Bullwhip Effect Analysis in Supply Chain for Demand Forecasting Technology

2007 ◽  
Vol 27 (7) ◽  
pp. 26-33 ◽  
Author(s):  
Hong LIU ◽  
Ping WANG
Keyword(s):  
Supply Chain ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Effect Analysis

scholarly journals An Empirical Study of Supply Chain Frangibility and Risk Management in Indian Automobile Industry

2021 ◽  
Author(s):  
Arora Ankit ◽  
Rajagopal Rajesh
Keyword(s):  
Risk Factors ◽  
Supply Chain ◽  
Automobile Industry ◽  
Lead Time ◽  
Risk Mitigation ◽  
Transportation Network ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
Supply Chain Efficiency

Abstract The automobile sector in India is one the key segment of Indian economy as it contributes to 4% of India’s GDP and 5% of India’s Industrial production. The supply chain of any firm is generally dependent on six driving factors out of which three are functional (information, inventory, and facilities) and 3 are logistic (sourcing, pricing, and transportation). The risk causing factors in supply chains consists of various levels of sub-factors under them. Say for instance, under supply risk, the sub-factors can be poor logistics at supplier end, poor material quality etc., under demand risk, the sub-factors can be inaccurate demand forecasting, fluctuating demand, bullwhip effect, and under logistics risk, the sub-factors can be poor transportation network, shorter lead time, stock outs. Through this study, we observe to find the effect of these factors in the supply chain. We use Failure Mode and Effect Analysis (FMEA) technique to prioritize the various types of risk into zones namely high, medium and low risk factors. Also, we use the Best Worst Method (BWM), a multi-criteria decision-making technique to find out the overall weightings of different risk factors. The combination of these methods can help an organization to prioritize various risk factors and proposing a proper risk mitigation strategy leading to increase in overall supply chain efficiency and responsiveness.

Bullwhip Effect Analysis in Two-Level Supply Chain Distribution Network Using Different Demand Forecasting Technology

2016 ◽  
Vol 33 (03) ◽  
pp. 1650016 ◽  
Author(s):  
Xi Gang Yuan ◽  
Nan Zhu
Keyword(s):  
Supply Chain ◽  
Moving Average ◽  
Minimum Mean Square Error ◽  
Distribution Network ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
First Order ◽  
Basic Work ◽  
The Impact

Following the basic work conducted by Lee et al. [(1997a), The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102; (1997b), Information distribution in a supply chain: The bullwhip effect. Management Science, 43(4), 546–558] and using two first-order autoregressive AR(1) models, respectively, this paper provides three quantitative models of the bullwhip effect of the two-level supply chain distribution network consisting of a single manufacturer and two retailers. The paper assumes that two retailers adopt the order point method, uses three kinds of demand forecasting technology, i.e., moving average, exponential smoothing and minimum mean square error methods, respectively, provides three corresponding models for analyzing the impact of bullwhip effect of two-level supply chain distribution network. At the same time, this paper compares and analyzes the results of the three models through simulation.

Bullwhip Effect Analysis in a Supply Chain

2012 ◽  
pp. 646-665
Author(s):  
Mehdi Najafi ◽  
Reza Zanjirani Farahani
Keyword(s):  
Supply Chain ◽  
Statistical Method ◽  
Moving Average ◽  
Bullwhip Effect ◽  
Effect Analysis ◽  
Forecasting Methods ◽  
Effect Measure ◽  
Forecasting Method ◽  
Efficiency And Effectiveness ◽  
Main Factors

In today’s world, all enterprises in a supply chain are attempting to increase both their and the supply chain’s efficiency and effectiveness. Therefore, identification and consideration of factors that prevent enterprises to attain their expected/desired levels of effectiveness are very important. Since bullwhip effect is one of these main factors, being aware of its reasons help enterprises decrease the severity of bullwhip effect by opting proper decisions. Now that forecasting method is one of the most important factors in increasing or decreasing the bullwhip effect, this chapter considers and compares the effects of various forecasting methods on the bullwhip effect. In fact, in this chapter, the effects of various forecasting methods, such as Moving Average, Exponential Smoothing, and Regression, in terms of their associated bullwhip effect, in a four echelon supply chain- including retailer, wholesaler, manufacturer, and supplier- are considered. Then, the bullwhip effect measure is utilized to compare the ineffectiveness of various forecasting methods. Owing to this, the authors generate two sets of demands in the two cases where the demand is constant (no trend) and has an increasing trend, respectively. Then, the chapter ranks the forecasting methods in these two cases and utilizes a statistical method to ascertain the significance of differences among the effects of various methods.

scholarly journals Alternative Forecasting Techniques that Reduce the Bullwhip Effect in a Supply Chain: A Simulation Study

1970 ◽  
Vol 25 (2) ◽  
pp. 177-188 ◽  
Author(s):  
Francisco Campuzano-Bolarín ◽  
Antonio Guillamón Frutos ◽  
Ma Del Carmen Ruiz Abellón ◽  
Andrej Lisec
Keyword(s):  
Supply Chain ◽  
Moving Average ◽  
Management Strategies ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Good Alternative ◽  
Critical Problems ◽  
Simple Exponential Smoothing ◽  
Demand Patterns ◽  
Forecasting Techniques

The research of the Bullwhip effect has given rise to many papers, aimed at both analysing its causes and correcting it by means of various management strategies because it has been considered as one of the critical problems in a supply chain. This study is dealing with one of its principal causes, demand forecasting. Using different simulated demand patterns, alternative forecasting methods are proposed, that can reduce the Bullwhip effect in a supply chain in comparison to the traditional forecasting techniques (moving average, simple exponential smoothing, and ARMA processes). Our main findings show that kernel regression is a good alternative in order to improve important features in the supply chain, such as the Bullwhip, NSAmp, and FillRate.

scholarly journals Demand forecasting and information platform in tourism

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 247-252 ◽  
Author(s):  
Yue Li ◽  
Qi-Jie Jiang
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Information Processing ◽  
Information Asymmetry ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Information Platform ◽  
On Demand ◽  
Processing Ability ◽  
Tourism Supply Chain

AbstractInformation asymmetry and the bullwhip effect have been serious problems in the tourism supply chain. Based on platform theory, this paper established a mathematical model to explore the inner mechanism of a platform’s influence on stakeholders’ ability to forecast demand in tourism. Results showed that the variance of stakeholders’ demand predictions with a platform was smaller than the variance without a platform, which meant that a platform would improve predictions of demand for stakeholders. The higher information-processing ability of the platform also had other effects on demand forecasting. Research on the inner logic of the platform’s influence on stakeholders has important theoretical and realistic value. This area is worthy of further study.

A new model development for demand forecasting of supply chain from data fusion perspective

2021 ◽  
pp. 1-7
Author(s):  
Zhonghuai Wang ◽  
Guoping Cheng ◽  
Yu Xiong
Keyword(s):  
Supply Chain ◽  
Data Fusion ◽  
Conceptual Model ◽  
Predictive Analytics ◽  
Demand Forecasting ◽  
Model Development ◽  
Bullwhip Effect ◽  
Profit Center ◽  
Source Data ◽  
Tremendous Impact

In the increasingly competitive society, the supply chain, as the third profit center of the enterprise, makes scholars and industry-related with supply chain more and more interesting in the study of relevant issues. Exiting studies believed that an application of predictive analytics could be a tremendous impact on supply chain management. The uncertainty of supply chain demand and bullwhip effect is a challenge in the supply chain. Data fusion can effectively reduce the uncertainty of demand and the amplification effect. In this study, a new conceptual model was established on the traditional supply chain based on data fusion. Results show that the conceptual model refers to data fusion for solving the uncertain and inconsistent multi-source data by Bayesian estimation and to providing reasonable decision information for supply chain managers.

System dynamics modeling with fuzzy logic application to mitigate the bullwhip effect in supply chains

2019 ◽  
Vol 14 (3) ◽  
pp. 610-627 ◽  
Author(s):  
Mehdi Poornikoo ◽  
Muhammad Azeem Qureshi
Keyword(s):  
Fuzzy Logic ◽  
Supply Chain ◽  
System Dynamics ◽  
Dynamic Models ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Automated System ◽  
Supply Chain Performance ◽  
Inference System ◽  
Content Type

Purpose A plethora of studies focused on the cause and solutions for the bullwhip effect, and consequently many have successfully experimented to dampen the effect. However, the feasibility of such studies and the actual contribution for supply chain performance are yet up for debate. This paper aims to fill this gap by providing a holistic system-based perspective and proposes a fuzzy logic decision-making implementation for a single-product, three-echelon and multi-period supply chain system to mitigate such effect. Design/methodology/approach This study uses system dynamics (SD) as the central modeling method for which Vensim® is used as a tool for hybrid simulation. Further, the authors used MATLAB for undertaking fuzzy logic modeling and constructing a fuzzy inference system that is later on incorporated into SD model for interaction with the main supply chain structure. Findings This research illustrated the usefulness of fuzzy estimations based on experts’ linguistically and logically defined parameters instead of relying merely on the traditional demand forecasting based on time series. Despite the increased complexity of the calculations and structure of the fuzzy model, the bullwhip effect has been considerably decreased resulting in an improved supply chain performance. Practical implications This dynamic modeling approach is not only useful in supply chain management but also the model developed for this study can be integrated into a corporate financial planning model. Further, this model enables optimization for an automated system in a company, where decision-makers can adjust the fuzzy variables according to various situations and inventory policies. Originality/value This study presents a systemic approach to deal with uncertainty and vagueness in dynamic models, which might be a major cause in generating the bullwhip effect. For this purpose, the combination between fuzzy set theory and system dynamics is a significant step forward.

scholarly journals CPN Based Modeling of Tourism Demand Forecasting

2016 ◽  
Vol 12 (1) ◽  
pp. 28
Author(s):  
Hua Bai ◽  
Haoyuan Zhang
Keyword(s):  
Supply Chain ◽  
Good Practice ◽  
Demand Forecasting ◽  
Bullwhip Effect ◽  
Service Level ◽  
Supply Chain Collaboration ◽  
Tourism Demand ◽  
High Volatility ◽  
Low Efficiency ◽  
Tourism Supply Chain

The tourism demand has become more and more diversified and sensitive to traveling environment, resulting in the high volatility of tourism market. Travel agencies, scenic spots, hotels and other tourism businesses in the tourism supply chain (TSC) need a tight collaboration in order to minimize cost and improve responsiveness and service level. The existence of the bullwhip effect will cause the waste of resources and low efficiency, thus collaborative demand forecasting becomes a good practice to enhance sharing of information and resources, and as a result improving the efficiency and effectiveness of tourism demand forecasting. This paper proposes a collaborative tourism demand forecasting framework based on Colored Petri Net (CPN), which can simulate and examine the effectiveness of tourism supply chain collaboration.

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