A Simulation Model for Decision Support in Business Continuity Planning

Enterprises with a global supply network are at risk of lost revenue as a result of disruptive disasters at supplier locations. Various strategies exist for addressing this risk, and a variety of types of research has been done regarding the identification, assessment and response to the risk of disruption in a supply chain network. This thesis establishes a decision model to support Business Continuity Planning at the first-tier supplier level. The decision model incorporates discrete-event simulation of supply chain networks (through Simio software), Monte Carlo simulation, and risk index optimization. After modeling disruption vulnerability in a supply chain network, costs of implementing all combinations of Business Continuity Plans are ranked and then tested in discrete-event simulation for further insight into inventory levels, unmet customer demand, production loss and related costs. A case study demonstrates the implementation of the decision support process and tests a historical set of data from a large manufacturing company. Discrete-event simulation modeling of loss is confirmed to be accurate. The relevance of the model concept is upheld and recommendations for future work are made

Modeling and analysis of supply chain risk system under the influence of partners' collaboration

Confining the focus to individual enterprise, traditional risk management literature seems to echo inadequately in the context of collaborative supply chain, partially due to its exclusion of correlation of risks across companies. In this study, we refine the notion of risk in supply chain and propose a model of supply chain risk system (SCRS) that consciously takes into account the correlation among risks resulting from partners' collaboration. Through analytical inference we found that the level of collaboration contributes to the resilience of supply chain. It implies that collaboration can positively affect the topology of SCRS, thus benefiting supply chain operations in terms of risk management. A simulation program has been developed with aim to demonstrate the practical feasibility of the proposed model. Implemented in simulation, two sets of experiments have been conducted for testing the model in actual business scenarios. The experimental results provide supporting evidence to consolidate the analytical findings. © 2011 IEEE.published_or_final_versionThe 44th Hawaii International Conference on System Science (HICSS 2011), Kauai, HI., 4-7 January 2011. In Proceedings of the Hawaii International Conference on System Sciences, 2011, p. 1-1

Supply Chain Management analysis: a simulation approach of the Value Chain Operations Reference model (VCOR)

International audienceThe impact of globalization and worldwide competition has forced firms to modify their strategies towards a real time operation with respect to customer's requirements. This behaviour, together with the communication possibilities offered by the actual Information and Communication Technologies, allows the top management to move towards the concept of extended enterprise in which a collaborative link is established among suppliers, commercial partners and customers. When the information flows involve each actor of the chain, from suppliers to the final distribution centers, the extended enterprise becomes a virtual firm, that can be defined as a set of stand-alone operational units that acts to reconfigure themselves as a value chain in order to adapt to the business opportunities given by the market. The present work is intended to verify through a simulation approach the quantitative advantages that can be obtained by the introduction of the Value Chain concept into the Supply Chain Management (SCM). The paper, after a description of the two most known (SCM) methods - SCOR and VCOR - makes a comparison between them by the customer's point of view. In the second part of the work a simulation model has been developed to verify the advantage that the VCOR is able to obtain, validating it on an industrial case study

Modeling the environmental impact of demand variability upon supply chains in the beverage industry

High demand variability can produce several inefficiencies in the supply chain, increasing cost and decreasing service level. This research focuses on the environmental impact of demand variability on supply chains especially in the beverage industry by investigating the relationship between demand variability and the emissions of carbon dioxide. The analysis was based on a beverage industry case, considering a two-stage supply chain. A simulation model was developed to represent the supply chain. The experimental factors demand variability, demand level, forecast method, system size, and truck fleet configuration were manipulated in order to represent different scenarios. A statistical Design of Experiment (DOE) model was used to understand the impact of these factors in relation to the emissions of carbon dioxide, cost and service level. The findings suggest that increments in demand variability result in an increment in carbon dioxide emissions due to the distribution of product. It was also observed that an increment in demand variability results in an increment in cost and a decrease in service level. The study also suggests that the factors that influence demand level and truck fleet configuration have a significant impact on the amount of carbon dioxide emissions. A significant interaction between demand variability and demand level was also identified in relation to carbon dioxide emissions, cost, and service level. Trade-offs were identified between carbon dioxide emissions and service level as well as between cost and service level

Risk from network disruptions in an aerospace supply chain

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 76-77).This thesis presents methods for determining the effects of risk from disruptions using an aerospace supply chain as the example, primarily through the use of a computer simulation model. Uncertainty in the current marketplace requires managers to be cognizant of the adverse impact of risk on their company's performance. However, managers who lack formal procedures for dealing with the potential impact of risk often are caught not knowing how much to invest in risk mitigation strategies. A computer simulation model representing a supply chain for a space vehicle was used to test different disruption scenarios to determine their impact on total production duration time. Scenarios ranging from suppliers not providing parts on time to quality test failures to disease pandemics were all considered. Randomness was incorporated through use of a stochasticity factor that was applied uniformly throughout the model. Output of the model was used to develop confidence percentiles for the complete duration times. Through testing of the various scenarios using the model we learned that most disruptions will add a deterministic time to the total estimated duration time of the system, regardless of the location of the disruption in the supply chain. In addition, we showed that a thorough review must be performed when choosing the stochasticity factor due to its large influence in determining total duration times and performance percentiles. The creation of the confidence percentiles allows the aerospace company to use the model throughout the entire 3 to 4 year production process to continually update and evaluate their buffer times and likelihood of meeting target completion dates. This buffer time can then be turned into a key performance index to better manage this supply chain. This model was created for a real supply chain, and it is currently being used by the aerospace company to help them plan and make appropriate decisions in regards to risk mitigation strategies in preparation for production of the space vehicle. They hope to expand the use of computer simulation models throughout the rest of their division to help drive down costs by increasing efficiencies in their planning.by Bryan K. Wilson.M.Eng.in Logistic

The development of a swarm intelligent simulation tool for sugarcane transport logistics systems.

Thesis (M.Sc.)-University of KwaZulu-Natal, 2008.Transport logistics systems typically evolve as networks over time, which may result in system rigidity and cause changes to become expensive and time consuming. In this study a logistics model, named TranSwarm, was developed to simulate sugarcane harvesting, transport and mill-yard activities for a mill supply area. The aim was to simulate produce flow, and allow individual working entities to make decisions, driven by rules and protocols, based on their micro-environments. Noodsberg mill was selected as a case study because of low current levels of synchronization. Growers were assumed to operate independent harvesting and transport systems causing inconsistent convergences at the mill. This diverse and fragmented system provided a suitable environment to construct a model that would consider interactions between individual growers and their respective transport systems. Ideally, by assessing the micro-decisions of individuals and how they influence the larger holistic supply chain, TranSwarm quantifies the impacts of different types of transport practices, such as staggering shift changes, transport scheduling, core sampling and consortium-based logistics. TranSwarm is visual, mechanistic and represents key entities, such as roads, farm groupings and the mill. The system uses discrete events to create a dynamic and stochastic environment from which observations and conclusions can be drawn. This approach potentially allows stakeholders to identify key components and interactions that may jeopardize overall efficiency and to use the system to test new working protocols and logistics rules for improving the supply chain

Propuesta metodológica para la gestión del riesgo en las redes abastecimiento: caso de estudio abasto de medicamentos oncológicos de una IPS (Institución prestadora de Salud) de Bogotá

Esta tesis de maestría presenta una propuesta metodológica para la Gestión de los riesgos de las redes de abastecimiento. La metodología propuesta plantea un nuevo marco conceptual sobre los orígenes y tipos de riesgo que pueden afectar los flujos de producto, información y dinero que se presentan en las redes de abastecimiento; así como herramientas y procedimientos para ejecutar las diversas etapas del proceso de gestión de riesgo en el contexto logístico (identificación, evaluación y priorización de riesgos y definición de estrategias de mitigación apropiadas). Por último, se presenta un caso de estudio en donde se aplica la metodología propuesta con el fin de identificar y evaluar los riesgos que afectan la disponibilidad de medicamentos oncológicos en una IPS (Institución Prestadora de servicios de Salud) de Bogotá. Los resultados del caso de estudio permitieron identificar los riesgos que mayor impacto negativo presenta sobre el nivel de servicio de uno de los medicamentos oncológicos más importantes de la IPS objeto de estudio.Abstract. This thesis presents a proposal for risk management in supply networks. The proposed methodology presents a new conceptual framework about the origins and types of risk that could affect the flow of product, information and money which take place within supply networks; as well as tools and procedures for implementing the various stages of risk management process in the logistics context (identifying, assessing and prioritizing risks, and defining the most appropriate risk mitigation strategies). A case study where the proposed methodology is used to identify and assess the risks affecting the availability of cancer drugs at an IPS (Health Center) applies Bogotá is presented. The results of the case study provided the list of the risks which have the highest negative influence over the service level of one of the most relevant cancer medicines at the chosen IPS (Health Center)Maestrí