TY - GEN
T1 - Digital Transformation of Supply Chain Management - Challenges and Strategies for Successfully Implementing Data Analytics in Practice
AU - Brandtner, Patrick
N1 - Publisher Copyright:
© 2024 Owner/Author.
PY - 2024/5/28
Y1 - 2024/5/28
N2 - Digital transformation has been a crucial endeavor for businesses for many years, with Supply Chain Management (SCM) standing out as an area with significant potential for enhancement through new technologies. Data Analytics (DA), in particular, presents numerous opportunities in this context. Nonetheless, the adoption of data-driven decision-making in SCM remains a challenge for many practitioners. This research paper conducts a thorough investigation into the obstacles encountered when integrating Data Analytics into SCM practices. Utilizing a qualitative research approach, the study gathers comprehensive insights from expert interviews, shedding light on the practical challenges organizations face in their digital transformation journeys. Drawing from the empirical evidence obtained through expert discussions and an extensive review of relevant literature, this paper offers both expert-recommended and theoretically grounded strategies to overcome the barriers to digital transformation in SCM. In total. seven key challenge areas are identified, such as issues with data integration and quality, organizational resistance to change, skills shortages among employees, and concerns about the opacity of AI systems and the trustworthiness of their outputs. The paper presents 22 specific recommendation strategies for the successful deployment of Data Analytics in SCM, including the use of explainable AI to enhance trust in analysis outcomes, showcasing successful internal and employee-centric use cases, establishing a Data Analytics function in a centralized, decentralized, or hybrid format, and creating a central role for data governance. By providing actionable strategies, this paper enriches the current knowledge base, aiding practitioners in overcoming digital transformation challenges and maximizing the benefits of Data Analytics in improving SCM efficiency and digitally transforming supply chains.
AB - Digital transformation has been a crucial endeavor for businesses for many years, with Supply Chain Management (SCM) standing out as an area with significant potential for enhancement through new technologies. Data Analytics (DA), in particular, presents numerous opportunities in this context. Nonetheless, the adoption of data-driven decision-making in SCM remains a challenge for many practitioners. This research paper conducts a thorough investigation into the obstacles encountered when integrating Data Analytics into SCM practices. Utilizing a qualitative research approach, the study gathers comprehensive insights from expert interviews, shedding light on the practical challenges organizations face in their digital transformation journeys. Drawing from the empirical evidence obtained through expert discussions and an extensive review of relevant literature, this paper offers both expert-recommended and theoretically grounded strategies to overcome the barriers to digital transformation in SCM. In total. seven key challenge areas are identified, such as issues with data integration and quality, organizational resistance to change, skills shortages among employees, and concerns about the opacity of AI systems and the trustworthiness of their outputs. The paper presents 22 specific recommendation strategies for the successful deployment of Data Analytics in SCM, including the use of explainable AI to enhance trust in analysis outcomes, showcasing successful internal and employee-centric use cases, establishing a Data Analytics function in a centralized, decentralized, or hybrid format, and creating a central role for data governance. By providing actionable strategies, this paper enriches the current knowledge base, aiding practitioners in overcoming digital transformation challenges and maximizing the benefits of Data Analytics in improving SCM efficiency and digitally transforming supply chains.
KW - Best Practices
KW - Data Analytics
KW - Data Literacy
KW - Digital Transformation
KW - Expert Interviews
KW - Supply Chain Management
UR - http://www.scopus.com/inward/record.url?scp=85204929056&partnerID=8YFLogxK
U2 - 10.1145/3675585.3675592
DO - 10.1145/3675585.3675592
M3 - Conference contribution
AN - SCOPUS:85204929056
T3 - ACM International Conference Proceeding Series
SP - 36
EP - 42
BT - ICEEG 2024 - 2024 8th International Conference on E-Commerce, E-Business, and E-Government
PB - Association for Computing Machinery
T2 - 8th International Conference on E-Commerce, E-Business, and E-Government, ICEEG 2024
Y2 - 28 May 2024 through 30 May 2024
ER -