Bullwhip Effect Calculator

It means Manufacturer Order Variance is six times Consumer Demand Variance. Because variance is a squared measure, that translates to order swings about sqrt(6) = 2.45 times as wide as consumer demand's in standard-deviation terms -- still a large distortion, but not six times the swing. A ratio of 1.0 would mean no amplification at all; anything above 1.0 signals the bullwhip effect is present.

Demand CV describes the end-consumer market itself -- it's the square root of Consumer Demand Variance (that is, the standard deviation of consumer demand) divided by Average Consumer Demand, showing how volatile real demand is on its own. The amplification ratios (Retailer, Wholesaler, Manufacturer) instead compare each supply chain tier's order variability to that same consumer demand variance, showing how much the chain distorts, not how volatile the underlying demand actually is.

Theoretical Amplification is a textbook reference point, not a measurement of your data -- it estimates the published lower bound on amplification from a moving-average forecast combined with order batching at your entered Lead Time and Forecast Moving-Average Window, independent of whatever variances you entered. Comparing it against your actual Total Bullwhip Ratio helps you judge whether your real amplification looks consistent with ordinary forecasting-and-batching dynamics or looks unusually severe.

It is the Chen, Drezner, Ryan and Simchi-Levi (2000) lower bound on the order/demand variance ratio for one tier using an order-up-to policy with a moving-average forecast: 1 + 2L/p + 2L^2/p^2, with L = Lead Time and p = Forecast Moving-Average Window. It uses only those two inputs, so it is a benchmark to compare your measured ratios against rather than a result derived from them. Both levers matter and both are quadratic at the tail: at a 4-period lead time a 4-period moving average implies at least 5.0x amplification, while a 12-period average implies only 1.9x.