Knowledge that Transforms

This article derives the first analytical pricing formulas for American-style Asian options of the so-called floating strike type. Geometric as well as arithmetic averaging is considered. The setup is a standard Black-Scholes framework where the price of the underlying security evolves according to a geometric Brownian motion. A decomposition result that splits up the value of the floating strike American option into the price of an otherwise equivalent European option and an early exercise premium is first presented. This decomposition result is then manipulated further for the two separate types of averaging. With geometric averaging we derive an exact pricing formula, whereas with arithmetic averaging we develop an analytical approximation formula that proves to be very precise. Numerical examples are provided.

This exploratory research on a decade of Total Quality Management in one factory opens up the black box of the learning curve. Based on the organizational learning literature, we derive a quality learning curve that links different types of learning in quality improvement projects to the evolution of the factory's waste rate. Only 25% of the quality improvement projects—which acquired both know-why and know-how—accelerated waste reduction. The other 75% of the projects either impeded or did not affect waste reduction. In complex and dynamic production environments, locally acquired knowledge is difficult to disseminate. The combination of know-why and know-how facilitates its dissemination.

This paper addresses the problem of jointly determining prices and production schedules for a set of items that are produced on the same production equipment. Under the assumptions that the production setup costs are negligible and that demand is seasonal but price dependent, we exploit the special structure of the problem to develop a solution procedure. Through a set of numerical examples, we demonstrate how a product's contribution to aggregate seasonality can increase its optimal price. Our examples also demonstrate that, among products that experience demand peaks during the firm's busy season, those that peak early in the busy season should be priced more aggressively than those that peak later.

Vendor-managed inventory (VMI) is a supply-chain initiative where the supplier is authorized to manage inventories of agreed-upon stock-keeping units at retail locations. The benefits of VMI are well recognized by successful retail businesses such as Wal-Mart. In VMI, distortion of demand information (known as bullwhip effect) transferred from the downstream supply-chain member (e.g., retailer) to the upstream member (e.g., supplier) is minimized, stockout situations are less frequent, and inventory-carrying costs are reduced. Furthermore, a VMI supplier has the liberty of controlling the downstream resupply decisions rather than filling orders as they are placed. Thus, the approach offers a framework for synchronizing inventory and transportation decisions. In this paper, we present an analytical model for coordinating inventory and transportation decisions in VMI systems. Although the coordination of inventory and transportation has been addressed in the literature, our particular problem has not been explored previously. Specifically, we consider a vendor realizing a sequence of random demands from a group of retailers located in a given geographical region. Ideally, these demands should be shipped immediately. However, the vendor has the autonomy of holding small orders until an agreeable dispatch time with the expectation that an economical consolidated dispatch quantity accumulates. As a result, the actual inventory requirements at the vendor are partly dictated by the parameters of the shipment-release policy in use. We compute the optimum replenishment quantity and dispatch frequency simultaneously. We develop a renewaltheoretic model for the case of Poisson demands, and present analytical results.

We consider the node clustering problem that arises in designing a survivable two-level telecommunication network. The problem simultaneously determines an optimal partitioning of the whole network into clusters (local networks) and hub locations in each cluster. Intercluster traffic minimization is chosen as the clustering criterion to improve the service quality. Various constraints on the clustering are considered which reflect both the physical structures of local networks, such as the connectivity requirement, and the node compatibility relations such as community of interest or policy. Additional constraints may be imposed on the hub selection to ensure network survivability. We propose an integer programming formulation of the problem by decomposing the entire problem into a master problem and a number of column generation problems. The master problem is solved by column generation and the column generation problems by branch-and-cut. We develop and use strong cutting-planes for the cluster generation subproblems. Computational results using real data are reported.

We assess the cost-effectiveness of maintenance treatment for heroin addiction, with emphasis on its role in preventing HIV infection. The analysis is based on a dynamic compartmental model of the HIV epidemic among a population of adults, ages 18 to 44. The population is divided into nine compartments according to infection status and risk group. The model takes into account disease transmission from drug injection and sexual contacts. The health benefits of methadone maintenance and the resulting HIV infections averted are measured in terms of life years gained and quality-adjusted life years gained. Costs considered include all health-care costs (including cost of HIV care and other health care) and the cost of methadone maintenance. The analysis shows that expanding existing methadone maintenance programs is a cost-effective health-care intervention that can play an important role in slowing the spread of HIV and improving the length and quality of life for injection drug users (IDUs), and that such expansion is cost-effective even in populations with low HIV prevalence among IDUs. Incremental expansion of methadone maintenance programs was found to have a cost-effectiveness ratio of between $9,700 and $17,200 per life year gained, and between $6,300 and $10,900 per quality-adjusted life year gained. Although methadone maintenance treatment is provided to IDUs, the analysis shows that significant benefits accrue to non-IDU members of the population. Sensitivity analysis shows that new methadone maintenance treatment slots will be cost-effective even if they are twice as expensive and half as effective in reducing risky behavior as current methadone maintenance programs.

In the newsvendor problem a decision maker orders inventory before a one period selling season with stochastic demand. If too much is ordered, stock is left over at the end of the period, whereas if too little is ordered, sales are lost. The expected profit-maximizing order quantity is well known, but little is known about how managers actually make these decisions. We describe two experiments that investigate newsvendor decisions across different profit conditions. Results from these studies demonstrate that choices systematically deviate from those that maximize expected profit. Subjects order too few of high-profit products and too many of low-profit products. These results are not consistent with risk-aversion, risk-seeking preferences, Prospect Theory preferences, waste aversion, stockout aversion, or the consequences of underestimating opportunity costs. Two explanations are consistent with the data. One, subjects behave as if their utility function incorporates a preference to reduce ex-post inventory error, the absolute difference between the chosen quantity and realized demand. Two, subjects suffer from the anchoring and insufficient adjustment bias. Feedback and training did not mitigate inventory order errors. We suggest techniques to improve decision making.

There have been many claims that the Internet represents a new nearly “frictionless market.” Our research empirically analyzes the characteristics of the Internet as a channel for two categories of homogeneous products—books and CDs. Using a data set of over 8,500 price observations collected over a period of 15 months, we compare pricing behavior at 41 Internet and conventional retail outlets. We find that prices on the Internet are 9–16% lower than prices in conventional outlets, depending on whether taxes, shipping, and shopping costs are included in the price. Additionally, we find that Internet retailers' price adjustments over time are up to 100 times smaller than conventional retailers' price adjustments—presumably reflecting lower menu costs in Internet channels. We also find that levels of price dispersion depend importantly on the measures employed. When we compare the prices posted by different Internet retailers we find substantial dispersion. Internet retailer prices differ by an average of 33% for books and 25% for CDs. However, when we weight these prices by proxies for market share, we find dispersion is lower in Internet channels than in conventional channels, reflecting the dominance of certain heavily branded retailers. We conclude that while there is lower friction in many dimensions of Internet competition, branding, awareness, and trust remain important sources of heterogeneity among Internet retailers.

We analyze two joint decisions in the management of HIV-infected patients on antiretroviral therapy: how frequently to measure a patient's virus level, and when to switch therapy. The underlying stochastic model captures the initial suppression and eventual rebound of the virus level in the blood of a typical HIV-infected patient undergoing treatment. We consider two classes of policies: a viral load policy, which triggers a change in therapy when the current virus level divided by the smallest level achieved thus far exceeds a prespecified threshold, and a proactive policy, which is similar to the viral load policy but also switches drugs at a prespecified time if no evidence of viral rebound has been seen. We find approximate analytical expressions for the probability of switching before the virus reaches its nadir (minimum value) and the mean delay in detection of viral rebound (i.e., the time interval from when the viral nadir occurs until the switch in therapy). Numerical results show that the proactive policy outperforms (i.e., a smaller detection delay for a given probability of prenadir switching) the viral load policy and recent recommendations by an expert AIDS panel, and may delay the onset of multidrug resistance in a significant proportion of patients who experience drug failure.

This article develops and illustrates a new knowledge discovery algorithm tailored to the action requirements of management science applications. The challenge is to develop tactical planning forecasts at the SKU level. We use a traditional market-response model to extract information from continuous variables and use datamining techniques on the residuals to extract information from the many-valued nominal variables, such as the manufacturer or merchandise category. This combination means that a more complete array of information can be used to develop tactical planning forecasts. The method is illustrated using records of the aggregate sales during promotion events conducted by a 95-store retail chain in a single trading area. In a longitudinal cross validation, the statistical forecast (PromoCast™) predicted the exact number of cases of merchandise needed in 49% of the promotion events and was within ± one case in 82% of the events. The dataminer developed rules from an independent sample of 1.6 million observations and applied these rules to almost 460,000 promotion events in the validation process. The dataminer had sufficient confidence to make recommendations on 46% of these forecasts. In 66% of those recommendations, the dataminer indicated that the forecast should not be changed. In 96% of those promotion events where “no change” was recommended, this was the correct “action” to take. Even including these “no change” recommendations, the dataminer decreased the case error by 9% across all promotion events in which rules applied.