Knowledge that Transforms

In many new product development (NPD) situations, the development process is characterized by uncertainty, and no single development approach will necessarily lead to a successful product. To increase the likelihood of having at least one successful product, multiple approaches may be simultaneously funded at the various NPD stages. The managerial challenge is to construct ex ante an appropriate NPD pipeline by choosing the right number of approaches to be funded at each stage. This so-called pipeline problem is also present in, among others, advertising copy selection and new products test markets problems. We describe here a normative model for structuring pipelines for such situations. The optimal structure of the pipeline is driven by the cost of the development approach, its probability of survival, and the expected profitability. We illustrate the workability and implications of the model by applying it to some real-world scenarios in the pharmaceutical industry, and by comparing its normative pipeline recommendations against actual pipelines. Our results suggest that, for the cases we studied, firms tend to use narrower pipelines for their new drug development than they should, and thereby they underspend on research and development. We also present general qualitative insights for one- and two-stage NPD optimal pipeline structures.

American universities are experimenting with new mechanisms for promoting the commercialization of academic research and generating revenue from university intellectual property. This paper discusses mechanisms available to universities in managing the commercialization of intellectual property, considering equity as a technology transfer mechanism that offers advantages for both generating revenue and aligning the interests of universities, industry and faculty. Employing data from a national survey of Carnegie I and Carnegie II institutions, we document the recent rise in university equity holdings. We present and estimate a model that considers the university's use of equity to be a function of behavioral factors related to the university's prior experiences with licensing, success relative to other institutions, and the organization of the technology transfer office, as well as structural characteristics related to university type.

We develop a model of customer choice in response to random variation in quality. The choice model yields closed-form expressions which reflect the effect of competing suppliers' service quality on the long-run fraction of purchases a customer makes at the various competitors. We then use the expressions as the basis of simple normative models for suppliers seeking to maximize their long-run average profits. The results provide insight into the effect of switching behavior on the service levels offered by competing suppliers.

This paper develops the analytics and geometry of the investment opportunity set (IOS) and the test statistics for self-financing portfolios. A self-financing portfolio is a set of long and short investments such that the sum of their investment weights, or net investment, is zero. This contrasts with a standard portfolio that has investment weights summing to one. Examples of self-financing portfolios are hedges, overlays, arbitrage portfolios, swaps, and long/short portfolios. A standard portfolio plus the IOS of self-financing portfolios form a restricted IOS hyperbola with restricted efficient set constants that differ from the usual constants. The restrictions affect statistical tests of portfolio efficiency, which are developed for the self-financing restrictions. As an application, we consider the self-financing portfolios formed by Fama and French (1992, 1993, 1995), based on market capitalization and value. In contrast to Fama and French (1992, 1993, 1995), we find that their restricted IOS is significantly different from the unrestricted IOS with the implication that the Fama-French tests are misspecified.

Researchers have recently been interested in studying the drivers of store-brand success as well as factors that motivate retailers to introduce store brands. In this paper, we study the effects of the introduction of a store-brand into a particular product category. Specifically, we are interested in the effect of store-brand introduction on the demand as well as on the supply side. On the demand side, we investigate the changes in preferences for the national brands and price elasticities in the category. On the supply side, we study the effects of the new entrant on the interactions between the national brand manufacturers and the retailer introducing the store brand, including how these interactions influence the retailer's pricing behavior. In doing so, we are also able to test whether the observed data are consistent with some of the commonly used assumptions regarding retailer pricing behavior. For the demand specification we use a random coefficients logit model that allows for consumer heterogeneity. The model parameters are estimated using aggregate data while explicitly accounting for endogeneity in retail prices. Our empirical results obtained from the oats product category based on store-level data from a multistore retail chain indicate that the store-brand introduction generates notable changes within the category. The store-brand introduction coincides with an increase in the retailer's margins for the national brand. We find that the preferences for the national brand are relatively unaffected by the introduction of the store-brand. While consumers are, in general, more price sensitive (in terms of elasticities) than they were prior to store-brand introduction, a statistical test of the differences in mean price elasticities across stores and between the two regimes fails to reject the hypothesis of no change in these elasticities. Elasticities in specific stores however, do increase after the store brand is introduced. We also find that there is considerable heterogeneity in the preferences for the store-brand. On the supply side, we test several forms of manufacturer-retailer interactions to identify retailer pricing behavior most consistent with the data. Our results indicate that the data reject several, commonly imposed, forms of interactions. In examining the nature of manufacturer interactions with the retailer, we find that the manufacturer of the national brand appears to take a softer stance in its interactions with the retailer subsequent to store-brand entry. This finding is consistent with academic research and with articles in the popular press which suggest that the store brand enhances the retailer's bargaining ability vis-à-vis the manufacturers of the national brands. We also provide results from a second product category frozen pasta) that are largely consistent with those found in the oats category.

Based on research on the polyhedral structure of lot-sizing models over the last 20 years, we claim that there is a nontrivial fraction of practical lot-sizing problems that can now be solved by nonspecialists just by taking an appropriate a priori reformulation of the problem, and then feeding the resulting formulation into a commercial mixed-integer programming solver. This claim uses the fact that many multi-item problems decompose naturally into a set of single-item problems with linking constraints, and that there is now a large body of knowledge about single-item problems. To put this knowledge to use, we propose a classification of lot-sizing problems (in large part single-item) and then indicate in a set of tables, what is known about a particular problem class and how useful it might be. Specifically, we indicate for each class (i) whether a tight extended formulation is known, and its size; (ii) whether one or more families of valid inequalities are known defining the convex hull of solutions, and the complexity of the corresponding separation algorithms; and (iii) the complexity of the corresponding optimization algorithms (which would be useful if a column generation or Lagrangian relaxation approach was envisaged). Three distinct multi-item lot-sizing instances are then presented to demonstrate the approach, and comparative computational results are presented. Finally, we also use the classification to point out what appear to be some of the important open questions and challenges.

Bottleneck analysis is a useful tool in capacity planning for centrally controlled network systems. However, under a decentralized network where individual users are allowed to select their own routes, straightforward application of bottleneck analysis does not necessarily yield an optimal performance. It may even hurt the system performance—an aspect of Braess's paradox. We investigate the capacity expansion problem for a decentralized system with general network topology. To this end, we first discuss the short-run problem and show that the externality pricing solves the joint problem of demand and routing control. We then study the capacity expansion/reduction problem for decentralized systems that may or may not be optimally controlled in the short run.

Many organizations have only recently recognized that sharing information with other members in their supply chain can lead to signficant reduction in the total costs.Usually these information flows are incorporated into existing operating policies at the various parties.In this paper we argue that, in some cases, it may be necessary to change the way the supply chain is managed to make complete use of the information flows. We support this argument by analyzing a supply chain containing a capacitated supplier and a retailer facing i.i.d. demands. In addition there are fixed ordering costs between the retailer and the supplier.In this setting, we consider two models: (1) the retailer is using the optimal (s,S) policy and providing the supplier information about her inventory levels; and (2) the retailer, still sharing information on her inventory levels, orders in a period only if by the previous period the cumulative end-customer demand since she last ordered was greater than δ. Thus, in Model 1, information sharing is used to supplement existing policies; while, in Model 2, we have redefined operating policies to make better use of the information flows. We will show, via a detailed computational study, that the total supply chain costs of Model 2 are 10.4% lower, on the average, than that of Model 1. We noticed that this reduction in costs is higher at higher capacities, higher supplier penalty costs, lower retailer penalty costs, moderate values of set-up cost, and at lower end-customer demand variances.

For multiobjective optimization problems, it is meaningful to compute a set of solutions covering all possible trade-offs between the different objectives. The multiobjective knapsack problem is a generalization of the classical knapsack problem in which each item has several profit values. For this problem, efficient algorithms for computing a provably good approximation to the set of all nondominated feasible solutions, the Pareto frontier, are studied. For the multiobjective one-dimensional knapsack problem, a practical fully polynomial-time approximation scheme (FPTAS) is derived. It is based on a new approach to the single-objective knapsack problem using a partition of the profit space into intervals of exponentially increasing length. For the multiobjective m-dimensional knapsack problem, the first known polynomial-time approximation scheme (PTAS), based on linear programming, is presented.

This paper investigates the impacts of a secondary market where resellers can buy and sell excess inventories. We develop a two-period model with a single manufacturer and many resellers. At the beginning of the first period resellers order and receive products from the manufacturer, but at the beginning of the second period, they can trade inventories among themselves in the secondary market. We endogenously derive the optimal decisions for the resellers, along with the equilibrium market price of the secondary market. The secondary market creates two interdependent effect—a quantity effect (sales by the manufacturer) and an allocation effect (supply chain performance). The former is indeterminate; i.e., the total sales volume for the manufacturer may increase or decrease, depending on the critical fractile. The latter is always positive; i.e., the secondary market always improves allocative efficiency. The sum of the effects is also unclear—the welfare of the supply chain may or may not increase as a result of the secondary market. Lastly, we study potential strategies for the manufacturer to increase sales in the presence of the secondary market.