Showing papers in "Manufacturing & Service Operations Management in 2003"

Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects

Abstract: Telephone call centers are an integral part of many businesses, and their economic role is significant and growing. They are also fascinating sociotechnical systems in which the behavior of customers and employees is closely intertwined with physical performance measures. In these environments traditional operational models are of great value--and at the same time fundamentally limited--in their ability to characterize system performance.We review the state of research on telephone call centers. We begin with a tutorial on how call centers function and proceed to survey academic research devoted to the management of their operations. We then outline important problems that have not been addressed and identify promising directions for future research.

Commissioned Paper: An Overview of Pricing Models for Revenue Management

Abstract: In this paper, we examine the research and results of dynamic pricing policies and their relation to revenue management. The survey is based on a generic revenue management problem in which a perishable and nonrenewable set of resources satisfy stochastic price sensitive demand processes over a finite period of time. In this class of problems, the owner (or the seller) of these resources uses them to produce and offer a menu of final products to the end customers. Within this context, we formulate the stochastic control problem of capacity that the seller faces: How to dynamically set the menu and the quantity of products and their corresponding prices to maximize the total revenue over the selling horizon.

Commissioned Paper: Capacity Management, Investment, and Hedging: Review and Recent Developments

Abstract: This paper reviews the literature on strategic capacity management concerned with determining the sizes, types, and timing of capacity investments and adjustments under uncertainty. Specific attention is given to recent developments to incorporate multiple decision makers, multiple capacity types, hedging, and risk aversion. Capacity is a measure of processing abilities and limitations and is represented as a vector of stocks of various processing resources, while investment is the change of capacity and includes expansion and contraction. After discussing general issues in capacity investment problems, the paper reviews models of capacity investment under uncertainty in three settings:The first reviews optimal capacity investment by single and multiple risk-neutral decision makers in a stationary environment where capacity remains constant. Allowing for multiple capacity types, the associated optimal capacity portfolio specifies the amounts and locations of safety capacity in a processing network. Its key feature is that it is unbalanced; i.e., regardless of how uncertainties are realized, one typically will never fully utilize all capacities. The second setting reviews the adjustment of capacity over time and the structure of optimal investment dynamics. The paper ends by reviewing how to incorporate risk aversion in capacity investment and contrasts hedging strategies involving financial versus operational means.

Matching Demand and Supply to Maximize Profits from Remanufacturing

Abstract: The profitability of remanufacturing depends on the quantity and quality of product returns and on the demand for remanufactured products. The quantity and quality of product returns can be influenced by varying quality-dependent acquisition prices, i.e., by using product acquisition management. Demand can be influenced by varying the selling price. We develop a simple framework for determining the optimal prices and the corresponding profitability. We motivate and illustrate our framework using an application from the cellular telephone industry.

Commissioned Paper: On the Interface Between Operations and Human Resources Management

Abstract: Operations management (OM) and human resources management (HRM) historically have been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications, and staffing. Human responses to OM systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields.

Product Differentiation and Capacity Cost Interaction in Time and Price Sensitive Markets

Abstract: In this paper, we study a profit-maximizing firm selling two substitutable products in a price and time sensitive market. The products differ only in their prices and delivery times. We assume that there are dedicated capacities for each product and that there is a standard industry delivery time for the regular (slower) product. The objective of the firm is to determine the delivery time of the express (faster) product and appropriately price the two products, taking into consideration the impact of delivery time reduction on capacity requirements and costs. We develop a model that integrates pricing and delivery time decisions with capacity requirements and costs, and study scenarios where the firm is constrained in capacity for none, one, or both product(s). We show how product differentiation decisions are influenced by capacity costs, and how the firm should adapt its differentiation strategy in response to a change in its operating dynamics. We first identify a market characteristic that governs the optimal pricing structure. We then show that the degree of product differentiation depends on both the absolute, as well as the relative values of the capacity costs. Provided that the capacity cost differential remains the same, higher capacity costs induce less time differentiation and less price differentiation. An increase in capacity cost differential increases price differentiation, but decreases time differentiation. The optimal prices depend, in addition to the above, on the market characteristic. We find that prices can actually decrease when the firm incurs capacity-related costs. We also explore the impact of substitutability on product differentiation, and illustrate our results in a numerical study.

Capacity Investments in Supply Chains: Sharing the Gain Rather Than Sharing the Pain

Abstract: In this paper, we investigate price-only contracts in supply chain capacity procurement games. For a two-party supply chain, comprising a manufacturer and a supplier that both invest in capacity, we prove the existence of a class of coordinating price-only contracts that arbitrarily allocate the supply chain profit. Moreover, if the supplier's reservation profit is below a certain threshold, the manufacturer's optimal contract is a quantity-premium price-only schedule, that is, the average wholesale price per unit increases in the order size. We prove that the manufacturer prefers simple piecewise-linear quantity-premium contracts to linear contracts and show numerically that such contracts are highly efficient. We extend our results for piecewise-linear price schedules toN-supplier assembly systems. We also enrich the voluntary compliance regime of Cachon and Lariviere (2001). With this enrichment, we prove that share-the-pain contracts, such as firm commitment and options contracts, increase supplier capacity in the full information case, a result that contrasts with that of Cachon and Lariviere. Finally, we investigate when a manufacturer prefers single-breakpoint quantity premiums to firm commitments.

Capacity Games in Assembly Systems with Uncertain Demand

Abstract: An assembler needs sets of components, each produced by a different supplier. To produce the components and assemble the final product, the firms involved need to construct their individual production capacities before observing the actual demand. The firms have an incentive scheme (contract) to induce a "proper" capacity build-up. The key parameters of the contract are the set oftransfer prices the assembler pays each supplier for a unit of its component. We consider two game settings as to how the terms of the contract are determined. The first is one where the assembler sets the prices, and the second is for the suppliers to simultaneously select the prices each wants to charge for its component.We first characterize the optimal capacity decision when the system is centralized, and then derive the decentralized equilibrium capacities under each of the two game settings. We show that the decentralized channel performances depend heavily on system structure/parameters. In particular, under the first setting, the performance improves as the assembler's share of the systemwide capacity cost increases and it is not affected by the number of suppliers in the system, while under the second setting, the performance degrades both in the assembler's share of capacity cost and in the number of suppliers. We show that the first setting dominates the second in terms of system performance if and only if the assembler's share of capacity cost is larger than the reciprocal of the number of firms involved.

Properties of the Periodic Review (R, T) Inventory Control Policy for Stationary, Stochastic Demand

Abstract: This paper compares the commonly used periodic review, replenishment interval, order-up-to ( R, T ) policy to the continuous review, reorder point, order quantity, ( Q, r) model. We show that long-run average cost function for the single-product ( R, T ) policy has a structure similar to that of the ( Q, r) model. Consequently, many of the useful properties of the latter model are applicable. In particular, the optimal cost is insensitive to the choice of the replenishment interval,T,provided the optimal order-up-to level,R, corresponding toT is used. For instance, a suboptimalT obtained from a deterministic analysis increases costs by no more than 6.125%. For continuous demand, we analytically prove that use of a ( R, T)policy instead of the optimal policy increases costs by at most 41.42% in the worst case. Computational experiments on Poisson demand demonstrate that the average-case relative error of using a ( R,T)policy is under 7.5%. This relative error is lower when the demand rate and leadtime are high and the fixed order costs are either very low or very high. When coordination of order placement epochs is desirable, the ( R,T) policy may sometimes be preferred to the ( Q, r) policy. In this context, we illustrate application of our single-product results to more complex systems. In particular, we show that a simple power-of-two, ( R,T) based heuristic for the stochastic multiproduct joint replenishment problem has a worst-case performance guarantee of 1.5. A similar result is explored for a special case of a two-echelon serial inventory system

Optimal Replenishment Policies for Multiechelon Inventory Problems Under Advance Demand Information

Abstract: Customers and downstream supply chain partners often place, or can be induced to place, orders in advance of future requirements. We show how to optimally incorporate advance demand information into periodic-review, multiechelon, inventory systems in series. While the state space for series systems appears to be formidably large, we decompose the problem across locations, as in Clark and Scarf (1960), and reduce the state space at each location by using modified echelon inventory positions (that nets known requirements). We prove the optimality of state-dependent, echelon base-stock policies for finite and infinite horizon problems. We also show that myopic policies are optimal and very easy to compute when costs and demands are stationary. We provide managerial insights into the value of advance demand information through a numerical study.

Analysis of a Decentralized Production-Inventory System

Abstract: We model an isolated portion of a competitive supply chain as aM/M/1 make-to-stock queue. The retailer carries finished goods inventory to service a Poisson demand process, and specifies a policy for replenishing his inventory from an upstream supplier. The supplier chooses the service rate, i.e., the capacity of his manufacturing facility, which behaves as a single-server queue with exponential service times. Demand is backlogged and both agents share the backorder cost. In addition, a linear inventory holding cost is charged to the retailer, and a linear cost for building production capacity is incurred by the supplier. The inventory level, demand rate, and cost parameters are common knowledge to both agents. Under the continuous-state approximation where theM/M/1 queue has an exponential rather than geometric steady-state distribution, we characterize the optimal centralized and Nash solutions, and show that a contract with linear transfer payments replicates a cost-sharing agreement and coordinates the system. We also compare the total system costs, the agents' decision variables, and the customer service levels of the centralized versus Nash versus Stackelberg solutions.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost

Abstract: We analyze an infinite horizon, single-product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are identically distributed random variables that are independent of each other, and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period, and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to maximize expected discounted, or expected average, profit over the infinite planning horizon. We show that a stationary s S p policy is optimal for both the discounted and average profit models with general demand functions. In such a policy, the period inventory is managed based on the classical s S policy, and price is determined based on the inventory position at the beginning of each period.

Managing Variety for Assembled Products: Modeling Component Systems Sharing

Abstract: Component sharing--using the same version of a component across multiple products--is an approach adopted by many assembled-product manufacturers to achieve high final product variety with lower component variety and cost. This paper presents a methodology for determining which versions of a set of related components should be offered to optimally support a defined finished product portfolio. We develop optimization models that determine which versions of each component should be introduced and which of these versions each product should use to minimize design and production costs. This approach is appropriate for components with a relatively low impact on consumers' perceptions about product differentiation, which can be shared across a set of products if they meet the most stringent performance requirements in the set. We illustrate our procedure on automotive braking systems, but also discuss its applicability to other components and industries. We identify three conceptually different organizational approaches to component sharing: a coordinated projects approach that requires higher-level organizational echelons above the individual project, a project-by-project approach that does not, and a hybrid partially coordinated approach. We use our model to examine how the gain from the coordinated projects approach relative to the project-by-project approach varies with the number of component versions in consideration, warranty costs, complexity costs, and demand variability. Further, we use our model to highlight the risk of using simplistic heuristics to determine design sequence within a component system in a partially coordinated approach.

Price-Directed Replenishment of Subsets: Methodology and Its Application to Inventory Routing

Abstract: The idea of price-directed control is to use an operating policy that exploits optimal dual prices from a mathematical programming relaxation of the underlying control problem. We apply it to the problem of replenishing inventory to subsets of products/locations, such as in the distribution of industrial gases, so as to minimize long-run time average replenishment costs. Given a marginal value for each product/location, whenever there is a stockout the dispatcher compares the total value of each feasible replenishment with its cost, and chooses one that maximizes the surplus. We derive this operating policy using a linear functional approximation to the optimal value function of a semi-Markov decision process on continuous spaces. This approximation also leads to a math program whose optimal dual prices yield values and whose optimal objective value gives a lower bound on system performance. We use duality theory to show that optimal prices satisfy several structural properties and can be interpreted as estimates of lowest achievable marginal costs. On real-world instances, the price-directed policy achieves superior, near optimal performance as compared with other approaches.

The Effectiveness of Several Performance Bounds for Capacitated Production, Partial-Order-Service, Assemble-to-Order Systems

Abstract: We consider an assemble-to-order (ATO) system: Components are made to stock by production facilities with finite capacities, and final products are assembled only in response to customers' orders. The key performance measures in this system, such as order fill rates, involve evaluation of multivariate probability distributions, which is computationally demanding if not intractable. The purpose of this paper is to develop computationally efficient performance estimates. We examine several ideas scattered in diverse literatures on approximations for multivariate probability distributions, and determine which approach is most effective in the ATO application. To do so, we first tailor different approximation ideas to the ATO setting to derive performance bounds, and then compare these bounds theoretically and numerically. The bounds also allow us to make connections between capacitated and uncapacitated ATO systems and gain various insights.

Supplier-Manufacturer Relationships Under Forced Compliance Contracts

Abstract: Today, many retailers are adopting lean strategies to improve efficiency (see Abernathy et al. 1999). Manufacturers that supply lean retailers must fulfill orders accurately, rapidly, and efficiently, despite demand volatility, by appropriately structuring their production and transportation processes. The outsourcing of transportation has become common; in this case a contract might be struck that specifies how much capacity a logistics provider guarantees to the manufacturer. Given that the demand for transportation services varies day by day in the “lean-retailer” context, the contract must take into account the manufacturer’s risk of not fulfilling the retailer’s demand if the transportation requirements exceed the agreed upon capacity and the logistics provider’s risk of not using all of the committed capacity. The use of electronic spot markets for transportation has become prevalent and offers one means to mitigate these risks—the transportation provider can sell unused capacity while the manufacturer can secure additional transportation services when the logistics provider’s promised capacity is insufficient. In this paper we analyze a contract in this context and its capability to mitigate the effects of demand and spot-price uncertainties, as well as how each party, and the supply chain in total, benefits from the contract. We analyze a contract for options for nonstorable products or services, such as transportation, between a single supplier and a single manufacturer in the presence of a spot market, where the supplier has limited capacity and the manufacturer must fulfill periodic stochastic demand from a downstream supplychain link, such as a lean retailer, in full. We assume that the quantity of goods desired by the manufacturer is always available on the spot market at some price, that the spot-market price is exogenous, and that neither the supplier nor buyer is of sufficient size to have a perceptible effect on it. We model our problem as a two-stage Stackelberg game in which the supplier is the leader. At stage one, the supplier offers the manufacturer a contract for options with a reservation price and an exercise price. In response, the manufacturer purchases a certain number of options from the supplier, after which the supplier determines the total quantity of goods or services to make available to the manufacturer or the spot market. At the beginning of the second stage, demand and spot price are realized. After observing this information, the manufacturer decides how many options to exercise with the supplier and how much to purchase on the spot market. The manufacturer can view the spot market as an alternative source of the product: If the spot market price is below the supplier’s exercise price, then the manufacturer buys only from the spot market; otherwise, she buys from the spot market only if the reserved capacity is insufficient to satisfy the demand in full. After the manufacturer’s order is filled, we assume that the supplier can sell all his excess inventory to the spot market at some price, which may or may not be profitable. We assess the effectiveness of such a contract in coordinating the channel, how the optimal policies are set, and how the value of the contract is shared between the parties. Several papers analyze whether forward options can coordinate the channel and ensure incentive compatibility for both players, or whether additional

Advance Demand Information and Safety Capacity as a Hedge Against Demand and Capacity Uncertainty

Abstract: To control a production-inventory system, a manager has to consider the variability in demand as well as variability in her production process. Both types of variability corrupt system performance and by alleviating either of them, the manager can improve the performance of the system. There has been a recent trend towards investing in better information systems to provide better advance demand information. Also, many firms have focused on having safety capacity (e.g., outsourcing or overtime) that they can rely on as needed to protect themselves against uncertainty in demand and production. In this paper, we first address the tactical decision of how a firm decides on productioninventory-safety capacity levels when faced with production and demand uncertainty. We use a multi-period production-inventory model with backordering to fully characterize the structure of optimal policies. We explore the sensitivity of optimal policies and costs to parameters such as demand and production variability, service level, and utilization. We also analytically show that uncertainty in capacity may result in nonintuitive behavior, such as more variable capacity resulting in less inventory. Using derived policy structure, through a computational study, we address the strategic decision of investing in better information or creating sources of safety capacity. Our study shows that reductions in costs are significant, with averages up to 30% for advance demand information, and up to 85% for outsourcing. Furthermore, conditions that make demand information more valuable tend to make safety capacity less valuable and vice versa and we identify when either will be more valuable. We also show that the benefits from both can exceed the sum of the benefits from either safety capacity or better information.

Interactive Multicriteria Optimization for Multiple-Response Product and Process Design

Abstract: We consider product and process design problems (hereafter collectively called process design problems) that address issues associated with the assessment of optimum levels for process inputs that influence multiple-process performance measures. While this problem context encompasses many possible applications, we focus primarily on multiple-response design problems that have been widely studied in the quality improvement and quality management literature. For such problems, several optimization criteria have been proposed, including maximization of process yield, maximization of process capability, minimization of process costs, etc. In this research, we propose a method that accounts for many of these criteria via a procedure that interacts with and relies on the preferences of a decision maker (DM). The interactive procedure evolves from the convergence of three areas of research: notably, the research in multiple-response design, the research in multicriteria optimization, and recent developments in global optimization. The proposed interactive method is illustrated and comparatively assessed via two well-known problems in multiple-response design. Although the interactive procedure is developed for application in multiple-response design, it is not limited to this problem context. The concepts and methods developed in this research have applicability to problems that can be characterized by process inputs and process performance, such as supply chain management and multidisciplinary design optimization.

Vehicle Routing with Time Windows and Time-Dependent Rewards: A Problem from the American Red Cross

Abstract: As is our tradition at the journal, we are indeed pleased to publish the extended abstracts from the winners of the 2002 MSOM Society Student Paper Competition. These abstracts represent the best work of the next generation of scholars in our field. We are delighted to recognize these young scholars for their achievements and provide our readers with an opportunity to learn about their work. The 2002 prize committee was chaired by Professor Don Eisenstein at the University of Chicago. The other committee members were: Dan Adelman (University of Chicago), Gerard Cachon (University of Pennsylvania, The Wharton School), Karen Donohue (University of Minnesota), Vishal Gaur (New York University, The Stern School), Stephen Gilbert (University of Texas, The McComb School), Roman Kapuscinski (University of Michigan), Itir Karaesmen (University of Maryland), Martin Lariviere (Northwestern, The Kellogg School), Erica Plambeck (Stanford University), Warren B. Powell (Princeton University), Lawrence W. Robinson (Cornell University), and Rob Shumsky (University of Rochester, The Simon School). The 2002 prizewinners are: First Place Xinxin Hu, University of Michigan Advisors: Izak Dueyas and Roman Kapuscinski, University of Michigan “Advance Demand Information and Safety Capacity as a Hedge Against Demand and Capacity Uncertainty”

Correspondence: Erratum: Optimizing Strategic Safety Stock Placement in Supply Chains

Abstract: In Graves and Willems (2000) we consider the problem of finding the optimal placement of safety stocks in a supply chain with bounded demand and guaranteed service times. We develop a dynamic programming algorithm for supply chains that can be modeled as spanning trees. The intent of this note is to correct an error in the algorithm. Ekaterina Lesnaia of MIT and Dr. Salal Humair of Optiant independently discovered the error. We wish to acknowledge and to thank Ms. Lesnaia and Dr. Humair for uncovering the error and for bringing it to our attention; they have also identified how to correct the error. To describe the error and its correction, we will repeat part of the algorithm from Graves and Willems (2000). The dynamic program evaluates a functional equation for each node in the spanning tree, in which the nodes have been labeled 1 2 N by the labeling algorithm in Graves and Willems (2000). We define Nk for each node k to be the subset of nodes 1 2 k that are connected to k on the subgraph with node set 1 2 k . There are two forms for the functional equation. First, the function fk S is the minimum holding cost for safety stock in a subnetwork with node set Nk, where we assume that the outbound service time for node k is S. Second, the function gk SI is the minimum holding cost for safety stock in a subnetwork with node set Nk, where we assume that the inbound service time for node k is SI . At node k for 1 ≤ k ≤ N − 1, the algorithm determines either fk S or gk SI , depending on the location of the node with the higher label that is adjacent to k. If this adjacent node is downstream (upstream) of node k, then we evaluate fk S gk SI . For node N , we can evaluate either functional equation. To develop the equations for fk S and gk SI , we define the function ck S SI to be the minimum inventory holding cost for the subnetwork with node set Nk, where node k has inbound service time SI and the outbound service time S. In Graves and Willems (2000) the equation for ck S SI is incorrect as it is based on faulty assumptions, namely the assumptions that fk S is nonincreasing in S and that gk SI is nondecreasing in SI . The correct expression is as follows: ck S SI = hk { Dk SI +Tk−S − SI +Tk−S k }

Erratum to Bounds in Serial Production/Distribution Systems Under Service Constraints

Abstract: We noticed an error in the upper bound on the optimal system stock in Boyaci and Gallego (2001). We provide a procedure to compute the correct bound.

A Continuous-Time Strategic Capacity Planning Model Based on the Minimum-Cut Problem

Abstract: The semiconductor industry has been one of the driving forces of the “new” economy. It boasts of the exponentially growing performance of semiconductor devices, coupled with rapidly decreasing chip prices; however, it faces highly volatile demands, and copes with astronomical fab costs, most of which can be attributed to tool costs. The leadtime for purchasing tools is between 6 and 18 months, upon which tools quickly become obsolete. Thus, semiconductor companies need to recover their capital investment in the tools over a short period of time. We develop models and algorithms for strategic capacity planning, which is to determine the sequence and timing of acquiring tools. Strategic planning decisions are made in the presence of high uncertainty. Uncertainty comes from factors such as technology, the market, and its products, and becomes amplified by long leadtimes. Although capacity planning decisions need to be made in the presence of high uncertainty, early research and even some current practices overlook the stochastic nature of planning, with the exception of simple case analyses. An extensive review of literature can be found in Cakanyildirim et al. (1999) and Roundy et al. (2000). Typical methods of stochastic optimization include stochastic-linear programming, stochastic-integer programming, and Markov decisions processes; yet, they have not been able to solve real-world capacity planning problems on the scale faced by the semiconductor industry. This paper takes the stochastic-optimization approach, which explicitly incorporates randomness in the model. We assume nonstationary stochastic demand, with the expected demand for product families increasing over time. We also assume lost sales and no finished good inventory. As in Cakanyildirim et al. (1999), we continue to explore alternative approaches based on continuous-time models. The time at which a machine is purchased becomes a continuous-decision variable. These models are more compact than traditional stochastic-programming methods based on discrete-time models. It is hoped that the small dimensionality of continuous-time models will make the strategic capacity planning problem computationally tractable. In this paper, we model multiple resource types used for multiple product families. The resulting problem is related to the continuous relaxation of the lot-sizing problem. We present an efficient divideand-conquer algorithm that will find a locally optimal solution of this problem. A subroutine to this algorithm is the parametric minimum-cut problem.