The bullwhip effect in supply chain networks (original) (raw)
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Quantifying the bullwhip effect in supply chains
Consider multiple companies operating as a serial supply chain. Within this environment, end users form the demand for the last company in the supply chain, but the demand for upstream companies is formed by the companies in the immediate downstream supply chain link. It has been shown that demand seasonality and forecast error can increase as we proceed up the supply chain. These demand distortions, called the "bullwhip" effect, create inefficiencies for upstream firms. This work seeks to identify the magnitude of the problem by establishing an empirical lower bound on the profitability impact of the bullwhip effect. Results indicate that the importance of the bullwhip effect to a firm differs greatly depending on the specific business environment. Given appropriate conditions, however, eliminating the bullwhip effect can increase product profitability by 10-30%.
Robust tests for the bullwhip effect in supply chains with stochastic dynamics
European Journal of Operational Research, 2008
This paper analyzes the bullwhip effect in single-echelon supply chains driven by arbitrary customer demands and operated nondeterministically. The supply chain, with stochastic system parameters, is modeled as a Markovian jump linear system. The paper presents robust analytical conditions to diagnose the bullwhip effect and bound its magnitude. The tests are independent of the customer demand. Examples are given. Ordering policies that pass these tests, and thus avoid the bullwhip effect in random environments for arbitrary customer demands, are shown to exist. The paper also presents possible extensions to multi-echelon chains.
THE BULLWHIP EFFECT IN SUPPLY CHAIN
Predicting of demand is the significant tool in order the production planning and provisions, managing the surface or creating levels of personalized services. Predicting demand by many technologies is relying on earlier data and their importance is setting up from patterns utilized heretofore earlier of demand for near future. Of values predicted with regard to high responsiveness for of the ones most current, this approach is obtaining in general high (low) values of demand predicted in accordance to periods high (low) of demand. It is being transferred by demand of clients to wholesalers, distributors or producers in the form of the retail order which is current demand for partners of the chain of supplies of the higher mark at the same time. Forecasts of demand are rarely in practice when thorough and what's more they are still refer to the poor quality higher marks in the chain of supplies. In the majority of chains of supplies, individual participants in the chain are trying to rationalize sizes of one's orders in accordance to economic decisions, what the distortion of real demand of clients is being created, through as well as bad redirection of demand at members of the chain of supplies from upper of its levels. Promotions and price hesitation also have influence for distorting demand The need to predict demand is increasing errors by chances to perform on every level of the chain of supplies in forecasts-called the bullwhip effect (BWE) this way-for the whole supply chain. The seeming effect is creating it of double predicting [8]. And therefore it is so very important determining the operating system correctly of predicting of demand which the bullwhip effect will limit. The regular, simple model of supply chain and its flows consist such participants as: supplier, producer, intermediary or distributor, retailer and customer, all with products and information flows. This structure is presented below on figure 1. So taking into consideration the above mentioned model it is possible to do the graphical presentation of the bullwhip effect in supply chain especially with pressure on its formation.
Simulation-Based Statistical Analysis of the Bullwhip Effect in Supply Chains
2000
The paper proposes both statistical and simulation-based analysis and evaluation of the bullwhip effect in supply chains. The demand distortion, called the bullwhip effect, is considered as an important characteristic of supply chain operation stability. A mathematical justification of the stochastic demand as a cause of the bullwhip effect is discussed. Results of simulation studies to analyse the impact of
A SIMULATION STUDY ON THE BULLWHIP EFFECT IN SUPPLY CHAIN
Under the competition among the global market place, enterprises must have skills of dealing with uncertainty. It is known that the lack of information sharing causes the uncertainty. The order variability increase as we move up the supply chain. In this study the bullwhip effect in supply chain is studied. The increase in inventory costs under the bullwhip effect is examined using simulation method. The effect of choosing the right forecasting technique for the demand pattern is taken into account to show its impact on the bullwhip effect.
On the bullwhip and inventory variance produced by an ordering policy
Omega, 2003
The Bullwhip Problem in supply chains is first outlined. A discrete control theory model of a generic model of a replenishment rule is presented. From this model, an analytical expression for bullwhip is derived that is directly equivalent to the common statistical measure often used in simulation, statistical and empirical studies to quantify the bullwhip effect. This analytical expression clearly shows that bullwhip can be reduced by taking a fraction of the error in the inventory position and pipeline position, rather then account for all of the errors every time an ordering decision is made as is common in many scheduling systems. Furthermore increasing the average age of the forecast reduces bullwhip, as does reducing the production lead-time.
A measure of the bullwhip effect in supply chains with stochastic lead time
International Journal of Advanced Manufacturing Technology, 2008
In this paper, we exactly quantify the bullwhip effect, the variance amplification in replenishment orders, for cases of stochastic demand and stochastic lead time in a simple two-stage supply chain with one supplier and one retailer. In most of the previous research, the impact of order lead time on the bullwhip effect in supply chains with pre-specified demand processes is investigated mostly for cases of deterministic lead time. In this paper, we deal with a first-order autoregressive, AR(1), demand process and investigate the behavior of a measure for the bullwhip effect with respect to autoregressive coefficient and stochastic order lead time. Extension to a mixed first-order autoregressive-moving average, ARMA(1,1), demand process is also considered.
A n important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple-stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.
Approximations of bullwhip effect function in a three-echelon supply chain
Supply chain includes all members who directly or indirectly involved in fulfilling a customer need. Successful lead of each supply chain requires extensive efforts and various policies that facilitate management of the main issues of supply chain, including materials, capital and information. One of the important and broad management issues in supply chain management is the bullwhip effect problem. This effect causes improper planning, increased inventory levels, reduced profits, and reduced service levels and other harmful effects on the organization. This paper is focused on studying the impact of some factors and estimating regression function, on bullwhip effect in a three-echelon supply chain including Manufacturer, Wholesaler and Retailer by considering customer demand on a Poisson process. In each echelon of supply chain six different scenarios by combining Moving Average, Exponential Smoothing and Regression methods have been used for forecasting demand. At the next step, the impact of the scenarios on Bullwhip, was evaluated by factor design and simulation methods. Finally A 6 2 experimental design is used to identify critical factors and estimate regression of Bullwhip Effect Function in a Three-Echelon Supply Chain. By approximating this function, if the values of different function's variables be given, the expected bullwhip effect can be estimated.
International Journal of Engineering & Technology, 2018
With supply chains becoming increasingly global, the issue of bullwhip effect, a phenomenon attributable to demand fluctuation in the upstream section of the supply chains, has received greater attention from many researchers. The phenomenon in which the variation of upstream members' orders is amplified than the variation of downstream members' demands in the supply chain is called the bullwhip effect (BWEF). Most of existing research studies did not realize the demand dependency of market demands. Thus, this research focused on the study of the influence of the demand correlation coefficient between two market groups on the BWEF. The incoming demand processes are assumed the separate first-order moving-average, [MA(1)] demand patterns. The scope of the supply chain structure used in this research is composed of one manufacturer and two distribution centers. The general result reveals that the coefficient of correlation is one of several factors affecting the BWEF.