From Data to Decision Support in Manufacturing

Digitalization is changing society, industry, and how business is done. For new companies that are more or less born digital, there is the opportunity to use and benefit from the capabilities offered by the new digital technologies, of which data-driven decision-making forms a crucial part. The manufacturing industry is facing the Fourth Industrial Revolution, but most manufacturing organizations are lagging behind in their digital transformation. This is due to the technical and organizational challenges they are experiencing. Based on this current state description and existing gap, the vision, aim, and research questions of this thesis are: Vision - future manufacturing organization to be driven by fact-based decision support based on data rather than of relying mainly on intuition and experience.Aim - to show manufacturing organizations the applicability of digital technologies in digitalizing manufacturing system data to support decision-making and how data sharing may be achieved.Research Question 1. How do manufacturing system lifecycle decisions influence the requirements of data collection towards interoperability? Research Question 2. What makes interoperability standardization applicable to sharing data in a manufacturing system’s lifecycle?This research is applied, addressing real-world problems in manufacturing. For this reason, the main objective is to solve the problem at hand, and data collection methods will be selected that can help address it. This can best be explained by taking a pragmatic worldview and using mixed methods approach that combines quantitative and qualitative methods. The research upon which this thesis is based draws on the results of three research projects involving the active participation of manufacturing companies. The data collection methods included experiments, interviews (focus group and semi-structured), technical development, literature review, and so on. The results are divided into three areas: 1) connected factory, 2) standard representation of machine model data, and 3) the digital twin in smart manufacturing. Connected factory addresses the question of how a mobile connectivity solution, 5G, may be used in a factory setting and demonstrates how the connectivity solution should be planned and how new data from a connected machine may support an operator in decision-making. The standard representation of machine model data demonstrates how an international standard may be used more widely to support the sharing and reuse of information. The digital twin in smart manufacturing investigates the reasons why there are so few real-world examples of this. The findings reveal that a manufacturing system’s lifecycle impacts data requirements, including a need for data accuracy in design, speed of data in operation (to allow operators to act upon events), and availability of historical data in maintenance (for finding root causes and planning). The volume of data was identified as important to all lifecycles. The applicability of standards was found to depend on: 1) the technology providers’ willingness to adapt standards, 2) enforcement by OEMs and larger actors further down a supply chain, 3) the development of standards that consider the user, and 4) when standards are required for infrastructure reasons. Based on the results and findings obtained, it may be stated that it is important to determine what actual manufacturing problem should be addressed by digital technology. There is a tendency to view this change from the perspective of what the digital technology might solve (a technology push), rather than setting aside the solution and focusing on what problem should be solved (a technology pull). This work also reveals the importance of the collaboration between industry and academia making progress in the digital transformation of manufacturing. Proofs-of-concept and demonstrators of how digital technologies might be used are also important tools in helping industry in how they can address a digital transformation

SYSTEMATIC APPROACH TO THE EDUCATION IN THE FIELD OF SMART PRODUCTS AND SERVICES ENGINEERING

Industry 4.0 has a huge impact on the entire social system. The speed, scope and impact of the changes it brings have an exponential trend. The biggest impact is related to the industry and industrial development. Full digitalization and automation of production is expected, i.e. networking of smart digital devices with products, machines, tools, robots and people. For the realization of such complex tasks, it is necessary to have adequate human resources. In the changes brought by I4.0, the engineering of smart products and services, i.e. the education of development engineers, has a very important role.The social and industrial transformations dictated by the fourth industrial revolution also define new trends in the education of development engineers. Development engineers are expected to be comprehensively educated and trained to work in interdisciplinary project teams, in order to create new intelligent and networked products through creativity, innovation and fascinating technique.The paper presents the development of education through the epochs of industrial development and presents trends and challenges related to the education 4.0. Special attention is paid to the education of development engineers and the relevant competencies they need to have in order to meet the tasks and expectations in modern conditions

A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation

The digitalisation of the value chain promotes sophisticated virtual product models known as digital twins (DT) in all asset-life-cycle (ALC) phases. These models. however, fail on representing the entire phases of asset-life-cycle (ALC), and do not allow continuous life-cycle-costing (LCC). Hence, energy efficiency and resource optimisation across the entire circular value chain is neglected. This paper demonstrates how ALC optimisation can be achieved by incorporating all product life-cycle phases through the use of a RAMS²-toolbox and the generation of a knowledge-based DT. The benefits of the developed model are demonstrated in a simulation, considering RAMS2 (Reliability, Availability, Maintainability, Safety and Sustainability) and the linking of heterogeneous data, with the help of a dynamic Bayesian network (DBN)

Chapter 3 How is production changing?

The unprecedented Covid-19 crisis revealed the scale and scope of a new type of economy taking shape in front of our very eyes: the digital economy. This book presents a concise theoretical and conceptual framework for a more nuanced analysis of the economic and sociological impacts of the technological disruption that is taking place in the markets of goods and services, labour markets, and the global economy more generally. This interdisciplinary work is a must for researchers and students from economics, business, and other social science majors who seek an overview of the main digital economy concepts and research. Its down-to-earth approach and communicative style will also speak to businesses practitioners who want to understand the ongoing digital disruption of the market rules and emergence of the new digital business models. The book refers to academic insights from economics and sociology while giving numerous empirical examples drawn from basic and applied research and business. It addresses several burning issues: how are digital processes transforming traditional business models? Does intelligent automation threaten our jobs? Are we reaching the end of globalisation as we know it? How can we best prepare ourselves and our children for the digitally transformed world? The book will help the reader gain a better understanding of the mechanisms behind the digital transformation, something that is essential in order to not only reap the plentiful opportunities being created by the digital economy but also to avoid its many pitfalls

The future of factories: Different trends

The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and methodologies that have to be data-driven, AI-assisted, smart, and service-oriented. Therefore, there is a great interest in experimenting with emerging technologies and evaluating how they impact the actual business processes. This paper reports a comparison among the major trends in the digitalization of a Factory of the Future, in conjunction with the two major strategic programs of Industry 4.0 and China 2025. We have focused on these two programs because we have had experience with them in the context of the FIRST H2020 project. European industrialists identify the radical change in the traditional manufacturing production process as the rise of Industry 4.0. Conversely, China mainland launched its strategic plan in China 2025 to promote smart manufacturing to digitalize traditional manufacturing processes. The main contribution of this review paper is to report about a study, conducted and part of the aforementioned FIRST project, which aimed to investigate major trends in applying for both programs in terms of technologies and their applications for the factory’s digitalization. In particular, our analysis consists of the comparison between Digital Factory, Virtual Factory, Smart Manufacturing, and Cloud Manufacturing. We analyzed their essential characteristics, the operational boundaries, the employed technologies, and the interoperability offered at each factory level for each paradigm. Based on this analysis, we report the building blocks in terms of essential technologies required to develop the next generation of a factory of the future, as well as some of the interoperability challenges at a different scale, for enabling inter-factories communications between heterogeneous entities

Collaborative networks: A pillar of digital transformation

UID/EEA/00066/2019 POCI-01-0247-FEDER-033926The notion of digital transformation encompasses the adoption and integration of a variety of new information and communication technologies for the development of more efficient, flexible, agile, and sustainable solutions for industrial systems. Besides technology, this process also involves new organizational forms and leads to new business models. As such, this work addresses the contribution of collaborative networks to such a transformation. An analysis of the collaborative aspects required in the various dimensions of the 4th industrial revolution is conducted based on a literature survey and experiences gained from several research projects. A mapping between the identified collaboration needs and research results that can be adopted from the collaborative networks area is presented. Furthermore, several new research challenges are identified and briefly characterized.publishe

Integrating Construction 4.0 Technologies: A Four-Layer Implementation Plan

This research explores the current state of Construction 4.0 and discusses a four-layer implementation of Construction 4.0 in the industry. The research methodology consists of an extensive literature review to gain insights about Construction 4.0 and frame the four-layer implementation plan. A case study is also presented to showcase the proposed implementation plan. Nine Construction 4.0 technologies were discussed, their integration throughout the project lifecycle was presented in a roadmap, their integration and connectivity with one another were outlined in an interaction roadmap, and the requirements necessary for achieving the 4.0 transformation were articulated. However, the proposed implementation plan is focused on nine Construction 4.0 technologies. The research presents a comprehensive plan for integrating Construction 4.0 technologies into the industry and serves as a guideline to help construction companies better understand the implications of Construction 4.0

Encourage risk and optimise the competitiveness of the Norwegian petroleum industry through a government digitalisation platform

Master's thesis in Industrial economics.Digital transformation is changing society as we know it. Business models for companies delivering consumer products has been changing for years, and we are seeing an increasing focus to do the same in traditional industries. National strategies for digital transformation were explored and the platforms established by the governments were investigated. The strategies originated from large economies like the USA, China, Germany, Japan and Sweden, involving broad spectre of different economies. The national strategies were found to build on the strengths of the traditional industry, with a focus on supporting and strengthening the small and medium enterprises in the countries. There was also found to be a strong focus on international cooperation, even between nations that may be considered competitors within the same industries. After exploring these national strategies and platforms, it is concluded that Norway should make an effort to mirror the initiatives taken by these countries. There should be a focus on a national platform to both facilitate for national cooperation and support to SMEs, while also being a gathering point to unite a Norwegian industry for international cooperation. As the petroleum industry is one of the most important industries in Norway, efforts should be made to strengthen the competitiveness of this with regards to digital transformation