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Purpose The purpose of this study is to contribute to the existing knowledge about the process of achieving Lean Manufacturing (LM) success. Design/methodology/approach This study uses interpretive structural modeling and captures the opinions of a group of LM experts from a world-class Japanese automobile manufacturer, to map the interrelationships among potential determinants of LM success. This study further uses the data from a survey of 122 leading automobile part manufacturers by performing structural equation modeling to empirically test the research model proposed. Findings Management support and commitment, financial resources availability, information technology competence for LM, human resources management, production process simplicity, supportive culture and supply chain-wide integration are the key determinants that directly or indirectly determine the level of achievement of LM success. Research limitations/implications The determinants of LM success as experienced by Asian automobile manufacturers might be different from determinants of LM success as experienced by Western automobile manufacturers. An interesting direction for future research would be to capture the experts' inputs from Western automobile manufacturers to complement the findings of this study. Practical implications The practical contribution of this study lays in the development of linkages among various LM success determinants. Utility of the proposed interpretive structural modeling and structural equation modeling methodologies imposing order, direction and significance of the relationships among elements of LM success assumes considerable value to the decision-makers and LM practitioners. Originality/value Building on opinions of a group of LM experts and a case study of leading auto part manufacturers, the present study strives to model the success of LM, a topic that has received little attention to date.

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Design-for-X (DfX) approaches are of great importance to support sustainable development of new products, since the goal of DfX practices is to improve life cycle cost, life cycle environmental performance, increase design flexibility, manufacturing efficiency, etc. Therefore DfX supports a better decision-making process whenever a new complex product is being developed. In this work the new Lean Design-for-X (LDfX) approach is presented embracing the principles of Lean Product Development and Modular Design, for a systematic applicability by design engineers and product managers, assessing the effectiveness and efficiency of a given product design. An LDfX index metric, ranging between 0-100%, and original scorecard were created for consistent decision support for the comparison of different design concepts or products, integrating different "X" domains. The approach was applied in a real design study of a machine tool (press-brake), integrating Ecodesign principles, Design for Structural Optimization and Modular Design. (C) 2018 The Authors. Published by Elsevier B.V.

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Tuberculosis is one of the major public health concerns. This highly contagious disease affects more than 10.4 million people, being a leading cause of morbidity by infection. Tuberculosis is diagnosed at the point-of-care by the Ziehl-Neelsen sputum smear microscopy test. Ziehl-Neelsen is laborious, prone to human error and infection risk, with a limit of detection of 10(4) cells/mL. In resource-poor nations, a more practical test, with lower detection limit, is paramount. This work uses a magnetoresistive biosensor to detect BCG bacteria for tuberculosis diagnosis. Herein we report: i) nanoparticle assembly method and specificity for tuberculosis detection; ii) demonstration of proportionality between BCG cell concentration and magnetoresistive voltage signal; application of multiplicative signal correction for systematic effects removal; iv) investigation of calibration effectiveness using chemometrics methods; and v) comparison with state-of-the-art point-of-care tuberculosis biosensors. Results present a clear correspondence between voltage signal and cell concentration. Multiplicative signal correction removes baseline shifts within and between biochip sensors, allowing accurate and precise voltage signal between different biochips. The corrected signal was used for multivariate regression models, which significantly decreased the calibration standard error from 0.50 to 0.03 log(10) (cells/mL). Results show that Ziehl-Neelsen detection limits and below are achievable with the magnetoresistive biochip, when pre-processing and chemometrics are used.

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