Dr Christopher Turner

Regulatory and technological changes have recently transformed the digital footprint of credit card transactions, providing at least ten times the amount of data available for fraud detection practices that were previously available for analysis. This newly enhanced dataset challenges the scalability of traditional rule-based fraud detection methods and creates an opportunity for wider adoption of artificial intelligence (AI) techniques. However, the opacity of AI models, combined with the high stakes involved in the finance industry, means practitioners have been slow to adapt. In response, this paper argues for more researchers to engage with investigations into the use of Explainable Artificial Intelligence (XAI) techniques for credit card fraud detection. Firstly, it sheds light on recent regulatory changes which are pivotal in driving the adoption of new machine learning (ML) techniques. Secondly, it examines the operating environment for credit card transactions, an understanding of which is crucial for the ability to operationalise solutions. Finally, it proposes a research agenda comprised of four key areas of investigation for XAI, arguing that further work would contribute towards a step-change in fraud detection practices.

Scholars often recommend incorporating context into the design of an ex-plainable artificial intelligence (XAI) model in order to deliver the successful integration of an explainable agent into a real-world operating environment. However, few in the field of XAI have expanded upon the meaning of context, or provided clarification as to what they consider its constituent parts. This paper answers that question by providing a thematic review of the extant literature , revealing an interaction between the contextual elements of Setting, Audience, Goals and Ethics (SAGE). This paper therefore proposes SAGE as a conceptual framework that enables researchers to build audience-centric and context-sensitive XAI, thereby strengthening the prospects for successful adoption of an XAI solution.

The field of Explainable Artificial Intelligence (XAI) is a relatively new approach to AI, with the aim to provide black box algorithms with human intelligible narrative functionality. It is most often in end-of-life considerations of the asset lifecycle that sustainability issues are encountered. Modern maintenance practice requires a holistic understanding of lifecycle and options for sustainable asset treatments. human in the loop solutions offer a way to leverage both machine and human skill sets to provide the next level of automaton solutions for industrial maintenance activities. This paper presents a framework for human in the loop Intelligent and Sustainable Maintenance. In bridging the gap between machines and humans XAI leverages the best of both worlds to provide a new level of agility to cyber assisted maintenance activities and full lifecycle consideration of assets; a notion that is necessary throughout the organization in the achievement of sustainability goals set by governments around the world in the achievement of a net zero carbon emission economy.

Human-robot collaboration (HRC) is at the heart of intelligent automation and is essential for increasing efficiencies in industrial systems by combining the strength, dexterity and precision of robots with the intelligence and adaptation skills of humans. HRC is enabled by Human-Robot Interaction (HRI) in which humans and robots co-work on a task communicating and physically interacting with each other. However, the principles of HRI are neither well understood nor well established and detailed investigation is difficult because real-life experimentation involving human and industrial robot interactions carry the risk of detrimental encounters. This paper presents an opportunity to investigate and assess human-robot interactions for collaboration efficacy and safety within a realistic Virtual Reality (VR) environment A simple maintenance activity involving the assembly of two blocks was designed with two scenarios: manual assembly; robot assisted assembly.

This research examines the opportunities provided by advances in digital manufacturing technologies for the provision of products designed to meet the needs of an individual consumer. The ability to co-create products with customers could enable mass personalisation to become a popular and fast-growing mode of production. Additive manufacturing, in both 3D and 4D printing forms, opens up new opportunities for circular economy-compliant production of such highly personalised products. Industry 4.0 has been seen by many as an agenda for the utilisation of interconnected digital technologies in industry, with a particular focus on manufacturing. Industry 5.0 seeks to address challenges that have grown in importance since the inception of Industry 4.0, such as the efficient inclusion of human worker skills in tandem with automation solutions, to address highly complex manufacturing scenarios while mitigating many of the environmental issues inherent with current manufacturing practices, while using circular economy principles. In examining the production of smart fabrics, this paper puts forward a framework for circular production of additively manufactured personalised products, co-designed with inputs from consumers.

This paper explores the use of Discrete Event Simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of Discrete Event Simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. The literature review has identified key research gaps as: The lack of example cases where multi-objective optimisation methods have been applied to simulation models; The need for a framework to visualise the relationship between inputs and outputs of simulation models. A framework is presented to enable the optimisation DES simulation models and optimise multiple objectives simultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. A real-life test case involving a turbine assembly process is presented to illustrate the use and validity of the framework. The generated regression tree expressed a general trend by demonstrating relationships between input variables and two conflicting objectives. Random forests were implemented for creating higher accuracy predictions and they produced a mean square error of ~0.066 on the training data and ~0.081 on test data.

High value manufacturing systems still require ergonomically intensive manual activities. Examples include the aerospace industry where the fitting of pipes and wiring into confined spaces in aircraft wings is still a manual operation. In these environments, workers are subjected to ergonomically awkward forces and postures for long periods of time. This leads to musculoskeletal injuries that severely limit the output of a shopfloor leading to loss of productivity. The use of tools such as wearable sensors could provide a way to track the ergonomics of workers in real time. However, an information processing architecture is required in order to ensure that data is processed in real time and in a manner that meaningful action points are retrieved for use by workers. In this work, based on the Adaptive Control of Thought—Rational (ACT-R) cognitive framework, we propose a Cognitive Architecture for Wearable Sensors (CAWES); a wearable sensor system and cognitive architecture that is capable of taking data streams from multiple wearable sensors on a worker’s body and fusing them to enable digitisation, tracking and analysis of human ergonomics in real time on a shopfloor. Furthermore, through tactile feedback, the architecture is able to inform workers in real time when ergonomics rules are broken. The architecture is validated through the use of an aerospace case study undertaken in laboratory conditions. The results from the validation are encouraging and in the future, further tests will be performed in an actual working environment.

•The paper focuses on the matching of digital technologies to maintenance practice in the automotive sector.•A framework for IOT enabled circular maintenance practice and treatment of automotive parts is proposed.•The framework proposed is capable of processing data from vehicle based sensors.•An Auto-circular simulator concept is outlined, providing visualisation and analytics toolbox capabilities.•A case study focused on a hydrogen car power unit is included, with a supporting ontology. The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research sets out an agenda and framework for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose.

Current OEM (Original Equipment Manufacturer) facilities tend to be highly integrated and are often situated on one site. While providing scale of production such centralisation may create barriers to the achievement of fully flexible, adaptable, and reconfigurable factories. The advent of Industry 4.0 opens up opportunities to address these barriers by decentralising information and decision-making in manufacturing systems through CPS (Cyber Physical Systems) use. This research presents a qualitative study that investigates the possibility of distributing information and decision-making logic into ‘smart workpieces’ which can actively participate in assembly operations. To validate the concept, a use-case demonstrator, corresponding to the assembly of a ‘flat-pack’ table, was explored. Assembly parts in the demonstrator, were equipped with computation, networking, and interaction capabilities. Ten participants were invited to evaluate the smart assembly method and compare its results to the traditional assembly method. The results showed that in its current configuration the smart assembly was slower. However, it made the assembly process more flexible, adaptable and reconfigurable.

The increasing demand for a sustainable, reliable and secure supply chain for food products has led to the application of digital technologies such as blockchain to improve operational effectiveness. The purpose of this paper is to investigate the integration barriers of Blockchain Technology (BCT) within Circular Food Supply Chains (CFSCs) towards firm’s operational effectiveness through a multi-methodological process. Initially the integration barriers are identified through a review of literature and these risks are categorised, using evidence obtained by survey questionnaire completed by experts in the integrated research arena. A further quantified prioritisation of these barriers is made by utilizing a Fuzzy Delphi approach, validated by expert practitioners drawn from the food production and supply organizations. Finally, through semi-structured interviews with Blockchain and FSC experts, an examination of how the integration barriers affect operational effectiveness may be mitigated is provided. This paper concludes that the identified barriers to blockchain integration have real impact on the operational effectiveness of the firm that can only be clarified through industry wide standardised processes.

Since it first appeared in literature in the early nineties, the Circular Economy (CE) has grown in significance amongst academic, policymaking, and industry groups. The latest developments in the CE field have included the interrogation of CE as a paradigm, and its relationship with sustainability and other concepts, including iterative definitions. Research has also identified a significant opportunity to apply circular approaches to our rapidly changing industrial system, including manufacturing processes and Industry 4.0 (I4.0) which, with data, is enabling the latest advances in digital technologies (DT). Research which fuses these two areas has not been extensively explored. This is the first paper to provide a synergistic and integrative CE-DT framework which offers directions for policymakers and guidance for future research through a review of the integrated fields of CE and I4.0. To achieve this, a Systematic Literature Review (SLR; n = 174) of the empirical literature related to digital technologies, I4.0, and circular approaches is conducted. The SLR is based on peer-reviewed articles published between 2000 and early 2018. This paper also summarizes the current trends in CE research related to manufacturing. The findings confirm that while CE research has been on the increase, research on digital technologies to enable a CE is still relatively untouched. While the “interdisciplinarity” of CE research is well-known, the findings reveal that a substantial percentage is engineering-focused. The paper concludes by proposing a synergistic and integrative CE-DT framework for future research developed from the gaps in the current research landscape.

Remanufacturing is a viable option to extend the useful life of an end-of-use product or its parts, ensuring sustainable competitive advantages under the current global economic climate. Challenges typical to remanufacturing still persist, despite its many benefits. According to the European Remanufacturing Network, a key challenge is the lack of accurate, timely and consistent product knowledge as highlighted in a 2015 survey of 188 European remanufacturers. With more data being produced by electric and hybrid vehicles, this adds to the information complexity challenge already experienced in remanufacturing. Therefore, it is difficult to implement real-time and accurate remanufacturing for the shop floor; there are no papers that focus on this within an electric and hybrid vehicle environment. To address this problem, this paper attempts to: (1) identify the required parameters/variables needed for fuel cell remanufacturing by means of interviews; (2) rank the variables by Pareto analysis; (3) develop a casual loop diagram for the identified parameters/variables to visualise their impact on remanufacturing; and (4) model a simple stock and flow diagram to simulate and understand data and information-driven schemes in remanufacturing.

In this paper we introduce a research agenda to guide the development of the next generation of Discrete Event Simulation (DES) systems. Interfaces to digital twins are projected to go beyond physical representations to become blueprints for the actual “objects” and an active dashboard for their control. The role and importance of real-time interactive animations presented in an Extended Reality (XR) format will be explored. The need for using game engines, particularly their physics engines and AI within interactive simulated Extended Reality is expanded on. Importing and scanning real-world environments is assumed to become more efficient when using AR. Exporting to VR and AR is recommended to be a default feature. A technology framework for the next generation simulators is presented along with a proposed set of implementation guidelines. The need for more human centric technology approaches, nascent in Industry 4.0, are now central to the emerging Industry 5.0 paradigm; an agenda that is discussed in this research as part of a human in the loop future, supported by DES. The potential role of Explainable Artificial Intelligence is also explored along with an audit trail approach to provide a justification of complex and automated decision-making systems with relation to DES. A technology framework is proposed, which brings the above together and can serve as a guide for the next generation of holistic simulators for manufacturing.

This paper explores the notion of the modular building construction site as an applied instance of redistributed manufacturing; in so doing, this research seeks to reduce the environmental footprint of building sites, treating them as small digitally connected subunits. In seeking to provide a whole lifecycle appreciation of a construction project, it is noted that the presence of a framework to provide guidance on the consideration of Internet of Things (IoT) data streams and connected construction objects is currently lacking. This paper proposes use of embedded IoT enabled sensing technology within all stages of a modular building lifecycle. An expanded four-phase model of intelligent assets use in construction is proposed along with an outline of the required data flows between the stages of a given building’s entire lifecycle that need to be facilitated for a BIM (Buildings Information Modelling) representation to begin to describe a building project as a sustainable asset within the circular economy. This paper also describes the use of concrete as a modular sensing structure; proposing that health monitoring of the material in situ along with the recoding of environmental factors over time could help to extend the longevity of such structures.

While the move towards Industry 4.0 has motivated a re-evaluation of how a manufacturing organization should operate in light of the availability of a new generation of digital production equipment, the new emphasis is on human worker inclusion to provide decision making activities or physical actions (at decision nodes) within an otherwise automated process flow; termed by some authors as Industry 5.0 and seen as related to the earlier Japanese Society 5.0 concept (seeking to address wider social and environmental problems with the latest developments in digital system, artificial Intelligence and automation solutions). As motivated by the EU the Industry 5.0 paradigm can be seen as a movement to address infrastructural resilience, employee and environmental concerns in industrial settings. This is coupled with a greater awareness of environmental issues, especially those related to Carbon output at production and throughout manufactured products lifecycle. This paper proposes the concept of dynamic Life Cycle Assessment (LCA), enabled by the functionality possible with intelligent products. A particular focus of this paper is that of human in the loop assisted decision making for end-of-life disassembly of products and the role intelligent products can perform in achieving sustainable reuse of components and materials. It is concluded by this research that intelligent products must provide auditable data to support the achievement of net zero carbon and circular economy goals. The role of the human in moving towards net zero production, through the increased understanding and arbitration powers over information and decisions, is paramount; this opportunity is further enabled through the use of intelligent products.

Industry 4.0 derived technologies have the potential to enable a new wave of digital manufacturing solutions for semi and fully automated production. In addition, this paradigm encompasses the use of communication technologies to transmit data to processing stations as well as the utilization of cloud based computational resources for data mining. Despite the rise in automation, future manufacturing systems will initially still require humans in the loop to provide supervisory level mediation for even the most autonomous production scenarios. Through a structured review, this paper details a number of key technologies that are most likely to shape this future and describes a range of scenarios for their use in delivering human mediated automated and autonomous production. This paper argues that in all cases of future manufacturing management it is key that the human has oversight of critical information flows and remains an active participant in the delivery of the next generation of production systems.

The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable 'Human in the loop' interactions.

The field of Explainable Artificial Intelligence (XAI) is a relatively new approach to AI, with the aim to provide black box algorithms with human intelligible narrative functionality. It is most often in end-of-life considerations of the asset lifecycle that sustainability issues are encountered. Modern maintenance practice requires a holistic understanding of lifecycle and options for sustainable asset treatments. human in the loop solutions offer a way to leverage both machine and human skill sets to provide the next level of automaton solutions for industrial maintenance activities. This paper presents a framework for human in the loop Intelligent and Sustainable Maintenance. In bridging the gap between machines and humans XAI leverages the best of both worlds to provide a new level of agility to cyber assisted maintenance activities and full lifecycle consideration of assets; a notion that is necessary throughout the organization in the achievement of sustainability goals set by governments around the world in the achievement of a net zero carbon emission economy.

Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.

The practice of optimising business processes has, until recently, been undertaken mainly as a manual task. This paper provides insight s into a n automated business process optimisation framework by using web services for the development of re - configurable business processes. The research presented here extends the framework of Vergidis (2008) by introducing web services as a mechanism for facilitating business process interactions, identifying enhancements to support business processes and undertaking three case studies to evaluate the proposed enhancements. The featured case studies demonstrate that an increase in the amount of available web services gives rise to improvements in the business processes generated. This research highlights an increase in the efficiency of the algorithm and the quality of the business proc ess designs that result from the enhancements . Future research directio ns are proposed for the further improvement of the framework

Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies.

This paper reviews the area of combined discrete event simulation (DES) and virtual reality (VR) use within in- dustry. While establishing a state of the art for progress in this area, this paper makes the case for VR DES as the vehicle of choice for complex data analysis through interactive simulation models, highlighting both its advantages and current limitations. This pa- per reviews active research topics such as VR and DES real-time integration, communication protocols, system design considera- tions, model validation, and applications of VR and DES. While summarizing future research directions for this technology combi- nation, the case is made for smart factory adoption of VR DES as a new platform for scenario testing and decision making. It is put that in order for VR DES to fully meet the visualization require- ments of both Industry 4.0 and Industrial Internet visions of digital manufacturing, further research is required in the areas of lower latency image processing, DES delivery as a service, gesture recog- nition for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets.

New trends in Knowledge-Based Engineering (KBE) highlight the need for decoupling the automation aspect from the knowledge management side of KBE. In this direction, some authors argue that KBE is capable of effectively capturing, retaining and reusing engineering knowledge. However, there are some limitations associated with some aspects of KBE that present a barrier to deliver the knowledge sourcing process requested by industry. To overcome some of these limitations this research proposes a new methodology for efficient knowledge capture and effective management of the complete knowledge life cycle. The methodology proposed in this research is validated through the development and implementation of a case study involving the optimisation of wing design concepts at an Aerospace manufacturer. The results obtained proved the extended KBE capability for fast and effective knowledge sourcing. This evidence was provided by the experts working in the development of the case study through the implementation of structured quantitative and qualitative analyses.

Fibre steering is involved in the development of non-conventional variable stiffness laminates (VSL) with curvilinear paths as well as in the lay-up of conventional laminates with complex shapes. Manufacturability is generally overlooked in design and, as a result, industrial applications do not take advantage of the potential of composite materials. This work develops a design for manufacturing (DFM) tool for the introduction in design of the manufacturing requirements and limitations derived from the fibre placement technology. This tool enables the automatic generation of continuous fibre paths for manufacturing. Results from its application to a plate with a central hole and an aircraft structure – a windshield front fairing – are presented, showing good correlation of resulting manufacturable paths to initial fibre trajectories. The effect of manufacturing constraints is assessed to elucidate the extent to which the structurally optimal design can be reached while conforming to existing manufacturing specifications.

In recent years a step change has been seen in the rate of adoption of Industry 4.0 technologies by manufacturers and industrial organizations alike. This paper discusses the current state of the art in the adoption of industry 4.0 technologies within the construction industry. Increasing complexity in onsite construction projects coupled with the need for higher productivity is leading to increased interest in the potential use of industry 4.0 technologies. This paper discusses the relevance of the following key industry 4.0 technologies to construction: data analytics and artificial intelligence; robotics and automation; buildings information management; sensors and wearables; digital twin and industrial connectivity. Industrial connectivity is a key aspect as it ensures that all Industry 4.0 technologies are interconnected allowing the full benefits to be realized. This paper also presents a research agenda for the adoption of Industry 4.0 technologies within the construction sector; a three-phase use of intelligent assets from the point of manufacture up to after build and a four staged R&D process for the implementation of smart wearables in a digital enhanced construction site.

The catchment area along a bus route is key in predicting bus journeys. In particular, the aggregated number of households within the catchment area are used in the prediction model. The model uses other factors, such as head-way, day-of-week and others. The focus of this study was to classify types of catchment areas and analyse the impact of varying their sizes on the quality of predicting the number of bus passengers. Machine Learning techniques: Random Forest, Neural Networks and C5.0 Decision Trees, were compared regarding solution quality of predictions. The study discusses the sensitivity of catchment area size variations. Bus routes in the county Surrey in the United Kingdom were used to test the quality of the methods. The findings show that the quality of predicting bus journeys depends on the size of the catchment area.

Discrete - Event Simulation (DES) is commonly used for the simulation of manufacturing systems. In many practical cases, DES practitioners ha ve to make simplifications or to use the software in an unconventional or convoluted fashion to meet their needs. Petri nets enable the development of transparent models which allow increased flexibility and control for designers. Furthermore, Petri nets t ake advantage of a solid mathematical ground and constitute a simple language. However, Petri nets lack the software capabilities to realise their full potential. This study investigates the suitability and relevance of Discrete - Event Simulation (DES) soft ware for Petri net modelling in the context of manufacturing systems. A framework is developed for the modelling of different classes of Petri nets on DES. Analytical models of asynchronous flow lines are developed. Initial results show that the analytical models are without closed - form solution and the explosion of the state space is observed, justifying the use of computational methods and simulation for the analysis of manufacturing systems. This study shows that the gain in flexibility provided by Petri nets provides a new insight into the effects of stochasticity on setup and failure times in manufacturing systems.

This paper proposes a framework for the facilitation of organisational capability for outsourcing innovation, enabling firms to take advantage of its many benefits (e.g., reduced costs, increased flexibility, access to better expertise and increased business focus), whilst mitigating its risks. In this framework a generic holistic model is developed to aid firms to successfully outsource innovation. The model is realised in two stages using a qualitative theory-building research design. The initial stage develops a preliminary model which is subsequently validated and refined during the second stage. The propositions which form the preliminary model are deductively explored to identify whether they also exist in a second data set. A semi-structured interview survey is executed with the aid of a rich picture survey instrument to gather data for this purpose. The model developed by this study describes innovation outsourcing as an open system of interrelated activities that takes established company strategy (in terms of people, organisational structures, environment, and technology), and transforms it into improved firm performance through innovation. The model achieves this through a three-stage process which enables the alignment of capability to outsourced innovation activity, and makes actual performance outcomes, rather than expected benefits, the focus of innovation outsourcing aims.

The emergence of new technologies such as the Internet of Things, big data, and advanced robotics, together with risks such as climate change, rising labour costs, and a fluctuating economy, are challenging the current UK manufacturing model. In this paper, business models for re-distributed manufacture (RdM) are developed using anIDEF (Icam DEFinition for Function Modelling) description to serve as a guide for the implementation of the RdM concept in the consumer goods industry. This paper explores the viability of a re-distributed business model for manufacturers employing new manufacturing technologies such as additive manufacturing or three-dimensional (3D) printing, as part of a sustainable and circular production and consumption system. An As-Is value chain model is presented alongside the proposed new business model for a sustainable re-distributed manufacturing system. Both are illustrated via a case study drawn from the shoe manufacturing industry. The case study shows that there is a need for robust facilities in close proximity to the customer. These facilities are store fronts which can also manufacture, remanufacture, and provide services. The reduction in transportation and increase in customer involvement throughout the process are the main benefits that would accrue if a re-distributed model is implemented in the given industry.

The purpose of this paper is to demonstrate a system architecture for integrating product lifecycle management (PLM) systems with cross supply chain maintenance information to support root cause analysis. By integrating product data from PLM systems with warranty claims, vehicle diagnostics and technical publications, engineers were able to improve the root cause analysis and close the information gaps. Data collection was achieved via in-depth semi-structured interviews and workshops with experts from the automotive sector. Unified Modelling Language (UML) diagrams were used to design the system architecture proposed. A user scenario is also presented to demonstrate the functionality of the system.

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel in- tegrated system for automatic job scheduling is presented; from concept formulation to the ex- amination of the data to information transitional level interface, and at the decision making level. The underlying architecture con fi gures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise de- gradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum sche- duling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account speci fi c cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The de- monstrator structure was found fi t for purpose with logical component relationships, o ff ering further scope for research and commercial exploitation