ABSTRACT
The global negative impact of Coronavirus 2019 (Covid-19) disease in vital sustainability points of social functionality, such as health and economics, is indisputable. State authorities and societies in general have found themselves confronted with new regularities while trying to understand and measure the behaviour of this new disease. Statistical process monitoring (SPM) constitutes a method of quality control which employs various powerful tools to monitor and control a process. Such tools should be able to illustrate the evolution over time of the behavior of quality characteristics, either measurable or countable, and at the same time detect anomalies. The use of SPM is very common among various organizations which make use of control charts for capturing the behavior of processes or systems that evolve over time. This chapter presents a real case study on Covid-19 cases regarding 10 countries of the Mediterranean basin based on the four-epoch/phase framework. © ISTE Ltd 2022.
ABSTRACT
Consider the problem of financial surveillance of a heavy-tailed time series modeled as a geometric random walk with log-Student's t increments assuming a constant volatility. Our proposed sequential testing method is based on applying the recently developed taut string (TS) univariate process monitoring scheme to the gaussianized log-differenced process data. With the signal process given by a properly scaled total variation norm of the nonparametric taut string estimator applied to the gaussianized log-differences, the change point detection procedure is constructed to have a desired in-control (IC) average run length (ARL) assuming no change in the process drift. If a change in the process drift is imminent, the proposed approach offers an effective fast initial response (FIR) instrument for rapid yet reliable change point detection. This framework may be particularly advantageous for protection against imminent upsets in financial time series in a turbulent socioeconomic and/or political environment. We illustrate how the proposed approach can be applied to sequential surveillance of real-world financial data originating from Meta Platforms, Inc. (FB) stock prices and compare the performance of the TS chart to that of the more prominent CUSUM and CUSUM FIR charts at flagging the COVID-19 related crash of February 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Because of health concerns and factory operational scale backs during the recent COVID-19 pandemic, we now need factory automation more than ever to maintain our productivity. However, most of our factories cannot operate remotely, and none can function without considerable human input and oversight. Trying to automate our factory highlights gaps in our technology, as it seems far behind our expectations, needs, and vision. Thus, this paper aims to fill this gap by showing how we have developed practical methodologies and applied technology to enhance legacy factories and their equipment. Specifically, we present the ORiON Production Interface (OPI) unit to run the factory as a smart networked edge device for virtually any machine or process. We have also implemented various computer vision algorithms in the OPI unit to detect errors autonomously, make decentralized decisions, and even control the quality. Although Industry 4.0 is a known concept to equip our factory to see, understand, and predict, we know that many machines today are closed source and cannot even communicate, let alone join a network. This research provides a workable solution to realize Industry 4.0 truly in existing factories with legacy equipment. Experimental results show that this system has a variety of applications, including process monitoring, part positioning, broken tool detection, etc. This novel intelligent networked system can enable our factories to be more innovative and responsive. It also allows for remote operations that can be unattended or lightly tended—a trend needed for the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Lean Six Sigma (LSS) is a methodological approach that originated in industry and has, over time, become increasingly popular in healthcare. Its tool-to, the DMAIC cycle, consisting of 5 main steps, offers methodological rigor that helps improve processes by comparing results quantitatively. In this study, the LSS and in particular the DMAIC cycle was used to investigate the impact of COVID-19 on patients' length of stay in the Emergency Department (ED-LOS) of the Evangelical Hospital "Betania” of Naples (Italy). The study revealed a general increase in ED-LOS due mainly to the new steps that the hospital added to the standard flow, such as those for performing screening swabs, and the reduction of treatment stations, with the exception of patients discharged home for whom there was a statistically significant reduction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The National Institute of Respiratory Diseases (INER. Its acronym in Spanish) is a public healthcare institution that provides medical attention, teaching and scientific research centered on diseases of the respiratory system. In 2019, the hospital held 228 beds and over six thousand medical devices for diagnosis, treatment, and rehabilitation of patients. In 2020, the COVID-19 pandemic pushed the Hospital to convert 215 conventional care beds to intensive care beds, assigned exclusively to critical COVID-19 patients. This resulted in a higher demand of Health Technology Management (HTM) resources which were reflected by a 60% increase in the Hospital’s medical devices. Therefore, the technical staff of the Department of Biomedical Engineering grew by 300%. Thus, the objective of this work was to innovate HTM procedures through the application of Six Sigma Methodology and two Lean tools: 5S and Kanban, to control the activities carried out at the Hospital. Procedures for dealing with work orders and inventory control of supplies (accessories, consumables, and spare parts) that medical equipment require for its operation were analyzed. Additionally, three activities related to HTM were analyzed: tool control, work area control and information flow among all 5 personnel work shifts. In total, seven innovation strategies were proposed and implemented in the Department of Biomedical Engineering at the INER. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Process Safety Management (PSM) is the backbone of any process industry and is fundamental to its businesses, plant reliability, safe operations, employee well-being and prevention of catastrophic events. In order to grow or sustain business and be responsible for its employees' health, good organizations never compromise on the foundation of PSM in their business and operational decisions. But what if a deadly disease alters the way how organizations and systems typically work? Can we quickly adapt to such unprecedented challenges? Are we able to fulfill all requirements of a process safety management system in this extraordinary scenario? The paper will cover this topic in detail and it will shed light on the encountered challenges and tested best practices in order to maintain PSM during a global pandemic. Effects of a pandemic on key important factors related to PSM will be discussed like management of change, employee motivation, process hazard analysis, job supervision, process monitoring, competency levels, training & development etc. Building on similar personal experience, the paper will also cover many interesting methods and suggestions for safety professionals and line managers to successfully adapt to the changes in this scenario while not compromising their process safety management systems. © 36th Center for Chemical Process Safety International Conference, CCPS 2021 - Topical Conference at the 2021 AIChE Spring Meeting and 17th Global Congress on Process Safety.
ABSTRACT
Process Safety Management (PSM) is the backbone of any process industry and is fundamental to its businesses,plant reliability,safe operations,employee well-being and prevention of catastrophic events. In order to grow or sustain business and be responsible for its employees health,good organizations never compromise on the foundation of PSM in their business and operational decisions. But what if a deadly disease alters the way how organizations and systems typically work? Can we quickly adapt to such unprecedented challenges? Are we able to fulfill all requirements of a process safety management system in this extraordinary scenario? The paper will cover this topic in detail and it will shed light on the encountered challenges and tested best practices in order to maintain PSM during a global pandemic. Effects of a pandemic on key important factors related to PSM will be discussed like management of change,employee motivation,process hazard analysis,job supervision,process monitoring,competency levels,training & development etc. Building on similar personal experience,the paper will also cover many interesting methods and suggestions for safety professionals and line managers to successfully adapt to the changes in this scenario while not compromising their process safety management systems. Copyright © American Institute of Chemical Engineers. All rights reserved.
ABSTRACT
Based on big data analysis, we discuss how to formulate an optimal coping mechanism for infectious diseases, especially major and emerging infectious diseases. First, by combining big data analysis and statistical analysis model and deducing whether the emerging disease is contagious, the strength of the contagion effect and the possible consequences, this study will determine whether the corresponding coping strategies should be implemented for infectious diseases, especially major and emerging infectious diseases. Secondly, according to the inspection results and actual situation, the optimal coping strategy is formulated to minimize the loss of life and property security of the country and the society by using the optimization principle and the objective management in management science. Finally, the statistical analysis method and the six sigma principle are combined to develop a feedback mechanism to evaluate whether the formulated coping strategies can achieve the expected results in practice. Our research has improved the research framework of infectious diseases in theory and provided scientific reference and experience for the major and emerging infectious diseases in practice for the future. © 2022 IEEE.
ABSTRACT
A challenge, in the era of economic crisis and uncertainty, is to provide health care services in an efficient and effective manner. The protection of public health, the provision of quality healthcare services to patients, the location of health centers, the geographical distribution of patients, and the provision of specialist services are some of the topics that the government and/or a health organization responsible for health care services provision has to arrange. Other topics are the assessment of quality, safety, and effectiveness of healthcare services provided by healthcare providers. Moreover, a central pylon in designing healthcare policy is expenditure monitoring and control. However, among all these topics the most significant is the protection of public health;especially now that viruses such as Coronavirus are spreading rapidly worldwide. This paper aims to review the use of Statistical Process Monitoring techniques in the public health domain in order to improve health care decision-making under uncertainty and further on to provide an innovative three-layer framework for the collection, processing, and real-time analysis of related data like Coronavirus or any other infectious disease that will emerge in the future for both proper and effective case management and effective health policy planning. © 2022 Taylor & Francis Group, LLC.
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Predictive monitoring is a key activity in some Process-Aware Information Systems (PAIS) such as information systems for operational management support. Unforeseen circumstances like COVID can introduce changes in human behaviour, processes, or computing resources, which lead the owner of the process or information system to consider whether the quality of the predictions made by the system (e.g., mean time to solution) is still good enough, and if not, which amount of data and how often the system should be trained to maintain the quality of the predictions. To answer these questions, we propose, compare, and evaluate different strategies for selecting the amount of information required to update the predictive model in a context of offline learning. We performed an empirical evaluation using three real-world datasets that span between 2 and 13 years to validate the different strategies which show a significant enhancement in the prediction accuracy with respect to a non-update strategy. © 2022, Springer Nature Switzerland AG.
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The Covid -19 is arguably the biggest pandemic in history and there are a lot of challenges that must be dealt with. One of the biggest challenges post Covid-19 is to tackle quality control challenges. This research paper discusses some of these challenges and solutions using an integrated internet of things (IoT) and internet of protocols (IoP) based approach and further showing its implementation in the industry world and hence, proving to be a solution for damage assessment. With the help of IoT- enabled quality control system, six-sigma rule is also analysed. Post Covid crisis, it is important for every institution to gain back customer trust so quality of materials should be maintained and IoT enables us to do the same. The unification of industrial IoT (IIoT) and industry 4.0 is also discussed as it leads us to understand that this unification is the next evolution of smart manufacturing and digital technologies. This methodology can lead us to accelerated innovation in applications for overcoming the eventual challenges post Covid in the near future. Also, small-scale/large-scale companies making use of the above research methodology can adhere to six-sigma criterion. © 2022 Institute of Physics Publishing. All rights reserved.
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This research aims at evaluating possible ways of implementing the lean six sigma tools and principles to micro, small and medium enterprises (MSMEs) to develop Competitiveness and resilience during and after the pandemic. The emergence of COVID-19's economic repercussion on MSMEs is part of a global a crisis that has positioned SMEs under enormous strain to survive, requiring them to effectively adapt to changes in crisis situations. SMEs make a substantial contribution to various economies globally, as evidenced by the fact that they account for a large portion of the countries' GDP while also providing a source of income for millions of individuals. SMEs are the most susceptible during the pandemic because of their limited access to capital, technology among other causes. This study will provide MSMEs' recommendations and remain competitive through resilience by developing inherent operational excellence for continuous improvement in their business process. Copyright © 2021 by Naudé Scribante. Permission granted to IAMOT to publish and use.
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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.
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This research is a case study on SQT a leading Irish Lean Six Sigma training provider and their transition to online training and the digitalisation of their Lean Six Sigma training programs and other associated programs during the COVID-19 pandemic. The changes and challenges in transitioning from the existing classroom-based training model are discussed. A quantitative survey and qualitative interviews were carried out with the customers (trainee’s and sponsoring employer organisations/clients) of the Lean Six Sigma trainer provider for 9–12 months. The results of the survey on the customers learning experiences with online Lean training is analysed. The results will demonstrate that the move to online Lean training was positive for both the customers and the training provider in terms of quality of delivery, cost minimisation, elimination of non-value-add travel and classroom time, improved online teamwork, program structure and engagement and enhanced benefits of the application of the learning in the workplace. © 2021, IFIP International Federation for Information Processing.
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This research discusses how lecturers in an Irish university transferred their classroom-based blended learning Lean Six Sigma modules to online delivery. The transfer from a practical classroom environment to an online classroom needed to be seamless in the students Lean active learning experiences. The output of the paper is to discuss the designing of appropriate delivery methods and practical examples, games, scenarios, exercises in a flipped online classroom. Problem-based learning is ideal for teaching lean manufacturing, driven by a problem-solving culture that values learning as a critical output. The design of a “practical problem based” online Kaizen utilising the virtual classroom as an obeya room enabled students to learn Lean Six Sigma tools and practically deploy the tools. Qualitative and quantitative measures were deployed to assess the success of the transition. © 2021, IFIP International Federation for Information Processing.
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As a result of the COVID-19 pandemic, organizations are forced to operate in a remote environment with several restrictions on travel, group meetings, and in-person interactions. This is especially difficult for lean six sigma projects where observing the process and interacting with the workers is essential to understanding and improving the process. The Army's Lean Six Sigma methodology includes five phases: Define, Measure, Analyze, Improve, Control;each of these phases includes interaction between the project and process team. This paper focuses on the application of Lean Six Sigma methodology at Tobyhanna Army Depot to help reduce overruns and repair cycle time within the sheet metal cost center. At the initiation of the project, the process incurred over 4000 hours of overruns, a situation in which it takes longer to repair an asset than the standard hours allocated for the repair. Additionally, the average repair cycle time, amount of time required to repair an individual asset, exceeded customer expectations by almost 4 days. The paper discusses how the lean six sigma team executed tradition tools for each phase, like process mapping, data analysis, communication plans, and brainstorming in a remote environment. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.
ABSTRACT
Emergency Care Networks (ECNs) are integrated healthcare systems comprised of emergency departments (EDs). ECNs are called to be the primary response of healthcare authorities to deal with the expected uptick in the future demands for emergency care during the current Covid-19 pandemic. Lean Six Sigma (LSS) has been proposed to address this challenge since it allows managers to detect factors contributing to the extended waiting times (WT) throughout the patient journey. The suggested framework follows the DMAIC cycle that was initiated with the project charter definition;in the meantime, a SIPOC diagram was drawn to analyze the emergency care process and pinpoint critical process variables. Following this, a nested Gage R&R study was undertaken to study the measurement system performance;subsequently, a normal-based capability analysis was carried out to determine how well the ECN process satisfies the specifications. The next step was to identify the potential causes separating the ECN nodes from the desired target. Afterwards, improvement strategies were devised to lessen the average WT. After suitable data collection, a before-and-after analysis was performed to verify the effectiveness of the implemented strategies. Ultimately, a control plan containing an I-MR control chart was designed to maintain the improvements achieved with the LSS implementation. The results revealed that the average WT of the showcased node passed from 190.02 min to 103.1 min whereas the long-term sigma level increased from −0.06 to 0.11. The proposed framework was validated through a case study including the involvement of a medium-sized hospital from the public sector. © 2021, Springer Nature Switzerland AG.
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During the current COVID-19 pandemic, decision-makers are tasked with implementing and evaluating strategies for both treatment and disease prevention. In order to make effective decisions, they need to simultaneously monitor various attributes of the pandemic such as transmission rate and infection rate for disease prevention, recovery rate which indicates treatment effectiveness as well as the mortality rate and others. This work presents a technique for monitoring the pandemic by employing an Susceptible, Exposed, Infected, Recovered, Death model regularly estimated by an augmented particle Markov chain Monte Carlo scheme in which the posterior distribution samples are monitored via Multivariate Exponentially Weighted Average process monitoring. This is illustrated on the COVID-19 data for the State of Qatar.
ABSTRACT
The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.