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Nowadays, most of the organizations have focused through the world on Six Sigma to reduce the costs, improve the productivity and enhance concerned individuals’ satisfaction, especially customers’ satisfaction. Annually, these organizations define and execute thousands of Six Sigma projects which involve a great deal of investments. But are all of these projects successful and do the organizations benefit from the above advantages? The purpose of this study is to proposing a methodology to to answer this question that: How can we reduce the risk of failure in Six Sigma projects? The first step to reduce the risk of failure in Six Sigma projects is selecting optimal ones which have the most profits and the least expected risks.

First, the effective criteria are recognized and defined in selecting Six Sigma projects. Then, a new data envelopment analysis (DEA) model is proposed for project selection process. A real example is resolved by the presented model. Finally, the authors use linear discriminate analysis (LDA) to examine the validity of obtained results from the proposed model.

The results show that the proposed model is a suitable tool for selecting Six Sigma Projects. The findings demonstrate that the selected projects by suggested integrated DEA model are those confirmed by LDA.

The paper, using a real case study, provides a mathematical model to enhance decision quality in Six Sigma project selection. Applying the specific DEA model is remarkable itself, which joined to a pioneering procedure to use LDA to validity evaluation of the results.

Nowadays, most of the organizations have focused through the world on Six Sigma to reduce the costs, improve the productivity and enhance concerned individuals’ satisfaction, especially customers’ satisfaction. Annually, these organizations define and execute thousands of Six Sigma projects which involve a great deal of investments. But are all of these projects successful and do the organizations benefit from the above advantages? How can we reduce the risk of failure in Six Sigma projects? The first step to reduce the risk of failure in Six Sigma projects is selecting optimal ones which have the most profits and the least expected risks.

In this paper, the effective criteria are recognized and defined in selecting Six Sigma projects. Then, the analytic hierarchy process (AHP) is used to rank the results. Then, a real example is resolved by two important techniques in decision-making process, that is the AHP and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), as well as data envelopment analysis (DEA). The results from the above three methods are compared.

The results of this paper show that by using fewer criteria, the results from AHP and TOPSIS are very similar. Also, the results from these techniques vary from DEA’s ones in many aspects. So regarding the different results and the importance of criteria in selecting the Six Sigma projects, multi-criteria decision-making (MCDM) techniques are more reliable in comparison with DEA, because decision-maker’s point of view is more effective in MCDM techniques.

The paper, using a real case study, provides important new tools to enhance decision quality in Six Sigma project selection.

The purpose of this paper is to consider the following problem; if the manager of the parallel network systems wants to add new sub-decision making units (sub-DMUs) to each parallel network system, he/she wants to know how much new fuzzy inputs allocate to new sub-DMUs and how much outputs these new sub-DMUs produce such that the efficiency of each parallel network system improve or preserve.

Resource allocation and target setting is a famous topic in management science, therefore many managers attention to this field. Data envelopment analysis is one approach to apply the resource. Resource allocation and target setting is a famous topic in management science, therefore many managers attention to this field. Data envelopment analysis is one approach to apply the resource allocation and target setting. In real application the structure of many DMUs are network and the data of them are imprecise. In this work first the authors calculate the fuzzy efficiency of parallel systems by common set of weights method, after that the authors propose an approach to find how much fuzzy inputs allocate to new sub-DMUs, and how much new fuzzy outputs produce, where the efficiency of each parallel network system improve or preserve.

It is found the value of inputs and outputs of new sub-DMUs, where the efficiency of each system not worse than before.

The method can be used in many organizations such as banks, chain stores, car factory, etc.

For the first time the authors allocate new sub-DMUs to each system, and the data of paper are fuzzy numbers.

The purpose of this paper is to investigate Six Sigma implementation in the subsidiaries of General Electric (GE) located in Brazil and to explore the role of the quality culture of headquarters in overcoming common obstacles to Six Sigma implementation reported by other studies.

An exploratory survey was the basis for gathering data for this study. A structured questionnaire was developed covering issues related to Six Sigma implementation, such as experienced benefits, main outcomes, and metrics adopted by companies. Data from eight GE subsidiaries were qualitatively analyzed. The findings were discussed in the light of other studies conducted in Brazil as well as in other developing countries in the context of the role of existing quality culture in overcoming barriers to Six Sigma implementation.

The findings revealed that Six Sigma at GE subsidiaries achieved better results in comparison with the results obtained by other Brazilian companies reported in the literature. GE quality culture aspects such as top management commitment, high investment in training, recognition schemes, and development of a well-planned infrastructure were identified as valuable to overcome common barriers to Six Sigma implementation. Moreover, the findings showed a strong alignment with the goals and practices of GE headquarters, which is an evidence of the quality culture that exists in GE and that allows all GE businesses achieve benefits with Six Sigma.

Since limited empirical research has been conducted concerning Six Sigma implementation in developing countries, this paper aspires to contribute to Six Sigma body of knowledge by illustrating the practices of a world benchmark corporation.

The purpose of this study is to develop a methodology in which alternate Six Sigma projects are prioritized and selected using appropriate multi-criteria decision-making (MCDM) methods in healthcare organizations. This study addresses a particular gap in implementing a systematic methodology for Six Sigma project prioritization and selection in the healthcare industry.

This study develops a methodology in which alternate Six Sigma projects are prioritized and selected using a modified Kemeny median indicator rank accordance (KEMIRA-M), an MCDM method based on a case study in healthcare organizations. The case study was hypothetically developed in the healthcare industry and presented to demonstrate the proposed framework’s applicability and validity for future decision-makers who will take place in Six Sigma project selection processes.

The study reveals that the Six Sigma project prioritized by KEMIRA-M assign the highest ranks to patient satisfaction, revenue enhancement and sigma level benefit criteria, while resource utilization and process cycle time receive the lowest rank.

The methodology developed in this paper proposes an MCDM-based approach for practitioners to prioritize and select Six Sigma projects in the healthcare industry. The findings regarding patient satisfaction and revenue enhancement mesh with the current trends that dominate and regulate the industry. KEMIRA-M provides flexibility for Six Sigma project selection and uses multiple criteria in two-criteria groups, simultaneously. In this study, a more objective KEMIRA-M method was suggested by implementing two different ranking-based weighting approaches.

This is the first study that implements KEMIRA-M in Six Sigma project prioritization and selection process in the healthcare industry. To overcome previous KEMIRA-M shortcomings, two ranking based weighting approaches were proposed to form a weighting procedure of KEMIRA-M. As the first implementation of the KEMIRA-M weighting procedure, the criteria weighting procedure of the KEMIRA-M method was developed using two different weighting methods based on ranking. The study provides decision-makers with a methodology that considers both benefit and cost type criteria for alternates and gives importance to experts’ rankings related to criteria and the performance values of alternates for criteria.

This paper aims to identify and review the critical success factors (CSFs) for successful Six Sigma implementation in service and manufacturing companies given in published literature.

A descriptive evaluation of the literature body is followed by future research opportunities. Studies published on the topic of Six Sigma during 2005-2016 are reviewed to retrieve identified CSFs.

From published literature, 48 CSFs vital for Six Sigma implementation were identified. The identified CSFs were classified under eight core categories. As per the categorization, 35 out of 48 CSFs identified are either Company Strategy related, Six Sigma Project related or Human Resources related. Only a limited number of studies are carried out mainly focusing on the stated three core areas. Hence, it is vital that these three core areas are further explored in future research.

With the outcome of this paper, the identified CSFs for Six Sigma will be taken for further studies, where they will be applied to service and manufacturing companies based in Sri Lanka. Hence, it would provide Sri Lankan service and manufacturing companies guidelines for successful implementation of Six Sigma for future endeavors.

For construction organisations to be effective at implementing an integrated safety, health and environmental (SHE) management system, they require the right level of organisational capability. This capability includes the policies, systems and resources of the organisation. However, within the academic literature, it is unclear which organisational attributes of construction companies are important for implementing integrated SHE management. This study aims to explore the organisational attributes that determine integrated SHE management capability and their relative priorities.

The study used a literature review supported by expert verification and a subsequent three-round expert Delphi technique accompanied by applying the voting analytical hierarchy process.

The study identified 20 attributes grouped under five main thematic categories. These are strategy (the organisation’s vision and top management commitment); process (the organisation’s procedures and processes for SHE management); people (organisation’s human resources, their competence, roles, responsibilities and involvement in SHE management); resources (organisation’s physical and financial resources for SHE management) and information (SHE related documents, data, records and their communication across an organisation). While these thematic categories and the attributes within carry different weights of importance, the strategy-related attributes are the most important, followed by the people-related attributes.

The results of this study should enable construction companies and key industry stakeholders to understand construction companies’ capability to successfully implement an integrated SHE management system. Furthermore, construction companies should be able to prioritise efforts or investments to enhance their SHE management capability.

Given the critical role of project prioritization and selection process in Six Sigma efforts, this study aims to analyse the relevant literature to answer this question: What types of project prioritization and selection methods have been used in Six Sigma research?

The study implemented the systematic literature review (SLR) method to identify and review all relevant previous studies.

The study revealed that 59 articles focused on the topic used 111 methods, analytic hierarchy process appeared as the most frequently used method with 12 articles (20%) and one-third of the methods used in the current Six Sigma project selection literature contained multi-criteria decision-making methods. In total, 61% of 59 articles were not published in the journals ranked by the ABDC’s list. Only 17% of the articles reviewed in this study were published in journals ranked as B category and 12% of the articles were published in A category journals.

The findings of this literature review may help Six Sigma practitioners and researchers accurately identify project prioritization and selection methods, considering that qualitative and quantitative scientific methods guarantee to make better decisions than “gut feelings” of the decision makers in this process.

Although a variety of studies focused on the topic, an SLR is lacking in the area of Six Sigma project prioritization and selection. Therefore, this study was constructed using the SLR method to analyse the topic.

The purpose of this paper is to find excellent banks on the basis of identified variables. First of all, banks are evaluated based on operation costs, deposits, staff, investments, net profit, and loans variables. Subsequently, these variables are categorized into inputs and outputs. The performances of the banks based on these variables are analyzed by data envelopment analysis (DEA) method to find efficiency and inefficiency of decision making units (DMUs).

This research is aimed to determine the best banks based on predetermined indicators. The indicators are categorized into inputs and outputs. DEA method is used to find efficiency and inefficiency of DMU. However, the aim is to find the efficient banks and to implement the model by using AP Super Efficiency method in order to find the most efficient unit for benchmarking. However, some inputs and outputs have more priority for banks than the others, as a result it will require some changes.

The results indicate that among 13 banks, including ten public and three private, solely five public banks are efficient. Moreover, DEA is used as a benchmarking tool for inefficient banks to be efficient. Among these banks ten of them are public banks and three are private. Among efficient ones, all are public banks. Moreover, five of public banks and three of private are inefficient.

In some cases, inputs and outputs have more priority for DMs than the others, as a result it will require some changes. Also, if one of the inputs or outputs is larger in number than the others, the DMU becomes efficient, despite its low priority. Thus, for solving this problem, the indicators of this research are ranked by Rembrandt method considering the existing ones to find the best banks (best DMU) based on their performance and the relevant indicators.

The aim of this analysis is to review the Indian manufacturing organizations practicing Lean Six Sigma (LSS) tools/techniques with an objective of monitoring the performance of an organization and to develop recommendation for strategies to benchmark organizational operational efficiency.

This study offers insights of the LSS performance measurement aspects of the Indian manufacturing organizations based on Data envelopment analysis (DEA) approach. The five inputs and two outputs are considered on the basis of literature review and discussed with the practitioners.

In this analysis, the relative efficiency score of 18 Indian manufacturing organizations has been determined in order to assist evaluation of the impact of monetary investment on the outputs. The present analysis not only investigates the optimum level of input variables but also lays down a significant observation that an organization having higher profit and inventory turnover ratio is not necessarily an efficient organization.

The results assist to determine the best practice units, potential source of inefficiency and deliver beneficial data for the consistent enhancement of the operational efficiency. The DEA results assist managers and decision makers to derive appropriate strategies to enhance their performance with reference to the efficient organization and to regard it as their role model.

This analysis renders a DEA based framework of LSS practicing Indian manufacturing organizations. The framework is unique in terms of its input-outputs variable selection and measurement procedure.