Methodology for Automated Master Data Management using Artificial Intelligence

Riesener, Michael; Kühn, Maximilian; Lender, Benjamin; Schuh, Günther

doi:10.1109/ieem55944.2022.9989629

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…By analyzing patterns in historical sales data, market trends, and even social media sentiment, AI algorithms can forecast future demand more reliably. This precision directly translates to more efficient inventory management, minimizing both overstock and stockout scenarios, thus reducing costs and enhancing customer satisfaction [9].…”

Section: Demand Forecasting and Inventory Managementmentioning

confidence: 99%

Digital Transformation in Supply Chain Management: Artificial Intelligence (AI) and Machine Learning (ML) as Catalysts for Value Creation

Singh

2023

IJSCM

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In the rapidly evolving landscape of supply chain management (SCM), digital transformation has become a cornerstone for achieving competitive advantage. This paper explores the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) as catalysts in this transformation, driving significant value creation across various facets of SCM. Through a comprehensive literature review, including an analysis of 12 key papers, this study examines the integration of AI and ML in enhancing supply chain operations, from predictive analytics in demand forecasting to real-time decision-making in logistics and inventory management. The findings highlight the transformative impact of these technologies in optimizing efficiency, reducing costs, and improving overall supply chain resilience. The paper also addresses the challenges and ethical considerations inherent in implementing AI and ML, such as data privacy and workforce implications. Concluding with a look towards the future, this study underscores the growing importance of AI and ML in shaping the next generation of SCM practices. This research not only contributes to the academic discourse on digital supply chain transformation but also offers practical insights for industry professionals navigating this digital shift.

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Section: Demand Forecasting and Inventory Managementmentioning

confidence: 99%

Digital Transformation in Supply Chain Management: Artificial Intelligence (AI) and Machine Learning (ML) as Catalysts for Value Creation

Singh

2023

IJSCM

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Data Catalogs in an Industrial SME Context – A Systematic Literature Review

Kick,

Rösl,

Auer

et al. 2024

Communications in Computer and Information Science

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Exploring the potential benefits and challenges of artificial intelligence for research funding organisations: a scoping review

Blatch-Jones,

Church,

Crane

2024

Preprint

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Background: Artificial Intelligence (AI) is at the forefront of today’s technological revolution, enhancing efficiency in many organisations and sectors. However, in some research environments, its adoption is tempered by the risks AI poses to data protection, ethics, and research integrity. For research funding organisations (RFOs), although there is interest in the application of AI to boost productivity, there is also uncertainty around AI’s utility and its safe integration into organisational systems and processes. The scoping review explored: ‘What does the evidence say about the current and emerging use of AI?’; ‘What are the potential benefits of AI for RFOs?’ and ‘What are the considerations and risks of AI for RFOs?’ Methods: A scoping review was undertaken with no study, language, or field limits. Due to the rapidly evolving AI field, searches were limited to the last three years (2022-2024). Four databases were searched for academic and grey literature in February 2024 (including 13 funding and professional research organisation websites). A classification framework captured the utility and potential, and considerations and risks of AI for RFOs. Results: 122 eligible articles revealed that current and emerging AI solutions could potentially benefit RFOs by enhancing data processes, administration, research insights, operational management, and strategic decision-making. These solutions ranged from AI algorithms to data management platforms, frameworks, guidelines, and business models. However, several considerations and risks need to be addressed before RFOs can successfully integrate AI (e.g., improving data quality, regulating ethical use, data science training). Conclusion: While RFOs could potentially benefit from a breadth of AI-driven solutions to improve operations, decision-making and data management, there is a need to assess organisational ‘AI readiness’. Although technological advances could be the solution there is a need to address AI accountability, governance and ethics, address societal impact, and the risks to the research funding landscape.

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Methodology for Automated Master Data Management using Artificial Intelligence

Cited by 3 publications

References 15 publications

Digital Transformation in Supply Chain Management: Artificial Intelligence (AI) and Machine Learning (ML) as Catalysts for Value Creation

Digital Transformation in Supply Chain Management: Artificial Intelligence (AI) and Machine Learning (ML) as Catalysts for Value Creation

Data Catalogs in an Industrial SME Context – A Systematic Literature Review

Exploring the potential benefits and challenges of artificial intelligence for research funding organisations: a scoping review

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