Meta-Prediction Models for Bullwhip Effect Prediction of a Supply Chain Using Regression Analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 36-71
Author(s):  
Navee Chiadamrong ◽  
Nont Sarnrak
Keyword(s):  
Regression Analysis ◽  
Supply Chain ◽  
Prediction Models ◽  
Bullwhip Effect ◽  
Decision Makers ◽  
Optimization Approach ◽  
Periodic Review ◽  
Simulation Based ◽  
Variance Ratios ◽  
A Chain

In this study, the main factors that can cause the bullwhip effect and stock amplification are investigated using a simulation-based optimization approach and regression analysis. A two-echelon supply chain with uncertain customer demand and delivery lead time operating with the periodic-review reorder cycle policy is studied. The parameters of smoothing inventory replenishment and forecasting methods are required. These parameters are optimized in terms of minimizing the Total Stage Variance Ratios (TSVRs) of both echelons. The results show that even though all factors of interest have an impact on the bullwhip effect, using smoothing proportional controllers can reduce TSVRs (the sum of the order varaince ratio and net stock amplification). The meta-prediction models can effectively help predict the amount of the bullwhip effect of a chain under various situations with an average MAPE of less than 11%. The results can assist decision makers in the management of a supply chain to realize, benchmark with the optimal results, and reduce the TSVRs under an uncertain environment.

Reducing the Bullwhip effect in a supply chain network by application of optimal control theory

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1377-1396 ◽  
Author(s):  
Ali Sabbaghnia ◽  
Jafar Razmi ◽  
Reza Babazadeh ◽  
Behzad Moshiri
Keyword(s):  
Supply Chain ◽  
Control Variable ◽  
Bullwhip Effect ◽  
Feedback Loops ◽  
Decision Makers ◽  
Supply Chain Network ◽  
Bicycle Industry ◽  
Inventory Costs ◽  
Network Layers ◽  
Total Inventory

Controlling the bullwhip effect and reducing the propagated inventory levels throughout the supply chain layers has an important role in reducing the total inventory costs of a supply chain. In this study, an optimal controller that considers demand as control variable is designed to dampen propagated inventory fluctuations for each node throughout the supply chain network. The model proves to be very useful in revealing the dynamic characteristics of the chain and provides a proper interface to study decisions taken into account at each node of the supply chain in different periods by decision makers (DMs). In the proposed approach, two feedback loops and online updated values of net stock quantities are used for calculation of the orders. To investigate the efficiency of the proposed approach, a real case of bicycle industry is conducted. The acquired results justify the efficiency of the proposed approach in controlling and dampening the bullwhip effect and reducing inventory levels, net stock quantities and inventory attributed costs throughout the supply chain network layers.

Empirical Assessment of Bullwhip Effect in Supply Networks

2021 ◽  
Vol 14 (2) ◽  
pp. 69-87
Author(s):  
Dazhong Wu ◽  
Joe Teng ◽  
Sergey Ivanov ◽  
Julius Anyu
Keyword(s):  
Regression Analysis ◽  
Supply Chain ◽  
Empirical Studies ◽  
Bullwhip Effect ◽  
Manufacturing Industries ◽  
The Other ◽  
Supply Chain Network ◽  
Supply Networks ◽  
Customer Base ◽  
Demand Variability

Previous empirical studies on bullwhip effects treat each industry or firm as isolated from its supply chain network. In this paper, the authors are interested in the role played by supply chain relational connection in moderating how demand variability signal is transmitted upstream. The paper conducts an empirical study based on a panel data of 55 manufacturing industries and 9 wholesale industries. The regression analysis shows that demand variability is propagated through supply chain upward and the transmission is influenced by the structural relationship between suppliers and customers, which is measured by customer-base concentration and customer interconnectedness. On the other hand, customer demand variability has a greater impact on industries with less concentrated customer base or with less interconnected customers.

A simulation-based optimization approach to integrated inventory management of a sawlog supply chain with demand uncertainty

2015 ◽  
Vol 45 (10) ◽  
pp. 1313-1326 ◽  
Author(s):  
Shashi Shahi ◽  
Reino Pulkki
Keyword(s):  
Supply Chain ◽  
Forest Management ◽  
Forest Products ◽  
Management Unit ◽  
Production Unit ◽  
Chain Model ◽  
Optimization Approach ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Flow Of Information

This paper develops a simulation-based optimization supply chain model for supplying sawlogs to a sawmill from a forest management unit. The simulation model integrates the two-way flow of information and materials under the stochastic demand of the sawmill production unit. The dynamic optimization model finds the optimum inventory policy (s, S) that minimizes the total inventory cost for the three supply chain agents — sawmill storage, merchandizing yard, and forest management unit. The model is used to analyze a real sawmill case study in northwestern Ontario, Canada. It was found that the merchandizing yard absorbs shocks of uncertain demand from the sawmill production unit and reduces idle time, but it increases the total cost of the supply chain by $11 802 (about 42%). The optimized model predicts that only 3.5 days of inventory is required at the sawmill storage. The simulation-based optimization supplier model will help in decision-making at the tactical and operational level in the forest products industry supply chain through a two-way flow of information and materials.

A simulation based optimization approach to supply chain management under demand uncertainty

2004 ◽  
Vol 28 (10) ◽  
pp. 2087-2106 ◽  
Author(s):  
June Young Jung ◽  
Gary Blau ◽  
Joseph F. Pekny ◽  
Gintaras V. Reklaitis ◽  
David Eversdyk
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Demand Uncertainty ◽  
Optimization Approach ◽  
Chain Management ◽  
Simulation Based ◽  
Simulation Based Optimization

scholarly journals Towards a simulation-based optimization approach to integrate supply chain planning and control

Procedia CIRP ◽  
2018 ◽  
Vol 72 ◽  
pp. 520-525 ◽  
Author(s):  
Matheus Cardoso Pires ◽  
Enzo Morosini Frazzon ◽  
Apolo Mund Carreirão Danielli ◽  
Mirko Kück ◽  
Michael Freitag
Keyword(s):  
Supply Chain ◽  
Optimization Approach ◽  
Supply Chain Planning ◽  
Planning And Control ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
And Control

scholarly journals A Simulation-based Optimization Approach to Logistic and Supply Chain Network Design

2021 ◽  
Vol 28 (97) ◽  
pp. 284-318
Author(s):  
Michael C. Jones ◽  
Thomas A. Mazzuchi ◽  
Shahram Sarkani
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Network Design ◽  
Network Design Problem ◽  
Optimization Approach ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Wide Range ◽  
Simulation Based ◽  
Simulation Based Optimization

The Department of Defense (DoD) operates a world-wide supply chain, which in 2017 contained nearly 5 million items collectively valued at over $90 billion. Since at least 1990, designing and operating this supply chain, and adapting it to ever-changing military requirements, are highly complex and tightly coupled problems, which the highest levels of DoD recognize as weaknesses. Military supply chains face a wide range of challenges. Decisions made at the operational and tactical levels of logistics can alter the effectiveness of decisions made at the strategic level. Decisions must be made with incomplete information. As a result, practical solutions must simultaneously incorporate decisions made at all levels as well as take into account the uncertainty faced by the logistician. The design of modern military supply chains, particularly for large networks where many values are not known precisely, is recognized as too complex for many techniques found in the academic literature. Much of the literature in supply chain network design makes simplifying assumptions, such as constant per-unit transportation costs regardless of the size of the shipment, the shipping mode selected, the time available for the delivery, or the route taken. This article avoids these assumptions to provide an approach the practitioner can use when designing and adapting supply chain networks. This research proposes a simulation-based optimization approach to find a near-optimal solution to a large supply chain network design problem of the scale faced by a theater commander, while recognizing the complexity and uncertainty that the practicing military logistician must deal with.

Optimising the Learning Potential of Simulations Through Structural Transparency and Exploratory Guidance

2020 ◽  
Vol 51 (4) ◽  
pp. 498-523
Author(s):  
Carlos Capelo ◽  
Ana Lorga Silva
Keyword(s):  
Supply Chain ◽  
Cognitive Processing ◽  
Instructional Strategy ◽  
Supply Chain Coordination ◽  
Bullwhip Effect ◽  
Learning Potential ◽  
Business Systems ◽  
Supply Chain System ◽  
Cognitive Aids ◽  
Simulation Based

Background. Simulation-based learning environments are used extensively to support learning in complex business systems. Nevertheless, studies have identified problems and limitations due to cognitive processing difficulties. In particular, previous research has addressed some aspects of model transparency and instructional strategy and produced inconclusive results. Aim. This study investigates the learning effects of using transparent simulations (that is, showing users the internal structure of models) and exploratory guidance (that is, guiding learners so they are able to explore the simulation by themselves, supported by specific cognitive aids) from a mental models perspective. Method. A test based on a simulation experiment with a system dynamics model, representing a supply chain system, was performed. Participants are required to use the simulator to investigate some issues related to the bullwhip effect and other supply chain coordination concepts. Results. Participants provided with the more transparent strategy and offered the more exploratory guidance demonstrated better understanding of the structure and behaviour of the underlying model. However, our results suggest that while exploratory guidance is a beneficial method for understanding both model structure and behaviour, making only the model transparent is more limited in its effect.

scholarly journals A Heuristic Simulation–Optimization Approach to Information Sharing in Supply Chains

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1319 ◽  
Author(s):  
KyoungJong Park
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Bullwhip Effect ◽  
Optimization Approach ◽  
Significance Level ◽  
Coordination System ◽  
Total Inventory ◽  
Asymmetric Flows ◽  
The Impact ◽  
Mean Variance

The sustainability of the supply chain is possible only if the profitability of all the tiers participating in that supply chain is guaranteed. The profitability of each of these tiers is ensured if information sharing as well as an effective and seamless coordination system are realized between the tiers. This process reduces the influence of an important risk factor known as the bullwhip effect. The purpose of the current study is to determine the necessary information sharing level to optimize the supply chain that has asymmetric flows of input and output values and to examine the effects of information sharing on the order fill rate (OFR) and total inventory cost (TIC) of the supply chain through analysis of variance (ANOVA) testing. In this work, the supply chain was optimized by using the particle swarm optimization (PSO) technique, with an objective function that assumes the maximization of OFR and minimization of TIC. The proposed method showed excellent results in comparing the mean, variance, and coefficient of variation. In addition, the method used the ANOVA test with a 5% significance level to verify the impact of the information sharing level.

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