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The purpose of this paper is to establish a link between Six Sigma and organizational change theory. Specifically, a framework that aligns Six Sigma critical success/hindering factors and the antecedents of successful organizational change process.

A theory‐derived framework containing Six Sigma's critical success and hindering factors at each stage of Lewinian change process is first proposed. Then, the framework is compared against the findings from a case study of Six Sigma improvement project in a UK, make‐to‐order, small to medium‐sized enterprise (SME).

There is a great deal of congruence (consistency) between Six Sigma's critical success factors and the antecedents of successful organizational change. Addressing people's “soft” skills (e.g. commitment, involvement, and communication) is necessary to “unfreeze” the equilibrium. The actual change and confrontation, which occur during “move” stage, requires a combination of both “software” and “hardware” of the organization (i.e. teamwork, methods/tools, organizational structure and culture). It is important for SMEs to provide resources during the “freeze” stage and justify the benefits of change, in order to sustain the change efforts.

This research was based on a single case of Six Sigma improvement project. However, future research will be conducted as a longitudinal study, to capture richer insights from the change process.

This paper offers a practical overview of how Six Sigma can be utilized as a change driver in SMEs and the enablers and barriers of success to be considered, especially during the early stage of adoption.

The Six Sigma and Lean Production methodologies suggest that creating value for customers is the objective of a production process or an organisation. In the production context, “added value” dominates the discussion about the creation of value to customers. However, “added value” is often only defined conceptually or discussed at a strategic level, and the link between added value and customer value has not yet been well conceptualised. Therefore, the purpose of the paper is to develop a methodology to measure added value in order to complement the existing performance measures in Six Sigma and Lean Production by conceptualising the link between customer value and added value. The conceptual link “confirms” that quality, time, and costs are the elements of added value, which are transformed into a metric to express customer value. The implementation of the metric recommends the adoption of Lean (Six) Sigma and Lean Accounting (Activity Based Costing), which thus implies that “leanness” is an important “feature” of added value.

This paper presents a methodology to nominate and select improvement projects that are perceived as adding value to customers (both internal and external). The structure of the methodology can be explained in three “stages”. First, the methodology suggests a new way of categorizing improvement opportunities, i.e. reactive‐proactive, to “upgrade” the little Q ‐ big Q categorisation. Then, it develops a roadmap that links performance indicators and improvement projects for both reactive and proactive improvements. Finally, it suggests an algorithm to select the improvement project, where the assessment of to what extent the nominated improvement projects add value to customers relies on the comparison between Overall Perceived Benefits (OPB) and Overall Perceived Efforts (OPE). The improvement project perceived as having the largest impact on adding value to customers receives the highest priority.

This paper seeks to present a way of estimating DisPMO, DePMO, left‐side and right‐side Sigma levels (as the “mutations” of DPMO and Sigma level when applied on customer satisfaction measurements), where all critical attributes (CTQs) contain data sets that are non‐normally distributed.

The calculation of DisPMO, DePMO, left‐side and right‐side Sigma levels is based on dynamic‐multiple CTQs without the need for assuming 1.5 Sigma shift from the mean. Using step‐wise multiple regression, CTQs are then the attributes that significantly influence overall customer satisfaction. This further developed method no longer takes normality assumption for granted, which means that, prior to calculating DisPMO, DePMO, left‐side and right‐side Sigma levels, the data should be proven as being normally distributed. To fulfil the assumption of normality, the primary data are being “replicated” by first generating random numbers that follow normal standard distribution and then adjusting (re‐calculating) these random numbers with the mean, standard deviation, and the skewness of the primary data. Simulation technique is then applied to generate a larger amount of secondary data as the basis for estimating DisPMO, DePMO, left‐side and right‐side Sigma levels.

The application of the method in a Swedish house‐building construction project suggests that: the use of multiple CTQs may reduce the risk for under‐/overestimation of Sigma levels, and DisPMO and DePMO are each other's “mirror” and both of them should be considered when calculating Sigma levels. The calculated Sigma levels suggest that the developer's performance is still quite far below Six Sigma level of performance.

Using the replica of the primary data as a way of approaching normality may be regarded as the main contribution of the paper in addressing one of the challenges in Six Sigma theory.

Facilitated by a decision support system tool, the purpose of this paper is to find the “best” allocated number of surgeons and medicine doctors that reduce patients' non‐value‐added time (NVAT) and total time in the system (TTS).

Interview and observation are first conducted in order to get general insights about (and to understand) the emergency ward of Sahlgrenska Hospital in Gothenburg (Sweden) and its value stream (flow). Then, time‐related data are collected by conducting time measurements empirically and through the triage database. The statistics of the collected empirical data represent the initial state of the system and are utilised as the input of ARENA^® simulation. A simulation scenario is designed by constructing a 3×3 table (= nine combinations) that contains a varying number of surgeons and medicine doctors allocated in the emergency ward. For each combination, 1,000 replications apply (=10 runs @ 100 replications). “Runs” are the cycles or how many times the simulation is executed, while “replications” refer to how many times a computer (automatically) repeats the simulation in a single execution. The simulation length of a single replication was set at 24 hours due to the fact that an emergency ward was always open. The selected feasible solution is the “best” combination of surgeons and medicine doctors that reduces the existing NVAT and TTS while ensuring that the resource utilisation is at a “reasonable” level (and did not exceed 100 per cent).

The simulation output indicates that the emergency ward may achieve considerable reduction in a patients' NVAT and total patients' time in the system by assigning three medicine doctors and three surgeons. This combination leads to (in average) 13 per cent reduction of NVAT while maintaining the TTS at approximately the same level.

An expanded simulation model with a higher level of complexity and ability to accommodate, e.g. cost of care, flow/layout reconfiguration would be greatly needed and is of interest. It would also be relevant to add greater flexibility by assigning more parameters in the simulation model (other than medicine doctor and surgeon).

Simulation can be considered as a valuable decision‐support tool in the adoption of lean in healthcare due to its flexibility in the sense that it is able to show the output (outcome) of various scenarios before any actual change is made. The results of our study present another side of the adoption of lean thinking besides layoff.

The purpose of this paper is to introduce a value improvement model (VIM) for repetitive processes applicable to any business where people and/or plant provide a service to support the overall business objective. Arguing competitive advantage can be realised through different amalgams of productive and strategic resources, the VIM introduced focuses on aligning resource bundles and influencing factors creating efficacious, efficient and effective processes by applying Lean thinking and Six Sigma tools and techniques more holistically.

The research methodology taken incorporated a case study approach complimented by the action research process of planning, observing and reflecting summarized as an action case study research design. The case study data examine the development of a management cycle of value improvement on an inter‐terminal shuttle transportation system within a busy international airport.

The VIM has been proven as a useful model for understanding the critical inputs and influencing factors for delivering sustainable improvements to repetitive processes in a service industry environment.

The research was completed in situ at a single business using a single case study example to develop and test the conceptual framework. The VIM would therefore benefit from being applied in both manufacturing and service industry environments to identify other potential environmental factors influencing the repetitive processes, increasing the usefulness to other potential users.

This research project has developed a visual and systematic framework that enables managers to understand, assess and improve repetitive processes within their businesses. The case study example presented in the paper show how this framework can be applied to the setting up of value improvement management cycles.

The purpose of this paper is to further develop a method to convert (dis)satisfaction on critical attributes (critical to qualities, CTQs) in the customer satisfaction survey into performance measures that are equivalent to defect per million opportunities (DPMO).

Stepwise multiple regression analysis is applied to identify the CTQs, where the overall satisfaction is the dependent variable and the attribute‐related (dis)satisfactions (i.e. performance score minus importance score) are the independent variables. To simulate the attribute‐related (dis)satisfaction for the identified CTQs, random numbers that follow normal standard distribution are generated and returned into random numbers that have similar characteristics (properties) with the primary data. The proportion of returned random numbers below the lower six sigma limit (SL) and above the upper six SL is adjusted into dissatisfaction per million opportunities (DisPMO) and delight per million opportunities (DePMO), respectively.

Applying the logic of DPMO outside the boundary of an organisation (i.e. in the market) leads to two distinct measures, DisPMO and DePMO. These two measures can be transformed into two types/variants of sigma levels, i.e. left‐side (approximated from DisPMO) and right‐side (from DePMO), which may describe organisational effectiveness in the market from two different but complementary approaches.

DisPMO and DePMO provide a basis for assessing the effectiveness of an organisation (manufacturing/non‐manufacturing) according to six sigma methodology, where the “importance” and “performance” dimensions are considered simultaneously. Hence, the applications of DPMO (as six sigma's main performance measure) within and outside the boundary of an organisation are consistent and comparable.

Identification of critical success factors (CSFs) for any continuous improvement initiative is important as it allows organisations to focus their efforts on these factors to ensure a success. The purpose of this paper is to present the CSFs for the effective implementation of Lean Six Sigma and to analyze the implementation of Lean Six Sigma, focusing on the CSFs identified in the literature, through a survey of companies, geographically dispersed, from both the manufacturing and service industry.

The approach taken by authors in this study has two fundamental parts. The first part was to analyse the current literature on CSFs for all continuous improvement initiatives such as TQM, Lean, Six Sigma and Lean Six Sigma. The second part was to design a survey questionnaire based on the literature. The questionnaire was sent to 600 companies (both manufacturing and service) and the response rate is approximately 17 per cent.

Analysis of key findings highlighted that the most important factors are: management commitment, cultural change, linking Lean Six Sigma to business strategy and leadership styles. The results also revealed that the least important factors are linking Six Sigma to HR rewards and extending Lean Six Sigma to supply chain.

A sample size of 101 companies is not sufficient to generalise our key findings. This will be rectified by carrying out further surveys in the forthcoming months and making this investigation a longitudinal study. Moreover, the authors have to execute semi‐structured interviews to obtain a better understanding of the current practice of Lean Six Sigma in participating organisations. An online survey was administered for this study; however future semi‐structured interviews with employees in those companies would enable one to have a better understanding of their practice of Lean Six Sigma programmes.

Although there are a number of papers published on CSFs of Lean and Six Sigma, it was found that there is a dearth of literature on CSFs of Lean Six Sigma implementation. The authors also compare and contrast the CSFs in both manufacturing and service organisations. The results showed what the most and least important factors are for a successful implementation of Lean Six Sigma, providing valuable insights for organizations which will be embarking on this journey.

The purpose of the paper is to develop existing tools or methodologies to measure customer value during acquisition and use, in such a way that the measures concurrently indicate the level of performance and more “accurately” identify the improvement opportunities.

The producer is the entity that creates the products that the customers acquire or consume. This view makes the “general agreement” that customer value is nothing other than customer perception in the market no longer relevant. Therefore, ValMEA (Value Modes Effects and Analysis) offers a “balanced” perspective on customer value, by recognizing that customer value exists in different “modes” in different stages of the product's life cycle. The link between different “modes” of customer value becomes an important basis to understand the contributions of producer activities on customer value.

Measuring customer value is necessary to capture the essential meaning of quality. However, the existing tools to measure customer value do not adequately manifest the concept of customer value itself. Therefore, the modification of these tools becomes the prerequisite to continuously improve quality performance. The measurement of customer value during acquisition and use is based on intangible aspects (cognitive judgement). Along the value stream, these measures are translated (transformed) into tangible aspects, which comprise aspects such as shorter lead‐time, reduced defects, and lower costs.

The customer value measures complement the existing methodology such as Six Sigma, Lean Production, and Quality Function Deployment (QFD).