Ebook: Transdisciplinary Engineering for Resilience: Responding to System Disruptions

This book of proceedings contains papers that have been peer-reviewed and accepted for the 28th ISTE International Conference on Transdisciplinary Engineering, organized by the University of Bath, United Kingdom, July 5–9, 2021. TE2021 has been the second conference in the series that was organized in a virtual manner due to the COVID-19 world-wide crisis. The papers published in this book of proceedings, as well as video presentations, were accessible from July 5 till July 9 in Teams, while questions and answers were being exchanged.

This is the tenth issue of the series “Advances in Transdisciplinary Engineering”, which publishes the proceedings of the TE (formerly: CE) conference series and accompanying events. The TE conference series is organized annually by the International Society of Transdisciplinary Engineering, in short ISTE (www.intsoctransde.org), formerly called International Society of Productivity Enhancement (ISPE, Inc.) and constitutes an important forum for international scientific exchange on transdisciplinary engineering. These international conferences attract a significant number of researchers, industry experts and students, as well as government representatives, who are interested in recent advances in transdisciplinary engineering research, advancements, and applications.

The concept of Transdisciplinary Engineering transcends Concurrent Engineering (CE). The concept of CE, developed in the 80’s, implies that different phases of a product life cycle are conducted concurrently and initiated as early as possible within the Product Creation Process (PCP), including the implications of this approach within the extended enterprise and networks. The main goal of CE is to increase the efficiency and effectiveness of the PCP and to reduce errors in the later phases, as well as to incorporate considerations for the full lifecycle, through-life operations, and environmental issues. In the past decades, CE has become the substantive basic methodology in many industries (e.g., automotive, aerospace, machinery, shipbuilding, consumer goods, process industry, environmental engineering) and is also adopted in the development of new services and service support. Collaboration between different disciplines is key to successful CE. The main focus, though, is an engineering focus.

While for several decades CE proved its value in many industries and still continues to do so, many current engineering problems require a more encompassing approach. Many engineering problems have a large impact on society. The context of these problems needs to be taken into account. For example, the development of self-driving cars requires taking into account changes in regulations for managing responsibilities, adaptation to road networks, political decisions, infrastructures for energy supply, etc. The impacted society may also be the business environment of networks of companies and supply chains. For example, the adoption and implementation of Industry 4.0 requires taking into account the changes to be expected in the business environment, the people, their jobs, the knowledge needed, technology, organizational rules and behaviours. These kind of engineering problems also require collaboration, but not only between technical disciplines. Disciplines from other scientific fields need to be incorporated in the engineering process, like disciplines from social sciences (governance, psychology, etc.), law, medicine, or other fields, relevant for the problem at hand.

The concept of transdisciplinary engineering transcends inter- and multi-disciplinary ways of working, like in CE. In particular, transdisciplinary processes are aimed at solving complex ill-defined problems or problems for which the solution is not obvious from the beginning. While such problems, including their solutions, have a large impact on society and the context in which the problems exist, it is important that people from society and practice collaborate with people from different relevant scientific communities. Neither one discipline nor one person can bring sufficient knowledge for solving such problems. Collaboration again is essential but has become even more demanding. Disciplines should be open to other disciplines to be able to share and exchange the knowledge necessary for solving the problem.

As indicated above any engineering problem can be put is a context in which the problem is to be solved or in which the solution for the problem is expected to be used. For researchers and engineers, it is important to take this context into account. This could be done, for example, by collaborating with researchers who can study user acceptance of the envisioned solution or with researchers who can apply suitable methods to acquire user preferences in the respective context and translate them into the necessary requirements for the solution to be developed. Validation of a proposed engineering solution will benefit also by incorporating people from other scientific fields.

The conference is entitled: ‘Transdisciplinary Engineering for Resilience: Responding to system disruptions’ indicating the dynamic and evolving nature of TE processes, requiring new knowledge, methods and tools to support the process. The TE2021 Organizing Committee has identified 36 thematic areas grouped into nine themes within TE and launched a Call for Papers accordingly. More than 80 papers have been submitted from all over the world. The submissions as well as invited talks have been collated into nine themes.

The Proceedings contains 58 peer-reviewed papers presented at the conference by authors from 24 countries. These papers range from the theoretical, conceptual to strongly pragmatic addressing industrial best practice. The involvement of industry in many of the presented papers gives additional importance to this conference.

This book on “Transdisciplinary Engineering for Resilience. Responding to System Disruption” is directed at three constituencies: researchers, design practitioners, and educators. Researchers will benefit from the latest research results and knowledge of product creation processes and related methodologies. Engineering professionals and practitioners will learn from the current state of the art in transdisciplinary engineering practice, new approaches, methods, tools, and their applications. The educators in the TE community gather the latest advances and methodologies for dissemination in engineering curricula, to prepare students for transdisciplinary collaboration in complex engineering processes, while the community also encourages educators to bring new ideas into the field. With the annual contributions of many researchers and practitioners the book series will contribute to the further development of the concept of Transdisciplinary Engineering.

The proceedings are subdivided into several parts, reflecting the themes addressed in the conference programme:

Part 1 is entitled Transdisciplinary Engineering Theory and contains five papers that address the concept of TE. Paper one presents a framework for assisting a TE approach to systemic risk detection. Paper two presents a comparison of a quality and a design approach in a TE context of setting up medical trials. Paper three presents findings of a preliminary exploration of the significance of TE in an industrial context. Paper four is an essay on the multi-dimensionality of TE. Paper five presents results of a workshop to develop an initial version of a disciplinary maturity grid to assess an industry’s engineering capability.

Part 2 contains three papers in the field of Transdisciplinary Engineering Education and Training, an important field in our conferences. In paper one, a framework is presented for the analysis of simulation effectiveness in training medical treatment procedures. A discussion of the need for a holistic curriculum design in digital manufacturing in presented in paper two. Design thinking is considered essential for people with different domain knowledge. In paper three, a framework developed by master students is proposed to facilitate design automation in different phases of design. Three SMEs have used variations of the framework.

Part 3, PD Methods and Digital TE, contains 11 papers. In paper one, a conceptual approach is presented to incrementally update a Digital Twin, especially suited for SMEs. In paper two, the basis for the method described in paper one, is presented with a use case. In paper three, a proposal for automated logo recognition and legal analysis for IP protection is presented. Activities to develop a tool to support the design of CPSs are described in paper four. In paper five, an NLP approach is presented for chatbots. The approach is applied to the management of patent trends. In paper six, a framework is proposed aimed at identifying, categorizing, prioritizing, and mitigating uncertainties in the process of digitization of life-cycle product models. In paper seven, IT tools are described for automating the generations of Digital Twins of machine tools. Paper eight contains a study into improving interoperability in the new manufacturing environment. In paper nine, also interoperability is addressed. An approach is presented for the automatic prediction of failure in the manufacturing process. The authors of paper 10 investigate issues in optimising end-to-end maintenance within manufacturing with DTs, IM, and Its. In paper 11, a five-dimensional DT framework has been proposed linking physical data and virtual data with an ERP for modelling the digital twin of electric vehicle batteries.

Part 4 contains 15 papers in the theme Industry and Society. In paper one, a method for implementing flex-time schedules in a service industry. An optimal production planning method for high-mix low-volume production is proposed in paper two. Paper three contains a literature study into the use of 4.0 technologies in Product Lifecycle Management with a focus on Sustainable Development. In paper four, an analysis is presented of different scenarios for resource planning in a multi-project environment. Based on a literature review the first of three artefacts is presented in paper five: a model to characterize Agriculture 4.0. In paper six, a proposal for a metro-car tracking system is proposed. A theoretical exploration into challenges and opportunities for the digitization of product development and manufacturing process is presented in paper seven. In paper eight, a study is presented into the existing gaps between reshoring drivers and critical operations capabilities. Paper nine contains a system dynamic modeling approach and an empirical study on innovation diffusion for subsystems in the automotive industry in the past decades. In paper 10, a cause-effect diagram is presented for analysing risks in the civil industry. Paper 11 contains a proposal for a framework of questions to identify risks in different phases of a civil engineering process. In paper 12, a model is proposed for the digitization of the railway industry, using the Balanced Score Card and Multi-Criteria Decision making. Paper 13 contains research that contributes to the reshoring literature by providing a multi-stage fuzzy-logic model that simultaneously handles different groups of criteria, and to practitioners by contemplating different key competencies within a company during the reshoring decision process. In paper 14, results of twenty-four multiple case studies in the construction sector are presented, which suggest new quality dimensions and ways to adapt to changed service-quality demands. Paper 15 contains a literature study and case studies into the level of alignment between product development and production. Several problems have been identified.

Part 5 is entitled Product Systems and contains 13 papers. Paper one contains a comparison between two tools for predicting human effort and ergonomic risk related to a series of tasks. In paper two, the TRIZ approach is applied to the non-trivial design of a Wire Electric Discharge Machining (WEDM). The work presented in paper three is an exploration of the possibility to analyse the performance of production lines through digital models. In paper four, a proposal is presented of an associative framework between processes and related data, which are following the recommendations of currently applied frameworks for Business Process Management and Big Data Analytics. In paper five, a literature study is described on APSs, as well as the impact of Industry 4.0 on the development of these systems. Paper six contains a simulation model developed for allowing the selection of appropriate parameters of a power supply system and a drive system for an electric go-kart to meet criteria assumed. In paper seven, research is presented into semantic and syntactic knowledge boundaries that play a role in introducing new products with its accompanying production processes. Paper eight contains a literature study into Advanced Manufacturing, the results of which have been applied to an experimental case. In paper nine, an exploration is presented of opportunities for Kazakhstan to recycle CFPR waste originating in this country and neighbouring countries. Paper 10 contains an exploration of the possibility to automate the design of a wing structure. In paper 11, the possibility of supporting the vertical take-off and landing unmanned aerial vehicle electric power systems by means of photovoltaic cells. An approach for optimizing floor plans using data collected from workplaces and a physics-based planning algorithm utilizing GPU-acceleration is presented in paper 12. In paper 13, a research step is presented to develop a methodology for designing and analysing a propeller, which can be used in a calculation background for a CAD model.

Part 6 contains 11 contributions on Individual and Teams. In paper one, a demonstration Is presented of a virtual tour in a cheese factory. The multi-faceted nature of a virtual tour is highlighted. Paper two contains a design of a rescue helicopter that can approach mountain tops and dangerous terrains. In paper three, a new AR/VR methodology is presented that allows an operator to touch any object in a virtual cabin design of a medical helicopter. Feedback from medical professionals is included. A patent portfolio analysis for VR tools is presented in paper four. Promising areas for further development in the medical domain have been identified. Paper five contains a patent analysis to discover trends in HCPSs in manufacturing. In paper six, an approach is presented to enhance human perspectives by introducing a semiotic framework for representing different aspects of human and organizational meaning formation. The approach is illustrated in a translational medicine organization. Paper seven contains a demonstration of a technology to detect behavioural states of team members during a meeting. In paper eight, a numerical method is presented that is a good step towards systematic design of attractive product shapes. In paper nine, a first design iteration is demonstrated in which a framework is applied that provide disciplines guidelines for achieving health-related objectives. Paper 10 contains a specification of a generic user interface that makes computational systems models more accessible to non-technical decision makers. Finally, paper 11 is a research paper containing research into the generation of a functional structure of a product connected with a Multi-interfaces Entity Model that supports risk assessment.

Systemic risks are potentially harmful events, that could severely disrupt an entire industry or economy. Examples include the bankruptcy of keystone companies and biosecurity incursions. According to the United Nations, detecting and managing systemic risk represents one of the main challenges of the 21st century. Due to increasing complexity and interconnectedness of today’s social-technological-biophysical systems, stakeholders relying on individual disciplines to systemic risk detection, or a combination of disciplines that are not well coordinated, will fail to promptly identify key early warning signals of threats. Our paper argues that transdisciplinary approaches are required to make comprehensive and integrative assessments of complex systems. To support stakeholders undertaking such assessments, we propose a framework that will assist them in: (1) better understanding their system and the risks to which it is exposed; (2) selecting complementary disciplines, theories and methods that are relevant to the system and risks in question; and (3) integrating knowledge from these different disciplines to detect a wide range of early warning signals of systemic risk. The framework can be used as a foundation to build transdisciplinary approaches to risk detection.

Translational Research in the health sciences endeavors to bring biomedical discoveries into clinical applications that improve human health. This work is complex and long-term with a substantial risk of failure. A key step of clinical trials is needed to evaluate the effects of those interventions on human biomedical or behavioral outcomes. Timely recruitment of human subjects and meeting recruitment milestones is recognized as one of the most significant contributors to delays and failures. Quality and Design approaches have been tried to address the problem but the scope has been limited. We proposed to determine how Quality and Design may lead to complementary solutions for these barriers of Translational Research. The first ten studios using this approach are presented here. Three themes emerged: (1) problems were investigated similarly but there was a difference in insights, (2) quality process-based solutions tended to be specific to the issue discussed whereas the design process often yielded solutions broader or even tangential, and (3) quality solutions demonstrated more immediacy while design solutions showed more systemic ideas. In conclusion, the paper demonstrates how Design and Quality in a transdisciplinary studio may lead to solutions with different characteristics for clinical trials and advance translational science.

This paper presents our findings from thirteen industrial interviews, to investigate the significance of transdisciplinarity (TD) in an industrial context. Thus to gain insight into the resilience of industrial manufacturing in rapidly changing environments and establish what enabling or disabling practices may currently exist. The interviews were conducted as an initial part of a wider case study approach being undertaken by the TREND research team and were semi-structured in format. We present the background and research questions being addressed and outline our exploratory research approach. The analysis of interview transcriptions is provided answering our research questions and identifying any emerging themes. Of the industry interviews, only five interviewees had heard of the term TD, the definition of TD varied between companies and did not align with the primordial system of Jantsch’s work. A number of focal enabling and disabling industrial themes emerge from the interviews and related discourse such as the positive and negative human contribution(s) and growing global teams involved in manufacture. For industry to be resilient and meet rapid technological and societal change, these themes should be core for manufacturing solutions. Secondary studies should investigate literature and collaborate with engineering industries to test any potential TD interventions.

Transdisciplinary engineering is composed of analysis and synthesis. Most of current engineering is focused on analysis and based on Euclidean Space, mathematical solutions are developed for product development. But if we pay attention to synthesis or design, we cannot satisfy the requirements of orthonormality and Euclidean distance with units. This paper discuss how we can challenge transdisciplinary engineering for market development, which requires Non-Euclidean Space approach.

The adoption of transdisciplinary capabilities within UK manufacturing could strengthen resilience in response to system disruptions. We propose a Disciplinary Maturity Grid (DMG) as a means through which industry can assess the disciplinarity of their engineering capability. The design of methods to assess maturity of disciplinary working is hindered by a lack of empirical evidence to support identification of the important dimensions. A workshop involving twelve academic experts was used to create a maturity grid. Workshop tasks focussed on defining the appropriate number of maturity levels, the dimensions of those levels, and the maturity assessment questions. The DMG contains five maturity levels and seven dimensions, providing a preliminary design from which to build in future studies.

Simulation in healthcare is rapidly replacing more traditional educational methods, becoming a fundamental step in the medical training path. Medical simulations have a remarkable impact not only on learners’ competencies and skills but also on their attitudes, behaviors, and emotions such as anxiety, stress, mental effort, and frustration. All these aspects are transferred to the real practice and reflected on patients’ safety and outcomes.

The design of medical simulations passes through a careful analysis of learning objectives, technology to be used, instructor’s and learners’ roles, performance assessment, and so on. However, an overall methodology for the simulation assessment and consequent optimization is still lacking.

The present work proposes a transdisciplinary framework for the analysis of simulation effectiveness in terms of learners’ performance, ergonomics conditions, and emotional states. It involves collaboration among different professional figures such as engineers, clinicians, specialized trainers, and human factors specialists. The aim is to define specific guidelines for the simulation optimization, to obtain enhanced learners’ performance, improved ergonomics, and consequently positively affect the patient treatment, leading to cost savings for the healthcare system. The proposed framework has been tested on a low-fidelity simulation for the training of rachicentesis and has allowed the definition of general rules for its enhancement.

Disruptive technologies such as 3D printing, artificial intelligence (AI), and robotics have changed how people think, learn, and work fundamentally. Engineering education must adapt to this digital transformation. There has been increasing interest in integrating design in the engineering curriculum around the world. While traditional problem solving is a linear and structured approach, design thinking is set by a human-centered innovation process which leads to better products and services. This concept is well aligned with the educational vision of transdisciplinary engineering. However, it is challenging to teach the mindset of design thinking for people with various domain knowledge. In this paper, the differences in how industrial designers and design engineers tackle a design project are explained. We intend to share a few successful examples regarding how design methodology captures customer requirements and explores creative solutions in the product development lifecycle within the current engineering curriculum. Also, the user experience research in response to the trend of cyberphysical integration is discussed. Finally, we conclude with the need for a holistic curriculum design in digital manufacturing as a case study to illustrate the role of design thinking for future transdisciplinary engineering education.

Maintaining high product quality while reducing cost is essential for mass-customised products, requiring continuous improvement of the product development process. To this end, design automation should be utilised in all stages of a product’s develop process and lay the foundation for automation of repetitive tasks throughout the process from interaction with the customer to design and production in order to mitigate errors and minimise costs. In this paper, a design automation and production preparation framework is proposed that can facilitate automation from initial stages via CAD to production. Examples of the framework are shown in the shape of proof-of-concepts systems developed by master students in the context of a course in design automation at Linköping University. Included disciplines such as automated planning of robot assembly paths, CNC manufacturing files and production drawings are described, based on design automation, Knowledge-Based Engineering, and design optimisation. Additionally, variations of the framework are implemented at three SMEs, and the results thereof are presented. The proposed frameworks enable interaction and connection between the “softer”, human centred, aspects of customer interaction within sales, with more traditional “harder” engineering disciplines in design and manufacturing.

While production plants are subjects of frequent change, for instance due to changed processes, new products or new machine tools, the process plans must be subsequently updated. This generally affects all planning processes for production management and thus in particular also modern planning methods such as the Digital Twin of a production system. The simulation of production processes using a Digital Twin is a promising means for prospective planning, analysis of existing systems or process-parallel monitoring. However, many companies, especially small and medium-sized enterprises, do not apply the technology, because the generation of a Digital Twin is cost-, time- and resource-intensive and IT expertise is required. Supposed that the process of generation of the Digital Twin was completed once using scans and deep learning applied to a point cloud of a production system, this paper describes a conceptual approach to provide an incremental update of this Digital Twin, as often as necessary. The solution alternatives are presented and discussed, in particular the use of flexible devices (360-camera) for the object acquisition. A particular attention is given to the integration of the entire change process for updating an existing Digital Twin.

The simulation of production processes using a Digital Twin is a promising tool for predictive planning, analysis of existing systems or process-parallel monitoring. In the process industry, the concept of Digital Twin provides significant support for process optimization. The generation of the Digital Twin of an already existing plant is a major challenge – in particular for small and medium-sized enterprises. In this sense, the twinning of the existing physical environment has got a particular importance due to high effort. Shape segmentation from unstructured (e.g. point cloud data) is a core step of the digital twinning process for industrial facilities. This is an inherent issue of Product Lifecycle Management how to acquire data of existing goods. The practice of Digital Twin is described based on object recognition by using methods of Machine Learning. The exploration of the pipeline semantics presents a particular challenge. The highly automated procedure for the generation of Digital Twin is described based on a use case of a biogas plant. Commercial deployment, pitfalls, drawbacks and potential for further developments are further explored.

A logo is s graphical emblem or mark used as an identification for a company and its products and services. Logos are legally protected as intellectual properties (IPs) if registered as trademarks (TMs). Logos^TM are widely distributed online nowadays in the digital economy. Due to their wide distributions online, the constant checking of TM legal usages becomes extremely challenging in the TM registration and protection system. The fact that users can easily imitate the registered TM logo designs casts serious IP legal issue, which highlights the importance of developing an automatic logo image retrieval system. Considering the complexity of TM visual semantics, this research proposes a deep embedding learning for logo image similarity analysis using triplet-network. We propose the optimization of sampling parameters to improve the TM image retrieval performance with robust model. The research aims to reduce discrepancy between human visual interpretation. This transdisciplinary engineering research incorporates deep learning (DL) modeling and TM legal analysis for image-centric TM protection. To demonstrate the model performance, more than 10,000 images for model training and 3000 images for model testing are adopted from Logo-2K+ database. Image retrieval performance shows excellent results with recall@10 exceeding 93%.

The approach presented in the paper is about the concept of a multi-criteria and multi-disciplinary tool supporting design activities while designing and developing CPS. Designers who solve CPS design problems try to build computer models, and examine, verify and validate them. Usually, these models, often created ad hoc, are very complex and large and evolve in time, so the entire processes have many stages and variants. These processes come to an end after one or several sequences of selected knowledge-based activities, which in general have been modified and improved before. These activities usually concern two groups of issues: substantial and decision-making. The presented activity supporting tool concept can be applied in the design process of CPS. The main goal of the new tool is to improve the design process through more precise, effective and problem-dedicated management of the design activity models. It also enables and supports the ad hoc modelling of the collaborative integration of activities for multidisciplinarity and multi-criteria optimization analysis.

Natural language processing (NLP) is an indispensable part of advancing the AI era, especially in the realm of the human-computer interface/interaction (HCI) for all state-of-the-art software applications. NLP enables interfaces between machines and humans allowing machines/computers/systems to understand human languages and engaging in dialogues. An intelligent chatbot development must incorporate NLP technologies to allow the understanding of users’ utterance and responding in understandable sentences in versatile scenarios. This research investigates the emerging technological trend of intelligent chatbot development. The systematic trend analysis is described in the research. First, patents related to intelligent chatbot domain are retrieved using a well-defined search query. The queries are derived from the knowledge ontology, which is extracted using text-mining algorithms - key term frequency analysis, clustering for sub-domain identification, and Latent Dirichlet Allocation (LDA) for topic modelling. Afterwards, the management and technology maps of a patent portfolio, such as patenting trends and technology function matrix, are extracted and drawn. The technology trend analysis also investigated the distributions of the relevant patent claims for specific industries.

Globally the manufacturing industry is undergoing a shift in the way product specifications are defined, used, and re-used from conventional drawing-based systems to a comprehensive 3D digital product model. This transformation is at the heart of the digitization processes. The true benefits lie in the adoption of this technology throughout the product lifecycle. However, this digital transformation is partial and many of the stages in the product lifecycle are still heavily reliant on traditional drawings. This is due to the involvement of several uncertainties in the process of adoption of model-based definition. In this paper, a framework is proposed for the systematic assessment of the prevailing uncertainties in the adoption of model-based definition and enterprise. The framework proposed in this paper is aimed at identifying, categorizing, prioritizing, and mitigating the uncertainties in this process.

The development of energy efficient production systems such as machine tools is a complex process. All specialised departments must work interdisciplinary during the design process in order to achieve an optimal result. In addition to the mechanical aspects, e.g., lightweight construction, the optimization of the Programmable Logic Controller (PLC) programs of tooling machines plays an increasingly important role. By optimizing the programs in terms of energy efficiency, the energy consumption of a machine can be significantly reduced. However, energy consumption depends on many parameters, so the optimization process is complex and a matter of all engineering disciplines working together. By using Digital Twins of tooling machines, simulations can be used to perform many parameter studies for optimizing energy consumption. However, the generation of Digital Twins of production systems is very expensive if they are to represent all relevant features of a production system. Through an IT system of networked software programs and using AutomationML as a special data interface these Digital Twins of machine tools can be generated automatically. The article describes the structure and function of this IT system and how it will be efficient within all involved engineering disciplines.

The development of interoperability is more and more an essential task for all kinds of organizations. It needs to be measured, verified, and continuously improved. With the advent of the Internet of Things, Industry 4.0, Digital Transformation, as well as all technologies it brings, such as big data, cloud computing, and mobile applications, this subject become quite ordinary and necessary, not only in a manufacturing scenario but also in a business process. As crucial benefits, it can bring possible gains for developers and users, also reduce the efforts to communicate multiple and different platforms. Like all new implementations, there are significant challenges that have to be overcome to allow satisfactory results. In this paper, after a systematic literature review to better understand the technologies and this new situation, we conduct a study involving a case in a business process, presenting some actions that can be executed and technologies that can be implemented to reach a well interoperable environment. For instance, to analyze the interoperability maturity level and to adopt ontologies as an alternative to integrate heterogeneous systems. Finally, in the last part of the paper, we give conclusions and perspectives for futures works.

Industry 4.0 has brought innovative principles to the entire world, especially for the manufacturing industry. The adaptation to a technological era showed limitations in the current processes, of which we can highlight the divergence between software and machinery technologies, cloud data processing, difficulty for the information to circulate within a manufacturing environment, so that it flows clearly and objectively, without ambiguity. These limitations end up generating errors between operations in the manufacturing process resulting in costs, customer dissatisfaction, low product quality, and reduced competitiveness. Thus, problems related to the semantic web, semantic interoperability, horizontal and vertical integration are responsible for such limitations in manufacturing processes. To resolve such restrictions and improve the final quality of the product, it is possible to apply Machine Learning techniques. Through the use of ensemble models of machine learning algorithm techniques, techniques with specific characteristics can be grouped, complementing each other, thus providing better prediction results during the manufacture of products, reducing costs, increasing the reliability and quality of the final product. In this way, it is expected to improve the final quality of the product and minimize the impacts that detract from the performance indicators, such as scrap, cost, rework, labor. This research will contribute scientifically to the creation of a system, which can be applied in different manufacturing production processes.

Over the years, there has been an advancement in how manufacturing companies conduct maintenance. They have begun transitioning from Preventive Maintenance (PM) to Predictive Maintenance (PdM). With the introduction of technologies such as Digital Twin (DT), Internet of Things (IoT), and Intelligent Manufacturing (IM), the world is rapidly changing, thus allowing companies to optimise existing processes, products and reduce costs. The existing literature offers limited investigations and best practices in the end-to-end optimisation for maintenance transformation. The current paper intends to explore (a) the transition from PM to PdM and (b) the utilisation of DTs and IM for maintenance optimisation. The paper articulates the scope and features of end-to-end maintenance optimisation for asset uptime and cost benefits. The findings can help industries understand the introductions and advancements of technologies for predictive maintenance and end-to-end optimisation with the benefit of investigating and illustrating how companies can move forward.

The successful operation of Electric-Vehicle Batteries (EVB) is paramount for the ever-continuing goal of approaching a low carbon emission future. The Lithium-ion battery (LIB) is currently the best wager to implement on Electric Vehicles (EV). Nonetheless, it comes with its fair trade of challenges. The complexity involved in the design, manufacturing and operating conditions for these batteries has made their control and monitoring paramount. Digital Twin (DT) is concretely defined as a virtual replica of a physical object, process or system. The DT can be implemented in conjunction with the EVB physical embodiment to analyse and enhance its performance. ERP is a system designed to control production and planning amongst others. This paper presents the state-of-the-art battery design, production with the combination of DT and Enterprise system. A five-dimensional DT framework has been proposed linking the physical data and virtual data with ERP. The proposed method was used to model the digital twin of EVB at the concept level and solve its challenges faced in the industry Also the potential application & benefits of the framework have been formalised with the help of a case study from Tesla EVBs.

In service industries personnel performance is key to achieve business excellence. Until recently, forty to forty eight hours per week labor contracts with equal daily schedules (eight hours per day) was the only way formal employees were hired in some countries. Recently, this trend has changed and some countries started to allow work contracts with less weekly hours and flexible daily schedules. This offers some degrees of freedom to employees to work less than eight hours per day and non-necessarily having the same timetable every workday. The fabric of society is impacted since now work is at reach for many people who cannot work full time schedules for diverse reasons. The contribution of this work is a Transdisciplinary Flextime Hiring Method that considers both employee and company needs. Using organisational design concepts (business management), flexibility is analysed through integer programming modelling (engineering optimisation) to evaluate cost changes resulting from implementing flextime (societal needs). Service companies may justify and implement flextime based on cost reductions, along with its associated improvement of employee satisfaction and commitment. Numerical analysis based on industry data illustrates these concepts and consequences.