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The purpose of this paper is to introduce the continuous auditing system based on continuous monitoring and its implementation methodology; also to present a systematic case study of actual continuous auditing systems implemented in the financial industry and the manufacturing industry.

The paper examines the method of implementing the continuous auditing system in the enterprise resource planning (ERP) environment, and suggests how the continuous auditing system can take firm root by looking at the successful introduction of the continuous auditing system in the financial industry and the manufacturing industry.

The proposed method of implementing the continuous auditing system has the 2stage approaches which can be applied to various kinds of companies in the ERP‐based environment. In addition, the proposed cases have the important practical implications acquired in the process of implementing the continuous auditing system in the financial industry and the manufacturing industry.

This study will help many corporations facing various types of corruption or circumvention of internal control, with their internal auditing, by showing them how to use the continuous auditing system to reinforce internal control. Also, it will make the independent auditor understand audited company's continuous monitoring system and lead to use the infrastructure for efficient and effective external auditing.

The proposed method and cases of implementing the continuous auditing system offer an innovative approach to auditing in the ERP‐based environment because it facilitates both internal auditor and external auditor to achieve the audit objectives efficiently and effectively.

Structural health monitoring (SHM) has gained significant attention due to its capability in providing support for efficient and optimal bridge maintenance activities. However, despite the promising potential, the effectiveness of SHM system might be hindered by unprecedented factors that impact the continuity of data collection. This research presents a framework utilising convolutional neural network (CNN) for estimating structural response using environmental variations.

The CNN framework is validated using monitoring data from the Suramadu bridge monitoring system. Pre-processing is performed to transform the data into data frames, each containing a sequence of data. The data frames are divided into training, validation and testing sets. Both the training and validation sets are employed to train the CNN models while the testing set is utilised for evaluation by calculating error metrics such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). Comparison with other machine learning approaches is performed to investigate the effectiveness of the CNN framework.

The CNN models are able to learn the trend of cable force sensor measurements with the ranges of MAE between 10.23 kN and 19.82 kN, MAPE between 0.434% and 0.536% and RMSE between 13.38 kN and 25.32 kN. In addition, the investigation discovers that the CNN-based model manages to outperform other machine learning models.

This work investigates, for the first time, how cable stress can be estimated using temperature variations. The study presents the first application of 1-D CNN regressor on data collected from a full-scale bridge. This work also evaluates the comparison between CNN regressor and other techniques, such as artificial neutral network (ANN) and linear regression, in estimating bridge cable stress, which has not been performed previously.

The purpose of this paper is to develop a keyword-based organizing framework for enterprise resource planning (ERP) intellectual contributions, and use the proposed framework to derive major areas of emphasis in ERP research.

Using keyword persistence, a time-based measure, and keyword dominance, a frequency-based measure, the authors constructed an empirical framework to classify ERP intellectual contributions.

Four major ERP themes were identified using the proposed framework; namely, emerging/phantom, intermittent, trendy, and core. Further examination of the keywords in each of these themes revealed that whereas the business aspects of ERP systems are discussed in an intermittent manner, its technical aspects are discussed in a trendy fashion.

The main limitation of this research is that it only used keywords from papers published in scholarly journals, and did not include conference proceedings, books, and other outlets. In this paper, the authors developed an objective keyword-based organizing framework. The proposed framework is simple, flexible, and is based on dimensions that are research domain independent, and thus can be used as an organizing framework for any research domain.

The proposed framework may help managers to better focus consulting engagements on ERP implementations through identifying areas that are not well covered by researchers to ensure that such areas they are well covered by consulting services.

The main contribution of this paper is the development of an objective keyword-based framework that can be used as an organizing tool for intellectual contributions. The framework can be used to derive insights in the macro characterizations of the research in a given domain, and is thus valuable to researchers – both academic and practitioner. To the best of the authors’ knowledge, this is the first such keyword-based research organizing framework.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

This paper aims to describe how, during the period after the scenarios have been sketched out but before the company leadership reaches the strategy‐development or investment‐decision stage, a continuous learning process can help planners and senior decision‐makers identify and track the unfolding events that are most relevant to the challenges and big issues facing their organization.

In the case example, a web‐connected Event, Pattern, Structure (EPS) tool allows all members of a multi‐stakeholder Scenario Planning Steering Group, as well as invited experts, to submit news articles, conference reports, research, scholarly articles and other published information into a database that is organized with links to four scenarios and their driving forces.

As the case demonstrates, an ongoing EPS process related to the scenarios and key drivers can support strategic monitoring of the business environment with the scenarios serving an organizing structure.

This continuous learning tool is particularly useful for companies that develop scenarios that evolve over long periods of time, a practice that is usually determined by the investment window or marketing cycle of an organization.

Research collected in the EPS system can be used to watch for developments that portend operating‐environment changes. Planners can generate early indicators that signal whether a particular scenario or trends within a scenario are emerging.

Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of analyses of large data sets. Many manufacturing companies can now seize this opportunity to leverage their data to gain competitive advantages for continuous improvement. Six Sigma has been among the most popular approaches for continuous improvement. The data-driven nature of Six Sigma applied in a big data environment can provide competitive advantages. In the traditional Six Sigma implementation – define, measure, analyze, improve and control (DMAIC) problem-solving strategy where a human team defines a project ahead of data collection. This paper aims to propose a new Six Sigma approach that uses massive data generated to identify opportunities for continuous improvement projects in a manufacturing environment in addition to human input in a measure, define, analyze, improve and control (MDAIC) format.

The proposed Six Sigma strategy called MDAIC starts with data collection and process monitoring in a manufacturing environment using system-wide monitoring that standardizes continuous, attribute and profile data into comparable metrics in terms of “traffic lights.” The classifications into green, yellow and red lights are based on pre-control charts depending on how far a measurement is from its target. The proposed method monitors both process parameters and product quality data throughout a hierarchical production system over time. An attribute control chart is used to monitor system performances. As the proposed method is capable of identifying changed variables with both spatial and temporal spaces, Six Sigma teams can easily pinpoint the areas in need to initiate Six Sigma projects.

Based on a simulation study, the proposed method is capable of identifying variables that exhibit the biggest deviations from the target in the Measure step of a Six Sigma project. This provides suggestions of the candidates for the improvement section of the proposed MDAIC methodology.

This paper proposes a new approach for the identifications of projects for continuous improvement in a manufacturing environment. The proposed framework aims to monitor the entire production system that integrates all types of production variables and the product quality characteristics.

This paper aims to focus on identifying key health-care issues amenable to digital twin (DT) approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare facilities management (FM) using four different scenarios. The scenario planning focused on monitoring and controlling the heating, ventilation, and air-conditioning system in real-time; monitoring indoor air quality (IAQ) to monitor the performance of medical equipment; monitoring and tracking pulsed light for SARS-Cov-2; and monitoring the performance of medical equipment affected by radio frequency interference (RFI).

The importance of a healthcare facility, its systems and equipment necessitates an effective FM practice. However, the FM practices adopted have several areas for improvement, including the lack of effective real-time updates on performance status, asset tracking, bi-directional coordination of changes in the physical facilities and the computational resources that support and monitor them. Consequently, there is a need for more intelligent and holistic FM systems. We propose a DT which possesses the key features, such as real-time updates and bi-directional coordination, which can address the shortcomings in healthcare FM. DT represents a virtual model of a physical component and replicates the physical data and behavior in all instances. The replication is attained using sensors to obtain data from the physical component and replicating the physical component's behavior through data analysis and simulation. This paper focused on identifying key healthcare issues amenable to DT approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare FM using four different scenarios.

The scenarios were validated by industry experts and concluded that the scenarios offer significant potential benefits for the deployment of DT in healthcare FM such as monitoring facilities’ performance in real-time and improving visualization by integrating the 3D model.

In addition to inadequate literature addressing healthcare FM, the study was also limited to one of the healthcare facilities of a large public university, and the scope of the study was limited to IAQ including pressure, relative humidity, carbon dioxide and temperature. Additionally, the study showed the potential benefits of DT application in healthcare FM using various scenarios that DT experts validated.

The study shows the practical implication using the various validated scenarios and identified enabling technologies. The combination and implementation of those mentioned above would create a system that can effectively help manage facilities and improve facilities' performances.

The only identifiable social solution is that the proposed system in this study can manually be overridden to prevent absolute autonomous control of the smart system in cases when needed.

To the best of the authors’ knowledge, this is the only study that has addressed healthcare FM using the DT approach. This research is an excerpt from an ongoing dissertation.

This paper aims to present an overview of the various ethical, societal and critical issues that micro- and nanotechnology-based small, energy self-sufficient sensor systems raise in different selected application fields. An ethical approach on the development of these technologies was taken in a very large international, multitechnological European project. The authors approach and methodology are presented in the paper and, based on this review, the authors propose general principles for this kind of work.

The authors’ approach is based on a great amount of experience working together in multi-disciplinary teams. Ethical issues have usually been handled in the authors’ work to some degree. In this project, the authors had the opportunity to emphasise the human view in technological development, utilise the authors’ experience from previous work and customise the authors’ approach to this particular case. In short, the authors created a wide set of application scenarios with technical and application field experts in the authors’ research project. The scenarios were evaluated with external application field experts, potential consumer users and ethics experts.

Based on the authors’ experiences in this project and in previous work, the authors suggest a preliminary model for construction activity within technology development projects. The authors call this model the Human-Driven Design approach, and Ethics by Design as a more focussed sub-set of this approach. As all enabling technologies have both positive and negative usage possibilities, and so-called ethical assessment tends to focus on negative consequences, there are doubts from some stakeholders about including ethical perspectives in a technology development project.

The authors argue that the ethical perspective would be more influential if it were to provide a more positive and constructive contribution to the development of technology. The main findings related to the ethical challenges based on the actual work done in this project were the following: the main user concerns were in relation to access to information, digital division and the necessity of all the proposed measurements; the ethics experts highlighted the main ethical issues as privacy, autonomy, user control, freedom, medicalisation and human existence.

Various technology assessment models and ethical approaches for technological development have been developed and performed for a long time, and recently, a new approach called Responsible Research and Innovation has been introduced. The authors’ intention is to give a concrete example for further development as a part of the development of this approach.

The authors’ study in this particular case covers various consumer application possibilities for small sensor systems. The application fields studied include health, well-being, safety, sustainability and empathic user interfaces. The authors believe that the ethical challenges identified are valuable to other researchers and practitioners who are studying and developing sensor-based solutions in similar fields.

The authors’ study covers various consumer application possibilities of small sensor systems. The studied application fields include health, well-being, safety, sustainability and empathic user interfaces. The findings are valuable to other researchers and practitioners who are studying and developing sensor-based solutions to similar fields.