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

An Integrated Manufacturing and Service Network (IMSN) is a grouping of companies, working together to offer a bundle of products and product-related services, that deliver value to customers over the entire useful life of the product, from purchase to disposal. The success of such an alliance is highly dependent on the seamless integration and interaction of business strategy, operational processes and enterprise systems between constituent companies and their design, manufacturing and sales operations. In this paper, we present system dynamics models to study the behavior of an IMSN and investigate the bullwhip effects that arise within such networks due to mismatches and delays between the manufacturing and service systems within the network. We show that integration and collaboration between the manufacturing and service operations with two-way information flow between them enhances profitability and minimizes the bullwhip effect within repair centers.

The design and planning of supply chain networks supporting production and distribution of multiple product generations overlapping with each other is of critical importance in the high-tech industry. We address the strategic supply chain network design problem in rapidly changing industry segments, where the selection of partners such as component suppliers, contract manufacturers and logistics providers is done, based on the capabilities of the partners for supporting the strategic needs of the current and near future generations of a finished product. We develop a mixed integer-programming model for integrated planning and scheduling across the supply chain and show how such a model may be used for making decisions related to introduction and rollovers of finished products and components from one generation to another. We assume that all stakeholders in the supply chain collaborate and share information on their capacities, schedules and cost structures. Based on this information the model addresses the issue of partner selection and planning for optimal profit. The model was solved using optimization tools from ILOG. Managerial insights are obtained by performing a series of simulated experiments on the model developed. For example, we show that an expensive supplier possessing the ability to develop and supply components required across a number of generations, might be preferred against a cheaper supplier supplying components suitable for a specific generation of the product. In addition, we show how the supply chain network configuration changes over the lifecycle of the product, wherein cheaper overseas suppliers slowly replace responsive and expensive local suppliers as the product matures. We also develop a framework to quantify and compare the costs and benefits of pursuing alternative product introduction plans and deadlines. Such a framework might be employed to determine the optimal product introduction schedule. We show here that in some cases it might not be profitable to launch a product in the market after a certain period of time.

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 strategic, operational and 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 also the associated consequences and impacts from these events. We can then focus our efforts on mapping out the propagation of events in the supply chain due to supplier non-performance, 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.

In this paper, we develop a framework for robust supply chain design, i.e., to build supply chains, which can handle disruptive events with minimal loss. There are two obvious steps to handle exceptions: (1) to design chains with built in risk-tolerance; and (2) to contain the damage once the undesirable event has occurred. In this paper, we consider design of robust (to risks) supply chains. First, we identify all the critical undesirable events that can occur at various stake holders and their interfaces and map out how each of them propagates through the supply chain. In this way, we identify the critical exceptions for various partners of the chain and understand the impact of all these exceptions, particularly on the supply. We develop a simple mixed integer programming optimization model, adapted from the credit risk minimization model, for partner selection as a means towards robust supply chain design. We choose the partners so that the effect of exceptions on the supply chain is minimized.

Supply chain configurations should change over the lifecycle of the product. During the early stages expensive suppliers with greater reliability and flexibility are desirable in order to meet uncertain market requirements, whereas in later stages with steady and declining demand, low cost suppliers should be chosen. Given the extremely short and overlapping product lifecycles in the Hi-Tech industry, it becomes necessary to use formal planning models to manage such dynamic supply chain configurations. In this paper, we develop a mixed integer-programming model for integrated planning across the supply chain and show how such a model may be used for decision-making across the product lifecycle. We assume that all stakeholders in the supply chain share information on their capacities, schedules and cost structures. Based on this information the model addresses the issue of partner selection and planning for optimal profit. The model was solved using optimization tools from ILOG.

In this paper, we develop a mixed-integer programming model for integrated partner selection and scheduling in an Internet-enabled dynamic manufacturing network environment. We assume that all stakeholders in the supply chain (SC) share information on their capacities, schedules, and cost structures. Based on this information, the model addresses the issue of partner selection and SC synchronization for profit maximization, while considering various manufacturing and logistics constraints. Furthermore, we study the dynamic configuration of the SC and its performance with respect to different buyer locations, different order patterns, and the utilization of transshipment hubs. The model is solved using optimization tools from ILOG, located in Paris, France, and Mountain View, CA.

In this paper we develop a quadratic programming model for integrated supply chain planning in an electronics-manufacturing environment supported by digital marketplaces. We consider a scenario where a sub-assembly manufacturer procures components from suppliers through an exchange and sells its manufactured sub-assemblies through another exchange. The model addresses for the sub-assembly manufacturer the issue of pricing and customer selection in the sub-assembly marketplace for maximum revenue and partner selection within the components procurement marketplace for minimal cost of manufacturing and delivery. Simultaneously, the model determines optimal schedules that synchronize activities across the supply chain.

We develop a linear programming model for integrated partner selection and scheduling in a web-enabled global manufacturing network environment. We assume that all stakeholders in the supply chain share information on their capacities, schedules and cost structures. Based on this information, the model addresses the issue of partner selection for minimal cost of manufacturing and delivery. The model is solved using ILOG optimization tools.