Version 1
: Received: 4 August 2024 / Approved: 5 August 2024 / Online: 5 August 2024 (08:24:42 CEST)
How to cite:
Brown, W.; Wilson, G.; Johnson, O. Understanding the Adoption of Advanced Analytics in Supply Chain Decision-Making. Preprints2024, 2024080235. https://doi.org/10.20944/preprints202408.0235.v1
Brown, W.; Wilson, G.; Johnson, O. Understanding the Adoption of Advanced Analytics in Supply Chain Decision-Making. Preprints 2024, 2024080235. https://doi.org/10.20944/preprints202408.0235.v1
Brown, W.; Wilson, G.; Johnson, O. Understanding the Adoption of Advanced Analytics in Supply Chain Decision-Making. Preprints2024, 2024080235. https://doi.org/10.20944/preprints202408.0235.v1
APA Style
Brown, W., Wilson, G., & Johnson, O. (2024). Understanding the Adoption of Advanced Analytics in Supply Chain Decision-Making. Preprints. https://doi.org/10.20944/preprints202408.0235.v1
Chicago/Turabian Style
Brown, W., George Wilson and Oliver Johnson. 2024 "Understanding the Adoption of Advanced Analytics in Supply Chain Decision-Making" Preprints. https://doi.org/10.20944/preprints202408.0235.v1
Abstract
This research explores the adoption of advanced analytics in supply chain decision-making, emphasizing its transformative impact on organizational efficiency and strategic advantage. The study investigates the key drivers behind the adoption of advanced analytics, including the growing complexity of data, the need for agility, and the pursuit of competitive advantage and cost reduction. It identifies significant challenges such as data integration difficulties, high implementation costs, and a shortage of skilled professionals, which organizations face when integrating these sophisticated tools. Despite these challenges, the research highlights the substantial benefits of advanced analytics, including enhanced forecasting accuracy, improved supply chain visibility, optimized processes, and cost savings. The findings indicate that while the implementation of advanced analytics varies across different sectors—such as retail, manufacturing, logistics, and healthcare—the overarching themes of improved efficiency and better decision-making are consistently observed. The study also discusses future trends, noting the integration of emerging technologies such as artificial intelligence, machine learning, and blockchain, which promise to further enhance analytics capabilities and accessibility. The successful adoption of advanced analytics relies not only on technological advancements but also on fostering a data-driven organizational culture, investing in continuous learning, and ensuring robust data governance. As supply chain management continues to evolve, advanced analytics will play a crucial role in driving strategic success and maintaining a competitive edge in a complex and dynamic market environment.
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
