This paper investigates the conditions under which frontline employees take initiative to improve their work systems to prevent operational failures. Drawing on the system improvement and team learning literatures, we develop a framework of frontline system improvement and test it using survey data from 37 workgroups. We find that psychological safety—the belief that one can talk about errors without risk of punishment—and problem-solving efficacy—the belief that the organization will support employees' system improvement efforts—were positively correlated with frontline system improvement (FLSI). Surprisingly, felt responsibility was negatively associated with FLSI. These findings suggest that rather than relying on hiring motivated individuals, managers need to support employees' efforts to improve their work systems by creating a work environment where it is safe to talk about operational failures and responding to employee communication about operational failures. Doing this may result in higher levels of FLSI efforts and ultimately improve work processes.

The decentralized supply chain management scheme of Lee and Whang (1999) can be viewed as operationalizing the decentralized management scheme implicit in Clark and Scarf (1960). This paper proposes the use of what are called responsibility tokens (RTs) to further facilitate that operationalization. The proposal assumes that a management information system, presumably electronic, is established to monitor inventories and shipment quantities, and to carry out transfer payments between players. As in Lee and Whang(1999), the incentives of the system are aligned, so if each player is brilliantly self-serving, the system optimal solution will result. While the system administrator need not know how the system should be managed, the most upstream player must know how to manage the system optimally for the system optimal solution to be achieved. RTs endow the system with an attractive self-correcting property: An example illustrates that upstream players are given a mechanism and the incentive to correct for downstream overordering. The downstream players who over-order are penalized, but system performance is not degraded much. Extensions and further research are also discussed.

We investigate a simple dynamic model of firm behavior in which firms compete by investing in capacity that is used to provide a good or service to their customers. There is a fixed total market of customers whose demands for the good or service are random and who divide their patronage between the firms in each period. Periodically, the market shares of the two firms can change based on the realized level of customer service provided in the prior period. We assume that the expected level of customer service can be expressed as a function of the (per customer) capacity of the firms' service delivery systems, and that service declines as the capacity decreases. The firms differ in their customers' willingness to defect when confronted by service failure. The primary issue we address is the firms' capacity decisions in response to customer service concerns and competitive pressure. We provide conditions under which the firms' optimal (i.e., equilibrium) capacity levels in a period are proportional to the size of their respective customer bases in that period. Further, we develop expressions for the value of a firm's customers and the implicit cost of service failure. Results for both single-period and finite-horizon problems are investigated and applied to two examples: (1) competition between Internet service providers who operate systems that we approximate by simple loss-type queueing models, and (2) competition between make-to-stock producers who operate systems that we approximate by newsvendor inventory models. For both examples, solutions are derived and interpreted.

I is a pleasure to announce that the 2014 Manufacturing & Service Operations Management Best Paper Award goes to Philipp Afeche for “IncentiveCompatible Revenue Management in Queueing Systems: Optimal Strategic Delay” for its contribution to theory and practice of operations management. The author will receive a $2,000 prize, contributed by the MSOM Society of INFORMS. Each year, all papers published in the journal during the past three years (2011–2013) are eligible for the award, and judging is done through a two-stage process. First, M&SOM Editor Steve Graves and a group of M&SOM associate editors select a small set of finalists from the nominations received. Second, an ad hoc committee, organized by the immediate past president of the MSOM Society, selects the best paper from among the finalists. This year, the ad hoc committee consisted of Charles Corbett, Jeremie Gallien, Amy Ward, and MSOM pastpresident Beril Toktay. They selected the winning paper from a set of five outstanding finalists. The winning paper studies the revenue-maximizing price and lead-time menu and scheduling policy in a queueing system when customers are heterogeneous with respect to their valuations and time sensitivity, and their type is not observable to the firm. A similar problem has been studied from the social optimality perspective. The novelty of this paper stems from the fact that it studies the problem from the point of view of revenue maximization. This makes a fundamental difference. From the social optimality perspective, it makes sense to consider only work-conserving scheduling policies (i.e., the server does not idle in the presence of a queue). This is not the case when the objective is revenue maximization: The firm may want to “strategically delay” the low-priority customers to make sure that the time-sensitive customers choose the high-price, fast-service option. The paper establishes the “strategic delay” concept and develops interesting managerial insights. The following reviewer commentary reflects points made by many of the reviewers:

A “cleantech” firm is one with an innovative technology and/or business model for serving an existing market with dramatically reduced environmental impact. This paper describes operations management (OM) challenges faced by five cleantech companies, and a few questions that these raise for OM and multidisciplinary research. It aims to fuel readers' motivation to identify and pursue others. The OM community has fundamental roles to play in the creation of an environmentally sustainable economy.

Isuggest that the prospering fields of physics, medicine, and finance illustrate the value of a strong empirical dimension to research that is well integrated with theoretical research. I use empirical research in these fields to formulate a framework for classifying empirical research and illustrate that framework with a few selected examples in operations management. I offer some advice on data sources and approaches to conducting empirical research and suggest ways strengthening empirical research in operations management. This is obviously a partial treatment of a large subject and represents my personal point of view. This paper should encourage comments by others to further develop the topic and to offer alternative points of view.

We extend previous work evaluating the quality versus time-to-market trade-off for a single product generation to the case of multiple generations. While a single generation framework is appropriate when either the technology is not extendable or when additional launch costs outweigh benefits, we find that it is important to recognize whether a technology is extendable and explicitly consider the potential for multiple generations. We evaluate the factors determining optimal development-cycle length and intensity using a forward-looking model that allows for multiple product generations. Comparisons are made with restricted versions of the model that reflect pure single generation and sequential single generation approaches. Against an active competitor, the multiple generation approach is much more profitable, with the greatest differences in fast-moving industries. More total time is spent in development when a multiple generation model is used. Further, this time is dedicated to the more frequent introduction of improved product generations—a “rapid inch-up” strategy—resulting in more, higher quality products over time. Factors affecting optimal time-to-market differ substantially for the single versus multiple generation approaches. A key difference is that faster rates of quality improvement lead to longer development cycles for the single and sequential single generation models, but shorter cycles with the forward-looking multiple generation model. With a single generation, variable costs have the biggest impact on cycle length (higher costs shorten cycles), but with multiple generations, fixed costs have the biggest impact on cycle length (higher costs lead to longer cycles).