69,648 research outputs found

    Enhancing Agility of Supply Chains Using Stochastic, Discrete Event and Physical Simulation Models

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    Managing supply chains in today’s distributed manufacturing environment has become more complex. To remain competitive in today’s global marketplace, organizations must streamline their supply chains. The practice of coordinating the design, procurement, flow of goods, services, information and finances, from raw material flows to parts supplier to manufacturer to distributor to retailer and finally to consumer requires synchronized planning and execution. Efficient and effective supply chain management assists an organization in getting the right goods and services to the place needed at the right time, in the proper quantity and at acceptable cost. Managing this process involves developing and overseeing relationships with suppliers and customers, controlling inventory, and forecasting demand, all requiring constant feedback from every link in the chain. First, a survey of existing stochastic models is presented. Base Stock Model and Q (r) models are applied to three tier single-product supply chains to calculate order quantities and reorder point at various locations within the supply chain. A computer based discrete event simulation model is created to study the three tier supply chain and to validate the results from the stochastic models. Results indicate that agility of supply chains can be enhanced by using the stochastic models to calculate order quantities and reorder points. In addition to reducing the total cost of inventory, probability of backorder and customer dissatisfaction is minimized. Results are further validated with physical simulations. Both computer based simulation and physical simulation demonstrate the improvement in the agility of the supply chain with reduced cost for inventory

    Modeling Multilevel Supply Chain Systems to Optimize Order Quantities and Order Points Through Mathematical Models, Discrete Event simulation and Physical Simulations

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    Managing supply chains in today\u27s distributed manufacturing environment has become more complex. To remain competitive in today\u27s global marketplace, organizations must streamline their supply chains. The practice of coordinating the design, procurement, flow of goods, services, information and finances, from raw material flows to parts supplier to manufacturer to distributor to retailer and finally to consumer requires synchronized planning and execution. Efficient and effective supply chain management assists an organization in getting the right goods and services to the place needed at the right time, in the proper quantity and at acceptable cost. Managing this process involves developing and overseeing relationships with suppliers and customers, controlling inventory, and forecasting demand, all requiring constant feedback from every link in the chain. Base Stock Model and (Q, r) models are applied to three tier single-product supply chain to calculate order quantities and reorder point at various locations within the supply chain. Two physical simulations are designed to study the above supply chain. One of these simulations is specifically designed to validate the results from Base Stock model. A computer based discrete event simulation model is created to study the three tier supply chain and to validate the results of the Base Stock model. Results from these mathematical models, physical simulation models and computer based simulation model are compared. In addition, the physical simulation model studies the impact of lean implementation through various performance metrics and the results demonstrate the power of physical simulations as a pedagogical tool for training. Contribution of present work in understanding the supply chain integration is discussed and future research topics are presented

    Supply chain risks: an automotive case study

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    The supply chain is a complex system exchanging information, goods, material and money within enterprises, as well as between enterprises within the value chain. An effective supply chain management contributes to large corporate profits and it is therefore a valid path to reinforce the enterprises' competitiveness. However, supply chain is exposed to influences from undesirable factors both from the outside environment and the entities in the chain. Moreover, industrial trends towards lean production, increasing outsourcing, globalisation and reliance on supply networks capabilities and innovations, increase the complexity of the supply chain . Therefore, managers need to identify, and manage risks, as well as opportunities, from a more diverse range of sources and contexts. This paper contributes to identify and categorise supply chain risks based on a literature study and an automotive manufacturer’s viewpoint. The empirical results indicate suppliers and raw material prices as the major internal and external potential risks

    Sustainable fibre for sustainable fashion supply chains: where the journey to sustainability begins

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    Adopting a sustainable business model is an essential element of gaining competitive advantage. Specifically, the management of fashion and textile supply chains characterized by geographical extension requires paying particular attention to environmental and social sustainability. Following an analysis of the literature on sustainable supply chains in the fashion and textile industries, this qualitatively based research examines – from a supply chain perspective – the sustainability initiatives implemented by a yarn and garment producer through a single case study. Subsequently, the classification of potential sustainability initiatives is presented. From this investigation, several good practices for sustainable fashion supply chains can be identified, providing a reference point for similar companies. Keywords: sustainable fashion supply chain, sustainable textiles, closed loop supply chai

    An integrated approach to supply chain risk analysis

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    Despite the increasing attention that supply chain risk management is receiving by both researchers and practitioners, companies still lack a risk culture. Moreover, risk management approaches are either too general or require pieces of information not regularly recorded by organisations. This work develops a risk identification and analysis methodology that integrates widely adopted supply chain and risk management tools. In particular, process analysis is performed by means of the standard framework provided by the Supply Chain Operations Reference Model, the risk identification and analysis tasks are accomplished by applying the Risk Breakdown Structure and the Risk Breakdown Matrix, and the effects of risk occurrence on activities are assessed by indicators that are already measured by companies in order to monitor their performances. In such a way, the framework contributes to increase companies' awareness and communication about risk, which are essential components of the management of modern supply chains. A base case has been developed by applying the proposed approach to a hypothetical manufacturing supply chain. An in-depth validation will be carried out to improve the methodology and further demonstrate its benefits and limitations. Future research will extend the framework to include the understanding of the multiple effects of risky events on different processe

    Supply chain uncertainty:a review and theoretical foundation for future research

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    Supply-chain uncertainty is an issue with which every practising manager wrestles, deriving from the increasing complexity of global supply networks. Taking a broad view of supply-chain uncertainty (incorporating supply-chain risk), this paper seeks to review the literature in this area and develop a theoretical foundation for future research. The literature review identifies a comprehensive list of 14 sources of uncertainty, including those that have received much research attention, such as the bullwhip effect, and those more recently described, such as parallel interaction. Approaches to managing these sources of uncertainty are classified into: 10 approaches that seek to reduce uncertainty at its source; and, 11 approaches that seek to cope with it, thereby minimising its impact on performance. Manufacturing strategy theory, including the concepts of alignment and contingency, is then used to develop a model of supply-chain uncertainty, which is populated using the literature review to show alignment between uncertainty sources and management strategies. Future research proposed includes more empirical research in order to further investigate: which uncertainties occur in particular industrial contexts; the impact of appropriate sources/management strategy alignment on performance; and the complex interplay between management strategies and multiple sources of uncertainty (positive or negative)
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