The bullwhip effect in a 3-stage supply chain considering multiple retailers using a moving average method for demand forecasting

Khosroshahi, Hossein; Husseini, S.M. Moattar; Marjani, Mohammad Reza

doi:10.1016/j.apm.2016.05.033

Cited by 33 publications

(12 citation statements)

References 60 publications

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“…Costantino et al [43] thought that information could not be shared is the root cause of the BWE; besides, inaccurate predictions and high-security stocks are factors that produce the BWE. A substantive amount of the literature shows that information sharing among supply chain participants can considerably alleviate the BWE [44,45] because the most crucial reason for the BWE is the existence of uncertain demand and information asymmetry [46]. Tai et al [25] paid much attention to SCC with the BWE and stochastic demand, but they did not consider the environment of DTs.…”

Section: Supply Chain Coordination Considering the Bullwhipmentioning

confidence: 99%

Coordination Mechanism of Supply Chain considering the Bullwhip Effect under Digital Technologies

Ran

Yan

Yang

et al. 2020

Mathematical Problems in Engineering

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The bullwhip effect (BWE) and stochastic demand coexist in supply chain operation management, which leads to a lower performance. In recent years, with the development of blockchain, cloud computing, big data, and other digital technologies (DTs), many enterprises hope to use them to weaken the BWE and improve profits. This paper focuses on improving the application level of DTs in the supply chain through coordination for enhancing the performance. We consider a supply chain with one-supplier-one-retailer and coordinate it with the cost-sharing contract and revenue-cost-sharing contract (the improved contract). We find out that the retailer and supplier did not always have the willingness to use DTs to alleviate the BWE. Even if they have the intention to use DTs, their ideal application level is not the same. After coordination, both the application level of DTs and the performance of the supply chain are improved, and we find that the improved contract has a better coordination effect. Finally, we validate our model by numerical simulation.

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Section: Supply Chain Coordination Considering the Bullwhipmentioning

confidence: 99%

Coordination Mechanism of Supply Chain considering the Bullwhip Effect under Digital Technologies

Ran

Yan

Yang

et al. 2020

Mathematical Problems in Engineering

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“…Instead of the variance ratio of demand to order, the ratio of coefficient of variation is used to measure the bullwhip effect (Trapero and Pedregal, 2016). Later, many other ratios such as inventory to demand, shipment ratio and manufacturing ratio (Khosroshahi et al, 2016;Cannella et al, 2013) were introduced to measure the bullwhip effect. Other methods such as Financial variables on bullwhip effect continues ratio method (Trapero and Pedregal, 2016) and Lyapunov exponent (Makui and Madadi, 2007) are also used to measure the bullwhip effect.…”

Section: Variablesmentioning

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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“…In supply chain management, a typical approach to demand forecasting involves the use of statistical tools, in the form of exponential smoothing, time series regression and autoregressive and integrated moving average (ARIMA) models (Fildes et al, 2009;Silva Filho et al, 2013;Babai et al, 2013;Khosroshahi et al, 2016;Huber et al, 2017). These models all assume linear relationship between independent and dependent variables, which is not always the case for demand forecasting in retail distribution (Gruen and Corsten, 2007).…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling wholesale distribution operations: an artificial intelligence framework

Bottani

Centobelli

Gallo

et al. 2019

IMDS

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Purpose The purpose of this paper is to propose an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks (OOSs) by jointly formulating price policies and forecasting retailer’s demand. Design/methodology/approach The framework is based on the cascade implementation of two artificial neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a real wholesale supply chain. Findings The application of the ANN framework to a real wholesale supply chain shows that the proposed methodology has the potential to decrease economic loss due to OOS occurrence by more than 56 percent. Originality/value The combined use of ANNs is a novelty in supply chain operation management. Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to more dependable demand forecasts.

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The bullwhip effect in a 3-stage supply chain considering multiple retailers using a moving average method for demand forecasting

Cited by 33 publications

References 60 publications

Coordination Mechanism of Supply Chain considering the Bullwhip Effect under Digital Technologies

Coordination Mechanism of Supply Chain considering the Bullwhip Effect under Digital Technologies

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

Modelling wholesale distribution operations: an artificial intelligence framework

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