Towards Developing a Digital Twin Implementation Framework for Manufacturing Systems

This research studies the implementation of digital twins in manufacturing systems. Digital transformation is relevant due to changing manufacturing techniques and user demands. It brings new business opportunities, changes organizations, and allows factories to compete in the digital era. Nevertheless, digital transformation presents many uncertainties that could bring problems to a manufacturing system. Some potential problems are loss of data, cybersecurity threats, unpredictable behavior, and so on. For instance, there are doubts about how to integrate the physical and virtual spaces. Digital twin (DT) is a modern technology that can enable the digital transformation of manufacturing companies. DT works by collecting real-time data of machines, products, and processes. DT monitors and controls operations in real-time helping in the identification of problems. It performs simulations to improve manufacturing processes and end-products. DT presents several benefits for manufacturing systems. It gives feedback to the physical system, increases the system’s reliability and availability, reduces operational risks, helps to achieve organizational goals, reduces operations and maintenance costs, predicts machine failures, etc. DT presents all these benefits without affecting the system’s operation. xv This dissertation analyzes the implementation of digital twins in manufacturing systems. It uses systems thinking methods and tools to study the problem space and define the solution space. Some of these methods are the conceptagon, systemigram, and the theory of inventive problem solving (TRIZ in Russian acronym). It also uses systems thinking tools such as the CATWOE, the 9-windows tool, and the ideal final result (IFR). This analysis gives some insights into the digital twin implementation issues and potential solutions. One of these solutions is to build a digital twin implementation framework Next, this study proposes the development of a small-scale digital twin implementation framework. This framework could help users to create digital twins in manufacturing systems. The method to build this framework uses a Model-Based Systems Engineering approach and the systems engineering “Vee” model. This framework encompasses many concepts from the digital twin literature. The framework divides these concepts along three spaces: physical, virtual, and information. It also includes other concepts such as digital thread, data, ontology, and enabling technologies. Finally, this dissertation verifies the correctness of the proposed framework. The verification process shows that the proposed framework can develop digital twins for manufacturing systems. For that purpose, this study creates a process digital twin simulation using the proposed framework. This study presents a mapping and a workflow diagram to help users use the proposed framework. Then, it compares the digital twin simulation with the digital twin user and system requirements. The comparison finds that the proposed framework was built right

A multi-dimensional model to the digital maturity life-cycle for SMEs

As companies try to maintain and strengthen their competitive advantage, they should be aware of the level of their digital maturity. The study aims to present a methodology that helps to determine the position of a small and medium-sized enterprise in the digital maturity life-cycle. This is performed on the basis of maturity and digital maturity models, and company growth theories. A number of studies and models have been prepared to determine digital maturity on the basis of various sectoral criteria, but these are all one-dimensional. The study therefore proposes a multi-dimensional model for determining the digital maturity life-cycle of small and medium-sized enterprises that takes into account companies’ digital maturity, the IT intensity of various sectors and their organizational characteristics. The model defines five maturity levels together with their relevant characteristics, classified into three levels in terms of data- information. It can help small and medium-sized enterprises adopt more accurate decisions regarding areas in need of development

Industrial Transformation Roadmap for Digitalisation and Smart Factories:The Danish SMEs Model

Today only some sections of the supply chain are digitalized, but some companies are also already far with Industry 4.0, where the virtual factory and the physical factory work closely together (digital twin). Industry 4.0, which started in Germany among the large OEMs, seems to have not resonated much with SMEs. There is an imminent challenge of coming up with a feasible transformation roadmap which will resonate effectively and efficiently with SEMs as they are the core backbone of every performing economy. This research investigates Smart Factories/Industry 4.0 in the Danish SMEs model perspective. This research's main objectives are to develop a feasible roadmap in the form of a conceptual framework for easy industrial transformation to the digitalizing and smart way of (doing things) developing products and/or services. This research employs quantitative research methods such as surveys and interviews where applicable as well as a literature review in the SMEs perspective. Previous research has shown that the digital evolution coined as Industry 4.0 was started among large companies. However, this initial precedence has not resonated very much with all-inclusive industrial evolution, especially within the SMEs perspective. The main industrial implication will be the definition of a clear feasible roadmap for what this research terms as an industrial transformation process - "digital change management process - Industry 4.0/Smart factory" in the industrial SMEs perspective - the Danish Model. This research seeks to propose a conceptual smart factory roadmap in an Industry 4.0 perspective, which could be adopted among manufacturing SMEs to effectively, and efficiently transform their production operations. The Danish model perspective or angle of Industry 4.0.</p

Sustainability transition of production systems in the digital era - a systems perspective for building resilient and sustainable production systems

Locked-in manufacturing industries with highly structured operations and path dependencies are major contributors to the sustainability challenges currently burdening our planet. The effects of the ongoing pandemic, large-scale environmental impacts due to climate change and constant economic and social downturns are just some examples of these sustainability challenges. Increased digitalisation, awareness, global initiatives and regulations are pressuring manufacturing industries to transition towards sustainable development. However, there exists a multitude of interpretations in implementing sustainability in manufacturing industries. This makes proposing tangible actions to translate global initiatives complicated, thus hindering the sustainability transition process.The purpose of this thesis is to support the advancement of resilient production systems which can overcome sustainability challenges in the Industry 4.0 era. Hence, the thesis aims to investigate: (i) the systemic challenges of manufacturing companies which hinder their sustainability transition process and (ii) the mechanisms by which a systems perspective may be applied to support the transition. A mixed-methods approach was used to carry out the research, using qualitative and quantitative data from three (empirical and theoretical) studies. Applying a systems perspective helped reveal the challenges which hinder the sustainability transition of production systems. Understanding the production ‘system’ as a whole (and the underlying web of intricate dependencies and challenges in production operations) required this holistic perspective. Regarding the challenges, it was observed that manufacturing industries across different domains face three main types of challenge: internal (such as organisational routines, strategies and cultural mindset), external (such as regulations and collaboration with stakeholders) and technological (such as maturity levels and data). Three different enabling mechanisms were explored which may help overcome the above sustainability challenges and support the sustainability transition of manufacturing industries: (1) Industry 4.0 technologies, (2) dynamic capabilities and (3) resilience engineering. It was observed that Industry 4.0 technologies (such as artificial intelligence/machine learning, virtual development tools and sensors) are largely implemented to enable sustainable manufacturing in the form of resource efficiency and waste reduction. The results also revealed five microfoundations of dynamic capabilities – communication, organisation, resources, collaboration and technology. Based on Industry 4.0 opportunities to promote sustainability transitions, the results revealed five industrial resilience factors – robustness, agility, resourcefulness, adaptability and flexibility.This research contributes to theory by studying the convergence of emergent research topics, such as Industry 4.0, dynamic capabilities and resilience engineering in the context of sustainability transitions. In terms of a practical contribution, the sustainability transitions model developed in this thesis may support industrial practitioners in gaining a holistic understanding of the systemic challenges to sustainability, plus corresponding mechanisms to promote the sustainability transition of industries and the building of resilient production systems

A multi-dimensional model to the digital maturity life-cycle for SMEs

As companies try to maintain and strengthen their competitive advantage, they should be aware of the level of their digital maturity. The study aims to present a methodology that helps to determine the position of a small and medium-sized enterprise in the digital maturity life-cycle. This is performed on the basis of maturity and digital maturity models, and company growth theories. A number of studies and models have been prepared to determine digital maturity on the basis of various sectoral criteria, but these are all one-dimensional. The study therefore proposes a multi-dimensional model for determining the digital maturity life-cycle of small and medium-sized enterprises that takes into account companies’ digital maturity, the IT intensity of various sectors and their organizational characteristics. The model defines five maturity levels together with their relevant characteristics, classified into three levels in terms of data-information. It can help small and medium-sized enterprises adopt more accurate decisions regarding areas in need of development

Maturity Models in the Age of Industry 4.0 – Do the Available Models Correspond to the Needs of Business Practice?

Maturity models (MMs) enable users to identify the need for change and to derive the necessary measures to accompany the change process. Existing literature reviews indicate that the number of available models has increased sharply in recent years. At the same time, it is found that the number of model applications does not keep up with the pace of development. Against the background of the current digitization trend, this article empirically investigates which models are actually used in business practice. We find that the degree of application is very low. Moreover, we also examine user-related model requirements, reasons for employing MMs, and the purpose of using MMs, which can support the user-centered development of future MMs

Towards a Framework for Smart Manufacturing adoption in Small and Medium-sized Enterprises

Smart Manufacturing (SM) paradigm adoption can scale production with demand without compromising on the time for order fulfillment. A smart manufacturing system (SMS) is vertically and horizontally connected, and thus it can minimize the chances of miscommunication. Employees in an SME are aware of the operational requirements and their responsibilities. The machine schedules are prepared based on the tasks a machine must perform. Predictive maintenance reduces the downtime of machines. Design software optimizes the product design. Production feasibility is checked with the help of simulation. The concepts of product life cycle management are considered for waste reduction. Employee safety, and ergonomics, identifying new business opportunities and markets, focus on employee education and skill enhancement are some of the other advantages of SM paradigm adoption. This dissertation develops an SM paradigm adoption framework for manufacturing SMEs by employing the instrumental research approach. The first step in the framework identified the technical aspects of SM, and this step was followed by identifying the research gaps in the suggested methods (in literature) and managerial aspects for adopting SM paradigm. The technical and the managerial aspects were integrated into a toolkit for manufacturing SMEs. This toolkit contains seven modular toolboxes that can be installed in five levels, depending on an SME’s readiness towards SM. The framework proposed in this dissertation focuses on how an SME’s readiness can be assessed and based on its present readiness what tools and practices the SMEs need to have to realize their tailored vision of SM. The framework was validated with the help of two SMEs cases that have recently adopted SM practices