Knowledge that Transforms

In models of optimal decision making, assumptions about managerial behavior are often made with the hope that the prescriptions offered by these models will be effective in practice, even if actual behavior occasionally strays from these assumptions. However, recent revenue management (RM) research has demonstrated what appear to be systematic deviations from normative models of decision making. These deviations can even be observed in relatively simple RM contexts. We suggest that technical errors in capacity allocation decisions are linked to issues such as arousal and stress associated with state conditions of RM tasks. Our study goes beyond existing findings by considering behavioral phenomena in concurrent task settings, where the decision maker is faced with managing decisions for more than one product or service. Physiological measures of eye dilation and blink rate are used as markers of arousal and stress in subjects engaged in RM tasks. Our analysis shows that physiological responses are indeed associated with both the state conditions of RM tasks and the number of capacity blocks managed concurrently by an individual. Deviations from modeled decision making appear to be significantly dependent upon these physiological responses. We conclude with a discussion of implications for further research and practice.

When a manufacturer relies solely on its own inputs in making products, the focus of negotiations between the manufacturer and retailer is exclusively on profits in the output (retail) market. In such cases, absent retail competition concerns, standard two‐part tariff negotiations set the per‐unit wholesale price equal to marginal cost, and require fixed transfers from the retailer to the manufacturer. In this article, we recognize that manufacturers often rely on imperfectly competitive markets for at least some inputs. Incorporating this seemingly natural feature has profound implications for manufacturer–retailer negotiations since it shifts their focus from being exclusively on output markets to one that balances strategic concerns in both input and output realms. The article's main result is that the added need to discipline input prices can lead the manufacturer and retailer to write contingent contracts that are cost‐plus and prescribe lump‐sum slotting allowances (i.e., fixed transfer from the manufacturer to the retailer).

The bold lines that have separated the application of specific production planning and control techniques to specific production systems are being blurred by continuous advances in production technologies and innovative operational procedures. Oral communication among dispatchers and production units has given way to electronic communication between production planners and these units by continuous progress in information technologies. Current production literature alludes to the idea that, collectively, these advances have paved the way for application of Just‐In‐Time (JIT) production concepts, which were originally developed for mass production systems, in intermittent production systems. But this literature does not actually consider the possibility. This article presents a modification to JIT procedures to make them more suitable for jumbled‐flow shops. This article suggests providing real‐time information about net‐requirements for each product to each work center operator for setting production priorities at each work center. Simulation experiments conducted for this study show that using Net‐Requirements in JIT (NERJIT) reduces customer wait time by 45–60% while reducing inventory slightly. The analysis of work centers’ input and output stock‐point inventories shows that using the information about net‐requirements results in production of items that are in current demand. NERJIT results in smaller input stock‐point inventory and availability of products with higher priority in the output stock‐points of work centers.

We analyze a supply chain consisting of a supplier and a retailer. The supplier's unit production cost, which characterizes his type, is only privately known to him. When trading with the retailer, the supplier demands a reservation profit that depends on his unit production cost. We model this problem as a game of adverse selection. In this model, the retailer offers a menu of contracts, each of which consists of two parameters: the ordering quantity and the supplier's share of the channel profit. We show that the optimal contract depends critically on a surrogate measure—the ratio of the types’ reservation profit differential to their production cost differential. An important implication from our analysis is that information asymmetry alone does not necessarily induce loss in channel efficiency. The optimal contract can coordinate the supply chain as long as the low‐cost supplier's cost efficiency is neither much overvalued nor much undervalued in the outside market. We further discuss the retailer's preference of the supplier's type under different market conditions, as well as evaluate the effects of the supplier's reservation profit, the retail price, and the demand uncertainty on the optimal contract.

We study the dynamic assignment of cross‐trained servers to stations in understaffed lines with finite buffers. Our objective is to maximize the production rate. We identify optimal server assignment policies for systems with three stations, two servers, different flexibility structures, and either deterministic service times and arbitrary buffers or exponential service times and small buffers. We use these policies to develop server assignment heuristics for Markovian systems with larger buffer sizes that appear to yield near‐optimal throughput. In the deterministic setting, we prove that the best possible production rate with full server flexibility and infinite buffers can be attained with partial flexibility and zero buffers, and we identify the critical skills required to achieve this goal. We then present numerical results showing that these critical skills, employed with an effective server assignment policy, also yield near‐optimal throughput in the Markovian setting, even for small buffer sizes. Thus, our results suggest that partial flexibility is sufficient for near‐optimal performance, and that flexibility structures that are effective for deterministic and infinite‐buffered systems are also likely to perform well for finite‐buffered stochastic systems.

Deciding to open the source code of a software product has advantages and disadvantages. The disadvantage is that the firm loses the revenue from the software. The advantage is that the users' network can contribute to the quality of the software code, which increases the demand for the software and for a complementary product. Demand for the complementary product also goes up, because demand for a product increases when the price of its complement decreases, and under open source, the price of the software product drops down to zero. This paper examines the strategic interactions at work here, within a duopoly framework, and tries to determine the circumstances under which it is optimal for a firm to open its code. We find that firms open the source code when there is a competitive software‐product market, a less competitive complementary‐product market, and when the complementary product is of high quality. Furthermore, it is more profitable for the firm to open the source code if its competitor also does so. When this happens the incentive to open the code can even be higher than in a monopoly situation. More intense competition induces symmetric equilibria in which both firms choose the same strategy.

We formally review the Author Affiliation Index (AAI) method as originally conceived by David Harless and Robert J. Reilly from the Economics Department at the Virginia Commonwealth University School of Business and as subsequently developed and interpreted by Gorman and Kanet in their 2005 article. Through this formal review, we first highlight and discuss two important informational inputs that can impact the stability of the AAI scores for journals in any given set of to‐be‐evaluated journals. We then identify and challenge interpretations related to these scores (one theoretical, one statistical) offered by Gorman and Kanet that result in misleading conclusions about journal quality and that may potentially motivate inappropriate editorial behavior. For important professional decisions of hiring, performance evaluation, promotion, and tenure, we conclude by cautioning against sole reliance on the AAI method for ranking journals and against exclusive interpretation of the score computed via the AAI method as an indicator of journal quality.

Previous work has considered the simultaneous (as opposed to sequential) optimization of a maintenance policy and a production policy in a multi‐product setting with random yield and product mix constraints. One of the sequential approaches to which the simultaneous approach is compared is a so‐called first‐come‐first‐served (FCFS) approach, i.e., an approach that generates randomized production policies that do not depend on the deterioration state of the machine. However, the model formulation for this approach does not generate policies consistent with this FCFS notion. Therefore, we present a revised FCFS model and analyze its performance using an existing experimental design. The results suggest that previous work overestimates the degree to which a FCFS approach is suboptimal, and underestimates the value of simultaneously optimizing the maintenance and production decisions. Lastly, we conduct additional experiments which suggest that the joint impact of using both simultaneous optimization and a deterioration dependent production policy is quite significant.

We consider the transport of containers through a fleet of ships. Each ship has a capacity constraint limiting the total number of containers it can carry and each ship visits a given set of ports following a predetermined route. Each container has a release date at its origination port, and a due date at its destination port. A container has a size 1 or size 2; size 1 represents a 1 TEU (20‐foot equivalent unit) and size 2 represents 2 TEUs. The delivery time of a container is defined as the time when the ship that carries the container arrives at its destination port. We consider the problem of minimizing the maximum tardiness over all containers. We consider three scenarios with regard to the routes of the ships, namely, the ships having (i) identical, (ii) nested, and (iii) arbitrary routes. For each scenario, we consider different settings for origination ports, release dates, sizes of containers, and number of ports; we determine the computational complexity of various cases. We also provide a simple heuristic for some cases, with its worst case analysis. Finally, we discuss the relationship of our problems with other scheduling problems that are known to be open.