Knowledge that Transforms

We formulate and evaluate the Automated Simulation Analysis Procedure (ASAP), an algorithm for steady-state simulation output analysis based on the method of nonover-lapping batch means (NOBM). ASAP delivers a confidence interval for an expected response that is centered on the sample mean of a portion of a simulation-generated time series and satisfies a user-specified absolute or relative precision requirement. ASAP operates as follows: The batch size is progressively increased until either (a) the batch means pass the von Neumann test for independence, and then ASAP delivers a classical NOBM confidence interval; or (b) the batch means pass the Shapiro-Wilk test for multivariate normality, and then ASAP delivers a correlation-adjusted confidence interval. The latter adjustment is based on an inverted Cornish-Fisher expansion for the classical NOBM t-ratio, where the terms of the expansion are estimated via an autoregressive-moving average time series model of the batch means. After determining the batch size and confidence-interval type, ASAP sequentially increases the number of batches until the precision requirement is satisfied. An extensive experimental study demonstrates the performance improvements achieved by ASAP versus well-known batch means procedures, especially in confidence-interval coverage probability.

Previous research has examined whether price dispersion exists in theoretically highly efficient Internet markets. However, much of the previous work has been focused on industries with low cost and undifferentiated products. In this paper, we examine the presence of price dispersion and product differentiation using data on the airline ticket offerings of online travel agents (OTAs). We find that different OTAs offer tickets with substantially different prices and characteristics when given the same customer request. Some of this variation appears to be due to product differentiation—different OTAs specialize by systematically offering different trade-offs between ticket price and ticket quality (minimizing the number of connections, matching requested departure and return time). However, even after accounting for differences in ticket quality, ticket prices vary by as much as 18% across OTAs. In addition, OTAs return tickets that are strictly inferior to the ticket offered by another OTA for the same request between 2.2% and 28% of the time. Overall, this suggests the presence of both price dispersion and product differentiation in the online travel market.

To what extent do firms go abroad to access technology available in other locations? This paper examines whether and when state technical capabilities attract foreign investment in manufacturing from 1987-1993. We find that on average state R&D intensity does not attract foreign direct investment. Most investing firms are in lower-tech industries and locate in low R&D intensity states, suggesting little interest in state technical capabilities. In contrast, we find that firms in research-intensive industries are more likely to locate in states with high R&D intensity. Foreign firms in the pharmaceutical industry value state R&D intensity the most, at a level twice that of firms in the semiconductor industry, and four times that of electronics firms. Interestingly, not only firms from technically lagging nations, but also some firms from technically leading nations are attracted to R&D intensive states. This suggests that beyond catching up, firms use knowledge-seeking investments also to source technical diversity.

A mathematical model is presented for studying the effects of interactions among team members on the process of replacing members of a team in an organization. The model provides the ability to control the number of members that interact with each individual on the team. Through the use of analysis and computer simulations, it is shown how the amount of interaction affects the tradeoff between the expected performance and the number of replacements and interviews needed to find a good team using various replacement policies. New managerial insights into this process—such as the fact that it is not necessarily optimal to replace the worst-performing team member—are provided.

In the economic evaluation of large public-sector projects, an aggregate social discount rate may be used in present worth comparison of alternatives. This paper uses the assumptions that individual discount rates are constant over time and approximately Normally distributed across the affected population, with mean μ and variance σ 2 , to derive an aggregate discount function that is exponential in form but with time-dependent aggregate discount rate ρ(t) = μ − σ 2 t/2, where t is the time of occurrence of the cost or benefit. This equation agrees with numerical simulations. If σ 2 > 0, then the aggregate discount rate is less than the mean individual discount rate, and use of the time-dependent aggregate discount rate ρ(t) = μ − σ 2 t/2 instead of the constant discount factor ρ(t) = μ would result in larger discounted present values for public-sector projects for which the benefits lie far in the future. This could mean that public-sector investments that would be rejected under the assumption ρ(t) = μ might be justified using the time-dependent aggregate discount rate ρ(t) = μ − σ 2 t/2.

We analyze a dynamic auction, in which a seller with C units to sell faces a sequence of buyers separated into T time periods. Each group of buyers has independent, private values for a single unit. Buyers compete directly against each other within a period, as in a traditional auction, and indirectly with buyers in other periods through the opportunity cost of capacity assessed by the seller. The number of buyers in each period, as well as the individual buyers' valuations, are random. The model is a variation of the traditional single leg, multiperiod revenue management problem, in which consumers act strategically and bid for units of a fixed capacity over time. For this setting, we prove that dynamic variants of the first-price and second-price auction mechanisms maximize the seller's expected revenue. We also show explicitly how to compute and implement these optimal auctions. The optimal auctions are then compared to a traditional revenue management mechanism—in which list prices are used in each period together with capacity controls—and to a simple auction heuristic that consists of allocating units to each period and running a sequence of standard, multiunit auctions with fixed reserve prices in each period. The traditional revenue management mechanism is proven to be optimal in the limiting cases when there is at most one buyer per period, when capacity is not constraining, and asymptotically when the number of buyers and the capacity increases. The optimal auction significantly outperforms both suboptimal mechanisms when there are a moderate number of periods, capacity is constrained, and the total volume of sales is not too large. The benefit also increases when variability in the dispersion in buyers' valuations or in the number of buyers per period increases.

The question of how initial resource endowments-the stocks of resources that entrepreneurs contribute to their new ventures at the time of founding-affect organizational life chances is one of significant interest in organizational ecology, evolutionary theory, and entrepreneurship research. Using data on the life histories of all 134 firms founded to exploit MIT-assigned inventions during the 1980-1996 period, the study analyzes how resource endowments affect the likelihood of three critical outcomes: that new ventures attract venture capital financing, experience initial public offerings, and fail. Our analysis focuses on the role of founders' social capital as a determinant of these outcomes. Event history analyses show that new ventures with founders having direct and indirect relationships with venture investors are most likely to receive venture funding and are less likely to fail. In turn, receiving venture funding is the single most important determinant of the likelihood of IPO. We conclude that the social capital of company founders represents an important endowment for early-stage organizations.

This study considers multistage production systems where defective units can be reworked repeatedly at every stage. Production, as well as rework, is in lots requiring set-up and variable production costs, and orders need to be filled in their entirety. The yield of each stage is uncertain, so several production runs may need to be attempted until the quantity of finished products is sufficient. The trade-off at each stage is between using small lots, possibly necessitating repeated rework set-ups and large lots, which may result in costly overproduction. Multistage manufacturing facilities with rework capabilities are quite common in practice, but their optimal operation when orders have to be met in full has been virtually unexplored. We show that a multistage system where only one of the stages requires a setup (a “single-bottleneck system”) can be reduced to a single-stage system. Moreover, if it is possible to arrange the operations in any order, we prove that it is best to make the “bottleneck” the first stage of the system. We also develop recursive algorithms for solving two- and three-stage systems, where all stages require set-ups, optimally. Generalizations to systems where rework yields and costs differ from those of initial processing are also discussed.

Increasingly, technological innovation creates markets for new products and services. To survive, firms must respond to these new markets. How do firms develop the capabilities necessary to succeed in such changing conditions? Some suggest that experience with previous entry builds such capabilities. Others suggest that capabilities arise from experience producing and selling to existing markets. The role of managers is also debated. Some argue that experience with existing markets causes managers to miss entry opportunities. Others argue that managers enter new markets when their firm possesses the experience needed to compete effectively. In this paper, we explore these issues by investigating entry patterns in the disk-drive industry. We investigate the effect of experience in existing markets and experience with previous market entry. We find that experience in previous markets increased the probability that a firm would enter a new market. We show that this experience had greater value if the firm entered the new market. We infer that managers chose to enter these markets to obtain this increase in value.