Search results

Process monitoring is a way to manage the quality characteristics of products in manufacturing processes. Several process monitoring based on machine learning algorithms have been proposed in the literature and have gained the attention of many researchers. In this paper, the authors developed machine learning-based control charts for monitoring fraction non-conforming products in smart manufacturing. This study proposed a relevance vector machine using Bayesian sparse kernel optimized by differential evolution algorithm for efficient monitoring in manufacturing.

A new approach was carried out about data analysis, modelling and monitoring in the manufacturing industry. This study developed a relevance vector machine using Bayesian sparse kernel technique to improve the support vector machine used to both regression and classification problems. The authors compared the performance of proposed relevance vector machine with other machine learning algorithms, such as support vector machine, artificial neural network and beta regression model. The proposed approach was evaluated by different shift scenarios of average run length using Monte Carlo simulation.

The authors analyse a real case study in a manufacturing company, based on best machine learning algorithms. The results indicate that proposed relevance vector machine-based process monitoring are excellent quality tools for monitoring defective products in manufacturing process. A comparative analysis with four machine learning models is used to evaluate the performance of the proposed approach. The relevance vector machine has slightly better performance than support vector machine, artificial neural network and beta models.

This research is different from the others by providing approaches for monitoring defective products. Machine learning-based control charts are used to monitor product failures in smart manufacturing process. Besides, the key contribution of this study is to develop different models for fault detection and to identify any change point in the manufacturing process. Moreover, the authors’ research indicates that machine learning models are adequate tools for the modelling and monitoring of the fraction non-conforming product in the industrial process.

The purpose of this paper is to provide industrial managers with insight into the real‐time progress of running processes. The authors formulated a periodic performance prediction algorithm for use in a proposed novel approach to real‐time business process monitoring.

In the course of process executions, the final performance is predicted probabilistically based on partial information. Imputation method is used to generate probable progresses of ongoing process and Support Vector Machine classifies the performances of them. These procedures are periodically iterated along with the real‐time progress in order to describe the ongoing status.

The proposed approach can describe the ongoing status as the probability that the process will be executed continually and terminated as the identical result. Furthermore, before the actual occurrence, a proactive warning can be provided for implicit notification of eventualities if the probability of occurrence of the given outcome exceeds the threshold.

The performance of the proactive warning strategy was evaluated only for accuracy and proactiveness. However, the process will be improved by additionally considering opportunity costs and benefits from actual termination types and their warning errors.

Whereas the conventional monitoring approaches only classify the already occurred result of a terminated instance deterministically, the proposed approach predicts the possible results of an ongoing instance probabilistically over entire monitoring periods. As such, the proposed approach can provide the real‐time indicator describing the current capability of ongoing process.

The purpose of this study is to investigate the current construction progress monitoring (CPM) process in relation to the contractual obligations, how project management teams carry out this activity in the field and why teams continue to adopt the current method. The study aims to provide a comprehensive understanding of the current monitoring process and its effectiveness, identify any shortcomings and propose recommendations for improvements that can lead to better project outcomes.

The study conducted semi-structured interviews with 28 construction management practitioners to explore their views on contractual requirements, traditional progress monitoring practices and advanced monitoring methods. Thematic analysis was used to identify existing processes, practices and incentives for advanced monitoring.

Standard construction contracts mandate current progress monitoring practices, which often rely on manual, document-centric and labor-intensive methods, leading to slow and erroneous progress reporting and project delays. Key barriers to adopting advanced tools include rigid contractual clauses, lack of incentives and the absence of reliable automated tools. A holistic automated approach that covers the entire CPM process, from planning to claim management, is needed as a viable alternative to traditional practices.

The study's findings can inform researchers, stakeholders and decision-makers about the existing monitoring practices and contribute to enhancing project management practices.

The study identified contractually mandated progress monitoring processes, traditional methods of collecting, transferring, analyzing and dispensing progress-related information and potential incentives and points of departure towards technologically advanced methods.

This paper aims to present disaster information and a monitoring system in order to utilize earth observation data in the operative process of early warning, mitigation and management of natural disasters. The system is aimed at integrating earth observation data analysis with modern ICTs including GIS, grid, mobile communication and web technology to support disaster monitoring and to share disaster information during a crisis.

The system development concerned outlining an operative disaster monitoring and management process. The process was derived from actual practices, suggestions and needs of different user groups involved in disaster management. After investigating state‐of‐the‐art ICTs and reviewing the existing tools and databases, a suitable system architecture was designed and a prototype system was implemented, adapting to a proven software development process.

The prototype system implementation demonstrated how satellite‐based data can be used to support disaster management processes. Disaster monitoring requires information system infrastructure that would enable communication and integrate various distributed information sources and services.

The result gives ideas for establishing an operative disaster management process involving local authorities, disaster analysts and the public. The process integrates earth observation data analysis with modern ICTs and improves the methods of early warning. The developed concept can be used as the basis for future development of automated real‐time disaster monitoring.

The advent of new enabling technologies and the surge in corporate scandals has combined to increase the supply, the demand, and the development of enabling technologies for a new system of continuous assurance and measurement. This paper positions continuous assurance (CA) as a methodology for the analytic monitoring of corporate business processes, taking advantage of the automation and integration of business processes brought about by information technologies. Continuous analytic monitoring-based assurance will change the objectives, timing, processes, tools, and outcomes of the assurance process.

The objectives of assurance will expand to encompass a wide set of qualitative and quantitative management reports. The nature of this assurance will be closer to supervisory activities and will involve intensive interchange with more of the firm s stakeholders than just its shareholders. The timing of the audit process will be very close to the event, automated, and will conform to the natural life cycle of the underlying business processes. The processes of assurance will change dramatically to being meta-supervisory in nature, intrusive with the potential of process interruption, and focusing on very different forms of evidential matter than the traditional audit. The tools of the audit will expand considerably with the emergence of major forms of new auditing methods relying heavily on an integrated set of automated information technology (IT) and analytical tools. These will include automatic confirmations (confirmatory extranets), control tags (transparent tagging) tools, continuity equations, and time-series cross-sectional analytics. Finally, the outcomes of the continuous assurance process will entail an expanded set of assurances, evergreen opinions, some future assurances, some improvement on control processes (through incorporating CA tests), and some improved data integrity.

A continuous audit is a methodology that enables independent auditors to provide written assurance on a subject matter, for which an entity’s management is responsible, using a series of auditors’ reports issued virtually simultaneously with, or a short period of time after, the occurrence of events underlying the subject matter.

• –

  CICA/AICPA Research Study on Continuous Auditing (1999)

CICA/AICPA Research Study on Continuous Auditing (1999)

Companies must disclose certain information on a current basis.

• –

  Corporate and Auditing Accountability, Responsibility, and Transparency (Sarbanes-Oxley) Act (2002)

Corporate and Auditing Accountability, Responsibility, and Transparency (Sarbanes-Oxley) Act (2002)

Researchers have traditionally investigated aspects of the interorganizational monitoring process in piecemeal fashion. This conceptual piece argues that juxtaposing the categorization process with interorganizational emulation, imitation, and competition, brings focus to organizations’ attempts to acquire information from other organizations, signal internal and external constituencies, and ultimately change. We argue that the depth or intensity with which the monitoring process is pursued as well as the breadth or degree of overlap in the sets of organizations chosen to monitor, determines the volume and diversity of information acquired, the strength of the signal sent to constituent groups, and the amount and type of change likely to emerge from the process. All of these factors will ultimately affect the firm's future performance.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Monitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.

Taxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.

The techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.

The review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.

The review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

E-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.

An approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.

The results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.

This research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.