Cost Differential Frontier Calculator

Switch to Cost Premium

Cost Premium Frontier Calculator

Switch to Cost Differential

Volatility Calculator

What is the real value of producing close to demand?

This calculator provides an estimate of full-lead-time demand volatility assuming that the forecast-evolution process is smooth. If there are sudden jumps in the demand forecast, or if information about demand arrives in clusters, the actual cost of lead time will be higher than what is estimated using the number here calculated, and these results should be interpreted as a lower bound.

The Cost-Differential Frontier uses quantitative finance techniques to calculate the mismatch cost arising from long lead times, answering the question "How much cheaper does a long-lead-time supplier have to be to compensate for the increase in demand-volatility exposure?". The Cost-Differential Frontier curve shows how the cost differential required to compensate for the increase in demand-volatility exposure increases as the relative lead time increases from 0 (the order is placed when demand is fully known, corresponding to the make-to-order cost) to 1 (the longest lead time under consideration).

The Cost-Premium Frontier begins from the cost offered by the supplier with the longest lead time, and answers the question "What cost premium should we be willing to pay to completely eliminate the exposure to demand volatility that comes from a long decision lead time?" The Cost-Premium Frontier curve shows how the cost premium worth paying increases as the Relative Lead Time decreases from 1 (the longest lead time under consideration) to 0 (demand is fully known).

This video further elaborates the basic concepts underlying the tool. The CDF was developed by Professors Suzanne de Treville and Norman Schürhoff of OpLab at the University of Lausanne's Faculty of Business and Economics (HEC). The underlying theory and publications describing its development are available at

This version of the CDF calculator assumes that the demand forecast evolves with a constant instantaneous volatility.


© 2014 OpLab, HEC Lausanne
University of Lausanne