Karen Donohue

Outsourcing via Service Competition

We consider a single buyer who wishes to outsource a fixed demand for a manufactured good or service at a fixed price to a set of potential suppliers. We examine the value of competition as a mechanism for the buyer to elicit service quality from the suppliers. We compare two approaches the buyer could use to orchestrate this competition: (1) a supplier-allocation (SA) approach, which allocates a proportion of demand to each supplier with the proportion allocated to a supplier increasing in the quality of service the supplier promises to offer, and (2) a supplier-selection (SS) approach, which allocates all demand to one supplier with the probability that a particular supplier is selected increasing in the quality of service to which the supplier commits. In both cases, suppliers incur a cost whenever they receive a positive portion of demand, with this cost increasing in the quality of service they offer and the demand they receive. The analysis reveals that (a) a buyer could indeed orchestrate a competition among potential suppliers to promote service quality, (b) under identical allocation functions, the existence of a demand-independent service cost gives a distinct advantage to SS-type competitions, in terms of higher service quality for the buyer and higher expected profit for the supplier, (c) the relative advantage of SS versus SA depends on the magnitude of demand-independent versus demand-dependent service costs, (d) in the presence of a demand-independent service cost, a buyer should limit the number of competing suppliers under SA competition but impose no such limits under SS competition, and (e) a buyer can induce suppliers to provide higher service levels by selecting an appropriate allocation function. We illustrate the impact of these results through three example applications.

Experimental Economics and Supply-Chain Management

One area in which experimental economics methods have been used to study operations problems is supply-chain management. We survey results from a series of human experiments based on the popular beer distribution game and find cognitive limitations on the part of managers, in particular an underweighting of the supply line. We suggest mechanisms that may alleviate this bias, including sharing inventory and point-of-sale data, and reducing ordering and shipping delays. Our research provides managerial lessons and identifies supply-chain issues that need further experimental study.

Using an optimized queueing network model to support wafer fab design

We develop an Optimized Queueing Network (OQNet) capacity planning tool for supporting the design of new and reconfigured semiconductor fabrication facilities that makes use of queueing network approximations and an optimization routine. The basic problem addressed by this tool is to minimize the facility cost required to meet specified volume and cycle time targets. Features common to semiconductor environments, such as batch processes, re-entrant flows, multiple product classes, and machine setups, are incorporated into the model. Comparisons with simulation show that the queueing and other approximations are reasonably accurate. Tests of the optimization routine demonstrate that it can find good solutions quickly.

Contract Preferences and Performance for the Loss-Averse Supplier: Buyback vs. Revenue Sharing

Prior theory claims that buyback and revenue-sharing contracts achieve equivalent channel-coordinating solutions when applied in a dyadic supplier–retailer setting. This suggests that a supplier should be indifferent between the two contracts. However, the sequence and magnitude of costs and revenues (i.e., losses and gains) vary significantly between the contracts, suggesting the supplier’s preference of contract type, and associated contract parameter values, may vary with the level of loss aversion. We investigate this phenomenon through two studies. The first is a preliminary study investigating whether human suppliers are indeed indifferent between these two contracts. Using a controlled laboratory experiment, with human subjects taking on the role of the supplier having to choose between contracts, we find that contract preferences change with the ratio of overage and underage costs for the channel (i.e., the newsvendor critical ratio). In particular, a buyback contract is preferred for products wit...

Optimal computing budget allocation for Monte Carlo simulation with application to product design

Abstract Ordinal optimization has emerged as an efficient technique for simulation and optimization, converging exponentially in many cases. In this paper, we present a new computing budget allocation approach that further enhances the efficiency of ordinal optimization. Our approach intelligently determines the best allocation of simulation trials or samples necessary to maximize the probability of identifying the optimal ordinal solution. We illustrate the approach’s benefits and ease of use by applying it to two electronic circuit design problems. Numerical results indicate the approach yields significant savings in computation time above and beyond the use of ordinal optimization.

An Empirical Study of Service Differentiation for Weapon System Service Parts

The question of how to effectively manage items with heterogeneous attributes and differing service requirements has become increasingly important to supply chains that support the delivery of after-sales service. However, there has been little investigation to date on how organizations actually manage inventory levels under such circumstances. This study provides such an investigation, focusing on the logistic system used to manage consumable service parts for weapon systems in the U.S. military. Our findings, based on interviews and rigorous analysis of part attribute and performance data, suggest that in practice a parts service level is negatively affected by an items cost and is less affected by attributes such as its priority code. We introduce a simple inventory model to explain our empirical findings and explore how variations in item attributes can interact with an inventory policy to affect system performance. Based on this model, we recommend using explicit service-level targets for priority categories to achieve performance consistent with part priority. We show, using military data, that a service differentiation strategy can be an effective way of allocating inventory investment by providing higher service for critical parts at the expense of accepting lower service levels for parts with less importance.

Improving the design of stochastic production lines : an approach using perturbation analysis

Abstract With the recent increase in expense and specialization of equipment, capacity decisions have taken on greater significance. As a result, companies are in need of a better understanding of the investment tradeoffs. In this paper, we examine the problem of determining the most profitable capacity configuration for a production line modelled as a series of single-server stations. In the context of a constant work-in-process (CONWIP) control system, an algorithm is developed for solving the general problem using a single-run simulation procedure. Various market structures are examined and sensitivity analysis is performed on the cost of capacity, quality and the amount of work-in-process allowed in the system.

Optimal design of stochastic production lines: A dynamic programming approach

We consider the problem of choosing the number and type of machines for each station in a new production line where the sequence of processes (i.e., manufacturing recipe) has already been established. We formulate a model to minimize cost (investment plus operating) subject to constraints on throughput and cycle time. Using queueing network approximations within a dynamic programming framework, we develop a line design algorithm that works in station-wise fashion. For computational tractability, we must discretize a continuous state space. However, we are able to compute bounds on the error in the cost function as a guide to the appropriate choice of grid size. We conclude by applying our algorithm to an industrial problem that motivated this work.

Supply Chain Management: A Teaching Experiment

How firms choose and manage their inventory is a question of interest for academics and practitioners in many fields, including Operations Management, Marketing, and Information Technology. Much recent attention has focused on the possibilities of information-sharing systems to aid in this setting, including sharing inventory information among firms (SAP) and sharing point-of-sale data (EDI). This classroom exercise illustrates the existence and implications of bounded rationality on the part of inventory managers, and shows how systems like these can help in inventory decision-making.

Efficient approach for Monte Carlo simulation experiments and its applications to circuit systems design

This paper presents an efficient method for managing Monte Carlo simulation experiments to select the optimal circuit design from a set of candidates. Simulation is a useful tool for evaluating and comparing circuit designs since it measures the impact of component variability. However, its use in circuit design has traditionally been limited to problems with a small number of design candidates due to its large computational requirements. We outline a solution method that has been successfully used in other contexts to improve the efficiency of simulation-based optimization. The method works in an iterative fashion to intelligently allocate a limited computing budget across multiple design alternatives in order to maximize the probability of correct selection. We illustrate the methods potential benefits for circuit design problems through two simple examples. The examples confirm that the method yields significant savings in computational time, making simulation-based experiments a feasible option for larger circuit design problems.