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Purpose The purpose of this paper is to develop a framework that illustrates the role of Lean Six Sigma (LSS) in creating organisational innovation climate by investigating the relationship between LSS’s intangible impacts and organisational innovation climate factors. Design/methodology/approach A self-administrated survey questionnaire was distributed among 145 public sector officials to get their opinions on the relationship between various observable elements of LSS’s intangible impacts and organisational innovation climate factors, where a response rate of 73.8% was achieved. The collected data were demographically, descriptively and statistically analysed. Accordingly, a house-of-pillars-based framework that illustrates the role of LSS’s intangible impacts in creating innovation climate in an organisation was developed. Findings Results from this study indicated that LSS’s intangible impacts on organisational structure and hierarchy, culture, change adaptability, utilisation of staff and staff’s behavioural aspects are positively related to many of organisational innovation climate factors such as trust and openness, challenge and involvement, support for ideas and freedom and autonomy. Research limitations/implications The findings of this study are based on the data collected from public sector organisations in the UAE and are supported by relevant literature. However, this study can provide useful guidance for further research for the generalisation of the results to wider scopes in terms of sectors and geographical domains. Practical implications The findings of this study will provide UAE public sector officials with a clear roadmap on how to use LSS for promoting innovation and fostering its implementation in practice. This study will also encourage professionals in public sectors to integrate LSS into their innovation strategies to enhance organisational innovativeness and improve service quality. Originality/value It is one of the first studies that explores LSS’s intangible impacts and assesses their relationship with organisational innovation climate factors. Hence, this study offers valuable insights for both academics and practitioners and is expected to lay a foundation for a better understanding of how LSS’s intangible impacts can be used in creating organisational innovation climate.

Early in the pandemic of coronavirus disease 2019 (COVID-19), face masks were used extensively by the general public in several Asian countries. The lower transmission rate of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Asian countries compared with Western countries suggested that the wider community use of face masks has the potential to decrease transmission of SARS-CoV-2. A risk assessment model named Susceptible, Exposed, Infectious, Recovered (SEIR) model is used to quantitatively evaluate the potential impact of community face masks on SARS-CoV-2 reproduction number (R

Background Nontuberculous mycobacteria (NTM) are emerging pathogens increasingly implicated in healthcare facility–associated (HCFA) infections and outbreaks. We analyzed the performance of statistical process control (SPC) methods in detecting HCFA NTM outbreaks. Methods We retrospectively analyzed 3 NTM outbreaks that occurred from 2013 to 2016 at a tertiary care hospital. The outbreaks consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition, cardiac surgery–associated extrapulmonary MABC infection, and a bronchoscopy-associated pseudo-outbreak of Mycobacterium avium complex (MAC). We analyzed monthly case rates of unique patients who had positive respiratory cultures for MABC, non-respiratory cultures for MABC, and bronchoalveolar lavage cultures for MAC, respectively. For each outbreak, we used these rates to construct a pilot moving average (MA) SPC chart with a rolling baseline window. We also explored the performance of numerous alternative control charts, including exponentially weighted MA, Shewhart, and cumulative sum charts. Results The pilot MA chart detected each outbreak within 2 months of outbreak onset, preceding actual outbreak detection by an average of 6 months. Over a combined 117 months of pre-outbreak and post-outbreak surveillance, no false-positive SPC signals occurred (specificity, 100%). Prospective use of this chart for NTM surveillance could have prevented an estimated 108 cases of NTM. Six high-performing alternative charts detected all outbreaks during the month of onset, with specificities ranging from 85.7% to 94.9%. Conclusions SPC methods have potential to substantially improve HCFA NTM surveillance, promoting early outbreak detection and prevention of NTM infections. Additional study is needed to determine the best application of SPC for prospective HCFA NTM surveillance in other settings.

PurposeDespite the increasing number of studies investigating environmentally sustainable practices in different sectors, no research exists on the issues related to the success factors for implementing environmentally sustainable practices in the utilities sector. To partially fill this research gap, the purpose of this study is to empirically examine these success factors in a public utilities organization in the United Arab Emirates.Design/methodology/approachAn integrated interpretive structural modeling and fuzzy cross-impact matrix multiplication applied to classification approach was implemented to identify the critical success factors (CSFs) and to model and analyze the relationships among them.FindingsThirteen CSFs were identified and the relationships among them were represented by a seven-level hierarchical graphical model, and six CSFs were identified as drivers of success: government policies, regulations and environmental awareness programs; organization sustainability strategies; top management commitment; organizational capabilities; social and environmental responsibility; and eco-knowledge.Originality/valueThis is first kind of study to identify CSFs for implementing environmentally sustainable practices in the utilities sector. The results could guide government policymakers and decision-makers in utilities organizations that are interested in implementing environmentally sustainable practices.

Purpose The purpose of this paper is to identify, present and categorise the main requirements, challenges and impacts of Lean Six Sigma (LSS) applications. Emphasis is given to the soft impacts of LSS applications, which are intangible in nature and difficult to quantify and measure, highlighting the most frequently cited ones. Design/methodology/approach A qualitative synthesis of the studies using the narrative synthesis approach is adopted to descriptively summarise and categorise the requirements, challenges and impacts of LSS applications. The studies were searched by using the following keywords: “LSS applications,” “LSS requirements,” “LSS challenges” and “LSS impacts” in almost all major electronic databases such as Emerald, Taylor and Francis, ScienceDirect and Wiley. A total of 116 articles published between 2007 and 2017 in 41 academic journals were collected and reviewed. Consideration was also given to a number of substantial publications in 2006, 2018 and 2019. Findings In addition to its process efficiency and financial impacts, LSS was found to have another impact category related to individual and organisational behaviours. Management commitment, training and organisational culture were concluded to be amongst the most important and required categories for successful LSS applications. It was also found that the lack of awareness of LSS tools and benefits and the lack of change management and resistance to change were amongst the most cited categories of implementation challenges. Research limitations/implications The studies published between 2007 and 2017 are mainly considered in this paper. It is believed that 10-year publication period considered in this research is sufficient to study the evolution, benefits, limitations and future trends of a particular research topic. However, the exclusion criteria used in the search process with respect to the articles’ year of publication and search terms and keywords may limit the generalisation of the research findings. In addition, the qualitative nature of this research study and the lack of empirical data to support its findings is another limitation that future research should consider. Practical implications This research paper may serve as a valuable source of information for LSS researchers as it will provide them with useful and new insights and directions for further research in LSS. It will also increase the awareness of LSS practitioners about the kind of impact LSS has, and therefore, achieve a better utilisation of its tools by ensuring availability of application requirements and overcoming application challenges. Originality/value This study differs from previous research studies as it focusses attention on the soft impacts of LSS applications and highlights them. The study identifies and prioritises LSS application impacts, requirements and challenges. The study on these aspects was found to be limited and lacking in previous research studies.

PurposeThis paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.Design/methodology/approachThe critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.FindingsFor a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.Research limitations/implicationsOnly three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.Practical implicationsThe proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.Originality/valueThis research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

PurposeThis study explores the role of leadership in organisational innovation by reviewing several publications that discuss the relationship between various leadership styles and innovation.Design/methodology/approachThe study followed a descriptive research methodology by reviewing 64 journal articles on the relationship between various leadership styles and innovation. The articles were analysed descriptively and then reviewed based on the leadership style it discusses to derive meaningful findings on the relationship between leadership and innovation.FindingsVarious leadership styles had a positive impact on organisational innovation either directly or indirectly, by influencing the organisational climate, employees' and leaders' behaviours or other organisational variables such as learning and knowledge sharing. Some leadership styles had both direct and indirect impacts on organisational innovation.Research limitations/implicationsThis study collected journal articles published in almost all major electronic databases such as Emerald, ScienceDirect, Taylor & Francis and Scopus. However, the review is limited to journal articles in which the title, abstract or author-specified keywords contain the search terms “leadership” and “innovation,” and published between 2000 and 2019. Therefore, this review may miss some relevant research insights mentioned in the literature that discussed innovation or leadership separately not combined.Originality/valueThis study contributes to the existing body of research on leadership and innovation by extensively discussing the role of various leadership styles in determining organisational innovation. The analysis reveals that prior studies had many limitations and focused on specific leadership styles only. The study goes a step further by explaining how the leadership and innovation aspects are related, and classifying various leadership styles according to their impact on organisational innovation being direct, indirect or both.

The Exponentially Weighted Moving Average (EWMA) schemes are a potent tool for monitoring small to moderate variations in the quality characteristics in production lines of manufacturing industries...

An effective way of managing a portfolio is to assign multiple projects to one project manager. A major step that helps in such an assignment is to form clusters of projects, an issue that is scarcely addressed in the literature. This study attempts to bridge that gap by proposing the use of a modified form of factor analysis for the formation of clusters of projects. The input for this method is a matrix representing the dependency levels among the projects, which is implemented in three major steps: measuring the level of dependencies among the projects; assessing the clusterability of the projects; and forming the clusters. The last step is complemented with a binary integer programing model for imposing constraints, such as maximum number of projects to be assigned to each project manager. The method is demonstrated using a real portfolio project adapted from the literature.

The response surface methodology (RSM) and Shewhart control charts have been widely used in manufacturing to reduce variation, improve quality and optimize the output. This article proposes an application of individuals & moving range chart (I&MR) and RSM in electrochemical machining. The Shewhart-type I&MR control chart and RSM are combined together in an effective way to successfully guarantee the statistical control of the surface roughness (Ra) of the items produced by wire electrochemical turning, and meanwhile optimize Ra by exploring the optimal values of the machining parameters including applied voltage, wire feed rate, wire diameter, rotational speed and overlap distance. The conducted experiments reveal that the optimal values of the aforementioned factors are 23.67, 0.5, 0.2, 900 and 0.02, respectively. A second-order regression model is also developed to predict the output (Ra) at different combinations of the input parameters. The developed regression model can predict the output values with a determination coefficient (R2) of 96.9%. The proposed combined scheme of Shewhart charts and RSM can be employed in other manufacturing processes and even in different service sectors to efficiently enhance the performance and reduce the cost.

This article presents a model for the optimisation design of the X¯ chart based on Duncan’s model for identifying unusual power loss through transmission and distribution system so that an immediat...

BackgroundTraditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data.MethodsWe performed a pilot study within a large network of community hospitals to evaluate performance of SPC methods for detecting SSI outbreaks. We applied conventional Shewhart and exponentially weighted moving average (EWMA) SPC charts to 10 previously investigated SSI outbreaks that occurred from 2003 to 2013. We compared the results of SPC surveillance to the results of traditional SSI surveillance methods. Then, we analysed the performance of modified SPC charts constructed with different outbreak detection rules, EWMA smoothing factors and baseline SSI rate calculations.ResultsConventional Shewhart and EWMA SPC charts both detected 8 of the 10 SSI outbreaks analysed, in each case prior to the date of traditional detection. Among detected outbreaks, conventional Shewhart chart detection occurred a median of 12 months prior to outbreak onset and 22 months prior to traditional detection. Conventional EWMA chart detection occurred a median of 7months prior to outbreak onset and 14 months prior to traditional detection. Modified Shewhart and EWMA charts additionally detected several outbreaks earlier than conventional SPC charts. Shewhart and SPC charts had low false-positive rates when used to analyse separate control hospital SSI data.ConclusionsOur findings illustrate the potential usefulness and feasibility of real-time SPC surveillance of SSI to rapidly identify outbreaks and improve patient safety. Further study is needed to optimise SPC chart selection and calculation, statistical outbreak detection rules and the process for reacting to signals of potential outbreaks.

The binomial Exponentially Weighted Moving Average (EWMA) control chart has been widely used as an effective tool for detecting small and moderate shifts in fraction nonconforming (p). This article presents a binomial EWMA chart employing a curtailment technique (abridged as Curt_EWMA chart) for monitoring p. The p shift is assumed to follow a Rayleigh distribution. It is found that the curtailment technique can improve the overall detection speed of the EWMA chart while maintaining the false alarm rate at a specified level. An algorithm is presented to find the optimal design of the Curt_EWMA chart. The results of the comparative study reveal that the Curt_EWMA chart outperforms the EWMA chart without curtailment by 41%, in terms of the average number of defectives, under different settings.