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Simulation Modelling Practice and Theory ; 122, 2023.
Article in English | Scopus | ID: covidwho-2240465


In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty. © 2022 The Authors

6th International Conference on Intelligent Computing and Control Systems, ICICCS 2022 ; : 366-373, 2022.
Article in English | Scopus | ID: covidwho-1922677


Increasing people's perception of their habitual face-touching behaviour and ameliorating their acknowledgment of self-inoculation as a medium of transmission may assist to curb the spread of novel coronavirus (COVID-19). On average, human beings generally touch their faces 23 times per hour. Therefore, hand hygiene is an essential preventive measure to stop the spread of COVID-19. This motivates to introduce an alert mechanis m using wearable technology that aims to alert a person whenever he/she brings his/her hands close to the face. The proposed face alert system is based upon deep learning technique to forecast hand movements followed by face touching and imparts sensory response to alert end-user to stop the face touching activities. The proposed system employs IMU to get features belonging to different hand movements resulting in face touching. The data can be effectively classified using CNN where the filters help in extracting temporal features from IMU data. The prediction model based upon CNN is developed with training data from four thousand eight hundred trials recorded from forty participants. The trained dataset of hand movements activities is collected during day-to-day activities, e.g., walking, sitting, etc. Results demonstrated a forecast accuracy of 90% is obtained with 550ms of IMU data. In a research study, the psychophysical experiment is conducted to compare the response time for sensational observation methods, e.g., auditory, visual and vibrotactile. It has been observed that the response time is remarkably higher for visual (VF) and auditory feedback (AF) in comparison to vibrotactile feedback (VTF). Moreover, the rate of success is analytically lesser for visual feedback compared to vibrotactile and auditory feedback. Practically, results indicate a prediction of the movement of hand, and timely generation of sensational response in less than a second, so that one does not touch the face, and thus curbing of the spread of COVID-19. © 2022 IEEE.