Search results

Describes the authority′s strategy, recording system, visiting programme, and guide to monitoring. Describes four developments in detail which South Lincolnshire′s review of its quality monitoring approach has stimulated. The strategy identifies three levels of monitoring activity: an overview, a means of identifying when closer monitoring is indicated, and causes for concern; a selective view, a means of assessing the severity of causes for concern; and an investigative view, to examine and identify solutions to confirmed problems. Explains how the strategy identifies corporate responsibility for quality monitoring, and how this may be achieved; how the “monitoring matrix” enables a comparative overview of each provider′s compliance to purchaser standards. An up‐to‐date record of progress on individual standards is constantly available. Quality monitoring visiting can be hit or miss. The South Lincolnshire approach attempts to minimize this through the management of the visit and the use of a pocket guide to monitoring. The guide′s objective is to provide a practical guide to monitoring. An attempt has also been made to unveil some of the mystique surrounding quality.

The increased use of contracting for service delivery involves new challenges in ensuring that quality is maintained. Performance monitoring involves both efficiency (costs) and effectiveness (quality) measures; however, there is little guidance from the literature to indicate the best approaches in different contexts. This paper therefore reports on an exploratory study in which approaches to performance monitoring, and respondents’ views on best practice, were explored in contracted services. Key findings are that organisations rely on inspections by their own employees or contractor checklists, but that these practices are in conflict with their views on best practice. However, the respondents agreed that performance monitoring has a large effect on quality outcomes. Using both the literature and the study, a model has been developed that provides managers with a framework for improving their performance and quality monitoring practices, and highlights areas for future academic research.

Adverse event monitoring is a problem‐oriented approach to clinical audit and health‐care quality improvement, which was developed and has been widely used in the USA. Briefly explores the technique itself and its evolution. Presents experience gained from the widespread use of the approach in a British acute hospital, and results from one specialty – ophthalmology. Suggests that the study of adverse events in patient care can produce significant improvements in patients’ care, that it is particularly suited to some specialties, and that it should be used alongside other techniques in hospital clinical audit programmes. Concludes that, as the demand for quality‐monitoring information from purchasers and within providers grows, adverse event monitoring may become one of the key techniques for quality assessment and improvement.

The main aim of this study is to examine the effect of investment in outside governance monitoring (IOGM), through non-executive directors' remuneration (NEDR) and external audit fees (AFEE), on real earnings management (REM) in an emerging market in the Southeast Asia region, Malaysia.

The data comprises 1,056 observations from manufacturing companies listed on Bursa Malaysia for the four-year period, 2013 to 2016. The study tests IOGM individually and aggregately with REM. Feasible generalized least squares (FGLS) regression is used to test the hypotheses.

The results show that NEDR is negatively and significantly associated with REM. Likewise, AFEE is significantly associated with lower REM. Aggregate IOGM significantly mitigates REM. Additional tests conducted show consistent findings.

This evidence supports agency theory and signaling theory, that a high level of investment in governance monitoring signals a high demand for monitoring and fewer agency problems. It justifies more investment in outside scrutiny and monitoring to limit the existence of managers' opportunistic behavior in concentrated markets. This study relies on an aggregate measure of REM and focuses on manufacturing companies in Malaysia; thus, the results may not be the same using other measurements and samples.

The study, to the best of the researchers' knowledge, is the first to document evidence in an emerging market suggesting that higher NEDR and AFEE are individually and aggregately associated with lower REM. Policymakers, shareholders and researchers may consider investment in these two mechanisms as a proxy of high-quality monitoring that mitigates REM.

In order to meet the requirements of 6σ management and to overcome the deficiencies of the theory for using the pre-control chart to evaluate and monitor quality stability, the purpose of this paper is to probe into the quality stability evaluation and monitoring guidelines of small batch production process based on the pre-control chart under the conditions of the distribution center and specifications center non-coincidence (0<ɛ≤1.5σ), the process capability index C _p≥2 and the virtual alarm probability α=0.27 percent.

First, the range of the quality stability evaluation sampling number in initial production process is determined by using probability and statistics methods, the sample size for the quality stability evaluation is adjusted and determined in initial production process according to the error judgment probability theory, and the guideline for quality stability evaluation has been proposed in initial production process based on the theory of small probability events. Second, the alternative guidelines for quality stability monitoring and control in formal production process are proposed by using combination theory, the alternative guidelines are initially selected based on the theory of small probability events, a comparative analysis of the guidelines is made according to the average run lengths values, and the monitoring and control guidelines for quality stability are determined in formal production process.

The results obtained from research indicate that when the virtual alarm probability α=0.27 percent, the shifts ɛ in the range 0<ɛ≤1.5σ and the process capability index C _p≥2, the quality stability evaluation sample size of the initial production process is 11, whose scondition is that the number of the samples falling into the yellow zone is 1 at maximum. The quality stability evaluation sample size of the formal production process is 5, and when the number of the samples falling into the yellow zone is ≤1, the process is stable, while when two of the five samples falling into the yellow, then one more sample needs to be added, and only if this sample falls into the green zone, the process is stable.

Research results can overcome the unsatisfactory 6σ management assumptions and requirements and the oversize virtual alarm probability α of the past pre-control charts, as well as the shortage only adaptable to the pre-control chart when the shifts ɛ=0. And at the same time, the difficult problem hard to adopt the conventional control charts to carry out process control because of a fewer sample sizes is solved.

This paper aims to examine whether external monitors (auditors and analysts) constrain earnings strings, an indicator of earnings management, and whether this monitoring is more effective after the implementation of the Sarbanes-Oxley Act of 2002 (SOX), given the emphasis of SOX on improving auditing, financial reporting and the information environment.

Agency theory establishes the premise between external monitoring and earnings strings. Auditor tenure and number of analysts following provide measures for external monitoring quality. Using prior research, empirical models explaining the presence of an earnings strings and earnings strings trend are developed to test the hypotheses.

Pre-SOX, extreme auditor tenure, indicating lower quality external monitoring, is associated with greater earnings strings trend, and analyst coverage is associated with increased likelihood of earnings strings and greater earnings strings trend consistent with analyst pressure on management. More effective auditor and analyst monitoring occurs post-SOX in terms of reduced likelihood of earnings strings and earnings strings trend.

The authors provide evidence on how elements of external monitoring are associated with increased earnings strings pre-SOX. Further, they contribute to the debate on the impact of SOX on external firm monitoring and the overall financial information environment. By focusing on earnings strings, the outcome of earnings management, the authors provide a unique understanding of external monitoring that also provides insight on the overvaluation of equity and ultimate destruction of firm value. The evidence demonstrates how regulation has contributed to an improved financial reporting environment and external monitoring.

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.

Construction schedule delays and quality problems caused by construction errors are common in the field of prefabricated buildings. The effective monitoring of the construction project process is one of the key factors for the success of a project. How to effectively monitor the construction process of prefabricated building construction projects is an urgent problem to be solved. Aiming at the problems existing in the monitoring of the construction process of prefabricated buildings, this paper proposes a monitoring method based on the feature extraction of point cloud model.

This paper uses Trimble X7 3D laser scanner to complete field data collection experiments. The point cloud data are preprocessed, and the prefabricated component segmentation and geometric feature measurement are completed based on the PCL platform. Aiming at the problem of noisy points and large amount of data in the original point cloud data, the preprocessing is completed through the steps of constructing topological relations, thinning, and denoising. According to the spatial position relationship and geometric characteristics of prefabricated frame structure, the segmentation algorithm flow is designed in this paper. By processing the point cloud data of single column and beam members, the quality of precast column and beam members is measured. The as-built model and as-designed model are compared to realize the visual monitoring of construction progress.

The experimental results show that the dimensional measurement accuracy of beam and column proposed in this paper is more than 95%. This method can effectively detect the quality of prefabricated components. In the aspect of progress monitoring, the visualization of real-time progress monitoring is realized.

This paper proposed a new monitoring method based on feature extraction of the point cloud model, combined with three-dimensional laser scanning technology. This method allows for accurate monitoring of the construction process, rapid detection of construction information, and timely detection of construction quality errors and progress delays. The treatment process based on point cloud data has strong applicability, and the real-time point cloud data transfer treatment can guarantee the timeliness of monitoring.

The tannery industry impacts strongly on water bodies due to the presence of chemical substances, such as chlorides, sulphates, chromium and other pollutants. This study has been carried out with a view to enhance the environmental compatibility of the Arzignano tannery district (northern Italy) which is the largest in Italy, by improving the quality of the Fratta-Gorzone River. Sediment quality, according to the driving forces-pressure-state-impact-response (DPSIR) scheme, has been developed as a significant impact indicator to be applied at basin level. The purpose of this paper is to discuss the above issues.

Data were produced during institutional monitoring and control activities carried out by ARPAV (Veneto Region Environmental Prevention and Protection Agency) according to official methods. To define the environmental impacts on the river basin, a statistical assessment was performed on water and sediment quality data to establish the degree of correlation for the main pollutants along the river stem.

In the basin considered, sediment quality presents high levels of contamination from heavy metals – in particular chromium (Cr) (III) – as well as organic pollutants. Investigation of the pressure sources revealed that the discharge from the Arzignano tannery district is the main source of Cr (III), according to background studies performed in the same area.

The quality of river sediments must be improved in order to avoid the re-suspension/solubility of pollutants; dredged materials with high polluting substances must be managed with specific precautions in order to safeguard the environment.

The river studied is currently the only significant water body in which sediment monitoring has been performed in the Veneto Region (Italy) since 2002. This study presents issues linked to the characterization of river sediment and the correlation between sediment quality and water pollution and prior and current pressure sources.

Advancements in real-time water monitoring technologies permit rapid detection of water quality, and threats from waste loads. Water Framework Directive mandating the establishment of Member States’ water resources monitoring, presence of hazardous contaminants in effluents, and perception of vulnerability of water distribution system to attacks, have spurred technical and economic interests. The paper aims to discuss these issues.

As alternative to traditional analyzers, chemosensors, operate according to physical principles, without sample collection (online), and are capable of supplying parameter values continuously and in real-time. Their low selectivity and stability issues have been overcome by technological developments. This review paper contains a comprehensive survey of existing and expected online monitoring technologies for measurement/detection of pollutants in water.

The state-of-the-art in online water monitoring is presented. Application examples are reported. Monitoring costs will become a lesser part of a water utility budget due to the fact that automation and technological simplification will abate human cost factors, and reduce the complexity of laboratory procedures.

An overview of applicable instrumentation, and forthcoming developments, is given. Technological development in this field is very rapid, and astonishing advances are anticipated in several areas (fingerprinting, optochemical sensors, biosensors, molecular techniques). Online monitoring is becoming an ever-important tool not only for compliance control or plant management purposes, but also as a useful approach to pollution control and reduction, minimizing the environmental impact of discharges.