Enhancing Risk Management in ERP Project through Structured RAID-Log Analysis : A Mixed-Method Approach to Continuous Learning and Governance
van Dijk, Inge (2025-06-13)
Enhancing Risk Management in ERP Project through Structured RAID-Log Analysis : A Mixed-Method Approach to Continuous Learning and Governance
(13.06.2025)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
suljettu
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2025062674593
https://urn.fi/URN:NBN:fi-fe2025062674593
Tiivistelmä
Introduction – This study explores how a structured analysis of RAID-logs can enhance risk management in ERP projects by supporting early risk detection, continuous learning, and as a result long-term organisational resilience.
Contribution – This study adopts a holistic perspective by combining quantitative and qualitative methods to address the underexplored long-term improvement of risk management practices in ERP implementations, shifting the focus from short-term mitigation to continuous learning through structured RAID-log analysis. It provides actionable insights for project managers by demonstrating how structured RAID-log analysis can improve early risk detection, support ongoing risk evaluation, and strengthen organisational resilience.
Methodology – This study employs an explanatory sequential mixed-methods design, combining quantitative analysis of RAID-log data with qualitative expert interviews to uncover patterns, validate findings, and provide a holistic understanding of how RAID-logs support risk management in ERP projects.
Results – The results reveal significant inconsistencies in how RAID-logs are used across ERP projects, with trends showing that effective RAID practices enable faster resolution, better risk-response alignment, and offer potential for continuous learning when supported by standardized labelling and active monitoring.
Conclusions – This study has shown that RAID-logs contribute to a better understanding of risks and enhance their impact on project risk management by revealing escalation patterns between RAID elements, supporting proactive decision-making, and enabling continuous learning.
Further research – Future research should explore longitudinal studies, and the role of organisational culture, while expanding to large, multi-organisational datasets to better capture RAID-log dynamics and enhance their application through advanced methods like machine learning.
Contribution – This study adopts a holistic perspective by combining quantitative and qualitative methods to address the underexplored long-term improvement of risk management practices in ERP implementations, shifting the focus from short-term mitigation to continuous learning through structured RAID-log analysis. It provides actionable insights for project managers by demonstrating how structured RAID-log analysis can improve early risk detection, support ongoing risk evaluation, and strengthen organisational resilience.
Methodology – This study employs an explanatory sequential mixed-methods design, combining quantitative analysis of RAID-log data with qualitative expert interviews to uncover patterns, validate findings, and provide a holistic understanding of how RAID-logs support risk management in ERP projects.
Results – The results reveal significant inconsistencies in how RAID-logs are used across ERP projects, with trends showing that effective RAID practices enable faster resolution, better risk-response alignment, and offer potential for continuous learning when supported by standardized labelling and active monitoring.
Conclusions – This study has shown that RAID-logs contribute to a better understanding of risks and enhance their impact on project risk management by revealing escalation patterns between RAID elements, supporting proactive decision-making, and enabling continuous learning.
Further research – Future research should explore longitudinal studies, and the role of organisational culture, while expanding to large, multi-organisational datasets to better capture RAID-log dynamics and enhance their application through advanced methods like machine learning.