Unraveling Behavioral Ordering: Relative Costs and the Bullwhip Effect

Author(s):  
Brent B. Moritz ◽  
Arunachalam Narayanan ◽  
Chris Parker

Problem definition: We study the bullwhip effect and analyze the impact of human behavior. We separate rational ordering in response to increasing incoming orders from irrational ordering. Academic/practical relevance: Prior research has shown that the bullwhip effect occurs in about two-thirds of firms and impacts profitability by 10%–30%. Most bullwhip mitigation efforts emphasize processes such as information sharing, collaboration, and coordination. Previous work has not been able to separate the impact of behavioral ordering from rational increases in order quantities. Methodology: Using data from a laboratory experiment, we estimate behavioral parameters from three ordering models. We use a simulation to evaluate the cost impact of bullwhip behavior on the supply chain and by echelon. Results: We find that cost increases are not equally shared. Human biases (behavioral ordering) at the retailer results in higher relative costs elsewhere in the supply chain, even as similar ordering by a wholesaler, distributor, or factory results in increased costs within that echelon. These results are consistent regardless of the behavioral models that we consider. The cognitive profile of the decision maker impacts both echelon and supply chain costs. We show that the cost impact is higher as more decision makers enter a supply chain. Managerial implications: The cost of behavioral ordering is not consistent across the supply chain. Managers can use the estimation/simulation framework to analyze the impact of human behavior in their supply chains and evaluate improvement efforts such as coordination or information sharing. Our results show that behavioral ordering by a retailer has an out-sized impact on supply chain costs, which suggests that upstream echelons are better placed to make forecasting and replenishment decisions.

2019 ◽  
Vol 14 (2) ◽  
pp. 360-384 ◽  
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas

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.


10.5772/56833 ◽  
2013 ◽  
Vol 5 ◽  
pp. 23 ◽  
Author(s):  
Francesco Costantino ◽  
Giulio Di Gravio ◽  
Ahmed Shaban ◽  
Massimo Tronci

The bullwhip effect is defined as the distortion of demand information as one moves upstream in the supply chain, causing severe inefficiencies in the whole supply chain. Although extensive research has been conducted to study the causes of the bullwhip effect and seek mitigation solutions with respect to several demand processes, less attention has been devoted to the impact of seasonal demand in multi-echelon supply chains. This paper considers a simulation approach to study the effect of demand seasonality on the bullwhip effect and inventory stability in a four-echelon supply chain that adopts a base stock ordering policy with a moving average method. The results show that high seasonality levels reduce the bullwhip effect ratio, inventory variance ratio, and average fill rate to a great extent; especially when the demand noise is low. In contrast, all the performance measures become less sensitive to the seasonality level when the noise is high. This performance indicates that using the ratios to measure seasonal supply chain dynamics is misleading, and that it is better to directly use the variance (without dividing by the demand variance) as the estimates for the bullwhip effect and inventory performance. The results also show that the supply chain performances are highly sensitive to forecasting and safety stock parameters, regardless of the seasonality level. Furthermore, the impact of information sharing quantification shows that all the performance measures are improved regardless of demand seasonality. With information sharing, the bullwhip effect and inventory variance ratios are consistent with average fill rate results.


2014 ◽  
Vol 6 (12) ◽  
pp. 986-1003
Author(s):  
Thokozani Patmond Mbhele

Information sharing in a retail supply chain presents challenges of mapping information flow in terms of collection and transfer capabilities from one point to other internal and external users. Efficient mapping information flow seems to be dependent on information availability, velocity and the level of volatility. This would strengthen partnerships between the upstream and downstream sites of a supply chain in terms of information capturing, transformation and exchange between both internal and external supply chain users. This study examines the relative magnitude of advance economic information sharing in optimizing integrated supply chain activities in the consumer goods industry. It further analyses the challenges of bullwhip effect from the perspective of electronically-enabled supply chain management (eSCM) systems and information sharing in the fast moving consumer goods (FMCG) industry. The study finds that information sharing is related to supply chain performance targets in the FMCG industry in terms of a higher order fulfillment rate and achieving shorter order cycle time through integrated e-SCM systems. The managerial implications of this study are that integrated IT infrastructure capability and top management support (in terms of visible involvement, commitment and participation of executives and the allocation of the necessary resources) are significant antecedents of the quality of shared information.


2012 ◽  
Vol 2012 (1) ◽  
pp. 000012-000017
Author(s):  
Chet Palesko ◽  
Alan Palesko

Demands on the electronics industry for smaller, better, and cheaper packages have made the supply chain more complex. Outsourcing, new technologies, and increasing performance requirements make designing and building the right product for the right price more difficult than ever. We will present a framework for understanding and managing the supply chain through cost modeling. Cost models that accurately reflect the cost impact from technology and design decisions enable a more precise understanding of supply chain behavior. Cost models can show the extra cost of adding a layer, the expected savings from relaxing design rules, or the cost of package on package assembly compared to 3D packaging with through silicon vias (TSVs). The models also provide context to understanding the ″should cost″ of a product and the path to achieving it. Since the guidance from cost models is based on the actual supplier cost drivers and pricing behavior, designer cost reduction efforts will result in higher savings compared to not using the cost models. Without cost models, designers risk missing their suppliers' real cost drivers and, therefore, the opportunity to decrease cost. This cost modeling framework allows the designers to realize the lowest cost product by matching the right design with the right supplier. It is a method for understanding a design decision's cost impact: a design change, a supplier change, or even the impact of new technology.


Author(s):  
Yunjie Wang ◽  
Albert Y. Ha ◽  
Shilu Tong

Problem definition: This paper investigates the issue of sharing the private demand information of a manufacturer that sells a product to retailers competing on prices and service efforts. Academic/practical relevance: In the existing literature, which ignores service effort competition, it is known that demand signaling induces an informed manufacturer to distort the wholesale price downward, which benefits the retailers, and so, they do not have any incentive to receive the manufacturer’s private information. In practice, many manufacturers share demand information with their retailers that compete on prices and service efforts (e.g., demand-enhancing retail activities), a setting that has not received much attention from the literature. Methodology: We develop a game-theoretic model with one manufacturer selling to two competing retailers and solve for the equilibrium of the game. Results: We show how an informed manufacturer may distort the wholesale price upward or downward to signal demand information to the retailers, depending on the cost of service effort, the intensity of effort competition, and the number of uninformed retailers. We fully characterize the impact of such wholesale price distortion on the firms’ incentive to share information and derive the conditions under which the manufacturer shares information with none, one, or both of the retailers. We derive conditions under which a higher cost of service effort makes the retailers or the manufacturer better off. Managerial implications: Our results provide novel insights about how service effort competition impacts the incentives for firms in a supply chain to share a manufacturer’s private demand information. For instance, when the cost of effort is high or service effort competition is intense, a manufacturer should share information with none or some, but not all, of the retailers.


2016 ◽  
Vol 11 (4) ◽  
pp. 967-984
Author(s):  
Anukal Chiralaksanakul ◽  
Vatcharapol Sukhotu

Purpose The purpose of this paper is to investigate the impact of backroom storage in supply chain replenishment decision parameters: the order quantity based on the well-established economic order quantity (EOQ) model. Design/methodology/approach The authors develop an EOQ-type model to investigate the operational cost impact of the order quantity with backroom storage. Because of the discrete and discontinuous nature of the problem, a modification of an existing algorithm is applied to obtain an optimal order quantity. Numerical experiments derived from a leading retailer in Thailand are used to study the cost impact of the backroom. Findings The paper shows that the backroom storage will significantly affect the decision regarding the order quantity. If its effect is ignored, the cost increase can be as high as 30 per cent. The costs and operations of additional shelf-refill trips from the backroom must be carefully analyzed and included in the decisions of replenishment operations. Research limitations/implications The model is a simplified version of the actual replenishment process. Validation from a real-world setting should be used to confirm the results. There are many additional opportunities to further integrate other issues in this problem such as shelf space decisions or joint order quantity between vendors and retailers. Practical implications The insights gained from the model will help managers, both retailers and vendors or manufacturers, make better decisions with regard to the order quantity policy in the supply chain. Originality/value Problems with backroom storage have been qualitatively described in the literature in the past decade. This paper is an early attempt to develop a quantitative model to analytically study the cost impact of backroom on order quantity decisions.


Author(s):  
Zhensen Huang ◽  
Aryya Gangopadhyay

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.


Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Salvatore Cannella ◽  
Roberto Dominguez ◽  
Jose M. Framinan ◽  
Manfredi Bruccoleri

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.


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