Case study in six sigma methadology : manufacturing quality improvement and guidence for managers

This article discusses the successful implementation of Six Sigma methodology in a high precision and critical process in the manufacture of automotive products. The Six Sigma define–measure–analyse–improve–control approach resulted in a reduction of tolerance-related problems and improved the first pass yield from 85% to 99.4%. Data were collected on all possible causes and regression analysis, hypothesis testing, Taguchi methods, classification and regression tree, etc. were used to analyse the data and draw conclusions. Implementation of Six Sigma methodology had a significant financial impact on the profitability of the company. An approximate saving of US$70,000 per annum was reported, which is in addition to the customer-facing benefits of improved quality on returns and sales. The project also had the benefit of allowing the company to learn useful messages that will guide future Six Sigma activities

Discrimination of biofilm samples using pattern recognition techniques

Biofilms are complex aggregates formed by microorganisms such as bacteria, fungi and algae, which grow at the interfaces between water and natural or artificial materials. They are actively involved in processes of sorption and desorption of metal ions in water and reflect the environmental conditions in the recent past. Therefore, biofilms can be used as bioindicators of water quality. The goal of this study was to determine whether the biofilms, developed in different aquatic systems, could be successfully discriminated using data on their elemental compositions. Biofilms were grown on natural or polycarbonate materials in flowing water, standing water and seawater bodies. Using an unsupervised technique such as principal component analysis (PCA) and several supervised methods like classification and regression trees (CART), discriminant partial least squares regression (DPLS) and uninformative variable elimination–DPLS (UVE-DPLS), we could confirm the uniqueness of sea biofilms and make a distinction between flowing water and standing water biofilms. The CART, DPLS and UVE-DPLS discriminant models were validated with an independent test set selected either by the Kennard and Stone method or the duplex algorithm. The best model was obtained from CART with 100% correct classification rate for the test set designed by the Kennard and Stone algorithm. With CART, one variable describing the Mg content in the biofilm water phase was found to be important for the discrimination of flowing water and standing water biofilms

Knowledge-based Lean Six Sigma Maintenance System for Sustainable Buildings

YesPurpose– This paper develops a Knowledge-based (KB) System for Lean Six Sigma (LSS) Maintenance in environmentally Sustainable Buildings (Lean6-SBM). Design/methodology/approach– The Lean6-SBM conceptual framework has been developed using the rule base approach of KB system and joint integration with Gauge Absence Prerequisites (GAP) technique. A comprehensive literature review is given for the main pillars of the framework with a typical output of GAP analysis. Findings– Implementation of LSS in the sustainable building maintenance context requires a pre-assessment of the organisation’s capabilities. A conceptual framework with a design structure is proposed to tackle this issue with the provision of an enhancing strategic and operational decision making hierarchy. Research limitations/implications– Future research work might consider validating this framework in other type of industries. Practical implications– Maintenance activities in environmentally sustainable buildings must take prodigious standards into consideration and, therefore, a robust quality assurance measure has to be integrated. Originality/value– The significance of this research is to present a novel use of hybrid KB/GAP methodologies to develop a Lean6-SBM system. The originality and novelty of this approach will assist in identifying quality perspectives while implementing different maintenance strategies in the sustainable building context.Ministry of Defence Engineering Services (Sultanate of Oman

Strategies to improve palatability and increase consumption intentions for Momordica charantia (bitter melon): A vegetable commonly used for diabetes management

<p>Abstract</p> <p>Background</p> <p>Although beneficial to health, dietary phytonutrients are bitter, acid and/or astringent in taste and therefore reduce consumer choice and acceptance during food selection. <it>Momordica charantia</it>, commonly known as bitter melon has been traditionally used in Ayurvedic and Chinese medicine to treat diabetes and its complications. The aim of this study was to develop bitter melon-containing recipes and test their palatability and acceptability in healthy individuals for future clinical studies.</p> <p>Methods</p> <p>A cross-sectional sensory evaluation of bitter melon-containing ethnic recipes was conducted among 50 healthy individuals. The primary endpoints assessed in this analysis were current consumption information and future intentions to consume bitter melon, before and after provision of attribute- and health-specific information. A convenience sample of 50, self-reported non-diabetic adults were recruited from the University of Hawaii. Sensory evaluations were compared using two-way ANOVA, while differences in stage of change (SOC) before and after receiving health information were analyzed by Chi-square (χ²) analyses.</p> <p>Results</p> <p>Our studies indicate that tomato-based recipes were acceptable to most of the participants and readily acceptable, as compared with recipes containing spices such as curry powder. Health information did not have a significant effect on willingness to consume bitter melon, but positively affected the classification of SOC.</p> <p>Conclusions</p> <p>This study suggests that incorporating bitter foods in commonly consumed food dishes can mask bitter taste of bitter melon. Furthermore, providing positive health information can elicit a change in the intent to consume bitter melon-containing dishes despite mixed palatability results.</p

Modeling causes of death: an integrated approach using CODEm

Background: Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting.Methods: We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance.Results: Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers.Conclusions: CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death

VSN: variable sorting for normalization

Spectrometric and analytical techniques in general collect multivariate signals from chemical or biological materials by means of a specific measurement instrumentation, usually in order to characterize or classify them through the estimation of one of several compounds of interest. However, measurement conditions might induce various additive (baseline) or multiplicative effects on the collected signals, which may jeopardize the accuracy and generalizability of estimation models. A common way of dealing with such issues is signal normalization and in particular, when the baseline is constant, the standard normal variate (SNV) transform. Despite its efficiency, SNV has important drawbacks, in terms of physical interpretation and robustness of estimation models, because all the variables are equally considered, independently on what their actual relationship with the response(s) of interest is. In the present study, a novel algorithm is proposed, named variable sorting for normalization (VSN). This algorithm automatically produces, for a given set of multivariate signals, a weighting function favoring signal variables that are only impacted by additive and multiplicative effects, and not by the response(s) of interest. When introduced in SNV preprocessing, this weighting function significantly improves signal shape and model interpretation. Moreover, VSN can be successfully used not only for constant but also with more complex baselines, such as polynomial ones. Together with the description of the theory behind VSN, its application on various synthetic multivariate data, as well as on real SWIR spectral data, is presented and discussed