Manufacturing companies usually expect strategic improvements to focus on reducing both waste and variability in processes, whereas markets demand greater flexibility and low product costs. To deal with this issue, lean manufacturing (LM) emerged as a solution; however, it is often challenging to evaluate its true effect on corporate performance. This challenge can be overcome, nonetheless, by treating it as a multi-criteria problem using the Hesitant Fuzzy linguistic and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. In fact, the hesitant fuzzy linguistic term sets (HFLTS) is vastly employed in decision-making problems. The main contribution of this work is a method to assess the performance of LM applications in the manufacturing industry using the hesitant fuzzy set and TOPSIS to deal with criteria and attitudes from decision makers regarding such LM applications. At the end of the paper, we present a reasonable study to analyze the obtained results.

dDegradation models have received much attention in the area of reliability estimation, mostly because it is possible to obtain robust information about the lifetime of highly reliable products and systems. However, in the last years, multivariate models have received more attention. This is because the quality of many products is a function of various environmental conditions, and the effect of these conditions over a performance characteristic can cause a failure of the product, either marginally or jointly. A gamma process is considered in this study to marginally model the degradation of a performance characteristic through two degradation test phases performed sequentially. The degradation increments obtained during the first test and the second test are modeled jointly considering the convolution of two marginal gamma processes. In this way, it is possible to obtain a robust model to get reliability estimates considering the effect of two serial degradation test, which can consider multiple covariates. This modeling is illustrated with crack propagation data and important results are presented.

PurposeThe objective of the study is to design and validate an instrument that allows organizations to assess their status regarding the adoption of the critical success factors (CSFs) that enable lean six sigma (LSS) implementation in order to achieve the expected benefits.Design/methodology/approachAn extensive literature review was conducted to define the LSS CSFs that have to be considered for the development of the questionnaire that would later be applied across all manufacturing companies on the Northern Mexican border. Once the database was built, a statistical verification of the assumptions required for factor analysis took place. Finally, the due construct validation was carried out to verify whether the proposed instrument measured reliably what it is intended to.FindingsA questionnaire measuring nine CSFs, as well as the benefits associated with the implementation of LSS, was designed and validated through 61 items.Research limitations/implicationsThe main limitations of this study are that the research is cross-sectional and that the study was carried out taking as a reference only exporting manufacturing companies located in the border area between Mexico and the United States.Practical implicationsThe validated instrument is expected to serve as a useful tool for companies interested in the implementation of LSS.Originality/valueThis study introduces a validated tool for the analysis of LSS CSFs while providing evidence of construct validity and the solid structure of the factors.

This research was carried out in the Mexican manufacturing industry, the second most important activity in the country's industrial sector, specifically in the transportation equipment manufacturing subsector, which generates 19% of the jobs in this industry. Thus, it is important to develop improvement strategies to strengthen the sector's competitiveness. Currently, Lean Manufacturing projects are considered the most important strategy for manufacturing companies to achieve world-class performance. However, such projects yield different results, depending on the level of Critical Success Factor (CSFs) implementation during their development. This work proposes the design and validation of an instrument to evaluate the implementation of CSFs during the project-improvement phase in the production of transportation equipment in the Mexican manufacturing industry. The instrument is made up of six CSFs selected from the reviewed literature on Lean Manufacturing methodology and improvement projects and measured through 31 items. The instrument was verified and empirically validated through exploratory factor analysis, confirmatory factor analysis, and reliability analysis, using the SPSS Amos ® software program and a sample of 240 valid surveys applied to experienced developers of Lean Manufacturing improvement projects. The results show that the proposed instrument holds enough statistical validity to be used by the companies in the sector in order to assess the impact of critical success factors on the development of improvement projects. Additionally, the survey can help companies to identify areas of opportunity by adopting the Lean Manufacturing methodology and fit models, to assess the interaction of the FCEs in achieving the expected results of improvement projects. INDEX TERMS Critical success factors, lean manufacturing, confirmatory factor analysis, construct validation, mexican manufacturing industry, improvement projects.

Present reliability models, which estimate the lifetime of electronic devices, work under the assumption that the voltage level must be constant when an Accelerated Life Testing is performed. Nevertheless, in a real operational environment, electronic devices are subjected to electrical variations present in the power lines; that means the voltage has a time-varying behavior, which breaks the assumption of reliability models. Thus, in this paper, a reliability model is presented, which describes the lifetime of electronic devices under time-varying voltage via a parametric function. The model is based on the Cumulative Damage Model with random failure rate and the modified Inverse Power Law. In order to estimate the parameters of the proposed model, the maximum likelihood method was employed. A case study based on the time-varying voltage induced by electrical harmonics when Alternate Current/Direct Current (AC/DC) transformer is connected to the power line is presented in this paper.