Application of Artificial Intelligence in Supply Chain Management
Khimanych, Dmitrii (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202405079693
https://urn.fi/URN:NBN:fi:amk-202405079693
Tiivistelmä
Modern societies rely heavily on efficient supply chains to deliver products and services. Currently, supply chain management (SCM) faces multiple challenges such as external disruptions, modern technology integration, cost pressures, and sustainability demands. Artificial Intelligence (AI), a significant development from the digital and industrial revolutions, holds tremendous potential to address these challenges.
This thesis aims to explore how AI can be applied in SCM and the value and benefits it provides. Additionally, it seeks to identify the key challenges associated with integrating and utilizing AI in SCM, thus providing a room for proactive preparation strategies.
Data for this study were collected using two main methods: first, through semi-structured interviews with industry professionals, and second, by reviewing and analyzing digitally available interviews related to AI applications in SCM.
The analysis identifies specific areas within SCM where AI has been beneficial, highlighting improvements such as enhanced speed and accuracy, significant cost reductions, streamlined process automation, increased transparency, reduced manual labor, and improved customer satisfaction.
Furthermore, it discusses the obstacles to AI adoption in SCM, such as issues related to data quality, system integration, lack of expertise, resistance to organizational change, and the associated financial challenges.
The study provides a snapshot of the varying levels of AI application in SCM, from minimal to advanced, and offers valuable insights, underscoring the dynamic and transformative impact of AI on supply chain management practices.
This thesis aims to explore how AI can be applied in SCM and the value and benefits it provides. Additionally, it seeks to identify the key challenges associated with integrating and utilizing AI in SCM, thus providing a room for proactive preparation strategies.
Data for this study were collected using two main methods: first, through semi-structured interviews with industry professionals, and second, by reviewing and analyzing digitally available interviews related to AI applications in SCM.
The analysis identifies specific areas within SCM where AI has been beneficial, highlighting improvements such as enhanced speed and accuracy, significant cost reductions, streamlined process automation, increased transparency, reduced manual labor, and improved customer satisfaction.
Furthermore, it discusses the obstacles to AI adoption in SCM, such as issues related to data quality, system integration, lack of expertise, resistance to organizational change, and the associated financial challenges.
The study provides a snapshot of the varying levels of AI application in SCM, from minimal to advanced, and offers valuable insights, underscoring the dynamic and transformative impact of AI on supply chain management practices.