Logistics System Risk Analysis Using FMEA-AHP Methodology: During and after the COVID-19 pandemic

The COVID-19 pandemic broke out, and the global logistics industry suffered severe losses, therefore, the FMEA-AHP (Failure Mode and Effects Analysis-Analytic Hierarchy Process) method is proposed to analyze the failure reasons of the logistics system in the COVID-19 pandemic. In this article, we have made an improvement on the basis of the traditional FMEA method: The AHP is integrated into the FMEA algorithm (referred to as RPWN (risk priority weighted number) in this article). In this algorithm, the AHP is to determine the weights of risk indicators. Meanwhile, in this article, we also consider about the new logistics failures, such as the failure modes and failure reasons of the logistics system under the COVID-19 pandemic. 12 failures have been identified, and corresponding preventive and corrective measures have been suggested to cut off the path of failure propagation and reduce the impact of failures. © 2022 ACM.

Analysis of Prediction Data for the Third Wave of COVID-19 in Bogor Regency

The COVID-19 pandemic is far from over. The government has carried out several policies to suppress the development of COVID-19 is no exception in Bogor Regency. However, the public still has to be vigilant especially now we will face a year-end holiday that can certainly be a trigger for the third wave of COVID-19. Therefore, researchers aim to make predictions of the increase in positive cases, especially in the Bogor Regency area to help the government in making policies related to COVID-19. The algorithms used are Gaussian Process, Linear Regression, and Random Forest. Each Algorithm is used to predict the total number of COVID-19 cases for the next 21 days. Researchers approached the Time Series Forecasting model using datasets taken from the COVID-19 Information Center Coordinationn Center website. The results obtained in this study, the method that has the highest probability of accurate and appropriate data contained in the Gaussian Process method. Prediction data on the Linear Regression method has accurate results with actual data that occur with Root Mean Square Error 1202.6262. © 2021 IEEE.