Data Analytics as an Enabler to Strengthen Supply Chain Resilience
Truong, An (2023-12-13)
13.12.2023
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20231215155053
https://urn.fi/URN:NBN:fi-fe20231215155053
Tiivistelmä
This thesis examines the role of data analytics in building supply chain resilience. The aim of the study is to investigate how companies can use data analytics to identify potential supply chain disruptions, mitigate risks, and improve supply chain performance.
To achieve this aim, case studies of a companies that has successfully implemented data analytics in its supply chain operations was conducted. The case study analytics included an examination of the specific tools and techniques used, the data sources and types of data analysed, and the insights gained from the analytics. The study also explored the challenges faced during the implementation of data analytics and analysed the effectiveness of these analytics in building supply chain resilience.
Case firms were selected based on business and product type. This study includes companies with electronic product and component supply chains. Interviewees were selected based on their data-driving experience and supply chain operations exposure. Seven supply chain specialists from six case firms were interviewed semi-structured.
The results show that data analytics provide valuable insights for supply chain management and help companies to proactively identify and mitigate risks. The study also highlights the importance of data quality, data integration, and the need for new skills and capabilities in implementing data analytics in the supply chain. The findings of
4
the study have practical implications for supply chain managers and provide a basis for future research in this area.
Overall, the thesis contributes to the growing body of literature on the role of data analytics in building supply chain resilience and provides insights into the challenges and opportunities associated with implementing these analytics in practice.
To achieve this aim, case studies of a companies that has successfully implemented data analytics in its supply chain operations was conducted. The case study analytics included an examination of the specific tools and techniques used, the data sources and types of data analysed, and the insights gained from the analytics. The study also explored the challenges faced during the implementation of data analytics and analysed the effectiveness of these analytics in building supply chain resilience.
Case firms were selected based on business and product type. This study includes companies with electronic product and component supply chains. Interviewees were selected based on their data-driving experience and supply chain operations exposure. Seven supply chain specialists from six case firms were interviewed semi-structured.
The results show that data analytics provide valuable insights for supply chain management and help companies to proactively identify and mitigate risks. The study also highlights the importance of data quality, data integration, and the need for new skills and capabilities in implementing data analytics in the supply chain. The findings of
4
the study have practical implications for supply chain managers and provide a basis for future research in this area.
Overall, the thesis contributes to the growing body of literature on the role of data analytics in building supply chain resilience and provides insights into the challenges and opportunities associated with implementing these analytics in practice.