Recent refinements in Industry 4.0 and Machine Learning demonstrate the positive effects of using deep learning models for intelligent maintenance. The primary benefit of Deep Learning (DL) is its capability to extract attributes and make fast, accurate, and automated predictions without supervision. However, DL requires high computational power, significant data preprocessing, and vast amounts of data to make accurate predictions for intelligent maintenance. Given the considerable obstacles, meta-learning has been developed as a novel way to overcome these challenges. As a learning technique, meta-learning aims to quickly acquire knowledge of new tasks using theminimal available data by learning through meta-knowledge. There has been less research in the area of using meta-learning for Predictive Maintenance (PdM) and we considered it necessary to conduct this review to understand the applicability of meta-learning’s capabilities and functions to PdM since the outcomes of this technique seem to be rather promising. The review started with the development of a methodology and four research questions: (1) What is the taxonomy of meta-learning for PdM?, (2) What are the current state-of-the-art methodologies? (3) Which datasets are available for meta-learning in PdM?, and (4) What are the open issues, challenges, and opportunities of meta-learning in PdM?. To answer the first and second questions, a new taxonomy was proposed and meta-learnings role in predictive maintenance was identified from selected 55 papers. To answer the third question, we determined which types of datasets and their characteristics exist for this domain. Finally, the challenges, open issues, and opportunities of meta-learning in predictive maintenance were examined to answer the final question. The results of the research questions provided suggestions for future research topics.

As digital transformation and Industry 4.0 have become a path for organizations to strengthen their performance, create value and acquire competitive advantages in different industries and regions, its appeal has increased among companies from diverse sectors and government institutions in the Colombian economy, but limited generalizability and replicability has been found in current models, which pose difficulties for their translation into the Colombian shipyard industry. Thus, this research study assessed the pertinence of utilizing digital transformation and industry 4.0 technologies as an enabler for the achievement of organizational goals, while focusing on the Colombian shipyard industry. To do so, a case study with mixed methods approach was utilized to collect data from interviews and surveys with the members of a Colombian Shipyard; which after applying analytical procedures and techniques, yielded the existence of an alignment between the Industry 4.0 and the achievement of their operational goals, as well as the identification of the strengths, challenges and implications of deploying digital technologies and a digital transformation strategy for a Colombian shipyard. Moreover, this study contributed to the understanding of digital transformation processes in relation to the specific characteristic, metrics, and factors of the Colombian shipyard industry, and it is a step forward into the further development of applications within different Colombian sectors by academics and practitioners in the field.

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Die „Industrie 4.0“ steht für eine neue Stufe der Organisation und Steuerung der gesamten Wertschöpfungskette über den Produktlebenszyklus, der sich an den zunehmend individualisierten Kundenwünschen hinweg orientiert und von der Idee, dem Auftrag über die Entwicklung und Fertigung, die Auslieferung eines Produkts an den Endkunden bis hin zum Recycling, einschließlich der damit verbundenen Dienstleistungen erstreckt. Hierfür ist die Basis die Verfügbarkeit aller relevanten Informationen in Echtzeit durch Vernetzung aller an der Wertschöpfung beteiligten Instanzen sowie die Fähigkeit, aus den Daten den zu jedem Zeitpunkt optimalen Wertschöpfungsfluss abzuleiten (Ovtcharova 2016). Weiterhin lässt sich die Industrie 4.0 als durchgängige Vernetzung der Produktion und des gesamten Produktlebenszyklus mit Hilfe von Internettechnologien verstehen. Dabei sind Kunden- und Maschinendaten vernetzt (vgl. Abbildung 1). Weiterhin tauschen die intelligenten Maschinen und Systeme Informationen untereinander in Echtzeit aus. Somit verschmilzt die Produktion mit der Informations- und Kommunikationstechnik und wird von Werkstücken sowie Maschinen selbstständig, flexibel, effizient und ressourcenschonend gesteuert. Weiterhin werden die ausgetauschten Informationen und entstandenen Daten ständig geprüft, sodass es zu keiner Zeit zu einem Stillstand oder Engpass in der Produktion kommen kann. Durch die Anwendung von Industrie 4.0 entsteht ein großer Nutzen, der das Unternehmen im globalen Wettbewerb stark macht. [... aus Punkt 1]

Sjöfartshögskolan i Kalmar har kurser i bl.a. programmerbara styrsystem. För att undervisningen ska ta steget in i Industri 4.0 vill skolan först ha utlåtande om svårighetsgraden att implementera en IO-linkgivare i befintlig process. Syftet med arbetet var att installera och konfigurera en IO-linkgivare i skolans ånganläggning för att på så sätt kunna utvärdera implementeringsarbetets svårighetsgrad. Detta sattes sedan i relation till att istället installera en traditionell analog tryckgivare. Genom att göra en installation och konfiguration implementerades IO-linkgivaren och för kontroll av funktion genomfördes en provning mot befintlig mätutrustning som referenser. Resultatet visade på en relativt komplicerad implementering men ett noggrant mätvärde samt många möjligheter utöver det. / The Maritime Academy in Kalmar wanted to learn about how the implementation of IO-link sensors in a steam process could be used in teaching. The work aimed to investigate how an IO-link sensor works and provide input to develop the content of the school's programming courses. This was done by first implementing a new pressure sensor (IO-Link) in a steam process and then performing the electrical installation in the existing automatic cabinet. In the control cabinet, the existing PLC system was expanded with an IO link master for communication between sensor and PLC system. The result was a fully functional IO link sensor that transferred the vapor pressure in the system to the PLC system. As the IO link sensor for this purpose does not use many of the functions that may be sought in an IO link sensor, it becomes an expensive and complicated installation. If the teaching and course content are developed to use more of the IO link donor's competence, it can justify a higher purchase price and a more complicated implementation.

Surface treatments are often used in the manufacturing industry to change the surface of a product, including its related properties and functions. The occurrence of degradation and corrosion in surface treatment processes can lead to critical breakdowns over time. Critical breakdowns may impair the properties of the products and shorten their service life, which causes increased lead times or additional costs in the form of rework or scrapping.  Prevention of critical breakdowns due to machine component failure requires a carefully selected maintenance policy. Predictive maintenance is used to anticipate equipment failures to allow for maintenance scheduling before component failure. Developing predictive maintenance policies for surface treatment processes is problematic due to the vast number of attributes to consider in modern surface treatment processes. The emergence of smart sensors and big data has led companies to pursue predictive maintenance. A company that strives for predictive maintenance of its surface treatment processes is Seco Tools in Fagersta. The purpose of this master's thesis has been to investigate the occurrence of critical breakdowns and failures in the machine components of the chemical vapor deposition and post-treatment wet blasting processes by mapping the interaction between its respective process variables and their impact on critical breakdowns. The work has been conducted as a Six Sigma project utilizing the problem-solving methodology DMAIC.  Critical breakdowns were investigated combining principal component analysis (PCA), computational fluid dynamics (CFD), and statistical process control (SPC) to create an understanding of the failures in both processes. For both processes, two predictive solutions were created: one short-term solution utilizing existing dashboards and one long-term solution utilizing a PCA model and an Orthogonal Partial Least Squares (OPLS) regression model for batch statistical process control (BSPC). The short-term solutions were verified and implemented during the master's thesis at Seco Tools. Recommendations were given for future implementation of the long-term solutions. In this thesis, insights are shared regarding the applicability of OPLS and Partial Least Squares (PLS) regression models for batch monitoring of the CVD process. We also demonstrate that the prediction of a certain critical breakdown, clogging of the aluminum generator in the CVD process, can be accomplished through the use of SPC. For the wet blasting process, a PCA methodology is suggested to be effective for visualizing breakdowns.

Industry 4.0 is a concept that emphasizes system and process automation, digitalization, and data interchange in the workplace. Its purpose is to create a smart factory that will cut lead times and increase system productivity by responding to client demand or unforeseen circumstances. Manufacturing, supply chain (SC), and logistics could all benefit from this notion. Industry 4.0 adoption in supply chain management (SCM) is a new and important topic that requires further investigation.  This study is to research the impact of Industry 4.0 on supply chains based on probable Industry 4.0 drivers and barriers. For future supply chains, Industry 4.0 is expected to introduce new difficulties and opportunities. The study addressed a number of issues and provided a method for effectively adapting and integrating the Industry 4.0 concept into supply chains. The research should help supply chain manager’s better grasp the challenges of integrating Industry 4.0 in their networks. Simulation study enables for the assessment of Industry 4.0 adoption in terms of its impact on SC performance, as well as the incorporation of both the drivers and impediments of this technological change. A conceptual framework for Industry 4.0 use in supply chains is proposed in addition to giving an empirical basis for this relationship.  Then we examined the 4.0 industry in Zagros Petrochemical Company. The statistical population in this study is the official employees of Zagros Petrochemical. Then, in order to evaluate the impact of Industry 4.0 on the supply chain management of Zagros Petrochemical Company, a supply chain management methods questionnaire was used. The tool of analysis in this research is a questionnaire which was converted into mathematical output through SPSS software and was statistically analyzed. Using inferential statistics (T-Test), it was found that Industry 4.0 has a significant effect on Zagros Petrochemical Company.  Then, using Friedman test, the rank of the studied features was compared. Cochran's sampling formula was used for sampling at 5% error level. In the end, based on inferential statistics, it was found that considering that in the first question of the research, the p-value obtained is more than 0.05, unlike the rest of the research questions, the null hypothesis is not rejected. Therefore, it can be inferred that Industry 4.0 is not effective in terms of strategic partnership with the supply chain management supplier of Zagros Petrochemical Company.

Background:  Industry 4.0 technologies are expected to play an important role in the near future. Among these, blockchain technology is in the spotlight and recognized to be revolutionary within the agri-food industry and its supply chains. However, the technology and its adoption is in an early phase and involves several challenges for agri-food organizations. Given its nascent nature, academic research is scarce and a need for research of blockchain technology adoption in different contexts has been identified.  Purpose:  The purpose of this thesis is therefore to analyze the reasoning behind Swedish agri-food organizations’ decisions to adopt or reject blockchain technology as well as their approach to its adoption in their supply chains.  Method:  A qualitative research design with an inductive approach was applied, where the primary data was gathered through 9 semi-structured interviews with agri-food organizations and experienced individuals within the field.  Conclusion:  The findings show that trustworthiness is the main goal and driver of blockchain technology adoption and identified several secondary reasons for adoption. The research also specifies challenges which act as reasons for rejection as well as two-edged critical factors affecting adoption decisions. Further, Swedish agri-food organizations were recognized to be in an immature adoption phase and hence two main approaches to blockchain adoption, proactive and pending, were determined. Based on these findings, the BAP framework visualizing the blockchain adoption process was developed.

The concept of sustainability is gaining visibility in recent years. Both society and companies are increasing their interest in every of its social, environmental, and economic dimensions. This interest is one of the reasons why the Circular Economy is escalating. One of the goals of this model of production and consumption is waste reduction through the creation of a closed-loop chain, where remanufacturing has a crucial role. Despite the benefits of remanufacturing, this process increases the complexity of the task, limiting access to this job due to the high level of knowledge required. This is the reason why Augmented Reality is presented in this thesis as a method to assist operators by guiding them and providing real-time feedback interactively. The main goal is to increase the efficiency, effectiveness, and accessibility of this task. At the same time, this project aims to contribute to all dimensions of sustainability to a greater or lesser extent. To meet the objectives mentioned above, and keeping in mind the Design Science Research Methodology (DSRM), an artifact is created. The case of study is an assembly operation reproduced in the IRMA-demonstrator in ASSAR Innovation Arena in Skövde (Sweden). A set of Augmented Reality instructions have been designed to guide the operator through the assembly task. First, with the help of the software provided by Microsoft, Dynamics 365 Guides, and afterward, by using Unity. The software is compared, and an attempt is made to justify the implementation of AR, specifically in the remanufacturing assembly task. The results seem to point to a reduction of errors in the operation. Finally, conclusions are extracted based on previous studies and the analysis of the design and implementation of the set of instructions. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>

Abstract Intro: Conventional training methods are intended to teach and offer the most human way of teaching but is it the most effective and can another method improve or eliminate nonvalue adding activities. This master thesis tackles the difference between traditional training versus AR supported training, studying different aspects of training to see the advantages and disadvantages of the methods. The research questions were the following:  RQ1: Is the learning process improved with or without AR supported technology?   RQ2: What are the benefits of AR use in industry?  RQ3: Will the investment of AR implementation in the learning process pay off?  Method: To answer the research questions, a case study at a case company was conducted. The case study consisted of working with two companies, an external company for a theoretical answer based on experiences and an experimental study at the internal case company. In parallel to this case study a literature review was done to answer the research questions.  Theory and Literature Review: Literature for the frame of reference was gathered to support the thesis and give the perspectives of the investigated area. The literature review sought to find answers to the research questions and the search was systematically formed to focus on data gathering to answer the questions.  Analyses: The findings from this research shows that AR supported training is beneficial and has the potential to eliminate nonvalue adding activities. AR has many capabilities and research shows that AR in general has positive impact.  Conclusion: AR-applied training has both advantages and disadvantages, but the potential of improvements is high. It is difficult to conclude if it is economical to invest in AR, with the advances the technology holds on to now since the outcome can depend on several factors from different parameters. However, AR does contribute to more effective learning and further benefits within the applied area. It has been proven to reduce the error rate and thus may increase the quality of the product.