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This study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.

A dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.

Our results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.

This study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.

Roadmapping has been used as an approach to support market, product and technology-integrated planning, resulting in a document commonly known as a roadmap. Despite the gains made in relation to the technique, recent studies indicate that most users leave or have difficulties in sustaining the process (i.e. maintaining the updated roadmaps). This paper aims to present a framework for continuous roadmap updating that incorporates principles from agile management fields.

The framework was developed through action research in a manufacturing firm in the construction industry.

The results demonstrate a positive impact on the degree of continuous information monitoring, roadmap credibility and use of the roadmap during innovation strategy decisions.

The key contribution of this framework is the demonstration of a new strategy for carrying out the maps in which information is internalized by the organization itself, using agile teams, without commissioned specialists and as part of the work standards.

The flexibility of batch process enables its wide application in fine-chemical, pharmaceutical and semi-conductor industries, whilst its complexity necessitates control performance monitoring to ensure high operation efficiency. This paper proposes a data-driven approach to carry out controller performance monitoring within batch based on linear quadratic Gaussian (LQG) method.

A linear time-varying LQG method is proposed to obtain the joint covariance benchmark for the stochastic part of batch process input/output. From historical golden operation batch, linear time-varying (LTV) system and noise models are identified based on generalized observer Markov parameters realization.

Open/closed loop input and output data are applied to identify the process model as well as the disturbance model, both in Markov parameter form. Then the optimal covariance of joint input and output can be obtained by the LQG method. The Hotelling's Tˆ2 control chart can be established to monitor the controller.

(1) An observer Markov parameter approach to identify the time-varying process and noise models from both open and closed loop data, (2) a linear time-varying LQG optimal control law to obtain the optimal benchmark covariance of joint input and output and (3) a joint input and output multivariate control chart based on Hotelling's T² statistic for controller performance monitoring.

This study aims to identify the factors that lead non-financial companies listed in the UK to use an internal audit function (IAF) as a monitoring mechanism. Although the use of an IAF in the UK is voluntary, no prior research has examined the drivers for using one.

Financial and non-financial data were collected from the annual reports of 332 UK non-financial companies listed on the London Stock Exchange (LSE) Main Market. Univariate tests and multivariate logistic regression tests were used to test the research hypotheses. A theoretical framework based on both agency theory and transaction cost economics (TCE) theory was used to explain the economic factors affecting the use of an IAF.

The study provides evidence that firm size, level of internal risks, agency problem between owners and managers and existence of an effective audit committee are associated with the existence of an IAF. Thus, the need to have strong internal control and risk management systems and to reduce both internal and external agency costs drives companies to have an IAF. These results suggest the importance of IAF as an internal corporate governance tool and the effectiveness of UK governance regulations in monitoring the effectiveness of internal control systems.

Given the importance of the IAF’s corporate governance role, the study provides some policy implications. Regulators should pay more attention to the issue of maintaining an IAF, especially by large companies, the relationship between the IAF and other governance parties, especially the audit committee, and the disclosure of more relevant information about the IAF’s characteristics and practices.

This is the first study to examine the factors affecting the existence of the IAF within the UK’s distinctive regulatory approach of “comply or disclose reasons”. Furthermore, it provides a theoretical framework that explains how both the agency theory and TCE theory can interpret the adoption of internal audit.

The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The study aimsto analyze the main elements associated with the evolution of Brazilian agtechs from the initial conception of the business model to becoming companies in the scale-up stage.

The exploratory research was conducted based on data collected through in-depth interviews. The answers were analyzed quantitatively using descending hierarchical classification (DHC) and correspondence factor analysis (CFA) and qualitatively using content analysis.

Five main elements were identified as responsible for the evolution of the companies up to their entering the scale-up phase: (1) governance, (2) decisions inherent to resource allocation, (3) monitoring of strategic, tactical and operational activities, (4) fostering human capital development and (5) business model validation. Each element presents a set of performance indicators that show the scalability of these companies.

The model developed can help companies that have not yet advanced from the conception of the business model to the scalability of different sectors, in addition to agribusiness.

Proposal of a model that presents the main elements that impact on scalability and respective indicators that contributed to the scalability process of Brazilian agtechs.

This study contributed to advancing the knowledge on the organizational life cycle (OLC) of agricultural startups, particularly regarding the factors responsible for their scalability.

This study aims to properly and objectively assess the students’ study progress in bachelor programmes by applying statistical process control (SPC). Specifically, the authors focused their analysis on the variation in performance rates in business studies courses taught at a Spanish University.

A qualitative methodology was used, using an action-based case study developed in a public university. Previous research and theoretical issues related to quality indicators of the training programmes were discussed, followed by the application of SPC to assess these outputs.

The evaluation of the performance rate of the courses that comprised the training programs through the SPC revealed significant differences with respect to the evaluations obtained through traditional evaluation procedures. Similarly, the results show differences in the control parameters (central line and control interval), depending on the adopted approach (by programmes, by academic year and by department).

This study has inherent limitations linked to both the methodology and selection of data sources.

The SPC approach provides a framework to properly and objectively assess the quality indicators involved in quality assurance processes in higher education.

This paper contributes to the discourse on the importance of a robust and effective assessment of quality indicators of the academic curriculum in the higher education context through the application of quality control tools such as SPC.

Quality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the digital transformation of their organisations. The purpose of this study is to provide a framework to guide quality practitioners with the implementation of digitalisation in their existing practices.

A review of literature assessed how quality management and digitalisation have been integrated. Findings from the literature review highlighted the success of the integration of Lean manufacturing with digitalisation. A comprehensive list of Lean Six Sigma tools were then reviewed in terms of their effectiveness and relevance for the hybrid digitisation approach to process improvement (HyDAPI) framework.

The implementation of the proposed HyDAPI framework in an industrial case study led to increased efficiency, reduction of waste, standardised work, mistake proofing and the ability to root cause non-conformance products.

The activities and tools in the HyDAPI framework are not inclusive of all techniques from Lean Six Sigma.

The HyDAPI framework is a flexible guide for quality practitioners to digitalise key information from manufacturing processes. The framework allows organisations to select the appropriate tools as needed. This is required because of the varying and complex nature of organisation processes and the challenge of adapting to the continually evolving Industry 4.0.

This research proposes the HyDAPI framework as a flexible and adaptable approach for quality management practitioners to implement digitalisation. This was developed because of the gap in research regarding the lack of procedures guiding organisations in their digital transition to Industry 4.0.

This study aims to investigate why and how shared service centres (SSCs) are implemented as well as how they affect audit firm practice and audit quality.

In this qualitative study guided by the theoretical framework of institutional theory, the authors conducted 25 semi-structured interviews in seven European countries, including 16 interviews with audit partners from Big 4 firms, 6 with audit team members, 2 with interviewees from second-tier audit firms and 1 with a member of an oversight body.

The authors show that the central rationale for audit firms to implement SSCs is economic rather than external legitimacy. The authors find that SSC implementation has substantial effects on audit practices, particularly those related to standardisation, coordination and monitoring activities. The authors also highlight the potential impacts on audit quality.

By exploring the motivation for and effects of SSC implementation amongst audit firms, the authors offer insights into the best practices related to subsequent change processes and audit quality.