M&SOM Volume 1:2

Copies of these published papers may be downloaded from Informs Online

Title: Quantity Flexibility Contracts and Supply Chain Performance

Author(s): A.A. Tsay and W. S. Lovejoy

Abstract: The Quantity Flexibility (QF) contract is a method for coordinating materials and information flows in supply chains operating under rolling-horizon planning. It stipulates a maximum percentage revision each element of the period-by-period replenishment schedule is allowed per planning iteration. The supplier is obligated to cover any requests that remain within the upside limits. The bounds on reductions are a form of minimum purchase commitment which discourages the customer from overstating its needs. While QF contracts are being implemented in industrial practice, the academic literature has thus far had little guidance to offer a firm interested in structuring its supply relationships in this way. This paper seeks to address this need, by developing rigorous conclusions about the behavioral consequences of QF contracts, and hence about the implications for the performance and design of supply chains with linkages possessing this structure. Issues explored include the impact of system flexibility on inventory characteristics and the patterns by which forecast and order variability propagate along the supply chain. The ultimate goal is to provide insights as to where to position flexibility for the greatest benefit, and how much to pay for it.

The consulting Senior Editor was Paul Zipkin.

The manuscript was submitted on January 26, 1998, subject to two reviews with 45 days in revision. The average review cycle time was 32 days.

Corresponding author: Dr. A. A. Tsay, Santa Clara University, Department of Operations & Management Information Systems, Leavey School of Business, Santa Clara, CA 95053-0382. Phone: (408) 554-4561; E-mail: ATSAY@SCULSB.SCU.EDU

Title: Worker Cross-Training in Paced Assembly Lines

Author(s): George Vairaktarakis, Case Western Reserve University and Janice Kim Winch, Lubin School of Business

Abstract: Paced or Synchronous assembly lines are a popular class of assembly systems consisting of a series of assembly stations arranged in tandem. Every job (or order) visits all assembly stations in the same sequence and spends the same amount of time (known as the production cycle) at each station. Industries such as aircraft, fire-engine, and automobile assembly have production cycles of a few hours and are labor intensive. In spite of increased automation in such industries, human capital remains the most expensive and important contributor to a flexible production system. In this article we formulate the cross-training problem on a paced assembly line with m stations (mCT). We assume that each worker possesses a number of skills referred to as a skill vector. Our objective is to schedule a set of work orders through the assembly system so as to minimize the size of the required workforce and/or the workforce cross-training costs. We analyze the complexity of mCT and identify polynomially solvable cases. A variety of lower bounds is developed based on optimization techniques. These lower bounds are used to develop a branch and bound algorithm as well as to evaluate our heuristics. A computational experiment reports the performance of all algorithms. Using these algorithms, we examine how the formation of skill vectors affects the workforce size and draw guidelines for cross-training programs in organizations with labor intensive assembly operations.

The consulting Senior Editor was Timothy Lowe.

The manuscript was submitted on June 12, 1997, subject to four reviews with 248 days in revision. The average review cycle time was 45.4 days.

Corresponding author: Dr. George Vairaktarakis, Case Western Reserve University, Department of OR and OM, 10900 Euclid Avenue, Cleveland, OH 44106-7235. Phone: (216) 368-5215; E-mail: gxv5@po.cwru.edu.

Title: A Model-Based Approach for Planning and Developing A Family of Technology-Based Products

Author(s): Viswanathan Krishnan, Rahul Singh and Devanath Tirupati

Abstract: In this paper, we address the product-family design problem of a firm in a market in which customers choose products based on some measure of product performance. By developing products as a family, the firm can reduce the cost of developing individual product variants due to the reuse of a common product platform. Such a platform, designed in an aggregate-planning phase that precedes the development of individual product variants, is itself expensive to develop. Hence, its costs must be weighed against the benefits of its reuse in a family. We offer a model for capturing costs of product development when the family consists of variants based on a common platform. It is shown that the model can be converted into a network-optimization problem, and the optimal product-family can be identified under a fairly general conditions by determining the shortest part of its network formulation. We also analytically examine the effect of using alternative product designs on product-family composition, and discuss the implications of investing in new-product technology. Finally, we illustrate our model and managerial insights with an application from the electronics industry.

The consulting Senior Editor was Marshall Fisher.

The manuscript was submitted on December 31, 1996, subject to three reviews with 201 days in revision. The average review cycle time was 86.7 days.

Corresponding author: Dr. Viswanathan Krishnan, The University of Texas at Austin, Department of Management, Austin, Texas 78712. Phone: (512) 471-9498; E-mail: krishnan@mail.utexas.edu

Title: Managing a Customer Following a Target Reverting Policy

Author(s): Sridhar Tayur and Srinagesh Gavirneni, Carnegie Mellon University

Abstract: We consider a stochastic, capacitated production-inventory model in which the customer provides information about the expected timing of future orders to the supplier.  We allow for randomness in customer order arrivals as well as the quantity demanded, but work under the assumption that the customer is making every effort to follow the schedule provided.  We term this as a target reverting policy. This gives rise to an interesting non-stationary inventory control model at the supplier. After characterizing the optimal policy, we develop solution procedures to compute the optimal parameters. An extensive computational study provides insights into the behavior of this model at optimality.  Further, comparing the cost of the optimal policy to the cost of simple policies that either ignore the customer’s information or the capacity constraint, we are able to provide insights as to when these simplifications could be costly.

The consulting Senior Editor was Paul Zipkin.

The manuscript was submitted on August 26, 1998, subject to two reviews with 111 days in revision. The average review cycle time was 32.3 days.

Corresponding author: Dr. Sridhar R. Tayur, Carnegie Mellon University, Graduate School of Industrial Administration, Schnenley Park, Pittsburgh PA 15213-3890. Phone: (412) 268-3584; E-mail: stayur@grobner.gsia.cmu.edu.

Title: Addendum to "A Single-Item Inventory Model for a Nonstationary Demand Process

Author(s): Stephen C. Graves

Abstract: In this paper, we consider an adaptive base-stock policy for a single-item inventory system, where the demand process is non-stationary. In particular, the demand process is an integrated moving average process of order (0, 1, 1), for which an exponential-weighted moving average provides the optimal forecast. For the assumed control policy we characterize the inventory random variable and use this to find the safety stock requirements for the system. From this characterization, we see that the required inventory, both in absolute terms and as it depends on the replenishment lead-time, behaves much differently for this case of non-stationary demand compared with stationary demand. We then show how the single-item model extends to a multi-stage, or supply-chain context; in particular we see that the demand process for the upstream stage is not only non-stationary but also more variable than that for the downstream stage. We also show that for this model there is no value from letting the upstream stages see the exogenous demand. The paper concludes with some observations about the practical implications of this work.

The consulting Senior Editor was Paul Zipkin.

The manuscript was submitted on July 29, 1998, subject to two reviews with 742 days in revision. The average review cycle time was 45 days.

Corresponding author: Dr. Stephen C. Graves, Massachusetts Institute of Technology, Sloan School of Management, Cambridge MA 02139-4307. Phone: (617) 253-6602; E-mail: sgraves@mit.edu.

Copies of these published papers may be downloaded from Informs Online