Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Assessing BPM’s role in a digital innovation project

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe world is changing. In the digitalization era, digital devices are everywhere, enabled by the quick proliferation of smart and connected products. The transformation we are witnessing is not only about the new digital artefacts, but also includes the alignment of the operations, business processes, strategy and organizational, and IT structures, resulting in the so-called maturity. Although it might not be trivial, this increased efficiency is closely connected with the processes, of how to create opportunities for optimizing and redesigning them. However, the combination of digital innovation and business process management, and how one benefits the other, is not very explored in the literature, which constitutes a research gap. Given this, the importance of business process management practices and their relationship with the remaining organisation’s dimensions was studied and assessed through a comprehensive and systematic literature review. Hence, insights were gathered to create a framework that allows answering the research question “What is the BPM’s role in a digital innovation project?”. It was expected to understand the challenges associated with digital transformation, what core requirements are the most valuable, and what is the role of process management in all of it. A focus group has confirmed the usefulness of the artefact, by showing the correlation between the different elements in scope and allowing an understanding of the capabilities needed in the organisation. Nonetheless, the feedback suggested the adaptation of the framework to include a maturity assessment pre-stage and cost evaluation per digital transformation category, so it can be completely transversal to all types of organisations and all budgets

Software Engineering Timeline: major areas of interest and multidisciplinary trends

Ingeniería del software. EvolucionSociety today cannot run without software and by extension, without Software Engineering. Since this discipline emerged in 1968, practitioners have learned valuable lessons that have contributed to current practices. Some have become outdated but many are still relevant and widely used. From the personal and incomplete perspective of the authors, this paper not only reviews the major milestones and areas of interest in the Software Engineering timeline helping software engineers to appreciate the state of things, but also tries to give some insights into the trends that this complex engineering will see in the near future

Evolution of a Lean Smart Maintenance Maturity Model towards the new Age of Industry 4.0

Over the last few years, the complexity of asset and maintenance management of industrial plants and machinery in the producing industry has risen due to higher competition and volatile environments. Smart factories, Internet of Things (IoT) and the underlying digitisation of a significant number of processes are changing the way we have to think and work in terms of asset management. Existing Lean Smart Maintenance (LSM) philosophy, which focuses on the cost-efficient (lean) and the learning organisation (smart) perspectives enables a value-oriented, dynamic, and smart maintenance/asset management. The associated LSM maturity model is the evaluation tool that contains the normative, strategic, and operational aspects of industrial asset management, based on which numerous reorganisation projects have already been carried out in industrial companies. However, due to the ever-increasing development of Industry 4.0 (I4.0), it is necessary to extend the model by selected aspects of digitisation and digitalisation. Based on a structured literature review (SLR) of state of the art I4.0 maturity models, we were able to investigate the essential maturity items for I4.0. To restructure and expand the existing LSM maturity model, the principle of design science research (DSR) was used. The architecture of the LSM maturity model was based on the structure of the Capability Maturity Model Integration (CMMI). Further development of a Lean Smart Maintenance maturity model thus covers the future requirements of I4.0 and data science. It was possible to enhance existing categories with new artefacts from the I4.0 range to represent the influence of cyber-physical systems (CPS), (big) data and information management, condition monitoring (CM) and more. Furthermore, the originally defined LSM-Model was restructured for a more simplified application in industrial use cases

Digital Servitization and Business Model Innovation in SMEs: A Model to Escape From Market Disruption

The progress and the adoption of digital technologies can rapidly make products, processes, and business models obsolete, until disrupting entire markets. In this context, small–medium enterprises (SMEs) operating in manufacturing are especially challenged due to their limited resources and smallness liabilities. Firms can implement, design, and deliver new smart and connected products that change the way they compete and trigger the provision of services—until redesigning the entire business model. However, little knowledge is available on how SMEs may effectively trigger and catalyze such transition. Using an interpretative research approach inspired by the design research methodology, in this article, we explore how SMEs may leverage digital servitization to escape from a disrupted market. Based on our findings, an original digital servitization model tailored for SMEs is proposed. Finally, the study provides a set of research and managerial implications on how SMEs can overcome market disruption in the manufacturing context through digital servitization and business model innovation

Analysis of the IoT platforms business models

In the last decade the world of manufacturing firms is completely changed thanks to the use of new technologies. Internet of Things (IoT) is one of these technologies that not only has the potential to impact how we live, but also how the businesses are being ran. Innovative companies are adopting IoT strategies and technologies to reengineer their products and services and redefine their relationships with customers, employees and partners. The IoT market is exploding at a significant pace as consumers, businesses, and governments are recognizing the benefits of connecting devices to the Internet. The purpose of this thesis is to explore in depth the different business model utilized by different companies, with no distinction of specific industry. Moreover, this thesis aims to study the IoT platforms business models and to understand how these platforms change the market competition by leveraging the IoT technologies. In order to reach this aim a structured literature review will be performed. Then, analysing different companies by using the business model canvas approach, three business scenarios will be identified and defined, as follows: servitisation, lean and world manufacturing and, digital platforms for manufacturing. Finally, it will be applied a mathematical model in order to discuss whether and how an IoT investment can give advantage to a manufacturing firm

Sistemas de informação na indústria 4.0 : mecanismos de apoio à transferência de dados para conhecimento em ambientes Lean

The paradigm that presently emerges in the organizational context, known as Industry 4.0 (I4.0) or Fourth Industrial Revolution, promises to bring principles of connectivity and flexibility to the companies that embrace it. Industry 4.0 enhances the efficiency in adapting in real time to the customers’ requirements, through the establishment of an intelligent shop floor capable of answering in a flexible and customized way to market changes. However, during the last three decades, it is known that the adoption of the Lean philosophy was absorbed by the industrial environment, with results that proved to be exuberant, considering the simplicity of the tools. In this way, the I4.0 implementation must be prepared to preserve the existing manufacturing systems, proceeding, whenever possible, to upgrade them on a Lean excellence basis. It is said that information systems will be decisive in the foundation of the I4.0 paradigm. Of these, MES systems, with greater connection to the shop floor, will tend to be aligned with existing practices, contributing, through their connectivity, to the introduction of knowledge management practices and data visualization mechanisms. In the specification and architecture phase of these systems, understanding the processes will be crucial. Thus, their documentation is an organizational pillar, with BPMN and UML being able to guide it. However, and in addition to its usefulness in the processes’ mapping, BPMN is also likely to be applied in capturing tacit knowledge, which can be a foundation for the constitution of knowledge repositories, impacting organizational excellence. It is in this context that the present work is implanted, aiming at the creation of guidelines and mechanisms that facilitate the implementation of I4.0 strategies in Lean industrial environments. The adopted methodology first went through an exhaustive literature review, in order to find possible bilateral effects between I4.0 technologies and lean tools. Then, the development of some applications aligned with the I4.0 paradigm, as a technological engine, and the Lean philosophy, as a tool for eliminating waste and / or creating value, was contemplated. From the various development experiences in an industrial context and considering the evidence reported in the literature, this study proposes a Lean 4.0 framework oriented to the shop floor.O paradigma que atualmente emerge no contexto organizacional, conhecido como Indústria 4.0 (I4.0) ou Quarta Revolução Industrial, promete trazer princípios de conectividade e flexibilidade às empresas que a adotam. A Indústria 4.0 potencia a eficácia no ajuste em tempo real aos requisitos dos clientes, através da constituição de um chão de fábrica inteligente e capaz de responder de forma flexível e customizada às mudanças do mercado. Contudo, durante as últimas três décadas, sabe-se que a adoção da filosofia Lean foi absorvida pelo meio industrial, com resultados que se demonstraram exuberantes, tendo em conta a simplicidade das ferramentas. Deste modo, a implementação I4.0 deve ser feita no sentido da preservação dos sistemas de manufatura já existentes, procedendo, desde que possível, ao seu upgrade numa base de excelência Lean. Conta-se que os sistemas de informação serão decisivos na fundação do paradigma I4.0. Destes, os sistemas MES, com maior conexão ao chão de fábrica, tenderão a ser alinhados com as práticas já existentes, contribuindo, através da sua conectividade, para a introdução de práticas de gestão do conhecimento e mecanismos de visualização de dados. Na fase de especificação e arquitetura destes sistemas, o entendimento dos processos será crucial. Assim, a documentação dos mesmos é um pilar organizacional, estando o BPMN e a UML capazes de a orientar. Porém, e a somar à sua utilidade na ilustração de processos, o BPMN está igualmente passível de ser aplicado na captação de conhecimento tácito, o que por si pode ser uma base para a constituição de repositórios de conhecimento, contribuindo para a excelência organizacional. É neste contexto que o presente trabalho se insere, tendo como objetivo a criação de linhas orientadoras e mecanismos que facilitem a implementação de estratégias I4.0 em ambientes industriais Lean. A metodologia adotada passou, primeiramente, por uma exaustiva revisão da literatura, por forma a encontrar possíveis efeitos bilaterais entre tecnologias I4.0 e ferramentas lean. De seguida, contemplou-se o desenvolvimento de alguns aplicativos alinhados ao paradigma I4.0, enquanto motor tecnológico, e à filosofia Lean, enquanto ferramenta de eliminação de desperdícios e/ou criação de valor. Das diversas experiências de desenvolvimento em contexto industrial e considerando as evidências reportadas na literatura o presente estudo propõe uma framework Lean 4.0 orientado ao chão de fábrica.Mestrado em Engenharia e Gestão Industria

Value creation and capture in business ecosystems from the business model’s perspective

Abstract. This thesis explores the concepts of business model, value creation and capture, business ecosystems and their relations to each other. While as the value network consists of linear relationships between actors, business ecosystems encompass relationships that are more diverse and tend to be more value capture-oriented. Business models are competing and collaborating simultaneously in ecosystems, this encourages value co-creation and co-capture. This co-evolution of business models enables and fosters ecosystem ecology. There are numerous actors interacting across the IoT ecosystem forming the complex interdependence and interconnection between and among different stakeholders, hence IoT is chosen as a context to study how ecosystem shapes value creation and value capture from the business model’s perspective. Four propositions are made based on the theoretical review and empirical evidence. 1) In comparison with the traditional value chain, value co-creation and co-capture are more dynamic in ecosystems. 2) In comparison with the traditional value chain, the value can be co-created and co-captured through platform business model in ecosystems. 3) In comparison with the traditional value chain, the value is co-created and co-captured through open innovation in ecosystems. 4) Value creation and capture can be maximized by creating own business ecosystem, yet it requires more resources and therefore lead to higher risks. The research methodology chosen for this thesis is a qualitative approach. Both the tradition of exploratory expert interviews focusing on exploring certain central dimensions and highlighting the expert status of the interviewee, and thematic interviews steered to stress on the flexible structure and open discussion is utilized. New themes are formed after transparent data analysis and reflecting on the theoretical and empirical findings. 1) Combining TVC and ecosystem value chain instead of choosing either or. 2) Platforms allow value co-creation and co-capture, yet a lack of track record of performance indicate higher risks for new platform business. 3) It is critical to managing the degree of openness in the open source business model but it is possible to face the challenge with some tools. 4) Creating one’s own ecosystem does not necessarily lead to maximum value creation or capture, but it certainly involves high risks and requires heavy investments. To answer the research question in short: value capture and value creation are more dynamic in ecosystems. It is not applicable for firms to maximize the creation and capture of value because increased value creation generally goes hand in hand with lower value capture. Value creation and capture are not monotone transformations of one another. Ecosystemic business models such as platform businesses and open innovation businesses enable value co-creation and co-capture. Among others, it is better for startups to find relevant ecosystems and become key players in them for optimizing value creation and capture. As business models other industry players are using affect value creation and capture, one needs to foresee the reaction of other firms when choosing, innovating, or reforming a business model