Bullwhip Effect In Supply Chain Research Articles

Metrics for bullwhip effect analysis

A bullwhip measurement system based on a two-criterion assessment—‘internal process efficiency’ and ‘customer service level’—is developed in this paper. The framework is designed to assess both individual (single member) and systemic (whole supply chain) performances. Data collection and calculation methods, update and monitoring mechanisms, as well as related procedures for each metric used, are detailed. A comparative analysis with a recent work by Barlas and Gunduz is performed, showing that the adoption of the proposed performance measurement system can help academics and practitioners to better understand, study and avoid the bullwhip effect. Such analysis also provides evidence on the relevance of considering when analysing the bullwhip effect in supply chains, the ‘customer importance’ aspect that is often forgotten in the published literature.

The bullwhip effect in supply chains with stochastic lead times

The bullwhip effect in supply chains with stochastic lead times

A robust optimization approach to reduce the bullwhip effect of supply chains with vendor order placement lead time delays in an uncertain environment

Supply chain management is important for companies and organizations to improve their business and enhance competitiveness in the global marketplace. The bullwhip effect problem of supply chain systems with vendor order placement lead time delays in an uncertain environment is addressed in this paper. Among the numerous causes of bullwhip effect, we focus on uncertainties with respect to demand, production process, supply chain structure, inventory policy implementation and especially vendor order placement lead time delays. Minimizing the negative effect of these uncertainties in inducing bullwhip effect creates a need for developing dynamical inventory policy that increases responsiveness to demand and decreases volatility in inventory replenishment. First, a dynamic model of supply chain with above uncertainties is developed. Then, a novel uncertainty-dependent robust inventory control method using inventory position information is proposed. Additionally, the maximum allowable vendor order placement lead time delay that ensures the stability of supply chains and the suppression of bullwhip effect under the proposed inventory control policy is explored and measured. We find that vendor order placement lead time delays do play important role in supply chain dynamics and contribute to its turbulence and volatility. The effectiveness and flexibility of proposed method is verified through simulation study.

The bullwhip effect phenomenon in automotive supply chains in South Africa

Purpose: The purpose of the article is to report on research that was completed to explore the concept of the bullwhip effect in supply chains and to illustrate empirically the presence of the bullwhip effect in automotive supply chains in South Africa. Problem Investigated: This article investigates the presence of the bullwhip effect - which was identified through an empirical study - and its causes and implications for supply chain management in the South African automotive component industry. Methodology: A literature study was conducted on the causes and implications of the bullwhip effect phenomenon. This was followed by an empirical study in the form of a survey among South African automotive component manufacturers. Descriptive and inferential statistics were used to determine the significant supply chain problems relating to the bullwhip effect in automotive supply chains. Findings and Implications: The results indicate that automotive component manufacturers are dependent on demand-forecasting information from their customers. They experience long lead times, fluctuating orders, cancellation of orders, excess and slow moving inventory and a lack of integration with suppliers and customers. There are also signs of relationship problems and a possible silo mentality. The mentioned results indicate the presence of the bullwhip effect in South African automotive supply chains. Since the bullwhip effect can have a major impact on organisations' costs, knowing where to invest effort and resources should be a high priority for supply chain managers. Value of the Research: Since the field of supply chain management is extremely dynamic, this article contributes to the body of knowledge and provides new insight into the bullwhip effect phenomenon. The results included in this article could assist parties in automotive supply chains to focus their attention on problems that might be within their control and if solved could lead to improved competitiveness. Furthermore, there is little empirical research on this topic in the South African automotive component industry. Conclusion: It is possible that the bullwhip effect is responsible for inefficiencies in automotive supply chains. Knowledge of the indicators of the bullwhip effect can enable supply chain managers to identify it at an early stage and thus be proactive in preventing its costly influence on the efficiency of the supply chain. The bullwhip effect can be experienced by any industry. This is possibly the case in the automotive component industry in South Africa.

Quantifying the impact of demand substitution on the bullwhip effect in a supply chain

In a supply chain, the distorted demand information when it goes upstream is commonly known as the bullwhip effect. In this paper, the impact of demand substitution on the bullwhip effect in a two-stage supply chain is investigated. In our model, a single retailer observes inventory levels for two products, among which product 1 can be used to substitute product 2. The retailer places orders to a single manufacturer following an order-up-to inventory policy and uses a simple moving average forecasting method to estimate the lead-time demand. The customers’ demands are modeled by an autoregressive process. By analyzing the bullwhip effect in such settings, quantitative relations between the bullwhip effect and the forecasting method, lead time, demand process, and the product substitution are obtained. Numerical results show that demand substitution can reduce the bullwhip effect in most cases.

Adaptive fuzzy vendor managed inventory control for mitigating the Bullwhip effect in supply chains

This paper proposes an adaptive fuzzy control application to support a vendor managed inventory (VMI). The methodology applies fuzzy control to generate an adaptive smoothing constant in the forecast method, production and delivery plan to eliminate, for example, the rationing and gaming or the Houlihan effect and the order batching effect or the Burbidge effects and finally the Bullwhip effect. The results show that the adaptive fuzzy VMI control surpasses fuzzy VMI control and traditional VMI in terms of mitigating the Bullwhip effect and lower delivery overshoots and backorders. This paper also guides management in allocating inventory by coordinating suppliers and buyers to ensure minimum inventory levels across a supply chain. Adaptive fuzzy VMI control is the main contribution of this paper.

Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains

Supply chain inventories are prone to fluctuations and instability. Known as the bullwhip effect, small variations in the end item demand create oscillations that amplify throughout the chain. By using system dynamics simulation, we investigate some of the structural sources of the bullwhip effect, and explore the effectiveness of information sharing to eliminate the undesirable fluctuations. Extensive simulation analysis is carried out on parameters of some standard ordering policies, as well as external demand and lead-time parameters. Simulation results show that (i) a major structural cause of the bullwhip effect is isolated demand forecasting performed at each echelon of the supply chain, and (ii) demand and forecast sharing strategies can significantly reduce the bullwhip effect, even though they cannot completely eliminate it. We specifically show how each policy is improved by demand and forecast sharing. Future research involves more advanced ordering and forecasting methods, modelling of other well-known sources of bullwhip, and more complex supply network structures.

A comparative study of simulation and time series model in quantifying bullwhip effect in supply chain

Bullwhip (or whiplash) effect is an observed phenomenon in forecast driven distribution channel and careful management of these effects is of great importance to managers of supply chain. Bullwhip effect refers to situations where orders to the suppliers tend to have larger variance than sales to the buyer (demand distortion) and the distortion increases as we move up the supply chain. Due to the fact that demand of customer for product is unstable, business managers must forecast in order to properly position inventory and other resources. Forecasts are statistically based and in most cases, are not very accurate. The existence of forecast errors made it necessary for organizations to often carry an inventory buffer called 'safety stock'. Moving up the supply chain from the end users customers to raw materials supplier there is a lot of variation in demand that can be observed, which call for greater need for safety stock. This study compares the efficacy of simulation and Time Series model in quantifying the bullwhip effects in supply chain management. .

Systematic Risk and the Bullwhip Effect in Supply Chains

The variance of demand can be divided into two components, a systematic risk component that occurs due to economic factors, and an idiosyncratic noise component. Operational hedging addresses the total variance of demand, whereas financial hedging is useful against systematic risk. We investigate the systematic risk component of demand uncertainty using industry-level data for the manufacturing, wholesale trade, and retail trade sectors of the U.S. economy, and applying sales as a proxy for demand. We show that the degree of systematic risk increases substantially from retailers to wholesalers to manufacturers. Investigating two potential sources of this phenomenon, amplification of production series as predicted by the bullwhip effect and aggregation of orders placed by downstream industries, we find evidence supporting the latter. Our result have implications for the relative values of different operational and financial hedging strategies chosen by a firm. It is particularly relevant to events such as the 2008-09 economic recession and the post-recession recovery.

Systematic Risk and the Bullwhip Effect in Supply Chains

Industrial production output is generally correlated with the state of the economy. Nonetheless, during the times of economic downturn some industries take the biggest hit, while at the times of economic boom they reap most benefits. To provide insight into this phenomenon we map supply networks of industries and firms and investigate how the supply network structure mediates the effect of economy on industry or firm sales. Previous research has shown that retail sales are correlated with the state of the economy. Since retailers source their products from other industries, the sales of their suppliers can also be correlated with the state of the economy. This correlation represents the source of systematic risk for an industry that propagates through a supply chain network. Specifically, we identify the following mechanisms that can affect the correlation between sales and the state of the economy in a supply chain network: propagation of systematic risk into production decisions, aggregation of orders from multiple customers in a supply chain network, and aggregation of orders over time. We find that the first effect does not amplify the correlation, however, the latter two intensify correlation and result in the amplification of correlation upstream in supply networks. We demonstrate three managerial implications of this phenomenon: implications for the cost of capital, for the risk-adjusted valuation of supply chain improvement projects, and for supplier selection and risk.

The analysis of bullwhip effect in a HMMS-type supply chain

The aim of the paper is to investigate the well-known bullwhip effect of supply chains. Control theoretic analysis of bullwhip effect is extensively analyzed in the literature with the Laplace transform. This paper tries to examine the effect for an extended Holt–Modigliani–Muth–Simon model. A two-stage supply chain (supplier–manufacturer) is studied with quadratic costs functional. It is assumed that both firms minimize the relevant costs. The order of the manufacturer is delayed with a known constant. Two cases are examined: supplier and manufacturer minimize the relevant costs decentralized, and a centralized decision rule. The question is answered, how to decrease the bullwhip effect.

Analyzing the Bullwhip Effect in a Supply Chain with ARMA(1,1) Demand Using MMSE Forecasting

Abstract This paper considers a supply chain with the first order autoregressive and the first order moving average (ARMA (1,1)) demand. The lead time demand is estimated using the minimum mean-square error (MMSE) forecasting technique. The expression of the bullwhip effect for a two stage supply chain consisting of one supplier and one retailer is derived, and the existence condition of the bullwhip effect as well as the impact of demand information sharing on the bullwhip effect are analyzed theoretically and numerically. At last, the extension analysis for bullwhip effect in a multiple stage supply chain is performed by using numerical examples.

Further Motivation for Continuous Improvement in Just-In-Time Logistics

In the supply chain literature, little work has been done on modeling the propagation pattern of quality defects in supply chains. We quantitatively model a defect bullwhip effect in supply chains that can be addressed logistically by reducing time intervals between product deliveries. We explicitly consider the cause and effect of this defect bullwhip effect and demonstrate analytically that there is a strong incentive for practitioners to continuously reduce shipment size. We also consider production characteristics such as the frequency of supplier failure and stringency of the buyer's inspection process. Implications for managers are suggested.

Analysis of bullwhip effect in supply chains with heterogeneous decision models

In modeling a supply chain, it is often assumed that agents of different echelons adopt similar inventory policies. As a result, bullwhip effect exists across different echelons up the supply chain when the product demand at the retailer level increases and decreases. However, a retailer and a manufacturer are two very different entities in a supply chain, which should employ different decision processes in managing their operations. In view of this, we study the application of heterogeneous decision models for retailers and manufacturers to analyze the bullwhip effect in a supply chain. In particular, we formulate a simple supply chain consisting of many retailers, a manufacturer and a warehouse. The inventory policy of the retailers is simply to use the inventory position to absorb variations in the demand. For the manufacturer, the demand can be met by keeping a higher inventory level or by varying the production rate more frequently. A multi-criteria objective function is formulated and analytic solutions are derived. We illustrate how the bullwhip effect varies for different selections of scaling parameters. By using optimal control theory, we show that the bullwhip effect can be suppressed if a smooth production requirement is enforced.

Changing risk preferences in supply chain inventory decisions

The risk preferences of managers may cause their inventory ordering decisions to deviate from the optimal policy. Past studies in operations management have produced mixed results. This study examines this proposition using decision data collected from a supply chain experiment. This article finds that changing the risk preferences of managers with respect to demand changes and supplier failures is a significant behavioural factor in explaining deviations in ordering decisions. This result provides an additional behavioural cause in explaining the bullwhip effect in supply chains. It also provides insights on the challenges for reducing the bullwhip effect in supply chains.

The bullwhip effect in supply chains—An overestimated problem?

A phenomenon that is now well known as the bullwhip effect suggests that the variability of orders increases as they move up the supply chain from retailers to wholesalers to manufacturers to suppliers. In this paper, we will focus mainly on measuring the bullwhip effect. Existing approaches that aim at quantifying the bullwhip effect neglect the network structure of supply chains. By only assuming a simple two-stage supply chain consisting of a single retailer and a single manufacturer, some of the relevant risk pooling effects associated with the network structure of supply chains are disregarded. Risk pooling effects arise when the orders, which a retailer receives from its customers, are statistically correlated with a coefficient of correlation less than one. When analyzing the bullwhip effect in supply chains, however, the influence of risk pooling has to be considered. The fact that these influences have not yet been analyzed motivates the research presented in this paper. We will show that the bullwhip effect is overestimated if just a simple supply chain is assumed and risk pooling effects are present.

A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process

In this paper, we quantify the impact of the bullwhip effect – the phenomenon in which information on demand is distorted as moving up a supply chain – for a simple two-stage supply chain with one supplier and one retailer. Assuming that the retailer employs a base stock inventory policy, and that the demand forecast is performed via a mixed autoregressive-moving average model, ARMA(1, 1), we investigate the effects of the autoregressive coefficient, the moving average parameter, and the lead time on the bullwhip effect.

A measure of the bullwhip effect in supply chains with stochastic lead time

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.

Robust tests for the bullwhip effect in supply chains with stochastic dynamics

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.

Reducing Bullwhip Effect in Supply Chains Using Control Variates

The bullwhip effect refers to the phenomenon where orders to the supplier tend to have a larger variance than sales to the buyer, and the distortion propagates as one moves upstream in an amplified form. The problem of variance propagation and amplification can be significant for even the existing optimal inventory policies. In this paper, we propose a control variate technique for reducing the bullwhip effect, or more generally the demand variability in supply chains. The method is effective and easy to implement in practical management and supply chain systems. The idea is to stabilize the inventory control policy to dampen the effect of propagation by imposing a correlated control variate to the original order. The proposed method is applied to stabilize the inventory policy and rectify the bullwhip effect discussed in Lee et al. [15] and Chen et al. [7, 8]. It shows that the method may lead to a system-wide optimal performance within a class of stabilizing policies.