Smart doors - A Machine Learning Approach to monitor body temperature and detect face mask

In recent years Internet of Things(IoT) plays a vital role in automation. Nearly millions of people have been affected by the threatening disease COVID-19 (coronavirus), who are either sick or being killed due to the spread of the disease. The densely populated world possesses a threat of spreading such infectious diseases rapidly. So, there is a need for supervision of people's health status working in large organizations/institutions. This paper emphasizes the automation in monitoring the temperature of human beings and face mask detection so that spreading of infectious disease like COVID-19 can be brought down. The proposed solution aids the institutions/organizations to find out the infected person and take necessary precaution at an earlier stage to avoid spreading of the disease to the other healthy persons. This prototype overcomes the drawback of existing ideas in which affected individuals are frequently exposed to high radiation devices. The idea comes with the provision of ensuring the operation of the system only in the presence of human beings and also it paves the way to install a low cost set-up. The system makes use of sensor technology to spot the common symptoms of the disease and machine learning algorithm to ensure people are wearing masks. The obtained data gets stored on the cloud and analyzed by the organizations/institution's authorities. The lid present in the entrance is opened for the people with normal constraints. The whole scheme helps the larger organizations/institutions to avoid spreading of infectious diseases. © 2022 IEEE.

A DIGITAL TWIN BASED APPROACH FOR ENSURING BUSINESS CONTINUITY PLAN AND SAFE RETURN TO WORKPLACE

Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of Covid19 pandemic related uncertainties. The uncertainty exists along multiple dimensions such as virus mutations, infectivity and severity of new mutants, efficacy of vaccines against new mutants, waning of vaccine induced immunity over time, and lockdown/opening-up policies effected by city authorities. Moreover, this uncertainty plays out in a non-uniform manner across nations, states, cities, and even within the cities thus leading to highly heterogeneous evolution of pandemic. While Work From Home (WFH) strategy has served well to meet ever-increasing business demands without compromising on individual health safety, there has been an undeniable reduction in social capital. With Covid19 pandemic showing definite waning trends, organizations are considering the possibility of safe transition from WFH to Work From Office (WFO) or a hybrid mode of operation. An effective strategy needs to score equally well on possibly interfering dimensions such as risk of infection, project delivery, and employee wellness. As large organizations will typically have a large number of offices spread across a geography, the problem of arriving at office-specific strategies becomes non-trivial. Moreover, the strategies need to adapt over time to changes that cannot be deduced upfront. This calls for an approach that is amenable to quick and easy adaptation. Our contribution in this regard is constructing a Digital Twin by leveraging various modelling techniques to realistically represent the above mentioned aspects of interest that can be subjected to what-if scenario analysis. We further demonstrate its efficacy using a case study from a large organization. © 2022 Society for Modeling & Simulation International (SCS)

A Digital Twin Based Approach For Ensuring Business Continuity Plan And Safe Return To Workplace

Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of Covid19 pandemic related uncertainties. The uncertainty exists along multiple dimensions such as virus mutations, infectivity and severity of new mutants, efficacy of vaccines against new mutants, waning of vaccine induced immunity over time, and lockdown / opening-up policies effected by city authorities. Moreover, this uncertainty plays out in a non-uniform manner across nations, states, cities, and even within the cities thus leading to highly heterogeneous evolution of pandemic. While Work From Home (WFH) strategy has served well to meet ever-increasing business demands without compromising on individual health safety, there has been an undeniable reduction in social capital. With Covid19 pandemic showing definite waning trends, organizations are considering the possibility of safe transition from WFH to Work From Office (WFO) or a hybrid mode of operation. An effective strategy needs to score equally well on possibly interfering dimensions such as risk of infection, project delivery, and employee wellness. As large organizations will typically have a large number of offices spread across a geography, the problem of arriving at office-specific strategies becomes non-trivial. Moreover, the strategies need to adapt over time to changes that cannot be deduced upfront. This calls for an approach that is amenable to quick and easy adaptation. Our contribution in this regard is constructing a Digital Twin by leveraging various modelling techniques to realistically represent the above mentioned aspects of interest that can be subjected to what-if scenario analysis. We further demonstrate its efficacy using a case study from a large organization. © 2022 SCS.

A Study in Performing Big Data Analytics with Limited Resources

Data Analytics are usually the purview of large organizations with significant human and computational resources. The purpose of the article is to identify how organizations with limited resources, including limited computing power and bandwidth, can carry out meaningful Data Analytics at a granularity that fits their particular needs. This paper presents a case study in creating and maintaining a COVID dashboard for a 5-county area in the Panhandle of Florida, using only free software and publication platforms. Results of this study are informative for any local or regional entity needing focused data analytics. © by the International Institute of Informatics and Systemics.

Enhancing the quality and competitiveness of small businesses: A pooled cross-sectional analysis

Compared with large organizations, small businesses have been slower to adopt quality management practices. This study combined the context-free perspective with the contingency theory of quality management to investigate the association between quality management and the quality and operational outcomes of a sample of 231 small businesses that applied for the Baldrige quality excellence award program. Using a pooled cross-sectional design and structural equation modeling, we examined the validity and reliability of the Baldrige model for measuring the quality management practices of small businesses, to determine the relationship between the Baldrige criteria and their impact on quality outcomes. Our analysis confirms the validity and reliability of the Baldrige quality excellence model when used as an assessment tool for improving the performance outcomes and competitiveness of small businesses. Based on the results, leadership was found to be the key determinant of quality outcomes during the application year, with leadership practice scores increasing by 40% over 16 years. This study found that Information analysis and knowledge management only influences Customer focus and satisfaction, but Strategic planning for quality and Management of process quality significantly and positively influence both Customer focus and satisfaction and Quality and operational results. However, the score for Quality and operational results diminishes over time for small businesses. This study provides some effective insights and recommendations for small businesses as well as policymakers looking to support small businesses’ quality and competitiveness, because quality improvements will enhance the stability, profitability, and survivability of small businesses when facing crises like the ongoing COVID-19 pandemic. © 2022 Elsevier B.V.

An innovation case study: A process, practice and learning perspective from a large hospitality and entertainment business

In the last several years, innovation has received an amplified focus as various traditional businesses have been interrupted by new business models as well as the Covid-19 pandemic. Large organizations are often challenged to keep innovating lest they be disrupted by new upstart business models or unforeseen shocks. This case study explores three dimensions of achieving systematic organizational innovation, specific to internal business optimization: a process framework for managing and tracking innovation activities and outcomes;an examination of the front-end of this process related to generating ideas for innovation;and a mapping of industrial engineering graduate courses to the innovation process framework. Being a case study, this paper will provide some key learnings for areas to focus on and what works when innovation becomes a priority. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.