Forecast Uncertainty as a Determinant for the Bullwhip Effect

The Bullwhip Effect, defined as the magnification of demand variability throughout the supply chain, poses a challenge to many firms. One of its contributing factors is inaccurate forecasts used to support demand planning. Forecast errors translate into higher inventory costs at a local level, but also impact other members in the supply chain as their decisions are based on the mis-estimated incoming orders. The conventional measure for the Bullwhip Effect does not reflect how forecast uncertainty evolves in the supply chain. This paper proposes a new metric to serve this purpose, which overcomes many of the limitations of the Bullwhip Ratio: the Ratio of Forecast Uncertainty, which benchmarks the upstream Mean Squared Errors of forecasts aggregated over lead time to the downstream’s. To study the properties and relevance of this measure, an inventory simulation is deployed. Our results indicate that this metric bears a connection to inventory costs at upstream level, and holds more explanatory power than the standard Bullwhip Ratio. Managers can make use of this ratio to better understand the upstream impact of the forecasting process in the entire supply chain and the cost-saving gains achieved by improving it