System Dynamics Simulation of a Supply Chain Intelligence Model

2020 ◽  
pp. 1285-1298
Author(s):  
Debasri Dey ◽  
D. Sinha
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Production Rate ◽  
Return On Investment ◽  
Systems Approach ◽  
External Environment ◽  
Dynamics Simulation ◽  
Order Fulfillment ◽  
Root Cause ◽  
Individual Nodes

Supply chains today are, primarily, measured by Key Performance Indicators (KPIs) such as order-fulfillment, availability to the consumer (percent in-stock) and cost reduction, as well as financial KPIs such as return on investment (ROI), return on brand equity and inventory. These KPIs measure the performance of supply chain as a whole. A supply chain is a network of nodes. The performances of individual nodes are measured with KPIs such as production rate, shipment rate, inventory and the like. These metrics may indicate the performance but may not indicate the cause of such performance. For example, a node whose production rate is below the desired level may be because of poor supply of inputs of production by its supplier node.Thus mere identification of KPIs and their evaluation will not enable to identify the root cause of a problem in a supply chain. Therefore, we need a business intelligence framework that will satisfy the objectives, namely, identification of outcome of each node of the supply chain and its cause. The existing Supply Chain Intelligence (SCI) frameworks aims at identifying metrics that reflect the performance of individual nodes and the total supply chain, but fail to identify the cause of such outcomes. It implies that the linkages or association between the KPIs of individual nodes are required to be identified and defined. In this paper, contingency and systems approach has been used to identify the dimensions of the firm, its internal environment, the complement and the external environment. A system dynamics based approach has been used to identify the causality and resulting behavior of the supply chain. The paper proposes a SCI framework and a System dynamics Model that help in identifying the reasons for supply chin performance and lead to the actions required to be taken for improvement in performance of the supply chain.

System Dynamics Simulation of a Supply Chain Intelligence Model

2016 ◽  
pp. 71-83 ◽  
Author(s):  
Debasri Dey ◽  
D. Sinha
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Production Rate ◽  
Return On Investment ◽  
Systems Approach ◽  
External Environment ◽  
Dynamics Simulation ◽  
Order Fulfillment ◽  
Root Cause ◽  
Individual Nodes

Supply chains today are, primarily, measured by Key Performance Indicators (KPIs) such as order-fulfillment, availability to the consumer (percent in-stock) and cost reduction, as well as financial KPIs such as return on investment (ROI), return on brand equity and inventory. These KPIs measure the performance of supply chain as a whole. A supply chain is a network of nodes. The performances of individual nodes are measured with KPIs such as production rate, shipment rate, inventory and the like. These metrics may indicate the performance but may not indicate the cause of such performance. For example, a node whose production rate is below the desired level may be because of poor supply of inputs of production by its supplier node.Thus mere identification of KPIs and their evaluation will not enable to identify the root cause of a problem in a supply chain. Therefore, we need a business intelligence framework that will satisfy the objectives, namely, identification of outcome of each node of the supply chain and its cause. The existing Supply Chain Intelligence (SCI) frameworks aims at identifying metrics that reflect the performance of individual nodes and the total supply chain, but fail to identify the cause of such outcomes. It implies that the linkages or association between the KPIs of individual nodes are required to be identified and defined. In this paper, contingency and systems approach has been used to identify the dimensions of the firm, its internal environment, the complement and the external environment. A system dynamics based approach has been used to identify the causality and resulting behavior of the supply chain. The paper proposes a SCI framework and a System dynamics Model that help in identifying the reasons for supply chin performance and lead to the actions required to be taken for improvement in performance of the supply chain.

scholarly journals Contingencies of Violent Radicalization: The Terror Contagion Simulation

Systems ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 90
Author(s):  
Timothy Clancy ◽  
Bland Addison ◽  
Oleg Pavlov ◽  
Khalid Saeed
Keyword(s):  
System Dynamics ◽  
Simulation Model ◽  
Future Development ◽  
Policy Development ◽  
Dynamics Simulation ◽  
Mass Violence ◽  
Testing Environment ◽  
Root Cause ◽  
The Future ◽  
System Dynamics Simulation

This paper builds confidence in the terror contagion hypothesis that violent radicalization leading to predatory mass violence operates as a system. Within this system, the contingent values of key root causes create channels within which violent ideologies and terrorism emerge. We built a system dynamics simulation model capable of replicating historical reference modes and sophisticated enough to test the contingent values of these propositions. Of 16 propositions, we identified six root-cause propositions that must simultaneously exist, act in concert and explain the dynamics of their interaction which generate a terror contagion. Other propositions can strengthen or weaken an existing contagion but not eliminate it. We use an experiment to demonstrate how changing the contingent values of these propositions creates downward channels. This experiment helps reconcile the swarm vs. fishermen debate over the true root causes of violent radicalization. Within these channels, the contingent values can favor swarm or fishermen manifestations. The simulation and experimentation results enable the future development of the terror contagion hypothesis, provide a testing environment for research on violent radicalization, and provide a pathway to policy development in the combating of terrorism that arises from violent radicalization.

Modelling complexity in retail supply chains

Kybernetes ◽  
2016 ◽  
Vol 45 (2) ◽  
pp. 297-322 ◽  
Author(s):  
Esra Ekinci ◽  
Adil Baykasoglu
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Supply Chains ◽  
Information Sharing ◽  
Performance Measures ◽  
Dynamics Simulation ◽  
Type Analysis ◽  
Content Type ◽  
Retail Supply Chains ◽  
And Performance

Purpose – The purpose of this paper is to describe the characteristics of complexity and how a retail supply chain can contain complexity in itself. A case has been provided to show the measurement of complexity with/without information sharing and the relation of complexity with the performance measures. Quantification of the complexity will help the practitioners to take strategic actions. Design/methodology/approach – System dynamics simulation has been used to model the retail supply chain with and without information sharing and data visibility. Entropy-based metric used for quantification and comparison of complexity based on the outputs of the models. Performance measures proposed for the retail supply chains to understand the effect of data visibility. Findings – Paper provides insight about the complexity of retail supply chain perspective. Using system dynamics modelling can be a useful way to perform what-if type analysis before business process changes. Including both complexity and performance measures can be useful to understand if the complexity is good or bad for the business and if it is in manageable amount. Research limitations/implications – Paper can encourage the future research on retail supply chains. Practical implications – Approach can be useful to analyse what-if type analysis in practice easily. It can support strategic decision making process. Originality/value – Combines retail supply chain with complexity and performance measurement.

scholarly journals System dynamics comparison of three inventory management models in an automotive parts supply chain

2017 ◽  
Vol 11 ◽  
Author(s):  
Andries Botha ◽  
Jacomine Grobler ◽  
V.S. Sarma Yadavalli
Keyword(s):  
Supply Chain ◽  
System Dynamics ◽  
Inventory Management ◽  
Bullwhip Effect ◽  
Dynamics Simulation ◽  
Management Approach ◽  
Damping Factor ◽  
Theoretical Derivation ◽  
Automotive Parts ◽  
Moving Parts

Background: The automotive parts supply chain measures its success in terms of parts availability and stock required to achieve the availability target, measured as allocation fill rate (AFR). The supply chain strives to achieve an AFR target of 95.5% while maintaining low levels of stock.Objective: The first objective of this study is to evaluate the current inventory management approach, namely the maximum inventory position (MIP) method, to understand the difference between the theoretical derivation and the actual implementation. The second objective is to develop and compare the performance of a new stock target setting (STS) method relative to the MIP methods.Method: The theoretical and actual equations behind the MIP and STS methods are derived for steady state as well as stochastic conditions. A system dynamics simulation model (SDSM) was developed to describe both the local and imported supply chains. The SDSM was used to simulate and confirm the parameters for the STS method. It was also used to compare the three inventory management methods against a theoretical environment and actual data sets.Results: The STS method requires a damping factor (DF) to ensure it does not cause the bullwhip effect. The SDSM was used to determine that a value equal to the lead time ensures effective damping. In the theoretical environment, the MIPTheory method requires the lowest stock, but also has the lowest AFR. MIPActual achieves the highest AFR, but with significantly higher stock holding. The STS method improves on the AFR achieved by the MIPTheory method, with lower stock holding than the MIPActual method. With the actual demand data sets, the results vary by parts movement type. With fast moving parts, all methods achieve the AFR target, the MIPActual method has a higher stock holding for all cases, and the STS method results in reduced stock holding for 7 of 12 cases. With medium moving parts, the MIPActual method improves on the AFR in all 15 cases, but with significantly higher stock. The STS method increases the AFR in 7 of 15 cases and reduces the stockholding in 11 of 15 cases. With slow moving parts, both the MIPActual and STS methods improve the AFR with increased stock holding. The increase in stock holding for the STS method is significantly lower. With erratic moving parts, the MIPActual method improves on the AFR in all 17 cases, but requires significantly higher stock holding. The STS method achieves lower AFR values in 10 of 17 cases, but also requires lower or equal stock holding in 10 of 17 cases.Conclusion: The STS method provides a new approach to inventory management in the automotive supply chain. It provides improved performance for lower stock holding than the implemented MIP method (MIPActual). The results for the different movement category suggest that there is further research to be done to confirm the effectiveness of the various methods with other demand distributions.

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