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This paper presents economic and economic–statistical designs of the adaptive exponentially weighted moving average (AEWMA) control chart for monitoring the process mean. It also aims to compare the effect of estimated process parameters on the economic performance of three charts, which are Shewhart, exponentially weighted moving average and AEWMA control charts with economic–statistical design.

The optimal parameters of the control charts are obtained by applying the Lorenzen and Vance’s (1986) cost function. Comparisons between the economic–statistical and economic designs of the AEWMA control chart in terms of expected cost and statistical measures are performed. Also, comparisons are made between the economic performance of the three competing charts in terms of the average expected cost and standard deviation of expected cost.

This paper concludes that taking into account the economic factors and statistical properties in designing the AEWMA control chart leads to a slight increase in cost but in return the improvement in the statistical performance is substantial. In addition, under the estimated parameters case, the comparisons reveal that from the economic point of view the AEWMA chart is the most efficient chart when detecting shifts of different sizes.

The importance of the study stems from designing the AEWMA chart from both economic and statistical points of view because it has not been tackled before. In addition, this paper contributes to the literature by studying the effect of the estimated parameters on the performance of control charts with economic–statistical design.

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.

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.

The purpose of this paper is to identify factors influencing implementation of control charts on key performance indicators (KPIs).

Factors driving organizational change described in literature are analyzed inspired by the affinity-interrelationship method. A holistic multiple-case design is used to conduct six workshops to affect the usage of control charts on KPIs at a global company in the automotive industry. The theoretical factors are compared with the result from the case study.

The important factors for implementation success differ to some extent between the theoretical and empirical studies. High-level commitment and a clear definition of the goal of change could be most important when creating a motivation for change. Thereafter, having a dedicated change agent, choosing an important KPI and being able to describe the gain in financial terms becomes more important.

By using control charts on KPIs, the organization in the case study has become more proactive, addressing the right issues upstream in the process, in the right way, cross-functionally.

Factors affecting the implementation of already available solutions in the industry are highlighted. This potentially provides a basis for improved decision making, which has a significant value.

The purpose of this research is to develop a better understanding of the hurdles in implementing Lean Six Sigma (LSS) for operational excellence in digital emerging technology companies.

We have conducted case studies of LSS implementations in six US-based companies in the digital emerging technology industry.

Critical success factors (CSF) for LSS implementations in digital emerging technology companies are: (1) organizational leadership that is engaged to the implementation, (2) LSS methodology that is rebranded to fit existing shared values in the organization, (3) restructuring of the traditional LSS training program to include a more incremental, prioritized, on-the-job training approach and (4) a modified LSS project execution methodology that includes (a) condensing the phases and tools applied in LSS projects and (b) adopting more iterative project management methods compared to the standard phased LSS project approach.

The qualitative nature of our analysis and the geographic coverage of our sample limit the generalizability of our findings.

Implications comprise the awareness and knowledge of critical success factors and LSS methodology modifications specifically relevant for digital emerging technology companies or companies that share similarities in terms of focus on product development, innovation and growth, such as R&D departments in high-tech manufacturing companies.

Research on industry-specific enablers for successful LSS implementation in the digital emerging technology industry is virtually absent. Our research informs practitioners on how to implement LSS in this and alike industries, and points to aspects of such implementations that are worthy of further attention from the academic community.

The purpose of this paper was to explore individual- and firm-level antecedents of the ability of a manufacturing firm's personnel to collaborate and integrate knowledge for organizational resilience practices.

The authors apply hierarchical regression analysis to study a sample of 192 European industrial equipment manufacturers. Data for each firm are collected from surveys of two key informants in each firm, as well as from public sources.

Firms' personnel’s ability to integrate information and knowledge for organizational resilience practices was positively related with the extent of the head of manufacturing's network of personal contacts inside the firm. This effect was stronger in firms with more formalized job descriptions and clearly defined roles. The head of manufacturing's orientation to teamwork and cooperation impacted this ability only in firms that did not financially incentivize cooperation. The authors also found that cooperation incentives and role formalization directly relate to firms' personnel’s ability to integrate information and knowledge for organizational resilience practices.

The study proposes to study organizational resilience practices through a transactive memory systems lens. The study is also the first to link characteristics of individual managers to firm-level resilience practices by examining the antecedents of firms' ability to integrate information and knowledge to recover from operational disruptions. Furthermore, the study serves to enhance the knowledge of resilience practices by examining the role of firm-level antecedents and their interplay with characteristics of individual managers.