Knowledge that Transforms

We consider two capacity choice scenarios for the optimal location of facilities with fixed servers, stochastic demand, and congestion. Motivating applications include virtual call centers, consisting of geographically dispersed centers, walk‐in health clinics, motor vehicle inspection stations, automobile emissions testing stations, and internal service systems. The choice of locations for such facilities influences both the travel cost and waiting times of users. In contrast to most previous research, we explicitly embed both customer travel/connection and delay costs in the objective function and solve the location–allocation problem and choose facility capacities simultaneously. The choice of capacity for a facility that is viewed as a queueing system with Poisson arrivals and exponential service times could mean choosing a service rate for the servers (Scenario 1) or choosing the number of servers (Scenario 2). We express the optimal service rate in closed form in Scenario 1 and the (asymptotically) optimal number of servers in closed form in Scenario 2. This allows us to eliminate both the number of servers and the service rates from the optimization problems, leading to tractable mixed‐integer nonlinear programs. Our computational results show that both problems can be solved efficiently using a Lagrangian relaxation optimization procedure.

For nearly all call centers, agent schedules are typically created several days or weeks before the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra‐day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents' schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade‐off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center.

An exploratory analysis of verbal protocols from a think‐aloud newsvendor experiment provided deeper insights into the decision‐making process, enabling us to formulate a number of questions that are worth answering in future research. In a think‐aloud experiment, subjects verbalize their cognitions while performing a task; responses are then recorded, transcribed, and analyzed. A majority of the subjects struggled with the abstractness of the business setting and were keen to know information on the product type, industry setting, decisions taken in the past, competitor's situation, etc. A large portion of the participants correctly identified the overage and underage costs, but failed to convert that information into the optimal order quantity. Finally, the bias in the order quantity was significantly influenced by the specific type of risk (overage or underage) that was identified closer to the decision, alluding to the presence of a recency effect. As a first application of verbal protocol analysis to inventory decision making, this study gives us an opportunity to highlight the strengths and weaknesses of this research methodology.

This paper focuses on the close relationship between statistical process control and preventive maintenance (PM) of manufacturing equipment. The context is very general: a production process that is characterized by multiple distinct operational states and a failure state. The operational states differ in terms of operational/quality costs and/or the proneness to complete failure. The times of shift from the normal operational state to an inferior one and the times to failure are random variables, not necessarily exponentially distributed. The process is monitored with a control chart with the purpose of quickly detecting shifts to an inferior operational state due to the occurrence of some unobservable assignable cause. At the same time, the information collected from the process may be used to re‐schedule the planned PM, if there is evidence that a failure is imminent. The two mechanisms are obviously related, especially if they are based on measurements of the same critical process characteristic. Yet, they are typically treated independently. We develop a fairly general mathematical model for the joint optimization of the control chart parameters and the maintenance times. Numerical investigation using this model shows that ignoring the close relationship between process control and maintenance results in inefficiencies that may be substantial. It also provides practical insights about the effects of some key problem characteristics on the optimal joint design of process control and maintenance.

Our research addresses a firm that sells a product to consumers who are sensitive to both price and return policy. The operational decisions of interest are the selling price, return policy, and quantity of new product to purchase. We model a single selling season that is split into two periods where the boundary between periods is delineated by the opportunity to recover product returns and resell them. That is, returns in the first period can be recovered and sold in the second period. Returns also arise in the second period, but these may only be salvaged. We first analyze both deterministic and stochastic models, finding that the deterministic results largely carry over to the stochastic case. In addition, our results indicate that the model is quite insensitive to errors in the estimates of the parameter values, except for purchase cost and parameters related to demand. Finally, we perform an analysis on the value of various investments to improve financial performance. Results indicate that investments to reduce the recovery cost of returns or reduce returns uncertainty are minimal, while investments to increase recovery speed, reduce market uncertainty, and reduce the return rate can be quite valuable.

This paper examines the optimal product portfolio positioning for a monopolist firm in a market where consumers exhibit vertical differentiation for product performance and horizontal differentiation for product feature. Our key results are as follows: (i) Variable costs drive vertical differentiation. In the presence of significant volume‐dependent manufacturing costs, the optimal portfolio contains a mix of vertically and horizontally differentiated products and an increase in the variable cost makes adding vertically differentiated products relatively more profitable; if fixed volume‐independent design costs dominate, the portfolio exhibits solely horizontal differentiation. (ii) Horizontal differentiation is the main profit lever, and vertical differentiation brings only a marginal benefit; this is true even when most of the consumers exhibit low willingness to pay for performance, which is often used as an excuse to offer low‐end products. (iii) There are more low‐quality products than high‐quality ones, and market coverage increases when the willingness to pay for performance increases. In summary, the model shows how portfolio composition decisions depend on the product cost structure and the consumer preferences.

Misplaced inventory is a major operational problem in many supply chains. Radio‐frequency identification (RFID) technology has been publicized as a promising solution for the misplaced inventory. Adoption of this technology has a fixed cost and variable cost of implementation, which can cause incentive issues in the supply chain. In this paper, we consider a supply chain under misplacement of inventory subject to uncertain demand. We study both centralized and decentralized cases and identify the conditions to coordinate the supply chain under implementation of RFID. We show that the incentives of the parties for investing in the technology are not perfectly aligned in the existence of the fixed cost of investment. Based on the relative payments of the parties for the fixed cost of investment, the incentives to adopt RFID can be characterized into regions, where we observe only one party or two parties benefiting from the technology when the tag price falls in a region specified in the paper. We further establish the effects of changes in mean and variance of a uniform demand on the incentives for investing in RFID and find that the incentives of the firms may indeed decrease as demand becomes more variable.

We study a distribution channel where a manufacturer relies on a sales agent for selling the product, and for investing in the most appropriate marketing effort. The agent's effort is hard to monitor. In addition, the cost of effort is the agent's private information. These impose challenges to the manufacturer in its endeavor to influence the agent's marketing effort provisions and to allocate profit between the two parties. We propose two contract forms. The franchise fee contract is a two‐part price schedule specifying a variable wholesale price and a fixed franchise fee. The retail price maintenance contract links the allowed retail price that the agent charges customers with total payment to the manufacturer and sales level. Under information asymmetry, for implementing either contract form, the manufacturer needs to offer a menu of contracts, hoping to invoke the “revelation principle” when the agent picks a certain contract from that menu. We show that the two contract forms perform differently, and each party's preference toward a particular contract form is linked with the total reservation profit level and/or the sales agent's cost type. We provide managerial guidelines for the manufacturer in selecting a better contract form under different conditions.

In this paper, we propose a new dynamic programming decomposition method for the network revenue management problem with customer choice behavior. The fundamental idea behind our dynamic programming decomposition method is to allocate the revenue associated with an itinerary among the different flight legs and to solve a single‐leg revenue management problem for each flight leg in the airline network. The novel aspect of our approach is that it chooses the revenue allocations by solving an auxiliary optimization problem that takes the probabilistic nature of the customer choices into consideration. We compare our approach with two standard benchmark methods. The first benchmark method uses a deterministic linear programming formulation. The second benchmark method is a dynamic programming decomposition idea that is similar to our approach, but it chooses the revenue allocations in an ad hoc manner. We establish that our approach provides an upper bound on the optimal total expected revenue, and this upper bound is tighter than the ones obtained by the two benchmark methods. Computational experiments indicate that our approach provides significant improvements over the performances of the benchmark methods.

We consider a continuous‐review inventory problem for a retailer who faces random disruptions both internally and externally (from its supplier). We formulate the expected inventory cost at this retailer and analyze the properties of the cost function. In particular, we show that the cost function is quasi‐convex and therefore can be efficiently optimized to numerically find the optimal order size from the retailer to the supplier. Computational experiments provide additional insight into the problem. In addition, we introduce an effective approximation of the cost function. Our approximation can be solved in closed form, which is useful when the model is embedded into more complicated supply chain design or management models.