Prescriptive Data Analytics in Digital Supply Chain Management: SAP Optimizer Feasibility Study
Hietalahti, Taneli (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024051411645
https://urn.fi/URN:NBN:fi:amk-2024051411645
Tiivistelmä
In recent years, supply chains have been increasingly disrupted by a variety of factors, including global pandemics, political conflicts, and even regional challenges like labor strikes. These disruptions have had a significant impact on pricing, product availability, and lead times. Given this ever-changing landscape, organizational resilience is becoming increasingly important for maintaining a competitive edge. In response to these challenges, this research investigates the potential of prescriptive data analytics to improve decision-making capabilities within a supply chain context.
This study examines the impact of digitalization, knowledge management, and data analytics on supply chain performance. It investigates the factors driving digital transformation in supply chains, the potential of knowledge management to enhance organizational effectiveness, and the opportunities and challenges presented by data analytics for supply chain organizations. Specifically, the research aims to under-stand the benefits of advanced data analytics such as prescriptive analytics for modern supply chains, assess the current level of data analytics adoption within the target organization, and evaluate the organization's readiness to implement more sophisticated prescriptive analytics tools.
A case study approach was employed to gain a deeper understanding of how a specific organization utilizes data analytics in its supply chain planning. Semi-structured interviews were conducted with members of the organization's supply chain planning team and a partner from an SAP consultant firm. The interview format allowed for flexibility and exploration of the research questions, while a predetermined thematic framework ensured data collection remained focused. The framework addressed current data analytics use, challenges and opportunities, and the organization's future vision.
The findings indicated that prescriptive data analytics has the potential to significantly enhance supply chain optimization. However, the current utilization of data analytics within the target organization is limited, primarily employed for broader tactical planning horizons. The identified challenges include the perception of limited added value for operational tasks, data quality issues, and the user-friendliness of the existing data analytics system. These findings suggest that the successful implementation of prescriptive data analytics necessitates addressing the current limitations.
This study examines the impact of digitalization, knowledge management, and data analytics on supply chain performance. It investigates the factors driving digital transformation in supply chains, the potential of knowledge management to enhance organizational effectiveness, and the opportunities and challenges presented by data analytics for supply chain organizations. Specifically, the research aims to under-stand the benefits of advanced data analytics such as prescriptive analytics for modern supply chains, assess the current level of data analytics adoption within the target organization, and evaluate the organization's readiness to implement more sophisticated prescriptive analytics tools.
A case study approach was employed to gain a deeper understanding of how a specific organization utilizes data analytics in its supply chain planning. Semi-structured interviews were conducted with members of the organization's supply chain planning team and a partner from an SAP consultant firm. The interview format allowed for flexibility and exploration of the research questions, while a predetermined thematic framework ensured data collection remained focused. The framework addressed current data analytics use, challenges and opportunities, and the organization's future vision.
The findings indicated that prescriptive data analytics has the potential to significantly enhance supply chain optimization. However, the current utilization of data analytics within the target organization is limited, primarily employed for broader tactical planning horizons. The identified challenges include the perception of limited added value for operational tasks, data quality issues, and the user-friendliness of the existing data analytics system. These findings suggest that the successful implementation of prescriptive data analytics necessitates addressing the current limitations.