How Can Organisations and Business Models Lead to a More Sustainable Society? A Framework from a Systematic Review of the Industry 4.0

The concept of Industry 4.0 has been mainly addressed by the current literature from a technological perspective, overlooking the organisational and even ethical challenges related to this recent paradigm. In order to become ‘4.0 compliant’, an enterprise must adapt its organisation and business approaches, and these changes may lead to a significant impact on sustainability. Therefore, we performed a systematic literature review to investigate the most recent Industry 4.0 research streams by adopting a multi-perspective approach. This analysis led to collect insights on the key traits of an Enterprise 4.0: integration, decomposed hierarchy, flexibility, and autonomy. Each of these keywords involves work environments, business and organisational models, and educational approaches, which constitute the key traits of the novel framework proposed in this study

How can organisations and business models lead to a more sustainable society? A framework from a systematic review of the industry 4.0

The concept of Industry 4.0 has been mainly addressed by the current literature from a technological perspective, overlooking the organisational and even ethical challenges related to this recent paradigm. In order to become '4.0 compliant', an enterprise must adapt its organisation and business approaches, and these changes may lead to a significant impact on sustainability. Therefore, we performed a systematic literature review to investigate the most recent Industry 4.0 research streams by adopting a multi-perspective approach. This analysis led to collect insights on the key traits of an Enterprise 4.0: integration, decomposed hierarchy, flexibility, and autonomy. Each of these keywords involves work environments, business and organisational models, and educational approaches, which constitute the key traits of the novel framework proposed in this study

Building a Simple Smart Factory

This thesis describes (a) the search and findings of smart factories and their enabling technologies (b) the methodology to build or retrofit a smart factory and (c) the building and operation of a simple smart factory using the methodology. A factory is an industrial site with large buildings and collection of machines, which are operated by persons to manufacture goods and services. These factories are made smart by incorporating sensing, processing, and autonomous responding capabilities. Developments in four main areas (a) sensor capabilities (b) communication capabilities (c) storing and processing huge amount of data and (d) better utilization of technology in management and further development have contributed significantly for this incorporation of smartness to factories. There is a flurry of literature in each of the above four topics and their combinations. The findings from the literature can be summarized in the following way. Sensors detect or measure a physical property and records, indicates, or otherwise responds to it. In real-time, they can make a very large amount of observations. Internet is a global computer network providing a variety of information and communication facilities and the internet of things, IoT, is the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. Big data handling and the provision of data services are achieved through cloud computing. Due to the availability of computing power, big data can be handled and analyzed under different classifications using several different analytics. The results from these analytics can be used to trigger autonomous responsive actions that make the factory smart. Having thus comprehended the literature, a seven stepped methodology for building or retrofitting a smart factory was established. The seven steps are (a) situation analysis where the condition of the current technology is studied (b) breakdown prevention analysis (c) sensor selection (d) data transmission and storage selection (e) data processing and analytics (f) autonomous action network and (g) integration with the plant units. Experience in a cement factory highlighted the wear in a journal bearing causes plant stoppages and thus warrant a smart system to monitor and make decisions. The experience was used to develop a laboratory-scale smart factory monitoring the wear of a half-journal bearing. To mimic a plant unit a load-carrying shaft supported by two half-journal bearings were chosen and to mimic a factory with two plant units, two such shafts were chosen. Thus, there were four half-journal bearings to monitor. USB Logitech C920 webcam that operates in full-HD 1080 pixels was used to take pictures at specified intervals. These pictures are then analyzed to study the wear at these intervals. After the preliminary analysis wear versus time data for all four bearings are available. Now the ‘making smart activity’ begins. Autonomous activities are based on various analyses. The wear time data are analyzed under different classifications. Remaining life, wear coefficient specific to the bearings, weekly variation in wear and condition of adjacent bearings are some of the characteristics that can be obtained from the analytics. These can then be used to send a message to the maintenance and supplies division alerting them on the need for a replacement shortly. They can also be alerted about other bearings reaching their maturity to plan a major overhaul if needed

A Conceptual Framework to Support Digital Transformation in Manufacturing Using an Integrated Business Process Management Approach

Digital transformation is no longer a future trend, as it has become a necessity for businesses to grow and remain competitive in the market. The fourth industrial revolution, called Industry 4.0, is at the heart of this transformation, and is supporting organizations in achieving benefits that were unthinkable a few years ago. The impact of Industry 4.0 enabling technologies in the manufacturing sector is undeniable, and their correct use offers benefits such as improved productivity and asset performance, reduced inefficiencies, lower production and maintenance costs, while enhancing system agility and flexibility. However, organizations have found the move towards digital transformation extremely challenging for several reasons, including a lack of standardized implementation protocols, emphasis on the introduction of new technologies without assessing their role within the business, the compartmentalization of digital initiatives from the rest of the business, and the large-scale implementation of digitalization without a realistic view of return on investment. To instill confidence and reduce the anxiety surrounding Industry 4.0 implementation in the manufacturing sector, this paper presents a conceptual framework based on business process management (BPM). The framework is informed by a content-centric literature review of Industry 4.0 technologies, its design principles, and BPM method. This integrated framework incorporates the factors that are often overlooked during digital transformation and presents a structured methodology that can be employed by manufacturing organizations to facilitate their transition towards Industry 4.0

Organising the Implementation of Industry 4.0 in a High Value German Manufacturing Firm: A Complex Adaptive Systems Approach.

Ph. D. ThesisThis thesis addresses an important research gap in empirical qualitative evidence regarding the organisational aspects of the implementation of Industry 4.0. Whereas there is a basic understanding of the technical implementation in the factory plant, the understanding of the implementation from an organisational perspective is limited. A holistic single case study with 35 semi-structured expert interviews enabled a deep exploration of an implementation in a real-world context at the firm level. The findings demonstrate how a high value German manufacturing company has implemented Industry 4.0, as well as why this firm implemented as it did. Several elements are thematically analysed, representing important examples of how manufacturing firms can organise the implementation of Industry 4.0 in praxis. Covering the three areas of actions, influences and relationships, the implications of the analysed elements are discussed in relation to six theoretical themes, namely centralisation vs. decentralisation, diffusion of new ideas, working in teams, trust, open innovation and path dependence. This thesis represents the first existing study that understands the implementation of Industry 4.0 as a Complex Adaptive System of interrelated system elements which continuously evolve over time. In this sense, a newly developed system model acknowledges important relationship characteristics that lead to a more comprehensive perspective on the complex implementation of Industry 4.0. This thesis contributes to the research field by being the first study to suggest a “dual approach” encompassing important decentralised as well as centralised implementation patterns for a successful process. It furthermore demonstrates how workforce concerns regarding job security significantly influence the emergence of system elements regarding change management during the implementation of Industry 4.0. The thesis offers academic contributions to the Industry 4.0 implementation literature, as well as organisational elements recommended for practitioners when organising the implementation of Industry 4.0