Supply chain analytics is the process of using descriptive, predictive, and prescriptive analytics methods to improve supply chain management. Its popularity has been increasing substantially over years due to advances in artificial intelligence and computational technology as well as the critical role supply chain management plays in global economic systems.
Organizations that excel in supply chain management can increase their revenues, reduce operational costs, and benefit their economic systems. However, achieving excellence in supply chain management is a very challenging task that requires professionals to have a clear understanding of (1) supply chain economics, (2) trade-offs, and (3) strategies. Only after organizations become competent along these three dimensions, they can benefit from digital technologies substantially to transform supply chains. Otherwise, and mostly happening in practice, targeted benefits of digital transformation projects do not materialize. In 2021, companies invested $4.1 trillion in digital transformations. Nevertheless, it is estimated that 70% for such investments fail. We have developed some sophisticated tools and methods to ease the adaption of digital technologies in supply chains. We published two academic books to help researchers and practitioners learn about digital transformation and supply chain analytics.
- Supply Chain Analytics: An Uncertainty Modeling Approach by Prof. Isik Bicer (Springer Nature, 2023)
- Re-Imagining Supply Chain Management: Uncovering the Hidden Trade-offs In the Digital Age by Prof. Isik Bicer (Cambridge University Press, 2025)
Please also visit our Blog page to read our posts about general topics of supply chain management and business analytics.
Who are we?
Please feel free to contact us to chat about uncertainties and analytical models used to solve supply chain problems. Visit the About Us page to learn more about the team.