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As cyber-attacks continue to grow, organisations adopting the internet-of-things (IoT) have continued to react to security concerns that threaten their businesses within the current highly competitive environment. Many recorded industrial cyber-attacks have successfully beaten technical security solutions by exploiting human-factor vulnerabilities related to security knowledge and skills and manipulating human elements into inadvertently conveying access to critical industrial assets. Knowledge and skill capabilities contribute to human analytical proficiencies for enhanced cybersecurity readiness. Thus, a human-factored security endeavour is required to investigate the capabilities of the human constituents (workforce) to appropriately recognise and respond to cyber intrusion events within the industrial control system (ICS) environment.

A quantitative approach (statistical analysis) is adopted to provide an approach to quantify the potential cybersecurity capability aptitudes of industrial human actors, identify the least security-capable workforce in the operational domain with the greatest susceptibility likelihood to cyber-attacks (i.e. weakest link) and guide the enhancement of security assurance. To support these objectives, a Human-factored Cyber Security Capability Evaluation approach is presented using conceptual analysis techniques.

Using a test scenario, the approach demonstrates the capacity to proffer an efficient evaluation of workforce security knowledge and skills capabilities and the identification of weakest link in the workforce.

The approach can enable organisations to gain better workforce security perspectives like security-consciousness, alertness and response aptitudes, thus guiding organisations into adopting strategic means of appropriating security remediation outlines, scopes and resources without undue wastes or redundancies.

This paper demonstrates originality by providing a framework and computational approach for characterising and quantify human-factor security capabilities based on security knowledge and security skills. It also supports the identification of potential security weakest links amongst an evaluated industrial workforce (human agents), some key security susceptibility areas and relevant control interventions. The model and validation results demonstrate the application of action research. This paper demonstrates originality by illustrating how action research can be applied within socio-technical dimensions to solve recurrent and dynamic problems related to industrial environment cyber security improvement. It provides value by demonstrating how theoretical security knowledge (awareness) and practical security skills can help resolve cyber security response and control uncertainties within industrial organisations.

The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques.

This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined.

This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable.

Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.

The purpose of this paper is to present a comparison of a number of business process mining tools currently available in the UK market. An outline of the practice of business process mining is given, along with an analysis of the main techniques developed by academia and commercial entities. This paper also acts as a primer for the acceptance and further use of process mining in industry, suggesting future directions for this practice.

Secondary research has been completed to establish the main commercial business process mining tool vendors for the market. A literature survey has also been undertaken into the latest theoretical techniques being developed in the field of business process mining.

The authors have identified a number of existing commercially available business process mining tools and have listed their capabilities within a comparative analysis table. All commercially available business process mining tools included in this paper are capable of process comparison and at least 40 per cent of the tools claim to deal with noise in process data.

The contribution of this paper is to provide a state‐of‐the‐art review of a number of commercial business process mining tools available within the UK. This paper also presents a summary of the latest research being undertaken in academia in this subject area and future directions for the practice of business process mining.

The purpose of this paper is to develop a technical product‐service systems (t‐PSS) for the BoX^® (Big OptiX) ultra precision free‐form grinding machine which has been designed and developed at Cranfield University. BoX is a new machine concept where advanced mass production and ultra precision technologies are combined.

This work utilises the machine as a demonstration case study to investigate t‐PSS for the machine tool providers. It develops three t‐PSS business models for the BoX machine: product‐, use‐ and result‐oriented.

The paper discusses the stakeholders' responsibilities, cash flows, application sectors, and consequently benefits and shortcomings of the three business approaches.

The enhancement of competition from low cost economies, vibrant market requirements and increasing customer demands cannot be addressed merely by the latest achievements in technology. As a response to this, the modern manufacturing industry is shifting its orientation towards t‐PSS. t‐PSS is an integrated product and service offering that delivers value in use.

The novelty of this paper lies in identifying and analysing the key implications of t‐PSS on machine tool industry using the BoX machine as an example case study.

The purpose of this paper is to propose a methodology for rapidly and reliably capturing, representing and analysing process systems to facilitate the potential for operations performance improvement within a complex product design and manufacturing facility operating in a high‐performance business environment.

This paper demonstrates the proposed methodology within an electro‐mechanical design and manufacturing environment producing complex non‐standard products for a global market. The technique has national and international application for a wide spectrum of design and manufacturing facilities.

The authors also demonstrate the effectiveness of the proposed methodology in capturing, representing and analysing processes to assess the feasibility of and identify the issues involved in realising further performance improvements in an existing complex product, high variety, design and manufacturing facility.

Through the enhanced level of understanding of business processes and knowledge intensive operations, the design and manufacturing facilities managers can now realise the next level of performance enhancement by fully exploiting the potential of performance improvement initiatives. Since the proposed methodology creates an environment in which risks inherent in new techniques are controlled, the facilities managers can also gain competitive advantage by becoming the early adopters of radical performance enhancement approaches.

Most major and many smaller manufacturing companies have adopted the popular performance improvement initiatives. These companies are now striving to achieve the next level of performance enhancement at the system or enterprise level and require a better understanding of their business processes and knowledge intensive operations. This understanding needs to be achieved rapidly, cost effectively and in a readily communicable and quantifiable form to allow analysis and ongoing re‐analysis within the volatile industrial environment. The proposed methodology achieves this through the identification, communication, understanding and analysis of the internal and external interactions and dependencies of the facility.

The techniques that help organisations implement leading edge cost and quality practices in manufacturing operations management are typically disparate and generic in nature. There is a need to identify integrated practices at the right level of granularity, based on a clear definition of the existing operations practices. This paper proposes a novel framework for achieving and maintaining good cost and quality operations management practice within a manufacturing environment.

The framework uses a new approach for identifying the profile of current activities and better practice activities for the roles of team leaders, cell leaders and operations managers within a manufacturing company.

The paper proposes a recommended set of context‐specific activities for these roles. These recommended activities are utilised to develop a cascade of deployable recommendations.

The framework is illustrated within a manufacturing environment producing complex product ranges. The implementation of the framework enables improved operational efficiency and effectiveness. It also enables the benefits of improved operational standardisation and consistency.

The demand for contracts on assets availability has increased. Recently published papers show that the use of asset health monitoring technologies is being encouraged to improve the asset performance. This is based on reason rather than analysis. This paper aims to understand and assess the effect of different types of business processes for maintenance resource levels on the behaviour of the maintenance operations and asset availability located at different customer locations using different asset monitoring levels.

A discrete event simulation (DES) model was developed to mimic complex maintenance operations with different monitoring levels (reactive, diagnostics, and prognostics). The model was created to understand and assess the influence of resources (labour and spare parts) on a particular maintenance operation. The model was created to represent different levels of asset monitoring to be applied in a case study. Subsequently, different levels of spare parts (ranging from deficient inventory to a plentiful spares inventory) and labour were applied to show the effects of those resources on the asset availability.

This research has found that the DES was able to discern different processes for asset monitoring levels in complex maintenance operations. It also provided numerical evidence about applying such asset monitoring levels and proved that the higher asset monitoring level does not always guarantee higher asset availability.

The developed model is a unique model that can provide the decision makers of maintenance operations with numerical evidence to select an appropriate asset monitoring level based on their particular maintenance operations.

A novel DES model was developed to support maintenance operations decision makers in selecting the appropriate asset monitoring level for their particular operations. This unique approach provides numerical evidence rather than reasoning, and also proves that the higher asset monitoring level does not always guarantee higher asset availability.

Asset management has recently gained significance due to emerging business models such as Product Service Systems where the sale of asset use, rather than the sale of the asset itself, is applied. This leaves the responsibility of the maintenance tasks to fall on the shoulders of the manufacturer/supplier to provide high asset availability. The use of asset monitoring assists in providing high availability but the level of monitoring and maintenance needs to be assessed for cost effectiveness. There is a lack of available tools and understanding of their value in assessing monitoring levels. The paper aims to discuss these issues.

This research aims to develop a dynamic modelling approach using Discrete Event Simulation (DES) to assess such maintenance systems in order to provide a better understanding of the behaviour of complex maintenance operations. Interviews were conducted and literature was analysed to gather modelling requirements. Generic models were created, followed by simulation models, to examine how maintenance operation systems behave regarding different levels of asset monitoring.

This research indicates that DES discerns varying levels of complexity of maintenance operations but that more sophisticated asset monitoring levels will not necessarily result in a higher asset performance. The paper shows that it is possible to assess the impact of monitoring levels as well as make other changes to system operation that may be more or less effective.

The proposed tool supports the maintenance operations decision makers to select the appropriate asset monitoring level that suits their operational needs.

A novel DES approach was developed to assess asset monitoring levels for maintenance operations. In applying this quantitative approach, it was demonstrated that higher asset monitoring levels do not necessarily result in higher asset availability. The work provides a means of evaluating the constraints in the system that an asset is part of rather than focusing on the asset in isolation.

The adoption of the product‐service system business model means that the designing company has greater scope and motivation to learn from experience of its products in use to improve their core design and engineering capabilities. Continuous improvement, however, depends crucially upon the implementation of effective knowledge and information management (KIM) systems within a dynamic learning environment that impinge on diverse communities throughout the product lifecycle. This paper aims to address these issues.

This paper consolidates literature reviews and presents empirical observations relating to the current KIM systems and practices within large aerospace and manufacturing companies. In particular, experiences from a case study to enhance reuse of in‐service feedback conducted with an aerospace company are reported.

The empirical observations suggest that the feedback processes rely on a combination of formal and informal personalization and codification approaches, but companies are placing greater emphasis on the development of information systems to support learning from in‐service experience. It is argued that greater value could be realized from collective reuse of in‐service information but should be considered from the outset such that its content and representation could be made more amenable to computational analysis and organization for knowledge discovery.

To achieve the full aspirations of learning from in‐service experience, issues and challenges of KIM need to be addressed. These are summarized as strategies to promote success of codification approaches.

The initial value of the techniques for improving in‐service information reuse has been demonstrated to the industrial collaborator.