Supply Chain Performance Measurement Methodology for COVID-19 Disruption Recovery- Natural Gas Retailing in India

The coronavirus(COVID-19)pandemic disruption adversely impacted the Supply Chain for Natural Gas(NG) retailing, jeopardizing committed targets for thesocio-economic transition toa gas-based economyleading to inclusive growth in India. Furthermore, Omicron'srapid spread is a cause of global concern. This research aims to develop a conceptual Supply Chain Performance (SCP) measurement methodology for reducing recovery time post exogenous disruption. Exploratory research involving an integrative literature review identifies different approaches, frameworks, models, and techniques for Supply Chain PerformanceMeasurement(SCPM). Analysis reveals that the application of DMAIC improves the reliability of Supply Chain Management (SCM) functional processes during disruption recovery while synthesizing the Balanced Scorecard (BSC) with Triple Bottom Line(TBL) accounting approach provides a rigorous robust method to represents financial, social, and environmental performance goals. Accordingly, findings present a methodology with nine systematic steps to evaluate performance by City Gas Distribution(CGD)entities to accelerate NG retail consumption and speed up network expansion to meetthe growing clean energy demand of the urban populace. Additionally, anIndian model for transition to a gas-based economy is also presentedto distinguish competing goals to harmonize growth with environmental sustainability and ecological modernization. Though SCP is a mature concept, the literature on its methodology during disruption recovery for NG retailing is scanty. Hence, the proposed action-oriented SCPM methodology has practical implications for improving SCP, contributing uniquely to the retail SCM domain for NG. Its application will instigate industry practitioners to review Supply Chain Management (SCM) processes and strategies to improve service effectiveness and delivery efficiency.

Six sigma DMAIC approach based mobile application for statistical analysis of COVID-19 data

Purpose Coronavirus disease 2019 is one of the novel diseases formed by a dreadful virus called Severe Acute Respiratory Syndrome Coronavirus 2. Various countries are affected by this viral disease, and many countries declare a lockdown with several rules and conditions. To prevent this rapid viral transmission, various researchers have introduced different mobile applications. This paper aims to study issues like viral transmission, mortality rates, vaccination rates, etc. and also provides suitable solutions based on the statistical analysis with the assistance of the Six-Sigma Define-Measure-Analyse-Improve-Control (DMAIC) concept. Design/methodology/approach Statistical analysis is done for different countries, and the required solutions are provided by using the DMAIC procedure. This application has the ability to represent the current risk status of the user and notify them to secure themselves. Findings The proposed work suggests the Aarogya Setu application to prevent large viral transmission by affording many preventive measures. This application also issues the current risk status of each individual user. Hence, it gives improved results in avoiding high viral transmission. Originality/value The proposed six-sigma DMAIC concept also affords the control measures to prevent viral transmission. Hence, the suggested application has the highest chance of avoiding the rapid viral transmission.

Machine Learning and Lean Six Sigma to Assess How COVID-19 Has Changed the Patient Management of the Complex Operative Unit of Neurology and Stroke Unit: A Single Center Study.

Background: In health, it is important to promote the effectiveness, efficiency and adequacy of the services provided; these concepts become even more important in the era of the COVID-19 pandemic, where efforts to manage the disease have absorbed all hospital resources. The COVID-19 emergency led to a profound restructuring-in a very short time-of the Italian hospital system. Some factors that impose higher costs on hospitals are inappropriate hospitalization and length of stay (LOS). The length of stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. Methods: This study analyzed how COVID-19 changed the activity of the Complex Operative Unit (COU) of the Neurology and Stroke Unit of the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy). The methodology used in this study was Lean Six Sigma. Problem solving in Lean Six Sigma is the DMAIC roadmap, characterized by five operational phases. To add even more value to the processing, a single clinical case, represented by stroke patients, was investigated to verify the specific impact of the pandemic. Results: The results obtained show a reduction in LOS for stroke patients and an increase in the value of the diagnosis related group relative weight. Conclusions: This work has shown how, thanks to the implementation of protocols for the management of the COU of the Neurology and Stroke Unit, the work of doctors has improved, and this is evident from the values of the parameters taken into consideration.

Digital transformations and supply chain management: a Lean Six Sigma perspective

Purpose>With the advent of technological connectivity and access to massive data, the possibilities of augmenting Lean Six Sigma’s Define-Measure-Analyze-Improve-Control (LSS’s DMAIC) problem-solving approach with advanced technologies are enormous. This paper aims to examine digital transformations (DT) of supply chains from a process improvement angle using the LSS DMAIC approach.Design/methodology/approach>This paper uses a case study approach. Three exemplary case studies were examined to shed light on how LSS can aid in DT to achieve enterprise-wide improvements and enhance value across the supply chains.Findings>The paper provides a conceptual framework based on a structured DMAIC problem-solving approach to drive improvements in supply chains. The conceptual framework also provides a systematic approach for big data to be integrated in LSS initiatives to achieve greater supply chain performance.Originality/value>This paper extends research in LSS supply chains by providing a guidance through a conceptual framework that integrate DT and LSS supply chains to support successful digital transformation and LSS supply chains.

ADDRESSING FOOD PRODUCTION WASTE IN AFRICA: INTEGRATION OF LEAN SIX SIGMA AND INDUSTRY 4.0 TECHNOLOGIES

This research aims to investigate a possible integration between Lean Six Sigma tools and principles and Industry 4.0 technologies to address pre-harvest and post-harvest food waste with a focus on Sub-Saharan Africa. With the projected increase in world population by United Nations to increase by 33% in 2050 and 99% increase in Sub-Saharan African in the same year. These foreseen changes will present global food security concerns in the future, with the greatest demand growth from the Sub-Saharan African region. The United Nations and the Food and Agriculture Organization predict that about 1.3 billion tons of food are globally wasted or lost per year, which also further adds to a global food security concern, bringing about a rise in food prices due to growing consumer demand. To address this impending challenge on food security globally, we aim to introduce analytics by applying advanced information systems synergies with the lean six sigma DMAIC methodology to systematically proffer solutions to production waste by understanding the critical factors responsible and curbing them with the latest innovations. This integration will bring about a more resilient food system, especially during and after the Covid-19. Copyright © 2021 by Naudé Scribante. Permission granted to IAMOT to publish and use.

Six Sigma to distinguish patterns in COVID-19 approaches

PurposeCOVID-19 has changed life as we know. Data are scarce and necessary for making decisions on fighting COVID-19. The purpose of this paper is to apply Six Sigma techniques on the current COVID-19 pandemic to distinguish between special cause and common cause variation. In the DMAIC structure, different approaches applied in three countries are compared.Design/methodology/approachFor three countries the mortality is compared to the population to distinguish between special cause variation and common cause variation. This variation and the patterns in it are assessed to the countries' different approaches to COVID-19.FindingsIn the DMAIC problem-solving approach, patterns in the data are distinguished. The special cause variation is assessed to the special causes and approaches. The moment on which measures were taken has been essential, as well as policies on testing and distancing.Research limitations/implicationsCross-national data comparisons are a challenge as countries have different moments on which they register data on their population. Furthermore, different intervals are taken, varying from registering weekly to registering yearly. For the research, three countries with similar data registration and different approaches in fighting COVID-19 were taken.Originality/valueThis is the first study with Master Black Belts from different countries on the application of Six Sigma techniques and the DMAIC from the viewpoint of special cause variation on COVID-19.

Implementation of lean Six-Sigma project in enhancing health care service quality during COVID-19 pandemic.

The recent outbreak of coronavirus (COVID-19) pandemic has exposed the weakness of the existing healthcare facilities in developing countries, and Pakistan has no exception. The increasing amount of patients has made this condition more vulnerable to failure. It became difficult for health care management to handle the surge of patients. This case study is based on the XYZ hospital system of Pakistan. The hospital initiates passive immunization as a savior in the absence of a vaccine. The process initiates numerous challenges as the same facility was using for passive immunization and routine operations of the hospital. DMAIC lean sig-sigma problem-solving methodology has been adopted to Define, Measure, Analyze, Implement and Control the improvement process for smooth special and routine activities. The staff and patients were interviewed, their issues were listed, and a comprehensive solution was suggested to deal with operational uncertainties. The results identified various factors through VOC and SIPOC processes, prioritized using fishbone diagram, analyzed through Kano model, and finally proposed process improvement by incorporating Kaizen process improvement methodology. Other industries could use this set of tools to evaluate and optimize routine problems, which ultimately enhances the quality and reduces cost.