focuses on all those digital technologies that are able to increase the interconnection and the cooperation of resources (persons or systems). It is based on the use of cyber-physical systems. CPS, cyber-physical systems connected to the Internet, facilitate human- machine and machine-machine interactions through the real-time exchange of information and allow industrial production to be automated and autonomous. (HYDAC, 2019)
Figure 1 - The Industrial Revolutions (Deloitte, 2019)
6 1.3 Industry 4.0: an overview
Industry 4.0 describes the organization of production processes based on technologies and devices that communicate autonomously with each other (via computer, virtual models), along the entire value chain. The expected 'intelligent' factory includes computer-guided systems that monitor production processes, creating virtual reproductions of the real world.
Many of the technologies related to Industry 4.0 have already been used for some time;
so, the evolution consists in the integrated use of all these technologies, which will lead to a significant transformation of production processes.
In fact, industry 4.0 has as its prerogative the complete control of the industrial production cycle connecting all the devices, and their behaviors are constantly stored and monitored;
this leads to a management that would be impossible for a human worker. The fact that machines are constantly connected, and that information is always and meticulously saved, opens countless benefits such as remote control, traceability, operation analysis, real-time updates, monitoring. The collection of information and the connection between devices will produce an output that will be much more useful and efficient than the sum of the results of all individual devices.
The application of 4.0 technologies has many facets that revolve mainly around the need to integrate new digital technologies (cyber-physical systems, cloud computing, sensors and connected devices) into industrial processes. Everything is linked to the automation and optimization of processes through their digitization (machine to machine languages, Industrial Internet of Things).(HYDAC, 2019)
Another important aspect to consider as the key factor of this new evolution is the need for customization; in this way you can get out of the standard format of the industry and create a working method tailored to your goals, which will lead to an increasingly optimized and unique result.
Smart and connected products are very different from the tangible products that typified the previous industrial era.
So, what is the 4.0 industry?
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We can define it as a production activity based on connected systems, capable of connecting the physical and digital worlds. In other words: optimization and digitalization in processes.
1.4 Optimization and digitization of production
The objective of the 4.0 industry is therefore to optimize and digitize production processes to the point of rendering them:
• Flexible
• Automatic
• Connected
As a matter of fact, production processes have evolved dramatically. The application of digital technologies allows to reinvent processes, increasing their efficiency and effectiveness and freeing humans from repetitive and unimportant tasks.
Using sensors, it is possible to have real time information about the progress of production processes; new automation and “robotization” (robotic automation) technologies allow to suspend or adjust a production process at any time and even remotely. Also, in this case the result is an enormous flow of data.
In this scenario, smart factories adopt an innovative approach to production as they allow a strong customization of products based on customer needs, traceability throughout the supply chain, greater energy and economic efficiency, given by the ability of the machines to self-regulate, and an acceleration of decision-making times thanks to the availability of information and the ability of the machines to make autonomous and predictive decisions. Industry 4.0, in fact, means that all steps and production processes such as production planning, logistics development and productivity control are fully connected and integrated.
At the base of this evolution there the concepts called machine learning (ML) and artificial intelligence (AI). It is important to understand the meaning, the link between them and why they are so important for the industry 4.0.
8 1.5 Machine learning (ML)
1.5.1 Definitions
• “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding those data and information in the form of observations and real-world interactions.” (Faggella, 2019a)
• “Machine learning is the science of getting computers to act without being explicitly programmed.” (Ng, 2019)
• “Machine learning research is part of research on artificial intelligence, seeking to provide knowledge to computers through data, observations and interacting with the world. That acquired knowledge allows computers to correctly generalize to new settings.” (Faggella, 2019b)
• “Machine Learning is the science of getting computers to learn as well as humans do or better.” (Faggella, 2019c)
Machine learning is a method that’s grounded in the idea that machines can learn from data, define patterns, and take actions with minimum human input. The lifecycle of machine learning could be explained as follow:
• Ask the right question/set the problem;
• Collect and prepare data;
• Train the algorithm;
• Test it;
• Collect feedback;
• Use feedback to improve the algorithm.
(SteelKiwi Inc., 2019)
Machines that learn are useful to humans because, with all their processing power, they’re able to more quickly highlight or find patterns in big data that would have otherwise been missed by human beings. Machine learning is a tool that can be used to enhance humans’
abilities to solve problems and make informed inferences on a wide range of problems.
9 1.6 Artificial intelligence (AI)
1.6.1 Definition
Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. (Techopedia, 2019)
Some of the activities that computers can perform thanks to artificial intelligence are:
speech recognition (technologies that enables the recognition and translation of spoken language into text by computers), learning, planning, problem solving.
AI has become an essential part of the new technology industry.
Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them.
1.7 Machine learning: progress in artificial intelligence
Machine learning is artificial intelligence. Yet artificial intelligence is not machine learning. We can consider machine learning as a subset of artificial intelligence.
Anyway, progress in artificial intelligence (AI) are conceivable thanks to the implementation of machine learning.
Companies today use machine learning in maintenance and support services. By means of sensors, artificial intelligence helps capture the energy consumption of individual machines, analyze maintenance cycles, and then optimize them in the following stage.
Have a look at what Gary Sims from Android Authority says about the differences between AI and machine learning: (SteelKiwi Inc., 2019)
• Artificial intelligence is the idea of a computer being able to do abstract thinking, analyze things within context, and be creative while not being intelligent itself.
It’s a machine with the ability to solve problems that are typically solved by humans with our natural intelligence. Artificial intelligence is much broader and more general than machine learning.
• Machine learning is a large area within artificial intelligence. It refers to the process of a machine learning from experience. It deals only with algorithms that automatically extract patterns from data. The idea of machine learning is that you
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take a data set, feed it into an algorithm that learns from it, and as the output the algorithm makes predictions.
Currently, the most commonly used learning method is image recognition, but the use of digital assistants, face recognition, speech recognition and speech processing, text and video analysis, autonomous driving, automated translation and automated transcription are established as well. (Hannover Messe, 2019)
The evolution we are living goes far beyond the simple automation of business processes but involves the optimization and therefore the reduction of waste. All this is made possible by the enabling technologies of Industry 4.0, which we will see below in 1.8.
1.8 Key concepts of industry 4.0
The revolution is characterized by several technical advancements which could even accelerate in the coming years:
• Big Data;
• Autonomous robots,
• Internet of things (IoT),
• Cloud computing
• Additive manufacturing (3D printing),
• Augmented reality (AR).
Table 1 - Summary of the concepts that define the future vision of industry 4.0 (Erboz, 2017)
THE CONCEPTS THE DEFINITIONS OF THE CONCEPTS
THE EXAMPLES OF THE CONCEPTS
BIG DATA Large, complex datasets
that affect the decision making of the companies
Big data analytics, algorithms, software programs
AUTONOMOUS ROBOTS
Solve complex tasks which cannot be solved by human
Kuka Iwaa has the learning ability to
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achieve some certain tasks
INTERNET OF THINGS Connection of the physical objects and systems
Smart home
CLOUD COMPUTING Shared platforms that serve to the multiple users
Gmail
ADDITIVE
MANUFACTURING
3D printing technology, producing in mass customization
3D printers
AUGMENTED REALITY Human-machine interaction
Pokémon Go!
1.8.1 Big data
The term big data generally means a huge amount of information so extensive in terms of volume, speed and variety that specific technologies and analytical methods are required for the extraction of value or knowledge. (Wikipedia, 2019c)
Big data is collected at every contact with the company and at every action in the network:
subscribing to a newsletter, a Google search, booking a trip, using GPS, a Facebook like, subscribing to a YouTube channel, buying online or at a store are just some of the interactions through which organizations collect data on consumers. The challenge for companies is to manage this huge flow of data to take advantage of it thanks to the analytics and data scientists. (Dal Porto, 2016)
In fact, analytics is the set of techniques and algorithms necessary to extract useful information from the data and to obtain a value, and a data scientist is a person employed to analyze and interpret complex data.
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Data can circulate and be reproduced, shared or manipulated instantaneously, on a huge scale and at little cost. These knowledge and information can be shared instantaneously among any number of actors, regardless of their location.
Business data are increasingly used to optimize processes within firms but also within supply chains. Companies exploit abundant real-time shop floor data in order to reduce waste, to increase energy savings, increased flexibility, and better asset utilization. Data are even concerned to predict the maintenance needs of production systems, significantly lowering maintenance costs. (OECD Publishing and OECD, 2019)
1.8.2 Internet of things (IOT)
The Internet of Things refers to information technology systems (IT systems) connected to all sub-systems, processes, internal and external objects, supplier and customer networks; that communicate and cooperate with each other and with humans. According to some estimates, the number of devices communicating with each other has surpassed the number of people communicating with each other. According to other projections, by 2020, 30 billion devices will be connected to the internet. (Smit et al., 2016)
The devices connected to the Internet and equipped with sensors to collect data and transmit information, has revolutionized consumer markets. An estimated 20 billion IoT devices exist today, connecting objects and data and people around the globe. In just two years’ time, that number is expected to exceed 30 billion, thanks in large part to approximately 3,000 U.S. IoT startups employing 340,000 workers. (Costello, 2018) Indeed, thanks to the IoT, product and process traceability can be achieved, which inevitably leads to improved product quality and inventory optimization. In addition, customer requirements will be directly linked to production and this will make orders much more immediate by reducing unsold and waste.
We are talking about real-time interaction that will benefit production lines, which are more flexible, and which will be able to meet customer demands and create an increasingly personalized product.
Among the benefits of the data made available by the Internet of Things we can mention better surveillance or the optimization of the conditions of workers: through the analysis of the movement of a worker you could even identify a physical problem not otherwise determinable.
13 1.8.3 Cloud computing
Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.
The cloud computing is based on a cloud infrastructure, that is the collection of hardware and software. The cloud infrastructure can be viewed as containing both a physical layer and an abstraction layer. The physical layer consists of the hardware resources that are necessary to support the cloud services being provided, and typically includes server, storage and network components. The abstraction layer consists of the software deployed across the physical layer, which manifests the cloud characteristics.
This cloud model is provisioned through three different service models:
• Software as Service (SaaS). The capability provided to the consumer is to use the provider’s applications running on a cloud infrastructure. The applications are accessible from various client devices such as a web browser (e.g. web-based email), or a program interface. The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
• Platform as a Service (PaaS). The capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages. The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, or storage, but has control over the deployed applications and possibly configuration settings for the application-hosting environment.
• Infrastructure as a Service (IaaS). The capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer can deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, and deployed applications; and possibly limited control of select networking components. (Mell and Grance, 2011)
14 1.8.4 Additive manufacturing
The additive manufacturing technique allows for the shaping of internal and external components with an additive method. (Tennø, 2017)
By owning their own 3D printing machines (and even entire labs), companies that introduce a number of products to the market a year, have an ability to fabricate a larger number of prototypes in a shorter time. It gives them more time for testing and perfecting their products before they go into mass production. Food industry giant Cadbury is a great example here. Thanks to 3D printing, their R&D department is able to quickly prototype and test new types of sweets which can go into production quicker than when they were using traditional manufacturing techniques.
With additive manufacturing technology at hand, industrial manufacturers, small companies, and makers can reduce the time it takes them from sketching the idea to manufacturing or from an order to shipping products to clients.
With a 3D printed prototype similar in size and properties to a final product, it’s much easier to communicate and verify the idea.
Additive manufacturing technology is also very economical when it comes to the amount of materials being used in the process. 3D printers use only the exact amount of filament they need for fabricating the object and its support structures. The latter can be melted back into filament instead of becoming waste. (ZMorph, 2016)
1.8.5 Augmented reality (AR)
According to the Apple’s CEO Tim Cook “Augmented Reality has the ability to amplify human performance instead of isolating humans.” (Fitzsimmons, 2019)
We’re living in a world of big data and designers and tech innovators have been long thinking about how to use the information and make sense of it.
Augmented reality is the technology that expands our physical world by adding layers of digital information. AR does not create the entire artificial environment to replace the real environment with a virtual one. AR appears in direct view of an existing environment and adds sound, video, graphics.
Basically, AR technology creates a connection between the digital and the physical realms. In this new field, data and complex analytics translate into interactive images and animations that be added to our real world.
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After the hype of “Pokémon Go!” in 2016, Augmented Reality became more approachable. People now understand how it works and, of course, they wait for more.
(ELEKS, 2017)
1.8.6 Autonomous robots
Robots are used in manufacturing industries in order to solve complex tasks which cannot be solved easily by a human.
There will be more automation and more robots that see and hear and can work together with the workers: specifically, the robots will be able to carry out autonomously the assembly or some operations such as the selection of objects and their handling, but above all help humans in tasks where cognitive skills and human manuals are required, but where the objects to be assembled to move are too large or the tasks too repetitive or dangerous or harmful to health. Thanks to the creation of robots and artificial intelligence systems, it is possible to make a job that is generally less tiring and repetitive.
1.9 Impact on employment
The world of work changes very quickly: new jobs disappear, but many more are created based on new knowledge.
The problem of jobs is common to the whole of the industrialized population of the world and is caused not only by an increase in technology in the different areas but also by the demand for the necessary knowledge to be able to exploit these technologies.
New technologies can not only easily replace workers who are engaged in "routine" tasks but have changed the relationship between machines and the type of tasks performed by workers. The machines also generate data during downtime.
In addition, companies can calculate production costs, the cost of each worker, and decide whether or not to outsource a certain stage of production, compare internal costs with the prices charged by any external suppliers: this creates competition between internal and external workers, imposing strong pressure on employees.
So, the Internet of Things (IoT) can greatly increase efficiency, but it can also turn the factory into an invasive surveillance system.
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In the best forecasts, robots will manage 90% of the daily work situations, but a human will be needed in the remaining 10% of cases. This suggests that there are no risky jobs in their entirety, but that most of the process can be automated. (Cipriani et al., 2018) The latest OECD study estimates a minimum of 30 to a maximum of 60% of jobs at risk of automation (32 countries based on the Survey of Adult Skills (PIAAC) which considers the characteristics of jobs and workers). (Simonazzi, 2019)
Workers no longer have to carry out large manual tasks, but more technical knowledge is required regarding the machines and the operation of the various devices supplied by the company.
Everything is accelerated by the development of new technologies and the emergence of new needs. The way in which all occupations are carried out, from the most demanding to the simplest, changes and the necessary skills are constantly evolving.
The introduction of robots in factories brings with it not only a more efficient production in several respects, but also an inevitable reduction in jobs. The work of a robot often equals that of 10 men, with the difference that the product will be more precise, identical to the previous and less subject to imperfections.
In recent years, a new professional figure has emerged who plays a fundamental role in data analysis: the data scientist. Data scientists are experts who are able to extract important information from huge amounts of data in order to help define or meet specific needs and business objectives. The role of data scientists in data analysis is becoming increasingly important as companies increasingly rely on Big Data and analytics to support their decision-making processes and on cloud, automation and machine learning technologies as key components of their IT strategies. (CIO, 2017)
17 1.10 Digital innovation across sectors
Figure 2 - Digital applications (OECD Publishing and OECD, 2019)
Figure 2 provides an overview of how the digital transformation is changing the agri- food, automotive and retail sectors. (OECD Publishing and OECD, 2019)
They are not the only sectors involved in this change.
The 4.0 industry is a revolution that is affecting several sectors which are slowly increasing their level of digitization by modern technologies.
At this stage, we will look specifically at 5 sectors to understand how deep digital innovation is:
• Agro-food;
• Tourism;
• Retail;
• Smart home;
• Automotive.
1.10.1 Agri-food sector
In agriculture, machinery is now equipped with many sensors that capture information about the conditions of the crop, allowing for the development of smart farming services.
Those data can be used to help farmers to optimize the use of water and other inputs to boost yields, for instance.
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Farm insurance services are also investing in advanced data collection technologies (e.g.
drones, sensors) to assess damages suffered after severe weather events, fire, etc.; these technologies significantly reduce costs linked to field inspections and accelerate insurance claim processes by farmers.
In agriculture, intelligent and digitally connected machineries (IoT) help farmers and contribute to improve the accuracy of operations and optimize the use of water, fertilizers, pesticides and whatever concerned and to give each plant exactly what it needs to grow optimally.
Agricultural machineries are equipped with sensors that collect information about crop conditions (e.g. soil conditions, irrigation, air quality, presence of pests).
Drones, covering sizeable area, including those that are difficult to access, are very useful because they are equipped with sensors used for crop scouting and spraying.
They take high-quality images, providing near real-time snapshots of the farm at a relatively low cost compared to satellite imagery.
The introduction of robots is also essential in farming. Fruit-picking, harvesting and milking are examples of the repetitive and standardized tasks that agricultural robots can perform. Although they are generally in the early stages of development, there are high expectations for these robots, which will contribute substantially to the improvement of agricultural conditions and efficiency: they will allow for more automated and precise agricultural practices.
Large agricultural machinery producers and suppliers are taking advantage of the big amounts of data collected with IoT farm applications and robots, that combined with other data and information about, for example, weather, market data, help to develop “smart farming” services.
Big data analytics and AI are employed to help farm-management in making decisions.
For instance, these systems can help the farmer decide when and where to plant, choose the type of crop to plant depending on soil conditions and market prices, and automatically give instructions to agriculture robots to perform certain tasks. The downside, given the large investments required for the deployment of such systems, is that the expansion of smart farming is still prerogative of large producers, but developments are unstoppable and will become increasingly accessible to all.
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Another important aspect, especially for consumers, is that in the agri-food supply chain, the IoT, deploying sensors connected to software systems, it is possible to trace products’
origins and track their trajectory as well as their transportation and storage conditions, improving the value chain’s transparency.
1.10.2 Tourism sector
The tourism sector is benefiting from the opportunities offered by digital innovation. For example, augmented reality is very useful for visitors to enjoy a new experience in historical sites and museums.
Several platforms are also changing the way of doing tourism and the tourism industry.
For example, on the one hand, platforms to search, compare and book accommodation and transportation options (e.g. Booking.com, Lastminute.com) lower the search and transaction costs of self-organizing trips, challenging traditional travel agencies.
On the other hand, platforms such as Airbnb provide peer-to-peer accommodation services, whereby private owners can easily rent their spare rooms or properties. This puts competitive pressure on the hotel industry.
1.10.3 Retail sector
Companies will have to re-structure their approaches to reaching their target segments and selling goods.
In the field of retail, digital innovations aim at enhancing the consumer experience (in both physical and online shopping) and optimizing processes (e.g. logistics, warehouse management). The largest investments focus on data collection (e.g. purchasing and browsing data) and data analytics capabilities. Such data provide insights on consumer needs and preferences that are used to customize the shopping experience, for instance by sending personalized advertisements and promotions.
Innovations in physical stores include smart dressing rooms, where customers can order the color and size of the product of choice via a screen in the fitting room and receive personalized recommendations based on previous selection of items; digital mirrors that enable customers to easily try clothes on virtually using augmented reality systems; and automatic payment systems that allow customers to skip check-out lines.
Whereas, innovations in online retail include applications that allow personalizing a product (for example shoes) through 3D visualizations.
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The automatic reordering of products may also become more common; the Amazon Dash Replenishment Service already allows connected devices (e.g. washing machines, coffee machines) to reorder products automatically (e.g. laundry detergent, coffee beans) when supplies are running low. All these innovations, however, remain marginal and are mainly deployed by large retailers.
The retail sector is also using IoT and robotics to optimize their inventories management and even other processes. Drones and autonomous vehicles may also offer new possibilities for products’ delivery in the future.
Big retailers are significantly investing in digital technology development to improve their activities, for instance investing intensively in data collection and data analytics capabilities (in order to create even more personalized promotions and to predict consumer trends), augmented and virtual reality (developing digital mirrors that enable customers to easily try clothes on virtually in the physical store) and IoT to improve inventory management. These investments aim at enhancing the consumer experience and optimizing retailers’ activities and profits.
1.10.4 Smart home
There is no official definition of Smart Home, but among the many definitions proposed in the literature, the two considered most effective are the following:
“Smart Home is the integration of technology and services through home networking for a better quality of living.” (DELL’ACQUA and CORIO, 2018)
“A Smart Home can be defined as a residence equipped with computing and information technology which anticipates and responds to the needs of the occupants, working to promote their comfort, convenience, security and entertainment through the management of technology within the home and connections to the world beyond.” (Kazmierzak, 2012)
According to McKinsey, the positive trend in increased computing power, advanced big data analytics, and the emergence of artificial intelligence (AI) could cause a tipping point in the smart home evolution. (Quarterly, 2017)
Those trends suggest that within a decade, many of us will live in “smart homes” that will feature an intelligent and coordinated ecosystem of software and devices, or “homebots”
which will manage and perform household tasks and even establish emotional connections with us.
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A smart home will be similar to a human central nervous system. A central platform, or
“brain,” will be at the core. Individual homebots will perform a wide variety of tasks, including supervising other bots.
Homebots can be as diverse as their roles: big, small, invisible (such as the software that runs systems or products). Some homebots will be companions or assistants, others wealth planners and accountants. We will have homebots as coaches, window washers, and household managers, throughout our home.
A good practical example of what a homebots is, is the Google Nest Learning Thermostat (Nest was an independent company acquired in 2014 by Alphabet, Google parent company). That is a smart thermostat, indeed its specificity is to learn what temperature the user likes through the day, and once learnt it, the thermostat will adjust autonomously the temperature through the day. Usually it takes one week to learn it. (Timokhina, 2017) Nest is able to understand you like eating breakfast at a specific degree and it warms up the house as you get out of bed or the Nest Thermostat can use sensors and your phone’s location to check if you have left, then sets itself to an Eco Temperature to save energy, but Nest perform a lot of other tasks to make life better and more comfortable. (Google, 2019)
Today the potential of a truly smart home is still unrealized, because the market of the home-technology remains fragmented. Each bot will need to follow standard protocols to communicate with one another. Not to mention the costs for the realization of such an advanced project.
Television certainly contributes to the expansion of the knowledge of these new robots.
Just think of "Alexa": Amazon Alexa, known simply as Alexa, is a virtual assistant developed by Amazon.
Talking to those who already own Alexa, it was discovered that several subjects said that they think of Alexa as a friend. That doesn’t develop from merely providing the train schedule when asked. It comes because Alexa evokes a sense of support, through its sensitive omnipresence and nuanced voice interaction. Interacting with Alexa really is like talking to a friend.
After all, those devices need to make people feel at home.
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Those are the main challenges that actors in the Smart Home world need to face, while the benefits of a smart home are easily imaginable.
1.10.5 Automotive industry
Digital technology developments are completely reshaping the automotive sector. The evolution is applied to vehicle, e.g. car connectivity, autonomous driving, and in production, with smart factories or Industry 4.0 applications.
Connected cars allow for enhanced driver safety and convenience, with services such as automatic emergency calls after an accident, real-time road hazard warnings to drivers, car repair diagnostics, systems of networked parking that reduce time for that chore, and navigation systems that optimize route planning by taking into account real-time traffic conditions.
There are different levels of automation. All new car models currently offer driving assistance systems, which support the driver with certain tasks such as parking.
Self-driving cars, currently tested in pilot projects, will be able to drive autonomously and react to their environment without the intervention of the driver.
With full automation, cars drive independently and react to their environment without intervention from the driver. Such systems are currently being tested in pilot projects, but opinions differ greatly on when full automation might be achieved. It is clear that evolution is only at the beginning.
Anyway, focusing on the benefits already generated by new technologies, especially thanks to connectivity, Car sharing allows members access through a mobile app to vehicles owned by car-sharing companies. Members typically pay an initial or yearly membership fee and usage fees by the mile, hour, or a combination of both. Members of Zipcar, for example, can view all available vehicles around their location and book them by the hour.
Ride-hailing platforms, such as Uber, allow matching real-time requests for rides with available drivers, speeding up the dispatch task and leading to greater utilization of the vehicles.
The automotive industry is also a leader in developing “smart factories”, adopting a variety of Industry 4.0 applications, including Internet-connected robotics, data analytics and cloud and high-performance computing, among others.
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The automotive industry is leader in the adoption of more advanced industrial robots.
While robots have begun to be present in other sectors (agriculture and retail), not all activities equally lend themselves to automation.
For instance, certain assemblies are not yet automated because they require manual skill and human cognitive abilities or various jobs of finishing or verification.
Digital innovations are often launched to the market even when they are not in their fully finished version (i.e. in beta versions), allowing for more experimentation and product fine-tuning based on consumers’ feedback and real-world product performance data.
That’s what Tesla did: Tesla Motors installed a “public beta” of its Autopilot software in more than 70 000 vehicles to test its robustness in different traffic scenarios. (OECD Publishing and OECD, 2019)
1.11 Policies - Preparing tomorrow's workforce
As described above, the impact on work is not irrelevant, especially for the new professional figures required.
In an increasingly technological context, it is essential to have qualified and competent figures capable of dealing with new technologies and with the reading and interpretation of the data produced, which is why companies should invest in the training of their staff and in the acquisition of new highly specialized figures.
It is fundamental to strengthening researchers’ digital skills, ensuring appropriate investments in digital tools and infrastructures for research, and setting incentives for build digital skills. Innovation authorities should collaborate with education and research authorities to identify the new skills needed in this era of digital transformation.
The full exploitation of productivity gains related to process and product innovations is not achieved if you do not have professional profiles capable of interacting with new technologies and everything related to them. In this context, the ability of companies to evaluate in depth the state of their skills and the possible need to update them can be crucial in order to quickly understand the opportunities for growth related to new technologies.
The need to update skills is closely linked to technological and organizational change in enterprises.(Deloitte, 2019)
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Focusing on youth training, according to the Education Commission, it is predicted that by 2030, more than half of the nearly 2 billion youth worldwide will not have the skills or qualifications necessary to participate in the emerging global workforce. In practical terms, this translates to more than 50 percent of tomorrow’s human capital being potentially unprepared to enter the workforce.
Improvement is needed in the approach to youth skill development, such as making learning and training interactive, multicultural, engaging, constructive, and practical.
Barriers to skill development and employment include lack of access to resources, lack of knowledge about careers and skills needed, lack of opportunities, tools and training.
The responsibility to prepare youth for future jobs depends on government, business community, schools, and civic society. The speed of technological updates often surpasses the speed at which current and future talent can be upskilled and trained, leaving a gap between skills needed and skills available.
Figure 3 - The Global Youth Survey (Deloitte, 2019)
As shown in Figure 3, according to a The Global Youth Survey conducted by the company Deloitte in order to understand the perspectives of youth from around the globe:
• 54% of youth respondents have not heard of Fourth Industrial Revolution;
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• 39% of youth report that their formal school did not prepare them with the skills for the job they want and required;
• 79% of youth report that they had to go outside of formal school to get the skills for the job they want and required;
• 79% of youth’s personal career interests do not align with jobs available in their community;
• 1/3 of youth respondents said that their college or university did not prepare them with the skills required in current era.
So, what skills are needed for 4IR and required by advanced companies?
1.11.1 Demand of skills
The skills important for the future of work include soft skills and technical skills.
• Soft skill: Personal attributes, social skills, and communication abilities that support interpersonal relationships and interactions with others, creativity, complex problem solving, relationship building, communication, emotional intelligence, and critical thinking.
• Technical skill: Knowledge and capabilities to perform specialized tasks;
understanding the industry-specific demands requires input from the industries themselves. Such input can create opportunities for industry-driven demand analysis, work-based learning, and talent-need projections to redefine the education to training to employment pipeline. (Deloitte, 2019)
The shortcomings highlighted by companies in terms of skills concern all professional groups, problem solving skills, together with the ability to plan the use of resources and technical skills.
Using their size and resources, governments can position themselves as a bridge between education, employers, and youth. This positioning varies in terms of engagement. Some governments, for example, establish standards qualifications frameworks, while others have implemented mandatory requirements for employee training.
In the same way enterprises should invest internally and adopt policies of education that will be useful for the growth of their employees and consequently of the company.
26 1.12 Structure and content of the thesis
The first chapter is aimed to understand the fundamental arguments that characterize the fourth industrial revolution. Some of these will be the subject of analysis in the next chapters.
The second chapter will focus on the Automatic Speech Recognition (ASR) Software.
The third chapter will concern the study of tools suitable to carry out a qualitative analysis, the methodology involved through the use of CAQDAS (Computer-assisted qualitative data analysis software).
The fourth chapter will deal with the qualitative analysis. It concerns the explanation of what I used in ATLAS.ti8 and the analysis of the results. The processing and analysis of the data will be used to describe the phenomena detected and to search for relationships between the variables built to detect them.
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2 SPEECH TO TEXT RECOGNITION
In order to draft this thesis, my starting point was joining a project of research on the diffusion of digital technologies in the automotive sector in Mexico.
The research design builds on the analysis of the supply chains and trade agreements, and on interviews with suppliers, with experts and with business associations of the automotive industry.
Interviews, in the form of audio files, needed interpretation and transcription. Automatic transcription software was used for the transcription, but the interpretation and revision of the transcriptions required human intervention.
During the initial work phase, in fact, I was responsible for reviewing the transcripts that the software has generated automatically.
The first step involved the use of automatic transcription software, a technology characteristic of the 4.0 industry and in continuous experimentation and evolution.
As already mentioned, the final objective of the thesis is to learn about the automotive sector in Mexico and its progress in terms of industry 4.0.
Having recorded interviews available, it was essential to understand how to make the most of them and to obtain the most interesting and unknown information.
In order to do that, I checked the efficiency of various automatic transcription software.
To their full potential, these software significantly reduce working time, but, as we will see, they have limitations for which human intervention is necessary. For this reason, much of the work has been devoted to the revision of the automatic transcriptions generated by ASR software, which we will discuss in this chapter.
The revision of automatic transcriptions has a twofold purpose:
• Verify the benefits and functionality of the software used
• Extrapolate the themes contained in the interviews
In the first analysis it is interesting to understand what a transcription is.
28 2.1 What is a transcription?
A transcription is a document created by typing or writing everything that is heard from either an audio or video. The transcript can be an exact word for word document, or the transcriptionist can clean up certain parts of the speech.
There are many reasons a transcription for audio or video content may be needed:
• Transcribing audio content for the deaf or hard of hearing.
Being a brand that offers accessibility alternatives to those with hearing impairments not only enhances corporate social responsibility and improves the brand image but has the potential to open up services to a whole new audience;
• Transcribing audio for subtitles or closed captions.
To create subtitles or closed captions you need a full transcription document from the video. We’ll then use the transcript to add time-code markers that will reference what is being said at concrete times in the video. This means that the subtitles or closed captions will appear on screen at the exact time it’s supposed to. The transcript may need to be condensed further to work as subtitles as there is a character limit implemented;
• Transcribing audio for voice over.
We transcribe the speech when the final voice over needs to be time-synced to a video.
This means the timing of the voice over speech will match up with specific timings in the video and therefore be in sync with what is happening on screen. This is a common requirement when translating the voice over in corporate videos or explainers;
• Transcribing for translation purposes.
The first step of translation is always transcribing. Once we have the time codes in place for subtitle or voice over use, we can then use that same transcription document to create as many different foreign language versions as required. The time code stamps will stay the same in every language and will mark where each section of speech starts and ends.
This is important as the translator may have to condense the translation to fit in with the voice over or subtitle timings so that they still match up with what’s on screen;
• A verbatim transcript can be kept for legal, technical, or as a professional reference.
