Algorithmic Anomaly Detection in R&D Decision-Making: Uncovering Hidden Bias and Missed Innovation Opportunities

While most R&D strategy research focuses on optimizing resource allocation or evaluating outcomes, few studies systematically examine the “unexpected” or anomalous decisions—those projects rejected despite high potential, or areas persistently underfunded. Building on the findings from Verheijen et al. (2024), where invasive testing led to significant alterations in clinical management, this project proposes using advanced anomaly detection algorithms to mine historical R&D decision data. By flagging outlier decisions (e.g., high-potential projects that were deprioritized), the research will investigate the root causes—be they organizational bias, risk aversion, or cultural inertia (see also Dr Gulab Dass Vaishnava, 2025). This approach differs from existing work by focusing on the “deviations from expectation” rather than norm-conforming strategies, offering a quantitative method to surface hidden innovation opportunities and challenge entrenched biases. This could significantly enhance innovation pipelines by making the invisible visible and prompting strategic re-evaluation.

References:

1. Invasive testing leads to significantly altered (surgical) management strategy in coronary anomalies with interarterial course: 3 year interim analysis of MuSCAT. D. Verheijen, F. van der Kley, A. Egorova, M. Jongbloed, D. Koolbergen, M. Hazekamp, M. Beijk, R. de Winter, P. Damman, L. Wagenaar, M. Post, M. Voskuil, J. Jukema, P. Kiès, H. Vliegen (2024). European Heart Journal.
2. Cross-Cultural Perspectives on Innovation Management in Multinational Organizations. Dr Gulab Dass Vaishnava (2025). International Journal of Latest Technology in Engineering Management & Applied Science.