The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
D Eversdyk - One of the best experts on this subject based on the ideXlab platform.
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integrated Safety Stock management for multi stage supply chains under production capacity constraints
Computers & Chemical Engineering, 2008Co-Authors: Joo Yung Jung, Gary Blau, Joseph F. Pekny, Gintaras V. Reklaitis, D EversdykAbstract:Abstract In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety Stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher Safety Stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in Safety Stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with Safety Stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of Safety Stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on Safety Stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based Safety Stock management model is developed, in which the Safety Stock control variables (the target inventory levels used in production planning and scheduling models, base-Stock levels for the base-Stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the Safety production capacity limits in Safety Stock management are properly represented. To demonstrate the effectiveness of the proposed Safety Stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3–8 warehouses supplying 10 final products to 30 sales regions.
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Integrated Safety Stock management for multi-stage supply chains under production capacity constraints
Computers and Chemical Engineering, 2008Co-Authors: Joo Yung Jung, Gary Blau, Joseph F. Pekny, Gintaras V. Reklaitis, D EversdykAbstract:In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety Stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher Safety Stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in Safety Stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with Safety Stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of Safety Stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on Safety Stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based Safety Stock management model is developed, in which the Safety Stock control variables (the target inventory levels used in production planning and scheduling models, base-Stock levels for the base-Stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the Safety production capacity limits in Safety Stock management are properly represented. To demonstrate the effectiveness of the proposed Safety Stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3-8 warehouses supplying 10 final products to 30 sales regions. © 2008 Elsevier Ltd. All rights reserved.
Joo Yung Jung - One of the best experts on this subject based on the ideXlab platform.
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integrated Safety Stock management for multi stage supply chains under production capacity constraints
Computers & Chemical Engineering, 2008Co-Authors: Joo Yung Jung, Gary Blau, Joseph F. Pekny, Gintaras V. Reklaitis, D EversdykAbstract:Abstract In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety Stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher Safety Stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in Safety Stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with Safety Stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of Safety Stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on Safety Stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based Safety Stock management model is developed, in which the Safety Stock control variables (the target inventory levels used in production planning and scheduling models, base-Stock levels for the base-Stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the Safety production capacity limits in Safety Stock management are properly represented. To demonstrate the effectiveness of the proposed Safety Stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3–8 warehouses supplying 10 final products to 30 sales regions.
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Integrated Safety Stock management for multi-stage supply chains under production capacity constraints
Computers and Chemical Engineering, 2008Co-Authors: Joo Yung Jung, Gary Blau, Joseph F. Pekny, Gintaras V. Reklaitis, D EversdykAbstract:In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety Stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher Safety Stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in Safety Stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with Safety Stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of Safety Stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on Safety Stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based Safety Stock management model is developed, in which the Safety Stock control variables (the target inventory levels used in production planning and scheduling models, base-Stock levels for the base-Stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the Safety production capacity limits in Safety Stock management are properly represented. To demonstrate the effectiveness of the proposed Safety Stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3-8 warehouses supplying 10 final products to 30 sales regions. © 2008 Elsevier Ltd. All rights reserved.
Sean P Willems - One of the best experts on this subject based on the ideXlab platform.
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analytical insights into two stage serial line supply chain Safety Stock
International Journal of Production Economics, 2016Co-Authors: Sean P WillemsAbstract:Effective inventory management is one of the most significant challenges facing today׳s global supply chains. Businesses are observing significant profitability gain by optimizing their inventory. This paper optimizes Safety Stock inventory in a two-stage serial line supply chain, inspired by real-life Cisco supply chains, under guaranteed-service Safety Stock model assumptions. We analytically show that the optimal Safety Stock levels depend on the cost and leadtime parameters of the supply chain. Intuitively, it is only worthwhile to hold Safety Stock inventory at the upstream stage when cost at the upstream stage is relatively low or its leadtime is relatively long. We also show that total supply chain Safety Stock cost can be reduced when cost allocated at the upstream stage is reduced or leadtime at the upstream stage is increased.
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Strategic Safety Stock Placement in Supply Networks with Static Dual Supply
Manufacturing & Service Operations Management, 2014Co-Authors: Steffen Klosterhalfen, Stefan Minner, Sean P WillemsAbstract:Many real-world supply networks source required materials from multiple suppliers. Existing multiechelon inventory optimization approaches either restrict their scope to multiple supply sources in two-echelon systems or single suppliers in multiechelon systems. We develop an exact mathematical model for static dual supply in a general acyclic N -echelon network structure, which builds on the guaranteed-service framework for Safety Stock optimization. It is assumed that the suppliers are allocated static fractions of demand. We prove that for normally distributed demand an extreme point property holds. We present a real example from the industrial electronics industry consisting of five echelons and three dual-sourced materials. This example forms the basis for a numerical analysis. Compared with the only previously published approximate solution, our exact approach results in considerable cost savings because the exact model captures inventory pooling in a way that the approximation is unable to do. For a set of test problems, total Safety Stock cost savings are 9.1p, on average.
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supply chain design Safety Stock placement and supply chain configuration
Handbooks in Operations Research and Management Science, 2003Co-Authors: Stephen C. Graves, Sean P WillemsAbstract:Publisher Summary This chapter discusses two approaches to Safety Stock placement, which are termed as “stochastic-service model” and the “guaranteed-service model.” In the stochastic-service model, each stage in the supply chain maintains a Safety Stock sufficient to meet its service level target. In this setting, a stage that has one or more upstream-adjacent supply stages has to characterize its replenishment time taking into account the likelihood that these suppliers will meet a replenishment request from Stock. In the guaranteed-service model, each stage provides a guaranteed service to its customer stages. In this setting, a supply stage sets a service time to its downstream customer and then holds sufficient inventory so that it can always satisfy the service-time commitment. A key assumption in this model is to assume that demand is bounded for the purpose of making the service-time guarantee. The chapter also discusses how the supply chain can be optimally configured. The notion of options are introduced for each stage in the supply chain, where the options differ in terms of lead-time and cost.
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optimizing strategic Safety Stock placement in supply chains
Manufacturing & Service Operations Management, 2000Co-Authors: Stephen C. Graves, Sean P WillemsAbstract:Manufacturing managers face increasing pressure to reduce inventories across the supply chain. However, in complex supply chains, it is not always obvious where to hold Safety Stock to minimize inventory costs and provide a high level of service to the final customer. In this paper we develop a framework for modeling strategic Safety Stock in a supply chain that is subject to demand or forecast uncertainty. Key assumptions are that we can model the supply chain as a network, that each stage in the supply chain operates with a periodic-review base-Stock policy, that demand is bounded, and that there is a guaranteed service time between every stage and its customers. We develop an optimization algorithm for the placement of strategic Safety Stock for supply chains that can be modeled as spanning trees. Our assumptions allow us to capture the stochastic nature of the problem and formulate it as a deterministic optimization. As a partial validation of the model, we describe its successful application by product flow teams at Eastman Kodak. We discuss how these flow teams have used the model to reduce finished goods inventory, target cycle time reduction efforts, and determine component inventories. We conclude with a list of needs to enhance the utility of the model.
Joseph F. Pekny - One of the best experts on this subject based on the ideXlab platform.
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integrated Safety Stock management for multi stage supply chains under production capacity constraints
Computers & Chemical Engineering, 2008Co-Authors: Joo Yung Jung, Gary Blau, Joseph F. Pekny, Gintaras V. Reklaitis, D EversdykAbstract:Abstract In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety Stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher Safety Stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in Safety Stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with Safety Stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of Safety Stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on Safety Stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based Safety Stock management model is developed, in which the Safety Stock control variables (the target inventory levels used in production planning and scheduling models, base-Stock levels for the base-Stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the Safety production capacity limits in Safety Stock management are properly represented. To demonstrate the effectiveness of the proposed Safety Stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3–8 warehouses supplying 10 final products to 30 sales regions.
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Integrated Safety Stock management for multi-stage supply chains under production capacity constraints
Computers and Chemical Engineering, 2008Co-Authors: Joo Yung Jung, Gary Blau, Joseph F. Pekny, Gintaras V. Reklaitis, D EversdykAbstract:In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety Stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher Safety Stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in Safety Stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with Safety Stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of Safety Stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on Safety Stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based Safety Stock management model is developed, in which the Safety Stock control variables (the target inventory levels used in production planning and scheduling models, base-Stock levels for the base-Stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the Safety production capacity limits in Safety Stock management are properly represented. To demonstrate the effectiveness of the proposed Safety Stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3-8 warehouses supplying 10 final products to 30 sales regions. © 2008 Elsevier Ltd. All rights reserved.
Gary Blau - One of the best experts on this subject based on the ideXlab platform.
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integrated Safety Stock management for multi stage supply chains under production capacity constraints
Computers & Chemical Engineering, 2008Co-Authors: Joo Yung Jung, Gary Blau, Joseph F. Pekny, Gintaras V. Reklaitis, D EversdykAbstract:Abstract In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety Stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher Safety Stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in Safety Stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with Safety Stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of Safety Stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on Safety Stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based Safety Stock management model is developed, in which the Safety Stock control variables (the target inventory levels used in production planning and scheduling models, base-Stock levels for the base-Stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the Safety production capacity limits in Safety Stock management are properly represented. To demonstrate the effectiveness of the proposed Safety Stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3–8 warehouses supplying 10 final products to 30 sales regions.
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Integrated Safety Stock management for multi-stage supply chains under production capacity constraints
Computers and Chemical Engineering, 2008Co-Authors: Joo Yung Jung, Gary Blau, Joseph F. Pekny, Gintaras V. Reklaitis, D EversdykAbstract:In the petrochemical, chemical and pharmaceutical industries, supply chains typically consist of multiple stages of production facilities, warehouse/distribution centers, logistical subnetworks and end customers. Supply chain performance in the face of various market and technical uncertainties is usually measured by service level, that is, the expected fraction of demand that the supply chain can satisfy within a predefined allowable delivery time window. Safety Stock is introduced into supply chains as an important hedge against uncertainty in order to provide customers with the promised service level. Although a higher Safety Stock level guarantees a higher service level, it does increase the supply chain operating cost and thus these levels must be suitably optimized. The complexities in Safety Stock management for multi-stage supply chain with multiple products and production capacity constraints arise from: (1) the nonlinear performance functions that relate the service level, expected inventory with Safety Stock control variables at each site; (2) the interdependence of the performances of different sites; and (3) finally the margin by which production capacity exceeds the uncertain demand. Given the complexities, the integrated management of Safety Stocks across the supply chain imposes significant computational challenges. In this research, we propose an approach in which the evaluation of the performance functions and the decision on Safety Stock related variables are decomposed into two separate computational frameworks. For evaluating the performance functions, off-line computation using a discrete event simulation model is proposed. A linear programming based Safety Stock management model is developed, in which the Safety Stock control variables (the target inventory levels used in production planning and scheduling models, base-Stock levels for the base-Stock policy at the warehouses) and service levels at both plant stage and warehouse stages are used as important decision variables. In the linear programming model, the nonlinear performance functions, interdependence of the performances, and the Safety production capacity limits in Safety Stock management are properly represented. To demonstrate the effectiveness of the proposed Safety Stock management model, a case study of a realistically scaled polymer supply chain problem is presented. In the case problem, the supply chain is composed of two geographically separated production sites and 3-8 warehouses supplying 10 final products to 30 sales regions. © 2008 Elsevier Ltd. All rights reserved.
