Rogue seasonality in supply chains: an investigation and a measurement approach

Abstract

Purpose -Shukla et al (2012) proposed a signature and index to detect and measure rogue seasonality in supply chains, but which however, were not effectively validated. The authors have sought to investigate rogue seasonality using control theory and realistic multi echelon systems and rigorously validate these measures, so as to enable their application in practice. The paper aims to discuss these issues.Design/methodology/approach -Frequency domain analysis of single echelon and simulated four echelon Beer game system outputs are used in the investigation, with the simulation incorporating realistic features such as non-linearities from backlogs and batching, hybrid make to order-make to stock ordering system and the shipment variable. Lead time, demand process parameters, ordering parameters and batch size are varied in the simulation to rigorously assess the validity of the index.Findings -The signature based on the cluster profiles of variables, specifically whether the variables cluster together with or away from exogenous demand, was validated. However, a threshold for the proportion of variables that could be clustered with exogenous demand and the system still being classified as exhibiting rogue seasonality, would require to be specified. The index, which is derived by quantifying the cluster profile relationships, was found to be a valid and robust indicator of the intensity of rogue seasonality, and which did not need any adjustments of the kind discussed for the signature. The greater effectiveness of the frequency domain in comparison to time for deriving the signature and index was demonstrated.Practical implications -This work enables speedy assessment of rogue seasonality in supply chains which in turn ensures appropriate and timely action to minimize its adverse consequences.Originality/value -Detailed and specific investigation on rogue seasonality using control theory and Beer game simulation and rigorous validation of the signature and index using these methods.

Sensing endogenous seasonality in the case of a coffee supply chain

“…Three studies, Thornhill and Naim (2006), Shukla, Naim, and Thornhill (2012) and Shukla and Naim (2015), have explored sensing of rogue seasonality, with 'sense and respond' (Haeckel 1999) as the underlying approach. While Thornhill and Naim's (2006) sensing technique has subjective elements, Shukla, Naim, and Thornhill's (2012) approach, that is based on a signature to assess presence/generation and an index to indicate the intensity of rogue seasonality in a supply chain is more objective, versatile and automation-friendly.…”

“…Spectra or amplitudes of sinusoids at different frequencies (after Fourier transform) was identified to be the most appropriate domain for representing variables when deriving the signature and the index. While Shukla, Naim, and Thornhill (2012) used different simulated contexts and an empirical steel context, Shukla and Naim (2015) established the signature's and index's validity with a more complex supply chain system that incorporated backlogs and quantity batching in ordering and shipping and considered more realistic ordering policies. While the index was seen to be valid as such, the signature required tweaking in the form of a threshold for proportion of variables that could be clustered with exogenous demand (and the context to still be classified as having rogue seasonality present/generated) needing to be specified.…”

“…Another deficiency of the previous work is that the signature's and index's effectiveness is not established for sensing rogue seasonality at different time intervals (in a monitoring mode); rather, the related evaluations in Shukla, Naim, and Thornhill (2012) and Shukla and Naim (2015) are done at one time with all available data. Success with the former could enable earlier detection of rogue seasonality, and thereby in quicker initiation of corrective response/s to ultimately lower the economic consequences of rogue seasonality.…”

“…Finally, the signature and index in Shukla, Naim, and Thornhill (2012) and Shukla and Naim (2015) are based on upstream supply chain contexts only; downstream or distribution/production-distribution contexts, whose dynamic characteristics are more complex are not considered. Even Shukla and Naim's (2015) work, which is based on the Beer game supply chain (Sterman 1989) that is technically a production-distribution or downstream context, does not incorporate these complexities, which are: a) Time-batching related cyclicality in ordering/despatch.…”

“…One way of managing rogue seasonality, as suggested by Shukla, Naim, and Thornhill (2012) and Shukla and Naim (2015) and discussed in resilience terms by Purvis et al (2016), could be through 'sense and respond' (Haeckel 1999), where 'sense' means assessing a problem context through system information and appropriate models/filters/data-mining techniques, and 'respond', to the subsequent initiation of suitable corrective action/s. Their suggested approach for sensing rogue seasonality involves a signature and an index, where the signature is to indicate if rogue seasonality is present/generated in a supply chain, and the index, to provide a measure of its intensity.…”