Volume 4:2

Copies of these published papers may be downloaded from Informs Online


Title: On the Complementary Value of Accurate Demand Information and Production and Supplier Flexibility

Author(s): Joseph Milner

Abstract: We study the value of information, production flexibility and supplier flexibility for a good for which an initial and a subsequent order may be placed. We consider a Bayesian model of demand in which the unknown mean demand rate is assumed to have a prior, which is a mixture of two normal distributions corresponding to the demand forecast for an innovative (fashion) good. We develop three models of production flexibility: a static model requiring initial placement of both orders, a partially dynamic model requiring a fixing of the time that the second order will be made and a fully dynamic model with no restrictions on ordering. Supplier flexibility is modeled through supply lead times. We observe that the magnitude of the savings from the static to the fully flexible model, corresponding to the sum of the values of information and production flexibility, reflects all sources of variability: differences between demand means of the prior mixture, variability within each prior, and variability about the observed mean. We observe that the greater the uncertainty within each prior distribution, the greater the value of demand information relative to the value of production flexibility. For short lead times, uncertainty around the mean demand reduces the relative value of demand information; for long lead times, it reduces the relative value of production flexibility. We conclude that for long lead times, investment in demand information updating takes precedence over investments in production flexibility. However, production flexibility has greater value after reducing supplier lead time constraints. Finally, we observe that the value of supply flexibility grows with lead time first in a concave manner and then in a convex manner.

The Consulting Senior Editor was Hau Lee

The manuscript was submitted on December 31, 1999 subject to five  reviews with 171 days in revision. The average review cycle time was 84 days.

Corresponding author: Joseph Milner, Washington University, John M. Olin School of Business, St. Louis, MO. 63130; Phone: 314-935-6331, Fax: 314-935-6359. E-mail: milner@olin.wustl.edu


Title: "Inventory-Service Optimization in Configure-to-Order Systems"

Author(s): David Yao, Grace Lin, Markus Ettl, Fangruo Chen

Abstract: This study is motivated by a process-reengineering problem in PC (personal computer) manufacturing, i.e., to move from a build-to-stock operation that is centered around end-product (machine type model) inventory, towards a configure-to-order (CTO) operation that eliminates end-product inventory --- in fact, CTO has made irrelevant the whole notion of pre-configured machine types--- and focuses instead on maintaining the right amount of inventory at the components.

Indeed, CTO appears to be the ideal operational model that provides both mass customization and a quick response time to order fulfillment. To quantify the inventory-service tradeoff in the CTO environment, we develop a nonlinear optimization model with multiple constraints, reflecting the service levels offered to different market segments. To solve the optimization problem, we develop an exact algorithm for the important case of demand in each market segment having (at least) one unique component, and a greedy heuristic for the general (non-unique component) case.

Furthermore, we show how to use sensitivity analysis, along with simulation, to fine-tune the solutions. The performance of the model and the solution approach is examined by extensive numerical studies on realistic problem data. We demonstrate that the model can generate considerable new insights into the key benefits of the CTO operation, in particular the impact of risk pooling and improved forecast accuracy. We present the major findings in applying our model to study the inventory/service impacts in the reengineering of a PC manufacturing process.

The Consulting Senior Editor was Lawrence Wein

The manuscript was submitted on December 26, 2001.  The average review cycle time was 14.5 days.

Corresponding author: David Yao, Columbia University, IEOR Department, 302 Mudd Building, New York, NY. 10027 Phone: (212) 854-2934, Fax: (212) 854-8103, E-mail: yao@ieor.columbia.edu


Title: "Optimal and Hierarchical Controls in Dynamic Stochastic Manufacturing Systems: A Survey"

Author(s): S.P. Sethi, H. Yan, H. Zhang, Q. Zhang

Abstract: Most manufacturing systems are large and complex and operate in an uncertain environment. One approach to managing such systems is that of hierarchical decomposition.  This paper reviews the research devoted to proving that a hierarchy based on the frequencies of occurrence of different types of events in the systems results in decisions that are asymptotically optimal as the rates of some events become large compared to those of others. The paper also reviews the research on stochastic optimal control problems associated with manufacturing systems, their dynamic programming equations, existence of solutions of these equations, and verification theorems of optimality for the systems. Manufacturing systems that are addressed include single-machine systems, dynamic flowshops, and dynamic jobshops producing multiple products. These systems may also incorporate random production capacity and demands, and decisions such as production rates, capacity expansion, and promotional campaigns. Related computational results and areas of applications are also presented. A more detailed survey is available at www.utdallas.edu/sethi/ITORMS/index.html.

This survey was submitted for publication and subsequently published by ITORMS, INFORMS net-based journal.  Because ITORMS will soon disappear, William Pierskalla, INFORMS Vice President for Publications, asked that M&SOM archive this survey, given its potential interest to our readers.


Copies of these published papers may be downloaded from Informs Online