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This paper aims to show that the extent to which convergence/divergence of a company's quality policies and practices towards/away from those of Six Sigma benchmark policies and practices mirror and anticipate the divergence of its sigma metric (SMs) from quantitative Six Sigma benchmarks. Further, the paper proposes to evaluate the robustness of the quality processes of these three companies and to compare them to that of the Six Sigma benchmark by subjecting these processes to the twin performance shocks of the benchmark Six Sigma 1.5σ allowance for process drift and a 25 percent tightening of customer requirements.

Using a novel methodology more appropriate to the critical quality characteristics of typical service industry companies, the paper computes a set of SMs for each company that is richer and broader than the metrics found in standard Six Sigma tables. This new methodology is based on the empirically observed defect rates that are currently being generated by a service process. Further, based on the available empirical data, the paper compared these metrics to the Six Sigma benchmarks.

First, the paper shows that it is possible to compute a broad array of Six Sigma metrics for service businesses based on defect rate data. Second, the results confirm the central proposition of the research to the effect that the divergence/convergence of the qualitative characteristics of a company's quality system from benchmark Six Sigma policies and practices mirror and anticipate the convergence/divergence of the company's quality metrics from the Six Sigma benchmark. Third, the research produced the unanticipated result that the quantitative quality performance of high‐performing service businesses on the Six Sigma metrics are much lower than anticipated and below what is normally achieved by their manufacturing counterparts. The results were also used to do an evaluation of the Taguchi robustness of service processes.

First, the paper demonstrates that traditional Six Sigma computational methodology for generating Six Sigma metrics that is prevalent in manufacturing applies equally to service businesses. Second, the parallel convergence of the qualitative characteristics of a company's quality system towards Six Sigma practices and its quantitative metrics towards the Six Sigma benchmark means that primacy must be given to quality practices as the drivers of quality improvement. Third, the fact that high‐performing service businesses achieve Six Sigma measures that are so low compared to their manufacturing counterparts seems to point either to some key measurement challenges in deploying Six Sigma in service industries or to the need to further change Six Sigma methodology to make it more applicable to these businesses.

While Six Sigma metrics have been studied by researchers in detail for normal distribution-based data, in this paper, we have attempted to study the Six Sigma metrics for two-parameter Weibull distribution that is useful in many life test data analyses.

In the theory of Six Sigma, most of the processes are assumed normal and Six Sigma metrics are determined for such a process of interest. In reliability studies non-normal distributions are more appropriate for life tests. In this paper, a theoretical procedure is developed for determining Six Sigma metrics when the underlying process follows two-parameter Weibull distribution. Numerical evaluations are also considered to study the proposed method.

In this paper, by matching the probabilities under different normal process-based sigma quality levels (SQLs), we first determined the Six Sigma specification limits (Lower and Upper Six Sigma Limits- LSSL and USSL) for the two-parameter Weibull distribution by setting different values for the shape parameter and the scaling parameter. Then, the lower SQL (LSQL) and upper SQL (USQL) values are obtained for the Weibull distribution with centered and shifted cases. We presented numerical results for Six Sigma metrics of Weibull distribution with different parameter settings. We also simulated a set of 1,000 values from this Weibull distribution for both centered and shifted cases to evaluate the Six Sigma performance metrics. It is found that the SQLs under two-parameter Weibull distribution are slightly lesser than those when the process is assumed normal.

The theoretical approach proposed for determining Six Sigma metrics for Weibull distribution is new to the Six Sigma Quality practitioners who commonly deal with normal process or normal approximation to non-normal processes. The procedure developed here is, in fact, used to first determine LSSL and USSL followed by which LSQL and USQL are obtained. This in turn has helped to compute the Six Sigma metrics such as defects per million opportunities (DPMOs) and the parts that are extremely good per million opportunities (EGPMOs) under two-parameter Weibull distribution for lower-the-better (LTB) and higher-the-better (HTB) quality characteristics. We believe that this approach is quite new to the practitioners, and it is not only useful to the practitioners but will also serve to motivate the researchers to do more work in this field of research.

The purpose of this paper is to propose an approach for studying the Six Sigma metrics when the underlying distribution is lognormal.

The Six Sigma metrics are commonly available for normal processes that are run in the long run. However, there are situations in reliability studies where non-normal distributions are more appropriate for life tests. In this paper, Six Sigma metrics are obtained for lognormal distribution.

In this paper, unlike the normal process, for lognormal distribution, there are unequal tail probabilities. Hence, the sigma levels are not the same for left-tail and right-tail defects per million opportunities (DPMO). Also, in life tests, while left-tail probability is related to DPMO, the right tail is considered as extremely good PMO. This aspect is introduced and based on which the sigma levels are determined for different parameter settings and left- and right-tail probability combinations. Examples are given to illustrate the proposed approach.

Though Six Sigma metrics have been developed based on a normality assumption, there have been no studies for determining the Six Sigma metrics for non-normal processes, particularly for life test distributions in reliability studies. The Six Sigma metrics developed here for lognormal distribution is new to the practitioners, and this will motivate the researchers to do more work in this field of research.

The selection of right projects in a Six Sigma program is a major concern for early success and long‐term acceptance within any organization. One of the ever‐increasing challenges is to define and select right measure for improvement and appropriate problem definition. Many projects encounter the problem of no linkage with business objectives or customer needs, too large or high‐level project scope along with unclear problem and goal statement. Improperly, chosen metrics lead to sub‐optimal behavior and can lead people away from the organization's goal instead of joining them. This paper aims to propose a project selection methodology for different situations.

This research develops a model for project identification; ensuring well‐defined projects are selected having large impact on customer satisfaction or bottom line. The model is described for the situations: availability of performance data, balanced business score card implemented and no data is available.

A “top‐down approach” model is developed for project selection, since top management support for Six Sigma initiatives is absolutely critical to see tangible, significant results. The authors suggest establishing the linkage with data (either reactive or survey), otherwise through prioritization tool for project selection. Finally, factors influencing successful Six Sigma projects include management commitment; project selection and control skill, irrespective of whether this is a define, measure, analyze, improve and control or define, measure, analyze, design and validate/verify project.

This approach will help the organizations to select the specific project from multivariate organizational and customer needs. Three different methods for project selection are explained with examples and reasons for selection. Merits and demerits of each method are also highlighted.

The purpose of this paper is to provide new insights in understanding the value of integrating the Balanced Scorecard (BSC) with Six Sigma.

The paper is based on a comprehensive literature review of the BSC and Six Sigma. The literature review provides the basis for a new integration framework that is grounded on the Plan‐Do‐Check‐Act cycle.

The literature review for both Six Sigma and the BSC crystallise how a fusion can add further value in comparison to a standalone implementation of either the BSC or Six Sigma. This new integration framework is identified through first, leveraging the strengths of both the BSC and Six Sigma and second, by incorporating the key themes of the literature review.

This paper provides practitioners with a greater understanding of the value of integrating Six Sigma with the BSC. Also, the study provides a framework that can serve as a basis for the integration of Six Sigma with the BSC.

Little research has been carried out on the integration of the BSC with Six Sigma. This paper takes a novel approach for the integration framework by identifying the integration leverage points through the strengths and weaknesses of both the BSC and Six Sigma. The key contribution of this paper is that it provides new insights in understanding how the BSC can be integrated with Six Sigma. In addition, the paper provides direction for future research that will address weaknesses in the way organisations execute integration of the BSC with Six Sigma today.

The purpose of this paper is to design a model of the port performance metrics for improving the quality in ports by integration of six sigma and system dynamics (SD) approach.

The port performance is measured by the sigma value (SV), the process capability indices (PCIs), and the cost of poor quality (COPQ) as the performance metrics. A port is a complex system that requires SD as an appropriate tool to simulate the model dynamically. The performance metrics focus on measuring the port performance in the entire flow of material in the cargo handling process.

With this model, the changing of the SV, the PCIs, and the COPQ can be identified and analyzed the results to improve the performance in ports. These metrics are utilized to eliminate “waste” in the cargo handling process at ports. This waste consists of lost and damaged cargo, equipment and transporter breakdown, and equipment and transporter delay time. The port performance metrics model can assess the causal relationships in ports as a complex system.

Studies on integration between the six sigma model and SD in ports are few and relatively limited. The port’s performance can be measured directly using the SV, the PCIs, and the COPQ in the simulation. The port performance metrics model will give the decision makers to make some scenarios to contribute for the optimization of performance in ports.

The purpose of this paper is to explore how Lean and Six Sigma’s distinctive practices relate to potential absorptive capacity (PACAP) and realized absorptive capacity (RACAP). The paper seeks to understand which of the practices in Lean Six Sigma are needed to manage absorptive capacity systematically.

Partial least square based structural equation modeling (PLS-SEM) was used to test the theoretical model drawing on a sample of 125 manufacturing organizations in Malaysia. In addition to examining direct effects, the study also examines indirect effects using bootstrapping method to identify possible mediation effects proposed in the model.

The results explain that Lean’s social practices (LSP), Six Sigma’s roles structure (RS) and structured improvement procedure (SIP) positively influence potential absorptive capacity (PACAP). Meanwhile LSP, RS and focus on metrics (FOM) positively influence realized absorptive capacity (RACAP). SIP was found to influence RACAP through PACAP. The analysis reveals the combination of Lean Six Sigma practices that are required in managing PACAP and RACAP differentially.

The study is only confined to manufacturing industries in Peninsular Malaysia. Data collected were cross-sectional in nature. The application of Lean Six Sigma and how it influences absorptive capacity may get sturdier across time, and this may change the effect toward sustainability of firm’s competitive advantage. A longitudinal study may be useful in that context. The study also makes specific recommendations for future research.

The results of this study can be used by Lean Six Sigma practitioners to prioritize the implementation of Lean Six Sigma practices to develop absorptive capacity of the organization through PACAP and RACAP, which needs to be managed differentially as they exert differential outcomes. This would enable organizations to tactfully navigate and balance between PACAP and RACAP in accordance to business strategies and market conditions.

Absorptive capacity in Lean and Six Sigma context has largely been studied as a unidimensional construct or used as a grounding theoretical support. Therefore, understanding the multidimensionality through PACAP and RACAP provide insights on how to enhance and maneuver absorptive capacity through Lean Six Sigma systematically. The findings may pave the way for future research in enhancing the current knowledge threshold in Lean Six Sigma.

The aim of this paper is to analyse the relationship between Six Sigma methodology and organisational ambidexterity (exploitation and exploration orientations). For this purpose, this study describes how Six Sigma practices may enhance both orientations simultaneously, contributing to organisational ambidexterity and performance improvements.

A systematic literature review was adopted as the research methodology. The authors analysed 512 publications in Social Science Citation Index journals in fields such as management, business, operation research management science, planning development, behavioural sciences, interdisciplinary social sciences and applied psychology from 1987 to 2016, as the first development and adoption of Six Sigma was in 1987 by Motorola.

This analysis describes how Six Sigma emphasises not only useful practices for exploitation orientation, such as customer input, design for manufacturability or improvement and control of processes, but also explorative practices, such as discovery, novelty or innovation. Consequently, an adequate combination of all these practices may enhance organisational ambidexterity and organisational success.

This study relies exclusively on previously published literature that fulfilled the selection criteria described in the search methodology. Further empirical research is necessary to test the propositions included in this paper.

This study has important implications for academics, practitioners and employers, as it furnishes new theoretical insights to the scarce literature that studies the relationship between quality management practices and organisational ambidexterity. The authors provide a better understanding of Six Sigma philosophy and some fresh and new insight on how Six Sigma practices may help organisation develop distinctive competitive competences by its influence over exploration and exploitation orientations (ambidexterity). Therefore, it might be of interest to those practitioners interested in achieving a successful competitive position and discover emerging business opportunities, as it may provide some guidance on the important implication of Six Sigma practices over exploration and exploitation orientations.

This study provides new insights into the non-existent literature about Six Sigma and organisational ambidexterity and to the scarce literature about quality management and ambidexterity. Propositions on how Six Sigma practices benefit organisational ambidexterity are also suggested.

The purpose of this paper is to explore the link between TQM/Six Sigma practices and their impact on performance.

Based on the literature review of research that investigated the TQM and Six Sigma best practices and performance, the TQM/Six Sigma practices are classified into three key categories: top management commitment and support (TMCS), infrastructure and core practices. This paper proposes a path model linking these practices and performance. The empirical data were obtained from a survey of 91 Tunisian companies in different sectors. The research model was tested using partial least squares (PLS) technique based on SmartPLS software.

The result shows that the implementation of Six Sigma practices is based majorly on the TQM practices. Also, it reveals the positive impact of TQM/Six Sigma practices on performance. Furthermore, the analysis of path model reveals the relative interdependence and significant link between TMCS, infrastructure, core practices and performance.

The path model tested in this study combines the TQM/Six Sigma practices and reveals their link with performance, which enhance the research theory of both two approaches. Moreover, it will be a useful support for the quality expert on the effective integration of TQM and Six Sigma methods.

This study is the first one which studies the link between TQM/Six Sigma practices and company’s performance in the Tunisian context based on PLS technique.

The purpose of this paper is to develop and validate a scale for infrastructure and core practices, including total quality management (TQM) and Six Sigma practices, in the Indian manufacturing organizations.

In this study, a psychometric systematic scale development procedure has been adopted and executed, which includes three phases. Phase 1 includes item generation and selection through expert opinion. In Phase 2, scale refinement using item analysis, exploratory factor analysis, confirmatory factor analysis (CFA), and convergent and discriminant validity have been carried out. Finally, in Phase 3, scale validation through the replication of CFA and nomological validity assessment has been conducted. Also, a higher order measurement and structural model has been used to assess predictive validity of infrastructure and core practices.

The findings of the empirical study resulted in a 45-item scale that measures Infrastructure practices (top management commitment, workforce management, supplier relationship, customer relationship management, Six Sigma role structure) and core practices (process management, product/service design, quality information and analysis, Six Sigma metric, Six Sigma structure), considering both TQM and Six Sigma practices. Also, the higher order predictive validity assessment model suggested that core practices have a higher impact on quality performance than infrastructure practices.

This study considered TQM and Six Sigma practices for defining infrastructure and core practices. However, future studies may include other quality tools in infrastructure and core quality practices such as Lean, Kaizen, and others in the measurement model to get more concrete results.

This reliable and valid scale will help managers measure the level of QM in order to enhance business performance.

This study symbolizes a first-time attempt for developing a validated tool to measure infrastructure and core practices in the Indian manufacturing organizations.