Case Studies & Resources

Kans Analytics, and its individual team members, have helped dozens of clients in decision-driven analytics. While most of our work is confidential, some of our team’s past work and projects have been published, a select few excerpts of which we have listed below.

Check Out Some of our Past Case Studies & Resources:

Index insurance handbook

In developing countries, smallholder farmers are often highly exposed to droughts and floods. Traditional insurance often proves too costly and slow, but index insurance—triggering payouts based on rainfall or satellite indicators—offers a practical and cheaper alternative.

We’ve supported the International Finance Corporation (part of the World Bank) with risk modeling that links weather data with crop yield losses to design and better understand affordable and sustainable products. Dr. Huybert Groenendaal co-authored the World Bank’s handbook on this topic.

References

Reference: “Mapfumo, Shadreck; Groenendaal, Huybert; Dugger, Chloe. 2017. Risk Modeling for Appraising Named Peril Index Insurance Products: A Guide for Practitioners. Directions in Development; Directions in Development—Agriculture and Rural Development. © World Bank.

Understanding risk of organ transplantation from pigs

Xenotransplantation, using pig organs for human transplants, could help solve organ shortages but raises concerns about infections from pig microorganisms and parasites (MP). Dr. Groenendaal co-led a comprehensive study using a risk-based framework, in which a large team of scientists evaluated over 250 MP. This systematic approach—the first of its kind—helps guide surveillance, screening, and monitoring strategies to protect both donor pigs and vulnerable human recipients, making xenotransplantation a safer option for addressing critical organ shortages.

References

Reference: Groenendaal H, Costard S, Ballard R, Bienhoff S, Challen DC, Dominguez BJ, Kern DR, Miller D, Noordergraaf J, Rudenko L, Schuurman HJ, Spizzo T, Sturos M, Zollers B, Fishman JA. Expert opinion on the identification, risk assessment, and mitigation of microorganisms and parasites relevant to xenotransplantation products from pigs. Xenotransplantation. 2023 Sep-Oct;30(5):e12815. doi: 10.1111/xen.12815. Epub 2023 Aug 24. PMID: 37616183.

Risk analysis to strengthen GlaxoSmithKline’s drug development decisions

Model-based drug development is transforming how pharmaceutical companies bring new medicines to market by improving efficiency, managing uncertainty, and enhancing decision quality. Dr. Huybert Groenendaal contributed to a GlaxoSmithKline team by applying probabilistic risk analysis to integrate uncertain data and expert opinion, strengthening strategic decisions and advancing the drug development process.

References

1. Mudd, P.N., Jr, Groenendaal, H., Bush, M.A. and Schmith, V.D. (2010), Probabilistic Risk Analysis: Improving Early Drug Development Decision Making. Clinical Pharmacology & Therapeutics, 88: 871-875.
2. Johnson, B., Groenendaal H., & Mudd, P.N. Jr. Novel application of Bayesian model averaging for more robust PK/PD simulations and improved decision making [abstr]. Annual Meeting of the American Society for Clinical Pharmacology and Therapeutics, Atlanta, GA, 17–20 March 2010.

Equipping organizations to make smarter choices under uncertainty

Dr. Huybert Groenendaal co-authored two influential textbooks—Practical Spreadsheet Risk Modeling for Management and Practical Spreadsheet Modeling Using @Risk—written to help organizations make better business decisions when facing uncertainty. These books emphasize building models around key decisions, applying proven methods such as Monte Carlo simulation, and clearly communicating results. By making advanced risk analysis practical and accessible, they provide managers and analysts with tools to improve planning, investments, and strategic choices.

References

1. Lehman, D., Groenendaal, H., & Nolder, G. (2012). Practical Spreadsheet Risk Modeling for Management (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b11254
2. Lehman, D., & Groenendaal, H. (2019). Practical Spreadsheet Modeling Using @Risk (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9780429056444

Turning uncertainty into profitable collaboration

Forming the right partnerships can make or break long-term profitability, yet such decisions are often clouded by uncertainty. We worked with a client to evaluate the risks and profitability of potential business alliances. By using simulation modeling to capture uncertainties in market demand, cost-sharing, and partner performance, we quantified risks and returns under different partnership structures. The results gave leadership a clear view of trade-offs, enabling them to negotiate confidently and select the partnership most likely to create lasting value.

References

Lessons from Oil and Chemical Case Studies: How to Build Accurate and Decision-Focused @RISK models. Webinar given by Dr. Huybert Groenendaal, February 15, 2016. https://youtu.be/RJ2U73CZcWQ?si=7OSMo5FdiDhahpe0.

Optimizing resources to unlock smarter investments

In a project investment case, Dr. Groenendaal used optimization modeling to guide a client through complex trade-offs. By balancing costs, timing, and resource constraints, we identified strategies that maximized value while managing risk. The results gave leadership a clear roadmap for selecting the most efficient investment mix. This same optimization approach can be applied across many areas—such as supply chains, portfolio management, and strategic planning—where making the best use of limited resources drives better decisions.

References

Measuring What Matters

It’s hard to manage or motivate action on a problem when its magnitude is unknown. The economic impact of liver abscesses in cattle was long recognized but poorly quantified because of scientific, technical, and economic uncertainties.

In a 2025 study that was co-authored by Dr. Groenendaal, stochastic modeling was used to integrate data from numerous studies, expert assessments, and market information. The model calculated the national losses of liver abscesses —along with the uncertainty in the estimate. This clarity provided a stronger foundation for prioritizing investment, research, and prevention. More broadly, stochastic modeling offers a powerful framework for quantifying uncertain quantities across science, business, and public policy—transforming vague concerns into measurable risks that can be managed and acted upon.

References

Daniel D Taylor, Huybert Groenendaal, Paul S Morley, Francisco Zagmutt, Economic costs of liver abscesses in U.S. beef feedlot cattle: a comprehensive economic analysis, Journal of Animal Science, Volume 103, 2025, skaf127, https://doi.org/10.1093/jas/skaf127.