Providing a measure for bullwhip effect in a two-product supply chain with exponential smoothing forecasts

Sadeghi, Ahmad

doi:10.1016/j.ijpe.2015.07.012

Cited by 17 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chaharsooghi and Sadeghi (2008) used the VAR demand process for two products in a two-level supply chain to investigate the bullwhip effect. This work was further extended by Sadeghi (2015), who investigated the bullwhip effect in a two-product, two-level supply chain. Here, the VAR demand was forecasted using the exponential smoothing method.…”

Section: Literature Review and Contributionmentioning

confidence: 97%

A unified theory of the dynamics of closed-loop supply chains

Hosoda

Disney

2018

European Journal of Operational Research

View full text Add to dashboard Cite

We investigate the dynamics of a closed-loop supply chain with first-order auto-regressive (AR(1)) demand and return processes. We assume these two processes are cross-correlated. The remanufacturing process is subject to a random triage yield. Remanufactured products are considered as-goodas-new and used to partially satisfy market demand; newly manufactured products make up the remainder. We derive the optimal linear policy in our closed-loop supply chain setting to minimise the manufacturer's inventory costs. We show that the lead-time paradox can emerge in many cases. In particular, the auto-and cross-correlation parameters and variances of the error terms in the demand and the returns, as well as the remanufacturing lead time, all influence the existence of the lead-time paradox. Finally, we propose managerial recommendations for manufacturers.

show abstract

Section: Literature Review and Contributionmentioning

confidence: 97%

A unified theory of the dynamics of closed-loop supply chains

Hosoda

Disney

2018

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…SC structures have various properties and different constraints. By using analytical models including mathematical modelling and simulations under different scenarios, the complex structures of SC can be modelled and the effects of IS can be analysed [18,34,43,68].…”

Section: Methodsmentioning

confidence: 99%

The Impact of Information Sharing on Different Performance Indicators in a Multi-Level Supply Chain

2021

View full text Add to dashboard Cite

Enterprises can use different methods/principles to obtain competitive advantages. Information sharing (IS) among supply chain (SC) partners is also one of these methods used in enterprises and it has positive effects on overall system performance like reduced inventory level, decreased cost, bullwhip effects and increased profit. In this paper, our aim is to present the impacts of IS on different costs like ordering, holding and penalty costs of each SC member and total system costs in multi SC. We want to show the effects of sharing different types of information simultaneously or separately on SC partners as cost change. Besides, this paper presents the situation of order quantity estimation according to the proximity of actual order quantity in decentralized or centralized demand sharing. A model is developed to determine IS influence on the cost of SC partners. Various IS scenarios are studied in this paper. The customer demand, warehouse order quantity and warehouse-manufacturer lead time are the shared information of scenarios. Results are tested and analysed by using analysis of variance (ANOVA).The findings of this study show that IS especially simultaneously sharing reduces system costs. Lead time sharing provides the lowest cost between other types of sharing. For every system member, holding cost reduces the most during IS. The more accurate demand forecasting is performed in centralized demand sharing compared to decentralized sharing.

show abstract

“…Second approach, -Beer Game‖ pioneered by Sterman [29] and followed by Croson and Donohue [30][31], Haines et al [32] and Sarkar and Kumar [33] has mainly addressed the behavioral aspect of bullwhip effect. The third approach initiated by Lee [4][5] introduced four main causes of bullwhip effect such as demand forecast updating (also [34][35][36][37] provide more elaborate analysis about forecasting methods), order inflation or order rationing, order batching, and price fluctuation and concludes that these factors methodically impair the performance of supply chains. In addition, Paik and Bagchi [38] identified other four causes of bullwhip effect such as material delays, information delays, purchasing delays and levels of echelons, but only the last one is followed by a very significant statistical result.…”

Section: Literature Review 21 Bullwhip Effectmentioning

confidence: 99%

Modeling Stock-out Loss and Overstocking Loss Generated by Bullwhip Effect

2018

View full text Add to dashboard Cite

Although the literature of the supply chain is teemed with the analysis of the bullwhip effect, few studies regarding the impact of the bullwhip effect or demand distortion on the supply chain profit have been done. Hence, we introduce the concept of Distance to Loss (DL), which is a function of the retailer's selling price, the manufacturer's wholesaler price, the end item's salvage value, the retailer's expected demand and the retailer's variance of demand. This concept can perfectly model both stock-out loss and overstocking loss emanated by the bullwhip effect and combines both the newsvendor model and credit risk concepts. Our findings are based on an experimental design and are profoundly in line with previous research. In particular, our model indicates that variations in demand parameters, retailer's selling price and manufacturer's wholesaler price impinge on the retailer's DL, whereas a slight increase in the salvage value negligibly affect the retailer's DL.

show abstract

Providing a measure for bullwhip effect in a two-product supply chain with exponential smoothing forecasts

Cited by 17 publications

References 24 publications

A unified theory of the dynamics of closed-loop supply chains

A unified theory of the dynamics of closed-loop supply chains

The Impact of Information Sharing on Different Performance Indicators in a Multi-Level Supply Chain

Modeling Stock-out Loss and Overstocking Loss Generated by Bullwhip Effect

Contact Info

Product

Resources

About