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Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach

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《机械工程前沿（英文）》 2014年第9卷第3期页码 218-232 doi: 10.1007/s11465-014-0309-7

摘要：

Productivity has often been cited as a key factor in a flexible manufacturing system (FMS) performance, and actions to increase it are said to improve profitability and the wage earning capacity of employees. Improving productivity is seen as a key issue for survival and success in the long term of a manufacturing system. The purpose of this paper is to make a model and analysis of the productivity variables of FMS. This study was performed by different approaches viz. interpretive structural modelling (ISM), structural equation modelling (SEM), graph theory and matrix approach (GTMA) and a cross-sectional survey within manufacturing firms in India. ISM has been used to develop a model of productivity variables, and then it has been analyzed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are powerful statistical techniques. CFA is carried by SEM. EFA is applied to extract the factors in FMS by the statistical package for social sciences (SPSS 20) software and confirming these factors by CFA through analysis of moment structures (AMOS 20) software. The twenty productivity variables are identified through literature and four factors extracted, which involves the productivity of FMS. The four factors are people, quality, machine and flexibility. SEM using AMOS 20 was used to perform the first order four-factor structures. GTMA is a multiple attribute decision making (MADM) methodology used to find intensity/quantification of productivity variables in an organization. The FMS productivity index has purposed to intensify the factors which affect FMS.

关键词： FMS ISM EFA SEM GTMA

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Evaluation of ICUs and weight of quality control indicators: an exploratory study based on Chinese ICU quality data from 2015 to 2020

《医学前沿（英文）》 2023年第17卷第4期页码 675-684 doi: 10.1007/s11684-022-0970-x

摘要： This study aimed to explore key quality control factors that affected the prognosis of intensive care unit (ICU) patients in Chinese mainland over six years (2015−2020). The data for this study were from 31 provincial and municipal hospitals (3425 hospital ICUs) and included 2 110 685 ICU patients, for a total of 27 607 376 ICU hospitalization days. We found that 15 initially established quality control indicators were good predictors of patient prognosis, including percentage of ICU patients out of all inpatients (%), percentage of ICU bed occupancy of total inpatient bed occupancy (%), percentage of all ICU inpatients with an APACHE II score ≥15 (%), three-hour (surviving sepsis campaign) SSC bundle compliance (%), six-hour SSC bundle compliance (%), rate of microbe detection before antibiotics (%), percentage of drug deep venous thrombosis (DVT) prophylaxis (%), percentage of unplanned endotracheal extubations (%), percentage of patients reintubated within 48 hours (%), unplanned transfers to the ICU (%), 48-h ICU readmission rate (%), ventilator associated pneumonia (VAP) (per 1000 ventilator days), catheter related blood stream infection (CRBSI) (per 1000 catheter days), catheter-associated urinary tract infections (CAUTI) (per 1000 catheter days), in-hospital mortality (%). When exploratory factor analysis was applied, the 15 indicators were divided into 6 core elements that varied in weight regarding quality evaluation: nosocomial infection management (21.35%), compliance with the Surviving Sepsis Campaign guidelines (17.97%), ICU resources (17.46%), airway management (15.53%), prevention of deep-vein thrombosis (14.07%), and severity of patient condition (13.61%). Based on the different weights of the core elements associated with the 15 indicators, we developed an integrated quality scoring system defined as F score=21.35%×nosocomial infection management + 17.97%×compliance with SSC guidelines + 17.46%×ICU resources + 15.53%×airway management + 14.07%×DVT prevention + 13.61%×severity of patient condition. This evidence-based quality scoring system will help in assessing the key elements of quality management and establish a foundation for further optimization of the quality control indicator system.

关键词： critical care medicine quality control evaluation exploratory factor analysis (EFA) model