Enna Hirata
Biographic Data
| ID | 6958747 |
|---|---|
| NAME | Enna Hirata |
| GIVEN NAMES | Enna |
| FAMILY NAME | Hirata |
| SIGNATURE | HIRATA E |
| AFFILIATIONS | Kobe University |
| ORCID | 0000-0002-3127-3170 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Measuring and predicting supply chain resilience under disaster and pandemic disruptions
This study investigates the supply chain resilience (SCR) of Japanese automotive manufacturers by integrating perspectives from disaster risk and operational disruption. A hybrid analytical framework that combines a logistic regression (LR) scorecard and a long short-term memory (LSTM) model is employed. This framework uses publicly available data from 2015 to 2024, which consists of information from seven major automakers. Resilience is assessed…
Exploring decarbonization priorities for sustainable shipping
The shipping industry is currently the sixth largest contributor to global emissions, responsible for one billion tons of greenhouse gas emissions. Urgent action is needed to achieve carbon neutrality in the shipping industry for sustainability. In this paper, we use natural language processing techniques to analyze policies, announcements, and position papers from national and international organizations related to the decarbonization of shippin…
No prominent works on this page.
Exploring decarbonization priorities for sustainable shipping
The shipping industry is currently the sixth largest contributor to global emissions, responsible for one billion tons of greenhouse gas emissions. Urgent action is needed to achieve carbon neutrality in the shipping industry for sustainability. In this paper, we use natural language processing techniques to analyze policies, announcements, and position papers from national and international organizations related to the decarbonization of shippin…
Measuring and predicting supply chain resilience under disaster and pandemic disruptions
This study investigates the supply chain resilience (SCR) of Japanese automotive manufacturers by integrating perspectives from disaster risk and operational disruption. A hybrid analytical framework that combines a logistic regression (LR) scorecard and a long short-term memory (LSTM) model is employed. This framework uses publicly available data from 2015 to 2024, which consists of information from seven major automakers. Resilience is assessed…
Archaeology (1 work) · Automotive industry (1 work) · Balanced scorecard (1 work) · Business (1 work) · COVID-19 Pandemic Impacts (1 work) · Economics (1 work) · Environmental economics (1 work) · Environmental Science (1 work) · Geography (1 work) · Maritime Ports and Logistics (1 work)