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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.

A major theme in the literature on bank regulation is that greater reliance on market forces can mitigate the moral hazard problem inherent in government sponsored deposit insurance. Specific proposals to impose greater market discipline on banks include minimum requirements on (1) uninsured subordinated debt financing (either fixed-term or with option-type features), and (2) private coinsurance on deposits. Both proposals amount to delegating the responsibility for bank regulation to various private sector claimholders. The results suggest that such delegation (with or without claims that include option-type features) may be ineffective in lowering bank risk, at least within the present regulatory and institutional framework. Alternative mechanisms exist that can mitigate the moral hazard problem; however, it may be necessary for the regulator/deposit insurer to be an integral part of the solution.

This paper responds to recent calls for deeper scrutiny of the institutional contexts of citizen science. In the last few years, at least two dozen civil society organizations in New York and Pennsylvania have begun monitoring the watershed impacts of unconventional natural gas drilling, also known as “fracking.” This study examines the institutional logics that inform these citizen monitoring efforts and probes how relationships with academic science and the regulatory state affect the practices of citizen scientists. We find that the diverse practices of the organizations in the participatory water monitoring field are guided by logics of consciousness-raising, environmental policing, and science. Organizations that initiate monitoring projects typically attempt to combine two or more of these logics as they develop new practices in response to macro-level social and environmental changes. The dominant logic of the field remains unsettled, and many groups appear uncertain about whether and how their practices might have an influence. We conclude that the impacts of macro-level changes, such as the scientization of politics, the rise of neoliberal policy ideas, or even large-scale industrial transformations, are likely to be experienced in field-specific ways.

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.

In this chapter, drone and vision camera technology have been combined for monitoring the crop product quality. Three vegetable crops such as tomato, cauliflower, and eggplant are considered for quality monitoring; hence, image datasets are collected for those vegetables only. The proposed method classified the vegetables into two classes as rotten and nonrotten products so the images were collected for rotten and nonrotten products. Three different features information such as chromatic features, contour features, and texture features have been extracted from the dataset and further used to train a Gaussian kernel support vector machine algorithm for identifying the product quality. The system utilized multiple features such as chromatic, contour, and texture features in classifier training which enhances the accuracy and robustness of the system. Chromatic features were utilized for detecting the crop while other features such as contour and texture features were utilized for further classifier building to identify the crop product quality. The performance of the system is evaluated based on the true positive rate, false discovery rate, positive predictive value, and accuracy. The proposed system identified good and bad products with a 97.9% of true positive rate, 2.43 % of false discovery rate, 97.73% positive predictive value, and 95.4% of accuracy. The achieved results concluded that the results are lucrative and the proposed system is efficient in agriculture product quality monitoring.