Parallel Innovation Contests

We study multiple parallel contests where contest organizers elicit solutions to innovation-related problems from a set of agents. Each agent may participate in multiple contests and exert effort to improve her solution for each contest she enters, but the quality of her solution also depends on an output uncertainty. We first analyze whether an organizer's profit can be improved by discouraging agents from participating in multiple contests. We show, interestingly, that organizers benefit from agents' participation in multiple contests when the agent's output uncertainty is sufficiently large. A managerial insight from this result is that when organizers elicit innovative solutions rather than low-novelty solutions, agents' participation in multiple contests may be beneficial to organizers. We further show that an organizer's profit is unimodal in the number of contests, and the optimal number of contests increases with the agent's output uncertainty. This finding may explain why many organizations run multiple contests in practice, and it prescribes a larger number of contests when organizations seek innovative solutions rather than low-novelty solutions

Dynamic Development Contests

Public, private, and not-for-profit organizations find advanced technology and product development projects challenging to manage due to the time and budget pressures, and turn to their development partners and suppliers to address their development needs. We study how dynamic development contests with enriched rank-based incentives and carefully-tailored information design can help these organizations outsource their development projects at the minimum project lead time by stimulating competition among suppliers. We show that an organization can use dynamically adjusted flexible rewards to achieve the absolute minimum expected project lead time at a significantly lower cost than a fixed-reward policy. Importantly, our flexible-reward policy pays the absolute minimum expected reward (i.e., achieves the first best). We further study the case where the organization does not have sufficient budget to offer a reward that attains the absolute minimum expected lead time. We propose that in this case, the organization can dynamically increase the contest reward until its budget constraint binds and then use information sharing as a strategic tool to incentivize suppliers. Specifically, we propose an easy-to-implement random-update policy where the organization periodically monitors the status of suppliers at random times and immediately discloses any partial progress. We show that such a random-update policy outperforms other canonical information disclosure strategies. Our results indicate that dynamic rewards and strategic information disclosure are powerful tools to help organizations outsource their development needs swiftly and cost effectively

A multi-stage stochastic programming approach in master production scheduling

Master Production Schedules (MPS) are widely used in industry, especially within Enterprise Resource Planning (ERP) software. The classical approach for generating MPS assumes infinite capacity, fixed processing times, and a single scenario for demand forecasts. In this paper, we question these assumptions and consider a problem with finite capacity, controllable processing times, and several demand scenarios instead of just one. We use a multi-stage stochastic programming approach in order to come up with the maximum expected profit given the demand scenarios. Controllable processing times enlarge the solution space so that the limited capacity of production resources are utilized more effectively. We propose an effective formulation that enables an extensive computational study. Our computational results clearly indicate that instead of relying on relatively simple heuristic methods, multi-stage stochastic programming can be used effectively to solve MPS problems, and that controllability increases the performance of multi-stage solutions

An anticipative scheduling approach with controllable processing times

In practice, machine schedules are usually subject to disruptions which have to be repaired by reactive scheduling decisions. The most popular predictive approach in project management and machine scheduling literature is to leave idle times (time buffers) in schedules in coping with disruptions, i.e. the resources will be under-utilized. Therefore, preparing initial schedules by considering possible disruption times along with rescheduling objectives is critical for the performance of rescheduling decisions. In this paper, we show that if the processing times are controllable then an anticipative approach can be used to form an initial schedule so that the limited capacity of the production resources are utilized more effectively. To illustrate the anticipative scheduling idea, we consider a non-identical parallel machining environment, where processing times can be controlled at a certain compression cost. When there is a disruption during the execution of the initial schedule, a match-up time strategy is utilized such that a repaired schedule has to catch-up initial schedule at some point in future. This requires changing machine–job assignments and processing times for the rest of the schedule which implies increased manufacturing costs. We show that making anticipative job sequencing decisions, based on failure and repair time distributions and flexibility of jobs, one can repair schedules by incurring less manufacturing cost. Our computational results show that the match-up time strategy is very sensitive to initial schedule and the proposed anticipative scheduling algorithm can be very helpful to reduce rescheduling costs

Incentives in Contests with Heterogeneous Solvers

In a contest in which solvers with heterogeneous expertise exert effort to develop solutions, a recent paper [Terwiesch C, Xu Y (2008) Innovation contests, open innovation, and multiagent problem solving. Management Sci. 54(9):1529–1543] argues that as more solvers enter the contest, every solver will reduce effort due to a lower probability of winning the contest. This paper corrects mistakes in this theory, and shows that there exist high-expertise solvers who may raise their effort in response to increased competition. This is because more entrants raise the expected best performance among other solvers, creating positive incentives for solvers to exert higher effort to win the contest. Because of this positive effect, we find that a free-entry open contest is more likely to be optimal to a contest organizer than what Terwiesch and Xu (2008) and other prior literature asserted

Product Development in Crowdfunding: Theoretical and Empirical Analysis

Crowdfunding goes beyond raising funds. Entrepreneurs often use crowdfunding to solicit feedback from customers to improve their products, and may therefore prefer to launch crowdfunding campaigns for a basic version of their products with few or no enhancements (i.e., limited features). Yet, customers may not be persuaded by a campaign if a product appears too basic. In view of this trade-off, a key question for an entrepreneur is how far a product should be enhanced before launching a crowdfunding campaign. Analyzing a game-theoretical model and testing its predictions empirically, we study how a product's level of enhancement at campaign launch influences both whether an entrepreneur continues to improve the product during the campaign and whether the campaign is successful. We show that as the product's level of enhancement at campaign launch increases, the likelihood of product improvement during a campaign at first increases (because customers are more likely to provide feedback) and then decreases (because of increased production cost for the entrepreneur). Furthermore, although our theoretical model intuitively predicts that the likelihood of campaign success will always increase when an entrepreneur launches a campaign for a more enhanced product, our empirical analysis shows that the likelihood of campaign success first increases and then decreases. This counterintuitive result may be due to customers being overwhelmed with the complexity of highly enhanced products. Finally, while crowdfunding experts believe that products should be enhanced as much as possible before a campaign, we show that this is not always the best strategy

Business Analytics Assists Transitioning Traditional Medicine to Telemedicine at Virtual Radiologic

Virtual Radiologic (vRad), the largest teleradiology company in the United States, faces the difficult problem of matching more than 400 radiologists with time-varying and seasonal demand. In addition to the constraints that traditional medical facilities face, vRad is subject to supply and demand requirements that are unique to the teleradiology business environment. In this paper, we present a forecasting and capacity-planning model that more accurately assesses demand and plans system capacity to provide better service to vRad’s customers. We discuss the underlying reasons for improvement and quantify the impact on vRad’s entire system. We explain managerial insights that will help both vRad and other companies in the service sector with similar service-response requirements and demand patterns. We also highlight the implementation challenges our teams faced

Design and analysis of mechanisms for decentralized joint replenishment

We consider jointly replenishing multiple firms that operate under an EOQ like environment in a decentralized, non-cooperative setting. Each firm's demand rate and inventory holding cost rate are private information. We are interested in finding a mechanism that would determine the joint replenishment frequency and allocate the joint ordering costs to these firms based on their reported stand-alone replenishment frequencies (if they were to order independently). We first provide an impossibility result showing that there is no direct mechanism that simultaneously achieves efficiency, incentive compatibility, individual rationality and budget-balance. We then propose a general, two-parameter mechanism in which one parameter is used to determine the joint replenishment frequency, another is used to allocate the order costs based on firms’ reports. We show that efficiency cannot be achieved in this two-parameter mechanism unless the parameter governing the cost allocation is zero. When the two parameters are same (a single parameter mechanism), we find the equilibrium share levels and corresponding total cost. We finally investigate the effect of this parameter on equilibrium behavior. We show that properly adjusting this parameter leads to mechanisms that are better than other mechanisms suggested earlier in the literature in terms of fairness and efficiency. © 2016 Elsevier B.V

Innovative online platforms: Research opportunities

Economic growth in many countries is increasingly driven by successful startups that operate as online platforms. These success stories have motivated us to define and classify various online platforms according to their business models. This study discusses strategic and operational issues arising from five types of online platforms (resource sharing, matching, crowdsourcing, review, and crowdfunding) and presents some research opportunities for operations management scholars to explore