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The iteration approach consists of applying Numerical Monte Carlo methods for calculation of linear functional of iterated functions to an integral form of the Boltz‐mann Equation. It has been successfully used for the time‐dependent homogeneous case. The same approach, we present in this paper, can be applied for the inhomo‐geneous case, important from practical point of view. Application of the iteration approach in the particular case of obtaining the Ensemble Monte Carlo algorithm is shown.

A Monte Carlo (MC) technique useful for calculation of the high energy tail of the distribution function (d.f) is proposed. The well known MC technique for simulation in rarely visited region splits the real history of the particle, that has entered in this region, in N subhystories with weights 1/N. But the lucky event for entering must be waited to happen during the simulation. A presentation of the d.f is found here, which allows, knowing the d.f in the low (common) energy region, to simulate only high energy events. This technique can be used for example when gate current in submicrometer MOS devices is calculated.

The purpose of this paper is to identify and evaluate potential synergies between the two management approaches Lean Six Sigma (LSS) and knowledge management. Although a strong interaction between them is suspected, there is only very little academic research on their possible interrelation available. This paper aims to close this research gap.

Based on a comprehensive literature review and a comparison of LSS and knowledge management two hypotheses on their interlocking application are formulated, discussed and evaluated.

Knowledge management supports and improves the application of LSS in various ways. In particular, the deliberate integration of communities of practice, information and communication technologies, and feedback and “lessons learned” sessions can develop potentially positive synergy effects and contribute positively to the success of LSS projects. In contrast, LSS turns out to be an inadequate methodology to significantly improve knowledge management, mainly due to the imprecise measurability of knowledge management processes.

The findings are based on a literature review and are not supported by empirical evidence. Therefore, empirical research in this field is suggested.

Organizations using LSS could enhance their performance by integrating instruments of knowledge management, whereas they should critically examine LSS as a methodology for improving knowledge management.

Considering the existing research gap in this field, this paper encourages organizations using LSS to reflect on if and how they could integrate instruments of knowledge management into their practice to achieve better LSS results.

This study aims to present an overview of leading research trends in the lean six sigma domain published in the International Journal of Lean Six Sigma (IJLSS) since its inception.

The study analyses articles published between 2010 and 2019 in IJLSS using the bibliometric technique. The results of data analysis identify the most prolific authors, their affiliation, citation trends and highly cited articles from the journal. Further, a graphical analysis involving bibliographic coupling and co-citation analysis of the corpus enriches the investigation.

The results of the bibliometric analysis suggest that the number of IJLSS’s publications and citations grew markedly over time (from 4 citations in 2010 to nearly 1,324 in 2019). The organizational diversity and collaboration among authors publishing in IJLSS are trending upwards. Case study and focus group are the two most common research designs in publications. In the study, three major themes emerged: implementation of lean on business, integration of lean and six sigma and the effects of lean six sigma on businesses.

The study finding informs and educates practitioners and scholars about various qualitative research tools, applications and methods of implementing lean six sigma tools in different industry sectors.

The study uses bibliometric analysis to propose a novel categorization of research published in IJLSS and to report on the utilization of various lean tools in the journal. The study provides guidance for new future research besides offering a thorough introspection of the lean and six sigma domains.

As program managers seek to improve the quality, speed and financial benefits of the programs they manage, many are turning to process improvement methodologies, such as Lean Six Sigma (LSS). However, although existing literature includes multiple studies that apply the methodology to non-manufacturing environments, there is no specific framework for applying LSS within program management (PM). Therefore, the purpose of this paper is to examine the relationships between LSS tools, project scope, program phase and functional area and project outputs, in PM organizations.

The study uses archival data from 511 LSS projects completed from 2006 to 2015 by a large government agency in the USA composed of 13 PM organizations. The study focuses on four types of input factors: LSS tools, project scope, program phase and functional area; and two output variables: LSS project average financial benefits and percentage of improvement. Multiple regressions are applied to determine what relationships exist between the input and output variables, as well as the nature of such relationships.

The results of this study show LSS is beneficial to PM and also indicate which tools and organizational contexts have positive and negative associations with project outcomes, serving as guide for future applications. In addition, this study can provide clarity and confidence to program managers who are currently skeptical of LSS, by showing that it can provide cost, schedule and performance improvements beneficial to their programs.

Limitations of this research include the use of a single government agency in the USA, the non-experimental design of the study and limitations associated with the nature and data collection process of the archival data. Future studies should include additional PM organizations, input variables and research designs.

There is no specific framework formalizing the concept of LSS application within PM. The literature includes several studies that apply the methodology to non-manufacturing environments, but not to PM specifically. Furthermore, the existing literature on PM does not explicitly cite any continuous improvement methodology as a critical success factor or provide any detailed guidelines for the application of LSS in PM. This paper contributes by studying the relationships between LSS tools, project scope, program phase and functional area, and project outputs, in a PM environment.

This paper aims to introduce a new mixed integer programming formulation and associated heuristic algorithm to solve the Military Nodal Capacity Problem, which is a type of supply chain network design problem that involves determining the amount of capacity expansion required at theater nodes to ensure the on-time delivery of military cargo.

Supply chain network design, mixed integer programs, heuristics and regression are used in this paper.

This work helps analysts at the United States Transportation Command identify what levels of throughput capacities, such as daily processing rates of trucks and railcars, are needed at theater distribution nodes to meet warfighter cargo delivery requirements.

This research assumes all problem data are deterministic, and so it does not capture the variations in cargo requirements, transit times or asset payloads.

This work gives military analysts and decision makers prescriptive details about nodal capacities needed to meet demands. Prior to this work, insights for this type of problem were generated using multiple time-consuming simulations often involving trial-and-error to explore the trade space.

This work merges research of supply chain network design with military theater distribution problems to prescribe the optimal, or near-optimal, throughput capacities at theater nodes. The capacity levels must meet delivery requirements while adhering to constraints on the proportion of cargo transported by mode and the expected payloads for assets.

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.

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.

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 examine, and present a comprehensive review of, the existing literature on the international manufacturing network (IMN).

The original data set used for reviewing the IMN literature consisted of 107 articles selected from 21 journals: more specifically, 40 articles are concerned with plant-level analysis, and 67 articles are related to IMN-level analysis. The literature is simultaneously reviewed by two researchers. The relevance and contribution of each reviewed paper is discussed and mutually agreed upon.

The paper highlights the different concepts related to IMN and traces the evolution of IMN-related research. Based on two levels of analysis (i.e. plant and network), this paper further reviews and discusses the IMN-specific literature in detail to determine the number of IMN articles published across the journals, the dominant methodologies employed, and the research focus reflected in IMN studies. A research trajectory is finally developed to provide an integrated and intuitional view on the development of IMN research.

This is the first effort that has been made towards thoroughly investigating the existing literature on IMN, aiming to trace different concepts related to IMN from a historical perspective, to review and discuss the IMN-specific literature in detail, to provide an overview of the evolution trajectory of different existing IMN research themes, and to propose future research directions. Keeping in mind the growing importance of IMN for practitioners as well as the academic community, this study provides a timely overview of existing and emerging IMN research themes.