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This work aims to focuse on improving the performance of the new exponentially weighted moving average (NEWMA) scheme for monitoring process dispersion. The authors use the generalized time-varying fast initial response (GFIR) to further enhance the detection ability of variability NEWMA control charts at the process startup. The performance of the proposed chart and other schemes discussed in this article are evaluated; and compared using the average run length (ARL) and standard deviation run length (SDRL) measures. It is observed that the ARL of the proposed scheme is quicker in detecting small and moderate shifts in the process dispersion than its counterparts. The real-life application of the proposed scheme is presented.

The dynamic parameter of GFIR is used to enhance the detection ability of variability NEWMA control charts. The authors apply GFIR to the control limit of variability NEWMA scheme. This further narrows the control limit, hence enabling it to swiftly detect small and moderate changes in process dispersion.

The authors present the performance comparisons by examining the ARL properties of the proposed chart and its counterparts. The performance comparison shows that the proposed chart is highly sensitive in detecting small and intermediate process shifts. The real-life application presented also supports the study’s conclusion from the simulation studies. The performance comparison of the proposed chart and its counterparts shows that the proposed scheme is efficient in detecting process abnormalities, especially at the startup.

In terms of the control limits, the proposed chart is the generalized variability NEWMA control chart in which all the previously proposed NEWMA variant schemes can be obtained. Also, the newly proposed control scheme is more efficient in detecting small or moderate persistent shifts in the process dispersion.

This study aims to analyse the influence of digitalisation on the management control function of small and medium-sized enterprises (SMEs). In particular, it aims to illuminate how digitalisation influences management control elements, organisation and roles/competencies and to identify obstacles to digitalisation of management control in SMEs and measures taken to overcome them.

The study is based on guideline-supported expert interviews conducted with 14 financial managers from SMEs in Germany, Austria and Switzerland.

This study reveals the influence of digitalisation on management control elements, organisation, and roles/competencies. The automation and standardisation of management control processes result in new elements for management control, such as strategic support for management. In addition, the increased availability and transparency of data enable the use of instruments within a company that allow for quick analyses of the company's development. Digitalisation leads to the integration of management control into the corporate network and, thus, a change in the organisation of management control. It also triggers the expansion of management control competencies, especially IT competencies. A shortage of internal digitalisation resources, unclear corporate roadmaps, and a lack of managerial experience loom as central challenges for digitalising the management control function. Measures derived from the interviews can help SMEs overcome the obstacles to the digitalisation of management control.

This research is the first interview-based study of the impact of digitalisation on management control in SMEs, potential obstacles to that digitalisation, and measures to overcome those obstacles. Thus, it contributes to the emerging debate on factors that may explain why SMEs lag in terms of the digitalisation of their internal processes.

The purpose of this paper is to propose and validate a robust industrial control system. The aim is to design a Multivariable Proportional Integral controller that accommodates multiple responses while considering the process's control and noise parameters. In addition, this paper intended to develop a multidisciplinary approach by combining computational science, control engineering and statistical methodologies to ensure a resilient process with the best use of available resources.

Taguchi's robust design methodology and multi-response optimisation approaches are adopted to meet the research aims. Two-Input-Two-Output transfer function model of the distillation column system is investigated. In designing the control system, the Steady State Gain Matrix and process factors such as time constant (t) and time delay (?) are also used. The unique methodology is implemented and validated using the pilot plant's distillation column. To determine the robustness of the proposed control system, a simulation study, statistical analysis and real-time experimentation are conducted. In addition, the outcomes are compared to different control algorithms.

Research indicates that integral control parameters (K_i) affect outputs substantially more than proportional control parameters (K_p). The results of this paper show that control and noise parameters must be considered to make the control system robust. In addition, Taguchi's approach, in conjunction with multi-response optimisation, ensures robust controller design with optimal use of resources. Eventually, this research shows that the best outcomes for all the performance indices are achieved when Kp11 = 1.6859, Kp12 = −2.061, Kp21 = 3.1846, Kp22 = −1.2176, Ki11 = 1.0628, Ki12 = −1.2989, Ki21 = 2.454 and Ki22 = −0.7676.

This paper provides a step-by-step strategy for designing and validating a multi-response control system that accommodates controllable and uncontrollable parameters (noise parameters). The methodology can be used in any industrial Multi-Input-Multi-Output system to ensure process robustness. In addition, this paper proposes a multidisciplinary approach to industrial controller design that academics and industry can refine and improve.

When a process experiences an out-of-control condition, identification of the change point is capable of leading practitioners to an effective root cause analysis. The change point addresses the time when a special cause(s) manifests itself into the process. In the statistical process monitoring when the chart signals an out-of-control condition, the change point analysis is an important step for the root cause analysis of the process. This paper attempts to propose a model approaching the artificial neural network to identify the change point of a multistage process with cascade property in the case that the process is modeled properly by a simple linear profile.

In practice, many processes can be modeled by a functional relationship rather than a single random variable or a random vector. This approach of modeling is referred to as the profile in the statistical process control literature. In this paper, two models based on multilayer perceptron (MLP) and convolutional neural network (CNN) approaches are proposed for identifying the change point of the profile of a multistage process.

The capability of the proposed models are evaluated and compared using several numerical scenarios. The numerical analysis of the proposed neural networks indicates that the two proposed models are capable of identifying the change point in different scenarios effectively. The comparative sensitivity analysis shows that the capability of the proposed convolutional network is superior compared to MLP network.

To the best of the authors' knowledge, this is the first time that: (1) A model is proposed to identify the change point of the profile of a multistage process. (2) A convolutional neural network is modeled for identifying the change point of an out-of-control condition.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

Researchers continue to address the concept of self-disclosure because it is foundational for helping social networking sites (SNS) function and thrive. Nevertheless, the authors' literature review indicates that uncertainty remains around the underlying mechanisms and factors involved in the self-disclosure process. The purpose of this research is to better understand the self-disclosure process from the lens of dual-process theory (DPT). The authors consider both the controlled factors (i.e. self-presentation and reciprocity) and an automatic factor (i.e. social influence to use an SNS) involved in self-disclosure and broaden The authors proposed a model to include the interactive facets of enjoyment.

The proposed model was empirically validated by conducting a survey among users of WeChat Moments in China.

As hypothesized, this research confirms that enjoyment and automatic processing (i.e. social influence to use an SNS) are complementary in the SNS self-disclosure process and enjoyment negatively moderates the positive relationship between controlled factor (i.e. self-presentation) and self-disclosure.

Theoretically, this study offers a new perspective on explaining SNS self-disclosure by adopting DPT. Specifically, this study contributes to the extant SNS research by applying DPT to examine how the controlled factors and the automatic factor shape self-disclosure processes and how enjoyment influences vary across these processes – enriching knowledge about SNS self-disclosure behaviors. Practically, the authors provide important design guidelines to practitioners concerning devising mechanisms to foster more automatic-enjoyable value-added functions to improve SNS users' participation and engagement.

This paper reports the developments of a project cost control system (PCCS) for construction projects to (1) measure its current level of cost control maturity, (2) examine the relationships between elements within PCCS processes and (3) identify improvement areas.

This study adopts a mixed approach of descriptive analysis and partial least squares structural equation modelling (PLS-SEM) to measure the current maturity level of PCCS and evaluate the relationships between elements within PCSS to identify improvement areas. Further importance-performance matrix analysis (IPMA) of priority constructs was conducted to improve a target construct and identify the most important areas of specific actions at indicator levels. The results of IPMA revealed the contrast that has the greatest importance on the performance of others so that the recommendations can be made accordingly. Data collected in New Zealand were used to develop the research model.

This study develops structural and measurement models with the constructs including pre-control, in-control and post-control processes, enablers and their proposed interrelationships. Then, data from survey of 184 experienced project cost control team members reveal that post-control has the lowest maturity or weakest areas in the PCCS. Data analysis facilitated by PLS-SEM confirmed that all the constructs in the structural model have positive and significant relationships with each other and suggested that systematic cost analysis reports, communication, skills and experience, defining roles and responsibilities, and top management's support should be the highest priority for improving the PCCS in a more effective manner.

This study presents one of the earliest attempts to develop and test an integrated model that links sub-processes in PCCS and their enablers. Secondly, this research adds to the construction project management literature by empirically verifying the roles of enablers in enhancing maturity level of PCCS.

Safety professionals' primary job is to execute safety control measures towards frontline personnel, and previous studies focus on the effectiveness of such controls. Rare research efforts, however, have been devoted to the effectiveness of management control measures towards safety professionals themselves. This study aimed to fill up this knowledge gap by examining whether safety professionals under differing management control configurations differ in their work attitudes, including affective commitment, job satisfaction, career commitment and intention to quit.

Drawing on a holistic view of control, five forms of management control, i.e. outcome control, process control, capability control, professional control and reinforcement, were investigated. A cross-sectional questionnaire survey targeting at construction safety professionals was conducted. The latent profile analysis approach was employed to identify how the five forms of management control are configured, i.e. identifying the distinctive patterns of control profiles. The Bolck–Croon–Hagenaars method was then used to examine whether safety professionals' work attitudes were different across the identified control profiles.

Seven distinct control profiles were extracted from the sample of 475 construction safety professionals. The overall test of outcome means showed that mean levels of affective commitment, job satisfaction and intentions to quit were significantly different across the seven profiles. The largest that was also the most desirable subgroup was the high control profile (n = 161, 33.9%). The least desirable subgroups included the low control profile (n = 75, 15.8%) and the low capability and professional control profile (n = 12, 2.5%). Pairwise comparison suggested that capability, professional and process controls were more effective than outcome control and reinforcement.

In theory, this study contributes to the burgeoning literature on how to improve the effectiveness of control measures targeted at safety professionals. The results suggested that effective management controls involve a fine combination of formal, informal, process and output controls. In practice, this study uncovers the ways in which managers leverage the efforts of safety professionals in achieving safety goals. Particularly, it informs managers that the control configurations, instead of isolated controls, should be executed to motivate safety professionals.

The purpose of this paper is to investigate the influence of environmental and cultural factors on the benchmarking implementation process and management control within organizations in the United Arab Emirates (UAE). By exploring the complex interplay of these factors, the study aims to uncover how environmental considerations and cultural dynamics shape the effectiveness and outcomes of benchmarking initiatives in the UAE's unique business environment. The research seeks to provide valuable insights for organizations in the UAE to optimize their benchmarking practices and enhance their overall performance and competitiveness.

The study adopts a mixed-methods approach, combining qualitative and quantitative methods to comprehensively explore the influence of environmental and cultural factors on benchmarking implementation and management control in the UAE. This study draws on the integration of two main theoretical perspectives: institutional theory and contingency theory. This is the first attempt to integrate these different frameworks in a single study. The study presents a case study of Emirates Industrial City (EIC), which has been recognized by global industries for boosting efficiency, cost control, quality and overall operations. The quality method known as benchmarking maximizes the potential for organizations to achieve optimal levels of production efficiency.

This paper provides compelling evidence that the benchmarking implementation process and management control in the UAE are significantly influenced by the complex interplay of environmental and cultural factors. By recognizing the importance of environmental sustainability and cultural values in guiding benchmarking practices, UAE organizations can optimize their performance and competitiveness. The findings contribute valuable insights to the existing literature, offering practical implications for UAE organizations seeking to leverage benchmarking as a strategic tool for growth and continuous improvement. The findings reveal that UAE organizations incorporating environmental considerations into benchmarking practices demonstrate a proactive approach to sustainability, aligning their goals with eco-friendly practices. Cultural influences, including a culture of collaboration and openness to external learning, contribute to successful benchmarking adoption and knowledge sharing. Moreover, the study highlights that the integration of benchmarking outcomes into the management control process positively correlates with organizational performance. UAE organizations that leverage benchmarking data for decision-making and performance evaluation exhibit higher levels of competitiveness and efficiency.

This paper has important implications for organizations in the UAE seeking to optimize their benchmarking practices and management control. The study's findings can guide organizations in aligning their benchmarking efforts with environmental sustainability goals and cultural values to enhance performance and competitiveness. Understanding the influence of environmental and cultural factors on benchmarking adoption and implementation allows organizations to foster a benchmarking culture that embraces knowledge sharing and learning. Managers can tailor their approaches to accommodate cultural nuances and enhance the effectiveness of benchmarking initiatives.

This paper contributes to the existing body of knowledge in several ways. Integrated approach: By examining the complex interplay of environmental and cultural factors, this study takes an integrated approach of institutional and contingency theories to understanding their influence on benchmarking implementation and management control. It offers a comprehensive view of how these factors interact to shape organizational practices and outcomes. UAE context: The study focuses specifically on the UAE, providing insights into benchmarking practices within the unique environmental and cultural context of the nation. This research addresses a gap in the literature by examining the influence of these factors in a distinct business environment.

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.