ABSTRACT
We study the impact of COVID-19 on the pairwise dependence between three indices, the COVID-19 Media Coverage Index, MSCI World Semiconductor Index, and the MSCI World Energy Index, as well as investigate the respective volatility spillovers. We find intervals of weak, moderate, and strong coherence between the Media Coverage Index and returns and volatility of semiconductor and energy sector companies. Low coherence intervals indicate a diversification potential of investments in these sectors and in their volatility-based products during periods of systemic crises such as the financial turmoil induced by COVID-19. Our results provide evidence that after the escalation of the pandemic in early 2020, the energy sector cedes its leading role in terms of volatility to the semiconductor industry. We report on appealing hedging attributes related to the decoupling between the trends in the global semiconductor industry and the global energy sector accelerated by the COVID-19 triggered crisis.
ABSTRACT
We study the impact of COVID-19 on the pairwise dependence between three indices, the COVID-19 Media Coverage Index, MSCI World Semiconductor Index, and the MSCI World Energy Index, as well as investigate the respective volatility spillovers. We find intervals of weak, moderate, and strong coherence between the Media Coverage Index and returns and volatility of semiconductor and energy sector companies. Low coherence intervals indicate a diversification potential of investments in these sectors and in their volatility-based products during periods of systemic crises such as the financial turmoil induced by COVID-19. Our results provide evidence that after the escalation of the pandemic in early 2020, the energy sector cedes its leading role in terms of volatility to the semiconductor industry. We report on appealing hedging attributes related to the decoupling between the trends in the global semiconductor industry and the global energy sector accelerated by the COVID-19 triggered crisis.
ABSTRACT
The semiconductor industry has faced supply chain manufacturing shortages that ultimately led to a worldwide chip shortage during the COVID-19 pandemic. These chip manufacturers use sophisticated and advanced manufacturing machinery in their fabs to manufacture chips. As experienced during the pandemic, manufacturing unavailability is often due to the lack of critical manufacturing-related spare parts. This thesis evaluates the effectiveness of machine learning algorithms to identify significant factors contributing to manufacturing part outages (i.e., zero-bin) to keep manufacturing equipment running at total capacity within the organization. We propose clustering methods to segment the data and use logistic regression, logistic lasso regression, and kNN approaches to identify important factors for those parts that could go to zero-bin. Extant research applies classic inventory management strategies based on expenditure, criticality, or usage to manage their parts' inventory throughout the year. Instead, the proposed methods explore whether predefined, static inventory parameters can predict whether a spare part reaches zero bin. To demonstrate the viability of this approach, we present a case study using one year's worth of data from a leading chip manufacturing company. Based on the modeling approaches, a lasso-based logistic regression proved the best predictive model amongst the five clusters with lead-time, current quantity available, days on inventory (usage remained relevant), and the part's reorder point being the most significant parameters. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.
ABSTRACT
This study examines how the mobility of engineers in the semiconductor industry changed after COVID-19. Through the analysis of patent data, differences in the impact on the probability of engineers moving across borders before and after the outbreak were compared. The analysis reveals that before the outbreak, the probability of engineers moving internationally was higher for top-ranked engineers, who accounted for 1% of all engineers, than that for second-, third-, and lowest-ranked engineers. However, after the outbreak, behavioral changes were observed only in top-ranked engineers, so they had a lower probability of mobility than any other rank of engineers.
ABSTRACT
This study evaluates the current situation and challenges of reshoring in the Japanese manufacturing industry, focusing on the semiconductor industry, which once dominated the world. After a recent document analysis and qualitative interviews with firms' representatives and policymakers, it is apparent that the globalised semiconductor industry is unlikely to reshore to Japan even amid supply chain disruptions due to the coronavirus disease 2019 pandemic. A review of Japan's semiconductor-related industries, such as semiconductor manufacturing materials, validated that they are embedded in Asian production networks and need to be optimised within a regionalised production system.