Abstract:The current economic environment in the Indian manufacturing industry is offering a perfect opportunity to SMEs of this country to develop and grow by acting as suppliers of large multinational original equipment manufacturers (OEMs). To meet the challenge of offering high standards of quality, cost and delivery (QCD) to these multinational OEMs, Indian manufacturing SMEs must implement effective approaches, such as lean manufacturing, to continually and systematically improve their operations. However, the academic literature indicates that the adoption of lean manufacturing and some of its tools, such as value stream mapping (VSM), by Indian manufacturing SMEs is feeble, or in many cases lean initiatives have been unsuccessful. This paper presents a successful application of VSM in an Indian manufacturing SME. The results of the study and operational improvements achieved indicate that the application of VSM is an effective 42 A. Saboo et al.strategy for organisations of this type to improve their processes and thus meet their current challenges. This paper contributes by providing empirical evidence of the application of VSM in India and thus it can be used as a guiding reference for managers and engineers to undertake specific process improvement projects, in their organisations, similar to the one presented in this paper.Keywords: India; lean operations; manufacturing; SME; value stream mapping; VSM.Reference to this paper should be made as follows: Saboo, A., Garza-Reyes, J.A., Er, A. and Kumar, V. (2014) 'A VSM improvement-based approach for lean operations in an Indian manufacturing SME', Int.

No abstract

Purpose While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. Methods We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). Results The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. Conclusion We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

Background COVID‐19‐associated pulmonary aspergillosis (CAPA) has been reported as an important cause of mortality in critically ill patients with an incidence rate ranging from 5% to 35% during the first and second pandemic waves. Objectives We aimed to evaluate the incidence, risk factors for CAPA by a screening protocol and outcome in the critically ill patients during the third wave of the pandemic. Patients/Methods This prospective cohort study was conducted in two intensive care units (ICU) designated for patients with COVID‐19 in a tertiary care university hospital between 18 November 2020 and 24 April 2021. SARS‐CoV‐2 PCR‐positive adult patients admitted to the ICU with respiratory failure were included in the study. Serum and respiratory samples were collected periodically from ICU admission up to CAPA diagnosis, patient discharge or death. ECMM/ISHAM consensus criteria were used to diagnose and classify CAPA cases. Results A total of 302 patients were admitted to the two ICUs during the study period, and 213 were included in the study. CAPA was diagnosed in 43 (20.1%) patients (12.2% probable, 7.9% possible). In regression analysis, male sex, higher SOFA scores at ICU admission, invasive mechanical ventilation and longer ICU stay were significantly associated with CAPA development. Overall ICU mortality rate was higher significantly in CAPA group compared to those with no CAPA (67.4% vs 29.4%, p < .001). Conclusions One fifth of critically ill patients in COVID‐19 ICUs developed CAPA, and this was associated with a high mortality.