Simulation-Based Measurement Of Supply Chain Risks (original) (raw)
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Simulation-Based Risk Measurements In Supply Chains
ECMS 2006 Proceedings edited by: W. Borutzky, A. Orsoni, R. Zobel, 2006
Because of modern supply chains complexity, supply chain managers are presently making decisions which are associated with some risk. This paper discusses risk problem in supply chains and presents a supply chain risk assessment approach. The Beer Game model, which is a simplified example of a supply chain, is used to demonstrate application of the suggested risk assessment approach. Two risk events are taken into account: inventory excessive accumulation and backordering. Simulation experiments with the computerised version of the Beer Game show that the approach presented can be used for assessing supply chain risk; at the same time, it is possible to illustrate risk occurrence using the Beer Game model.
Risk management in supply chains : a simulation and model driven engineering approach
2018
La maitrise des risques est un enjeu majeur pour les entreprises. Loin d’etre l’apanage des seules catastrophes naturelles, les perturbations des chaines logistiques actuelles peuvent parfois etre causees par des evenements mineurs amplifies par les failles d’organisations industrielles de plus en plus complexes. Nombreux sont les exemples de ces perturbations avec des consequences economiques graves.La gestion des risques dans les chaines logistiques est un theme recent et les methodes et outils actuels ne repondent pas encore totalement aux preoccupations des gestionnaires de ces chaines logistiques. Une grande aide peut etre apportee par la simulation des evenements affectant les chaines. Cependant malgre son efficacite pour couvrir la complexite de la chaine, la simulation reste encore difficile a mettre en œuvre, notamment dans les phases de creation et d’exploitation des modeles.Le but de cette these est de faciliter l’utilisation de la simulation pour l’analyse des risques da...
Risk analysis for supply chain networks
2014
The research is focused on the development of an integrated generic supply chain risk-, crises and performance management and monitoring system under the aspect of a comprehensive approach. Building on existing results from previous research activities we created a generic, integrated and systemic supply chain network model. Based on this model we described various scenario portfolios and single scenarios. We developed a risk assessment and analysis process model. The research results should create a unique risk rating and crises management monitoring system for decision making under the systemic view and criteria of: • event orientation • organisational orientation • cause/trigger orientation • time frame • region • level of abstraction Beside the generation of the systemic supply chain network model, the scenario development, the risk assessment and analysis process and the risk rating and crises management monitoring model our research result can be used as well as for organizati...
Short Papers Analytical Framework for the Management of Risk in Supply Chains
2007
In this paper, we develop a framework to classify supply chain risk-management problems and approaches for the solution of these problems. We argue that risk-management problems need to be handled at three levels: 1) strategic, 2) operational, and 3) tactical. In addition, risk within the supply chain might manifest itself in the form of deviations, disruptions, and disasters. To handle unforeseen events in the supply chain, there are two obvious approaches: 1) to design chains with built-in risk tolerance and 2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the supply chain and the associated consequences and impacts from these events. Having described these approaches, we then focus our efforts on mapping out the propagation of events in the supply chain due to supplier nonperformance, and employ our insight to develop two mathematical programming-based preventive models for strategic level deviation and disruption management. The first model, a simple integer quadratic optimization model, adapted from the Markowitz model, determines optimal partner selection with the objective of minimizing both the operational cost and the variability of total operational cost. The second model, a simple mixed integer programming optimization model, adapted from the credit risk minimization model, determines optimal partner selection such that the supply shortfall is minimized even in the face of supplier disruptions. Hence, both of these models offer possible approaches to robust supply chain design. Index Terms-Cause-consequence diagrams, failure analysis, mean-variance optimization, partner selection, risk management, supply chain design, supply chain planning, supply chain risk management.
Modelling and analysis of risk and reliability for a contingency logistics supply chain
Contingency logistic supply chain design and performance are very important for rare events such as natural disasters, terrorist attacks, and other emergency conditions. Although there is ample work on the evaluation of supply chain systems in terms of their profitability and efficiency, there is little work on the effectiveness of the response time of the supply chain, as well as quantitative measures for the robustness of the design and the reliability of the supply chain. Depending on the contingency, the perceived risk may be different for different situations. The level of risk aversion is dependent on the decision maker and the uncertainties associated with the supply and demand for the contingency. In this study the risk associated with shortages due to the uncertainties in supply and demand in the supply chain are modelled using distortion. In addition, a numerical analysis is performed and the sensitivity of the parameters in a contingency logistic supply chain is analysed. Methods to improve the effectiveness of the contingency operation are examined by efficiently allocating available resources, supplies, and time as well as modelling the decision maker's perception associated with the failure of a particular node. An array of optimization models are formulated and discussed. Each of these models enables a decision maker to examine a variety of trade-offs in a contingency logistics supply chain. For example, one model examines the acquisition of mitigation strategies and the reallocation of resources while taking into consideration the decision maker's risk aversion. The main contribution of this paper is the establishment of an analysis framework for modelling and analysing the risk and reliability of contingency logistic supply chains.
A Systems approach for modelling Supply Chain Risks
A Systems approach for modelling Supply Chain Risks, 2013
""Purpose- With increasing exposure to disruptions, it is vital for supply chains to manage the risks proactively. Prediction of potential failure points and overall impact of these risks is challenging. In this paper, systems thinking concepts are applied for modelling supply chain risks. The aim of this research is to develop a holistic, systematic and quantitative risk assessment process for measuring the overall risk behaviour. Design/Methodology/Approach- A framework for Supply Chain Risk Management (SCRM) is developed and tested using an industrial case study. A systematically developed research design is employed to capture the dynamic behaviour of risks. Additionally, a system based supply chain risk model is conceptualized for risk modelling. Sensitivity modelling results are combined for validating the supply chain risk model. Findings- The systems approach for modelling supply chain risks predicts the failure points along with their overall risk impact in the supply chain network. System based risk modelling provides a holistic picture of risk behavioural performance which is difficult to realise through other research methodologies commonly preferred in the SCRM research. Practical Implications- The developed framework for SCRM is tested in an industry setting for its viability. The framework for SCRM along with the supply chain risk model is expected to benefit practitioners in understanding the intricacies of supply chain risks. The system model for risk assessment is a working tool which could provide a perspective of future disruptive events. Originality- A holistic, systematic and quantitative risk modelling mechanism for capturing overall behaviour of risks is a valuable contribution of this research. The paper presents a new perspective towards using systems thinking for modelling supply chain risks. ""
Methods of reliability management in supply chain
22nd International Scientific Conference Engineering for Rural Development Proceedings
The main problem of choosing an adequate method of managing the reliability of supply chains is that for both participants and consumers all the main properties of an effective supply chain (failure, economy and security of supply) are equally relevant. Therefore, it is necessary to jointly use the tools of reliability theory, planning methods built on the basis of operation research and risk management methods. The work considers mathematical models and methods for assessing and improving the reliability of supply chains and proposes an algorithm for calculating the reliability of supplies that corresponds to the exponential and normal laws of the distribution of failure intensity. According to the algorithm, on the example of the supply chain of spare parts of the “Sfera-Auto” enterprise, the reliability indicators of the supply chain were calculated, namely: failure time, failure intensity, average recovery time, recovery intensity and probability of failure-free delivery, and ch...
International Journal of Supply Chain Management, 2014
This paper explores the effects of Supply, Production and Distribution reliabilities in a thre e- tier supply chain. The central thrust of the work i s to examine and analyse on conceptual and analytical bases the effects of reliabilities of the systemic components of the Supply Chain on the overall cost of the Supply Chain. A linear cost-minimization Mathematical Programming Model of a three-stage supply chain is built in which the supply, producti on and distribution reliabilities determine supplier, production facility and distributor selections and contributing quantities to supplies, productions an d distributions of goods respectively. Test experiments are designed as trade-offs of costs and reliabiliti es incorporated into the variants of the Mathematical Programming Model to study combinations of effects reliabilities on the cost structure of the Supply C hain.
Modeling Supply Chain Risk for Operational Supply Chain Planning
As today's supply chain (SC) networks are globalized complex systems planning and optimizing SC processes is harder than ever. Unexpected deviations and disruptions can lead to devastating and far-reaching consequences. Hence, the management of SC risk (SCR) is of fundamental importance. The literature on SCR is, however, mostly of anecdotic nature; only few authors present empirical research. So far no unanimous framework has been developed to explicitly quantify SCR and the underlying SC vulnerability drivers. To ensure that SCR management can be realized as a continuous process, SC managers, seek solutions that integrate SCR analysis in their routines and planning processes. Therefore, the challenge is to model SCR in a way that is both transferable to models of proprietary operational SC planning engines and suitable for quantitative SCR analysis. In our work, we combine simulation and operational SC planning to identify factors that enhance the vulnerability of complex SC systems.
Int. J. Comb. Optim. Probl. Informatics, 2016
Measure the reliability of a supply chain is an important step to promote the construction of more resilient chains. The resilience is necessary to guaranty the efficient and secure global movement of goods. This paper proposes an instrument to analysis and evaluates the reliability of a system. Also compare scenarios of reliability between a basic supply chains versus complex systems. Finally present useful conclusion to the construction of resilient and robust supply chain.