Effects of time aggregation, product aggregation, and seasonality in measuring bullwhip ratio

TL;DR

This paper investigates how time aggregation, product correlation, and seasonality affect the measurement of the bullwhip ratio, revealing complex interactions and clarifying conflicting prior results.

Contribution

It provides a detailed decomposition of the bullwhip ratio considering these factors, offering new insights into their individual and combined effects.

Findings

Time aggregation can increase, decrease, or maintain the bullwhip ratio.

Uncorrelated products' bullwhip ratio averages out with demand variance weights.

Seasonality acts as a standalone factor with a bullwhip ratio of one.

Abstract

The bullwhip study has received a lot of attention in the literature, but with conflicting results, especially in the context of data aggregation. In this paper, we investigate three widely studied factors in bullwhip measurement: time aggregation, product aggregation, and seasonality. In time aggregation, we decompose the variance into two components: the expectation of the subset variances and the variance of subset expectations, thus decomposing the bullwhip ratio into four components to explore the underlying mechanism of time aggregation. In product aggregation, the bullwhip ratio is analyzed in the context of products with either uncorrelated or correlated demands and orders. Seasonality is also examined to study its effect on the bullwhip ratio. Our key findings are: (a) Time aggregation can increase, decrease, or maintain the bullwhip ratio in different scenarios. (b) Aggregated bullwhip ratio of uncorrelated products is a weighted average of bullwhip ratios from individual products, with corresponding demand variance as the weights. However, aggregated bullwhip ratio of correlated products could break the boundaries. (c) Seasonality can be considered as a standalone product with a bullwhip ratio of one, which can drive the overall bullwhip ratio closer to one.