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In 2001, Caterpillar's Building Construction Products Division (BCPD) began a project to reduce and stabilize order-to-delivery times (termed product availability within Caterpillar) while reducing cost and inventory. Using a six-sigma method and a multiechelon inventory optimization model, BCPD sets dynamic inventory policies for assembled machines stocked at the assembly plant and for subassemblies and components stocked at the assembly plant and at supplier locations. The project has improved product availability, reducing the mean time for orders with identified customers by 20 percent and reducing the standard deviation of product availability for all orders by 50 percent. BCPD's initial implementation and resulting improved product availability enabled BCPD to win more orders (a two percent increase in revenues) and reduce total chain inventory by 16 percent, and it has motivated other Caterpillar manufacturing and assembly facilities to replicate this project across their supply chains. [ABSTRACT FROM AUTHOR]

Motivated by real applications, we consider the problem of shipping products to multiple customers from limited inventory. After formulating the optimization problems under different restrictions on partial shipments, we find that commercially available packages, applied directly, are unsatisfactory, as are simple greedy approaches. We develop a scheme of heuristics that enables the user to select a good balance between computation time and effectiveness. A detailed computational study of one- and two-period industrial-sized problems indicates that these heuristics are computationally practical and generate solutions that are, on average, within 3%--4% of the optimum. [ABSTRACT FROM AUTHOR]

We address several aspects related to managing re-entrant lines in a unified manner capacity allocation, inventory management and production control. Our approach to study these systems is through simulation based optimization. Simulation offers the flexibility to model the complexities adequately while the gradient computation (via infinitesimal Perturbation Analysis (IPA)) helps identify good solutions quickly. Our framework is a discrete time capacitated multiple product production-inventory system operating under a base stock policy. We analyze several different production and capacity allocation rules. We develop expressions for and validate the appropriate derivatives. These derivatives can then be used in an optimization tool which enables the determination of optimal parameters. For the several policies proposed. We also present a summary of insights obtained through an extensive computational study. [ABSTRACT FROM AUTHOR]

We present an efficient solution method -- Direct Derivative Estimation (DDE) -- for computing optimal order-up-to levels for a discrete time non-stationary inventory control model. We generalize a number of non-stationary inventory control models (which underlie larger models of supply chains) that include forecast updates. seasonality, information sharing, and exchange-rate fluctuations. This procedure is different from the existing ones in that it computes, in a recursive manner, the derivative of the cost function and not the cost function itself. It can also handle a much wider variety of fluctuations in the problem parameters. In our computational testing it was found to be considerably faster than Dynamic Programming and Infinitesimal Perturbation Analysis. [ABSTRACT FROM AUTHOR]

As part of its growth strategy, Caterpillar Inc. is launching a new P2000 product line of 'compact' construction equipment and worktools. In anticipation of this, they asked the authors to construct and analyze potential P2000 supply chain configurations. Using decomposition and results from network flow theory, inventory theory, and simulation theory, we were able to provide solutions to this problem for different supply chain scenarios provided by Caterpillar. Novel features of our model include expedited deliveries, partial backlogging of orders, and realized sales that are responsive to service. Caterpillar made their decision regarding the P2000 supply chain based on our recommendations. [ABSTRACT FROM AUTHOR]

The integration of product design and operations could potentially enhance the ability of manufacturers to provide large variety in the product line while keeping their operations competitive. At the plant level, an improved assembly sequence can lead to superior operational response to customer demands. While this has been intuitively accepted, there is a scarcity of models that can adequately reflect the operational benefits of a costlier yet better design. In this paper, we present integrated models applicable at a plant level that can provide quantification of certain operational benefits. Further, through a computational study, we also provide qualitative insights on several issues such as the effect of : (i) features in the product line; (ii) variance of demand for the different features on the optimal assembly design; (iii) set-up times; and (iv) life cycle of the product line. [ABSTRACT FROM AUTHOR]

We consider a model for managing a single stage that produces multiple items. The production rates are finite, and there are switchover times. The interarrival times and quantities of demands for the items are random, and demand may occur for a set of items. We consider order focussed measures: cost based on response times: service levels based on quoted lead times and Type-1 service. We operate the stage in the following manner: (1) there is a cyclic schedule that determines the sequence of items and the number of times a particular item is produced in a cycle; (2) given a cyclic schedule, production of each item follows a modified base-stock policy or a (s, S) policy. We present a simulation based procedure to obtain good values for the base stock levels or S (for any fixed S-s) for each of the above performance measures. Numerical results indicate that good solutions can be obtained with modest computational effort. We also report on a real world implementation of this model. [ABSTRACT FROM AUTHOR]