Coordinating of multi-echelon supply chains through the generalized (R, S) policy

SIMULATION ◽  
2020 ◽  
Vol 96 (9) ◽  
pp. 767-778 ◽  
Author(s):  
Ahmed Shaban ◽  
Francesco Costantino ◽  
Giulio Di Gravio ◽  
Massimo Tronci
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Information Sharing ◽  
Negative Impact ◽  
Bullwhip Effect ◽  
Coordination Mechanism ◽  
Chain Model ◽  
Demand Forecast ◽  
Modeling Methodology ◽  
Information Demand

Numerous studies have confirmed the negative impact of the lack of coordination on supply chain performance. In particular, the lack of coordination leads to the bullwhip effect, which has severe impacts on supply chain stability. This paper evaluates a proposed coordination mechanism that allows a decentralized information sharing in multi-echelon supply chains. The proposed mechanism “Info-Smooth” utilizes the ordering rule of the generalized (R, S) policy in which a replenishment order can be transferred to upstream echelons including two pieces of information (demand forecast and inventory balance). As the generalized (R, S) can allow order smoothing, Info-Smooth combines the power of both information sharing and order smoothing. A simulation modeling methodology is employed to investigate the effectiveness of Info-Smooth in a multi-echelon supply chain. The factorial design results have shown that Info-Smooth is successful in mitigating the bullwhip effect whilst keeping acceptable inventory stability, compared to the traditional supply chain model.

Investigating the impact of inventory inaccuracy on the bullwhip effect in RFID-enabled supply chains using colored petri nets

2019 ◽  
Vol 14 (2) ◽  
pp. 360-384 ◽  
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Petri Nets ◽  
Information Sharing ◽  
Bullwhip Effect ◽  
Colored Petri Nets ◽  
Content Type ◽  
Multi Stage ◽  
The Impact ◽  
Serial Supply Chains

PurposeInformation distortion results in demand variance amplification in upstream supply chain members, known as the bullwhip effect, and inventory inaccuracy in the inventory records. As inventory inaccuracy contributes to the bullwhip effect, the purpose of this paper is to investigate the impact of inventory inaccuracy on the bullwhip effect in radio-frequency identification (RFID)-enabled supply chains and, in this context, to evaluate supply chain performance because of the RFID technology.Design/methodology/approachA simulation modeling method based on hierarchical timed colored petri nets is presented to model inventory management in multi-stage serial supply chains subject to inventory inaccuracy for various traditional and information sharing configurations in the presence and absence of RFID. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results.FindingsThe bullwhip effect is increased in RFID-enabled multi-stage serial supply chains subject to inventory inaccuracy. The information sharing supply chain is more sensitive to the impact of inventory inaccuracy.Research limitations/implicationsInformation sharing involves collaboration in market demand and inventory inaccuracy, whereas RFID is implemented by all echelons. To obtain the full benefits of RFID adoption and collaboration, different collaboration strategies should be investigated.Originality/valueColored petri nets simulation modeling of the inventory management process is a novel approach to study supply chain dynamics. In the context of inventory errors, information on RFID impact on the dynamic behavior of multi-stage serial supply chains is provided.

The Bullwhip Effect in Supply Chain and Countermeasures

2013 ◽  
Vol 711 ◽  
pp. 799-804 ◽  
Author(s):  
Yu Fang Chao
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Supply And Demand ◽  
Bullwhip Effect ◽  
Market Demand ◽  
Strategic Partnerships ◽  
Wide Spread ◽  
Demand Forecast ◽  
Demand Fluctuations ◽  
The Competitiveness Of Enterprises

As supply chain involves a wide spread of enterprises, it is inevitable to have a bullwhip effect. The reason, why bullwhip effect occurs, includes such factors as demand forecast, delay in delivery, bulk orders and others. Bullwhip effect results increased inventory, differences in supply and demand, posing great risks on enterprise operation. To reducing the bullwhip effect in supply chains, such strategies as establishing an information-sharing platform, establishing strategic partnerships, direct ship and transit, stabling market demand fluctuations, should be taken, which will improve the competitiveness of enterprises in supply chain.

Information Sharing in Supply Chain Management with Demand Uncertainty

2011 ◽  
pp. 44-62 ◽  
Author(s):  
Zhensen Huang ◽  
Aryya Gangopadhyay
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Demand Uncertainty ◽  
Bullwhip Effect ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Simulation Approach ◽  
Supply Chain Model ◽  
Trading Partners ◽  
The Impact

Information sharing is a major strategy to counteract the amplification of demand fluctuation going up the supply chain, known as the bullwhip effect. However, sharing information through interorganizational channels can raise concerns for business management from both technical and commercial perspectives. The existing literature focuses on examining the value of information sharing in specific problem environments with somewhat simplified supply chain models. The present study takes a simulation approach in investigating the impact of information sharing among trading partners on supply chain performance in a comprehensive supply chain model that consists of multiple stages of trading partners and multiple players at each stage.

scholarly journals Demand Sharing Inaccuracies in Supply Chains: A Simulation Study

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Salvatore Cannella ◽  
Roberto Dominguez ◽  
Jose M. Framinan ◽  
Manfredi Bruccoleri
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Negative Impact ◽  
Positive Impact ◽  
Bullwhip Effect ◽  
Sources Of Information ◽  
Short Delay ◽  
Demand Information ◽  
Demand Variability ◽  
The Impact

We investigate two main sources of information inaccuracies (i.e., errors and delays) in demand information sharing along the supply chain (SC). Firstly, we perform a systematic literature review on inaccuracy in demand information sharing and its impact on supply chain dynamics. Secondly, we model several SC settings using system dynamics and assess the impact of such information inaccuracies on SC performance. More specifically, we study the impact of four factors (i.e., demand error, demand delay, demand variability, and average lead times) using three SC dynamic performance indicators (i.e., bullwhip effect, inventory variability, and average inventory). The results suggest that demand error has a negative impact on SC performance, which is exacerbated by the magnitude of the error and by low demand variability scenarios. In contrast, demand delay produces a nonlinear behavior in the supply chain response (i.e., a short delay may have a negative impact and a long delay may have a positive impact), being influenced by the supply chain configuration.

scholarly journals A multi-period and multi-product optimizer for cooperative supply chains

2012 ◽  
Vol 43 (1) ◽  
pp. 77-92 ◽  
Author(s):  
M. Sepehri ◽  
K. Fayazbakhsh
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Minimum Cost ◽  
Bullwhip Effect ◽  
Coordination Mechanism ◽  
Holding Costs ◽  
Multi Stage ◽  
Long Run ◽  
Operational Information ◽  
Supply Chain Simulation

Members in a traditional supply chain compete to reduce their individual costs. But total cost is minimized in a cooperative, or a corporate managed, supply chain. A lower average cost and a lower cost variation are achieved by cooperative individual members in the long-run. The problem is formulated and solved as an integrated flow network. Previous research is expanded to include multi-period and multi-product cooperative supply chain with possibility of holding inventory in a multi-stage, multi-member setup. A Cooperative Supply Optimizer System (CSOS), a software-based coordination mechanism, is developed for large chains. It gathers operational information from members of the supply chain, and then guides them on ordering decisions for a minimum cost of the entire supply chain. Simulation results indicate an approximately 26% reduction in total supply chain costs, utilizing this formulation over a competitive setup. As the holding costs increase, the problem decomposes into single period (Just-in-time) again. The disturbing bullwhip effect disappears in cooperative supply chains.

scholarly journals Measuring and eliminating the bullwhip in closed loop supply chains using control theory and Internet of Things

2021 ◽  
Author(s):  
Christos I. Papanagnou
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Supply Chains ◽  
Closed Loop ◽  
Bullwhip Effect ◽  
The Internet ◽  
Chain Model ◽  
Closed Loop Supply Chains ◽  
The Impact ◽  
The Internet Of Things

AbstractClosed-loop supply chains are complex systems as they involve the seamless backward and forward flow of products and information. With the advent of e-commerce and online shopping, there has been a growing interest in product returns and the associated impact on inventory variance and the bullwhip effect. In this paper, a novel four-echelon closed-loop supply chain model is presented, where base-stock replenishment policies are modelled by means of a proportional controller. A stochastic state-space model is implemented, initially to capture the supply chain dynamics while the model is analysed under stationarity conditions with the aid of a covariance matrix. This allows the bullwhip effect to be expressed as a function of replenishment policies and product return rates. Next, an optimisation method is introduced to study the impact of the Internet of Things on inventory variance and the bullwhip effect. The results show that the Internet of Things can reduce costs associated with inventory fluctuations and eliminate the bullwhip effect in closed-loop supply chains.

Vertical Contracts in a Supply Chain and the Bullwhip Effect

2020 ◽  
Author(s):  
Zhan Qu ◽  
Horst Raff
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Operations Management ◽  
Bullwhip Effect ◽  
Price Effect ◽  
Demand Shocks

This paper shows that decentralized supply chains, in which upstream firms use linear wholesale prices, may experience lower upstream production and downstream sales volatility than vertically integrated supply chains and may be less susceptible to the bullwhip effect by which the variance of upstream production exceeds the variance of downstream sales. The reason is that decentralized supply chains exhibit a price effect, whereby upstream producers raise wholesale prices in the case of positive demand shocks and lower wholesale prices in the case of negative demand shocks. Whereas upstream producers benefit from the price effect and, thus, from a dampening of the bullwhip effect, downstream firms may lose, and overall supply chain profit may decrease. This paper was accepted by Vishal Gaur, operations management.

scholarly journals Simulation Research on Information-Sharing Value of Two-Level Supply Chain

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaheng Zhang ◽  
Zekai Lin ◽  
Lin Xiao
Keyword(s):  
Supply Chain ◽  
Performance Evaluation ◽  
Information Sharing ◽  
Chain Model ◽  
Simulation Research ◽  
Incentive Effect ◽  
Actual Evaluation ◽  
Demand Information ◽  
And Performance ◽  
The Impact

In the two-stage supply chain model, the incentive effect to the supplier’s sharing of demand information and performance evaluation and the effect of various parameters on the incentive effect of the supply chain are studied through a multiagent simulation model constructed for the purpose. It is found that the incentive coefficient of demand information-sharing degree, the number of selected suppliers, the order allocation coefficient, and the order proportion are positively related to the incentive effect of demand information sharing. So, the greater the demand information sharing is, the greater the impact of these parameters on the incentive effect is. Based on the demand information sharing, the supplier performance evaluation rules are shared, and when the actual evaluation rules are inconsistent with the supplier’s expectations, the incentive effect is further enhanced. Other parameters do not affect the incentive effect of demand information sharing and performance evaluation rule sharing.

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