David L. Olson

David L. Olson is affiliated with the University of Nebraska-Lincoln in the United States. Their research spans multiple areas within business, management, and computer science, focusing particularly on management information systems, artificial intelligence, and operations research.

Their recent scholarly contributions include papers such as Data Analytics for Operational Risk Management (2020) published in Decision Sciences, Role of Analytics for Operational Risk Management in the Era of Big Data (2020) also in Decision Sciences, Technologies and applications of Industry 4.0: insights from network analytics (2021) in the International Journal of Production Research, Financing with preferential credit to coordinate the capital-constraint supply chain (2021) in the International Journal of Production Research, and Who should finance the supply chain? Impact of accounts receivable mortgage on supply chain decision (2023) in the International Journal of Production Economics.

These works underline a consistent engagement with operational risk management, supply chain finance, Industry 4.0 technologies, and big data analytics.

Olson frequently collaborates with other researchers. Some of the most common coauthors include:

• Desheng Wu
• Enrique Herrera-Viedma
• Karen Panetta
• Giancarlo Fortino
• David Mendonça

The scientist's publications appear primarily in these venues:

• IEEE Transactions on Systems Man and Cybernetics Systems
• SSRN Electronic Journal
• Decision Sciences
• International Journal of Production Research
• Information Sciences

David L. Olson has contributed to the field through various book publications, primarily with Springer International Publishing. Notable titles include TOPSIS and its Extensions: A Distance-Based MCDM Approach (2022), Enterprise Risk Management Models (2020 and 2023 editions). They have also published with Springer Nature, authoring Pandemic Risk Management in Operations and Finance (2020), and with Springer Science+Business Media, producing Data Mining and Analytics in Healthcare Management (2023).

Their research covers major topics such as:

• Supply Chain Resilience and Risk Management
• Big Data and Business Intelligence
• Imbalanced Data Classification Techniques
• Data Mining Algorithms and Applications
• Risk and Safety Analysis
• Quality and Supply Management
• Artificial Intelligence in Healthcare

This diverse portfolio demonstrates a focus on both theoretical and applied aspects of risk, supply chains, artificial intelligence, and data analytics within business and management contexts. Their work integrates methodologies from artificial intelligence and information systems to address complex challenges in operational and financial management.