Rogue seasonality, or endogenously generated cyclicality (in variables), is common in supply chains and known to adversely affect performance. This paper explores a technique for sensing rogue seasonality at a supply chain echelon level. A signature and index based on cluster profiles of variables, which are meant to sense echelon-level generation and intensity of rogue seasonality, respectively, are proposed. Their validity is then established on echelons of a downstream coffee supply chain for five stock keeping units (SKUs) with contrasting rogue seasonality generation behaviour. The appropriateness of spectra as the domain for representing variables, data for which is daily sampled, is highlighted. Time-batching cycles which could corrupt the sensing are observed in variables, and the need to therefore filter them out in advance is also highlighted. The knowledge gained about the echelon location, intensity and time of generation of rogue seasonality could enable timely deployment of specific mitigation actions
