Measuring the bullwhip effect for supply chains with seasonal demand components

Nagaraja, Chaitra H.; Thavaneswaran, A.; Appadoo, S. S.

doi:10.1016/j.ejor.2014.10.022

Cited by 39 publications

(13 citation statements)

References 26 publications

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“…Most bullwhip effect research on perishable product using simulation, because the age of product based on time is not fixed. (Appendix A) [8], [10], [11], [12], [13], [16], [17], [20], [21], [22], [24], [25], [28], [29], [30], [31], [32], [34], [37], [38], [39], [40], [42] Simulation [6], [9], [11], [14], [7], [15], [18], [19], [23], [25], [26], [27], [28], [33], [35], [41], [43] C. Identification Presentage of Perishable Product and Non-perishable Product Journal After searching from several related journals, it is known that of the 38 journals 7 of them are journals about perishable products. From the minimum number of existing numbers, the authors are interested in developing a model for mitigating bullwhip effect on perishable product.…”

Section: B Identification Evaluation Methodsmentioning

confidence: 99%

Bullwhip Effect in Supply Chain for Perishable Product (A Systematic Literature Review)

Novitasari¹,

Damayanti²

2018

JOAMS

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Bullwhip effect is the most feared phenomenon by the majority of companies because it leads to high operating costs. Bullwhip will also grow along with the increase in network tier or echelon of a distribution system. Bullwhip will become more severe when distributed products are fragile food products and have uncertain lead times and demand. In the systematic literature of this review, we will review the previous research on how to minimize the occurrence of the bullwhip effect. We also compared the differences and similarity models between common bullwhip mitigating models with bullwhip mitigating models for perishable products that have short life cycles. In further research, we will create optimal model to mitigate bullwhip, integrate the use of SCOR Framework to assess supply chain performance in food companies to minimize bullwhip effect. By using SCOR framework, we will be able to know the supply chain structure that is priority to be improved so that bullwhip on perishable product will decrease.1 Index Terms-bullwhip effect, cause bullwhip effect, variable of bullwhip effect, perishable product, systematic literature-review (SLR).

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Section: B Identification Evaluation Methodsmentioning

confidence: 99%

Bullwhip Effect in Supply Chain for Perishable Product (A Systematic Literature Review)

Novitasari¹,

Damayanti²

2018

JOAMS

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“…Mathematical modeling techniques such as quadratic programming (Fu et al, 2014), multi-objective optimization, goal programming (Dhahri and Chabchoub, 2007), differential equations, theory of constraints (Costas et al, 2014), genetic algorithm (Tosun et al, 2013) and fuzzy logic (Shore and Venkatachalam, 2003) are also used to predict the dynamic nature of supply chain. Price and demand management is also used to deal with bullwhip effect in various scenarios such as direct and substitute product (Duan et al, 2015), seasonal demand (Nagarajaa et al, 2015), agent-based supply chain (Blos et al, 2015), discount (Sodhi et al, 2014), product life cycle (Nepal et al, 2012) and demand and supply elasticity are also used to predict the complex nature of bullwhip effect. Machine learning is used to identify the set of operational and financial variables affecting bullwhip effect .…”

Section: Jgoss 132mentioning

confidence: 99%

Predicting the impact of operational and financial variables on bullwhip effect using threshold regression: Indian context

Gupta¹,

Saxena

2020

JGOSS

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Purpose The operational aspects of supply chain, when handled correctly, results in diminishing the impact of the bullwhip effect. The purpose of this study is to analyze the impact of operational and financial variables on the bullwhip effect. Various operational factors that contribute to the bullwhip effect in a supply chain are identified and their impact on variability in production is measured at manufacturer’s end in the supply chain. Design/methodology/approach Ten different sectors of the Indian economy are identified and analyzed on the basis of bullwhip effect. The ratio of change in production with respect to change in demand is taken as a metric to measure the bullwhip effect. Initially, the impact of identified variables on bullwhip effect is analyzed using the linear regression analysis and then to gain more insights, the threshold regression model is applied according to the change in bullwhip ratio. Findings The study identifies four threshold regions in which bullwhip ratio is changing its slope considerably. The operational and financial variables impacting bullwhip effect differently in these four regions provide useful insights about how the variables are impacting the bullwhip effect. Research limitations/implications Past 11 years of observations on identified operational and financial variables are studied for ten different sectors. The operational and financial variables are identified on basis of available literature but may not be exhaustive in nature. Practical implications The present study implies that the emphasis must be given to the magnitude of the bullwhip ratio. Strategies must be adopted that result in mitigation of bullwhip effect. Such mitigation strategies must not only be restricted on the basis of type of product or sector, perhaps they must be on the basis of threshold region of bullwhip ratio. Originality/value The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of threshold regression considering the bullwhip ratio as a threshold variable.

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“…La tendencia en el estudio del efecto látigo apunta al análisis del efecto del error humano en la toma de decisiones y el impacto de la colaboración entre empresas en la red de suministro [26].…”

Section: Medición Efecto Látigounclassified

Medición del efecto látigo en redes de suministro

Rodríguez

Acosta²,

Obregón³

et al. 2016

ingeniare

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RESUMENLa variabilidad de la demanda que se origina de un agente a otro en la cadena de suministro, aumenta o amplifica a medida que la señal se aleja del punto de origen; estas distorsiones se denominan Efecto Látigo. En este artículo se aborda una red de suministros multieslabón, que estima los intervalos de medición de la distorsión de la demanda de la red, de acuerdo a comportamientos extremos en el sistema que permiten diagnosticar su desempeño en términos de la amplificación de demanda. Para la investigación se aplican mediciones estáticas locales y globales del efecto látigo, en cada nivel de la red de suministros del juego de la cerveza de cuatro etapas; cada etapa o eslabón tiene múltiples nodos. Tradicionalmente, esta medición se ha realizado en un nodo a la vez, o en el caso de mediciones globales se asume un solo nodo en cada eslabón del sistema. La medición en este estudio se realiza a una red de suministro no lineal, en la que, a diferencia de casos anteriores, se tienen dos o más nodos por cada eslabón, lo que representa un avance en la medición del efecto látigo en sistemas de mayor complejidad. Palabras claves: Efecto látigo, amplificación de demanda, indicadores cadena de suministro, redes de suministro, juego de la cerveza. ABSTRACTThe variance demand originating from one agent to another is amplified as the demand signal moves away from the point of origin, these distortions are called bullwhip effect. This paper looks for a multi echelon supply network, estimating intervals distortion measurement of network demand, according to extreme behavior in the system, which will diagnose their performance in terms of demand amplification. In this research of the bullwhip effect static local and global measurements at each level of a four-stage beer game supply network, where each stage or echelon has multiple nodes. Traditionally, this measurement has been implemented on a node at a time, or in the case of global measures a single node is assumed for in the system. Measurement is performed in a non-linear supply chain, where there are more than two nodes per echelon, increasing the understanding of measuring the bullwhip effect in complex systems.

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Measuring the bullwhip effect for supply chains with seasonal demand components

Cited by 39 publications

References 26 publications

Bullwhip Effect in Supply Chain for Perishable Product (A Systematic Literature Review)

Bullwhip Effect in Supply Chain for Perishable Product (A Systematic Literature Review)

Predicting the impact of operational and financial variables on bullwhip effect using threshold regression: Indian context

Medición del efecto látigo en redes de suministro

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