Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems–a simulation approach

2009 ◽  
Vol 48 (15) ◽  
pp. 4501-4517 ◽  
Author(s):  
P. Wangphanich ◽  
S. Kara ◽  
B. Kayis
Keyword(s):  
Supply Chain ◽  
Bullwhip Effect ◽  
Simulation Approach ◽  
Multi Stage

Investigating the impact of inventory inaccuracy on the bullwhip effect in RFID-enabled supply chains using colored petri nets

2019 ◽  
Vol 14 (2) ◽  
pp. 360-384 ◽  
Author(s):  
Maria Drakaki ◽  
Panagiotis Tzionas
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Petri Nets ◽  
Information Sharing ◽  
Bullwhip Effect ◽  
Colored Petri Nets ◽  
Content Type ◽  
Multi Stage ◽  
The Impact ◽  
Serial Supply Chains

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.

Information Sharing in Supply Chain Management with Demand Uncertainty

2011 ◽  
pp. 44-62 ◽  
Author(s):  
Zhensen Huang ◽  
Aryya Gangopadhyay
Keyword(s):  
Supply Chain ◽  
Information Sharing ◽  
Demand Uncertainty ◽  
Bullwhip Effect ◽  
Supply Chain Performance ◽  
Chain Model ◽  
Simulation Approach ◽  
Supply Chain Model ◽  
Trading Partners ◽  
The Impact

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.

scholarly journals A multi-period and multi-product optimizer for cooperative supply chains

2012 ◽  
Vol 43 (1) ◽  
pp. 77-92 ◽  
Author(s):  
M. Sepehri ◽  
K. Fayazbakhsh
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Minimum Cost ◽  
Bullwhip Effect ◽  
Coordination Mechanism ◽  
Holding Costs ◽  
Multi Stage ◽  
Long Run ◽  
Operational Information ◽  
Supply Chain Simulation

Members in a traditional supply chain compete to reduce their individual costs. But total cost is minimized in a cooperative, or a corporate managed, supply chain. A lower average cost and a lower cost variation are achieved by cooperative individual members in the long-run. The problem is formulated and solved as an integrated flow network. Previous research is expanded to include multi-period and multi-product cooperative supply chain with possibility of holding inventory in a multi-stage, multi-member setup. A Cooperative Supply Optimizer System (CSOS), a software-based coordination mechanism, is developed for large chains. It gathers operational information from members of the supply chain, and then guides them on ordering decisions for a minimum cost of the entire supply chain. Simulation results indicate an approximately 26% reduction in total supply chain costs, utilizing this formulation over a competitive setup. As the holding costs increase, the problem decomposes into single period (Just-in-time) again. The disturbing bullwhip effect disappears in cooperative supply chains.

Minimizing the bullwhip effect in a supply chain: a simulation approach using the beer game

SIMULATION ◽  
2020 ◽  
Vol 96 (9) ◽  
pp. 737-752
Author(s):  
Abdullah A Alabdulkarim
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Simulation Modeling ◽  
Reference Model ◽  
Bullwhip Effect ◽  
Optimal Decision ◽  
Simulation Approach ◽  
Management Policies ◽  
The Cost ◽  
Beer Game

In this research, the aim is to find the most appropriate inventory management logic and set of rules along with the optimal decision values that will minimize the bullwhip effect in a supply chain, taking the beer game supply chain as a reference model. In order to achieve this, a simulation model of the beer game supply chain is developed along with an ordering strategy based on the Economic Order Quantity with additional rules, such as no backorder policy, vendor-managed inventory, and taking into consideration route deliveries, all of which are implemented in the ordering algorithm. In the literature, there is extensive research conducted on the causes of the bullwhip effect and in the presence of certain inventory management policies. However, these terms are rarely combined with simulation modeling to provide satisfactory proven results. In this article, our proposed ordering algorithm avoids the bullwhip effect to a very large extent. The results show that approximately half the cost is incurred compared to recent studies with the same settings.

A Review on the Application of Neural Networks for Decreasing Bullwhip Effect in Supply Chain

2015 ◽  
Vol 9 (5) ◽  
pp. 438
Author(s):  
Milad Yousefi ◽  
Moslem Yousefi ◽  
Ricardo Poley Martins Ferreira
Keyword(s):  
Neural Networks ◽  
Supply Chain ◽  
Bullwhip Effect

Classification of Operational and Financial Variables Affecting the Bullwhip Effect in Indian Sectors: A Machine Learning Approach

2019 ◽  
Vol 12 (3) ◽  
pp. 171-179 ◽  
Author(s):  
Sachin Gupta ◽  
Anurag Saxena
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Product Life Cycle ◽  
Consumer Preference ◽  
Bullwhip Effect ◽  
Machine Learning Techniques ◽  
Chain Management ◽  
Financial Variables

Background: The increased variability in production or procurement with respect to less increase of variability in demand or sales is considered as bullwhip effect. Bullwhip effect is considered as an encumbrance in optimization of supply chain as it causes inadequacy in the supply chain. Various operations and supply chain management consultants, managers and researchers are doing a rigorous study to find the causes behind the dynamic nature of the supply chain management and have listed shorter product life cycle, change in technology, change in consumer preference and era of globalization, to name a few. Most of the literature that explored bullwhip effect is found to be based on simulations and mathematical models. Exploring bullwhip effect using machine learning is the novel approach of the present study. Methods: Present study explores the operational and financial variables affecting the bullwhip effect on the basis of secondary data. Data mining and machine learning techniques are used to explore the variables affecting bullwhip effect in Indian sectors. Rapid Miner tool has been used for data mining and 10-fold cross validation has been performed. Weka Alternating Decision Tree (w-ADT) has been built for decision makers to mitigate bullwhip effect after the classification. Results: Out of the 19 selected variables affecting bullwhip effect 7 variables have been selected which have highest accuracy level with minimum deviation. Conclusion: Classification technique using machine learning provides an effective tool and techniques to explore bullwhip effect in supply chain management.

scholarly journals A COVID-19 Supply Chain Management Strategy Based on Variable Production under Uncertain Environment Conditions

2021 ◽  
Vol 18 (4) ◽  
pp. 1662
Author(s):  
Mohammed Alkahtani ◽  
Muhammad Omair ◽  
Qazi Salman Khalid ◽  
Ghulam Hussain ◽  
Imran Ahmad ◽  
...  
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Production Rate ◽  
Manufacturing System ◽  
Working Environment ◽  
Data Set ◽  
Manufacturing Firm ◽  
Chain Management ◽  
Ripple Effects ◽  
Multi Stage

The management of a controllable production in the manufacturing system is essential to achieve viable advantages, particularly during emergency conditions. Disasters, either man-made or natural, affect production and supply chains negatively with perilous effects. On the other hand, flexibility and resilience to manage the perpetuated risks in a manufacturing system are vital for achieving a controllable production rate. Still, these performances are strongly dependent on the multi-criteria decision making in the working environment with the policies launched during the crisis. Undoubtedly, health stability in a society generates ripple effects in the supply chain due to high demand fluctuation, likewise due to the Coronavirus disease-2019 (COVID-19) pandemic. Incorporation of dependent demand factors to manage the risk from uncertainty during this pandemic has been a challenge to achieve a viable profit for the supply chain partners. A non-linear supply chain management model is developed with a controllable production rate to provide an economic benefit to the manufacturing firm in terms of the optimized total cost of production and to deal with the different situations under variable demand. The costs in the model are set as fuzzy to cope up with the uncertain conditions created by lasting pandemic. A numerical experiment is performed by utilizing the data set of the multi-stage manufacturing firm. The optimal results provide support for the industrial managers based on the proactive plan by the optimal utilization of the resources and controllable production rate to cope with the emergencies in a pandemic.

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