Christian Terwiesch

Innovation Contests, Open Innovation, and Multiagent Problem Solving

In an innovation contest, a firm (the seeker) facing an innovation-related problem (e.g., a technical R&D problem) posts this problem to a population of independent agents (the solvers) and then provides an award to the agent that generated the best solution. In this paper, we analyze the interaction between a seeker and a set of solvers. Prior research in economics suggests that having many solvers work on an innovation problem will lead to a lower equilibrium effort for each solver, which is undesirable from the perspective of the seeker. In contrast, we establish that the seeker can benefit from a larger solver population because he obtains a more diverse set of solutions, which mitigates and sometimes outweighs the effect of the solvers underinvestment in effort. We demonstrate that the inefficiency of the innovation contest resulting from the solvers underinvestment can further be reduced by changing the award structure from a fixed-price award to a performance-contingent award. Finally, we compare the quality of the solutions and seeker profits with the case of an internal innovation process. This allows us to predict which types of products and which cost structures will be the most likely to benefit from the contest approach to innovation.

Idea Generation and the Quality of the Best Idea

In a wide variety of settings, organizations generate a number of possible solutions to a problem---ideas---and then select a few for further development. We examine the effectiveness of two group structures for such tasks---the team structure, in which the group works together in time and space, and the hybrid structure, in which individuals first work independently and then work together. We define the performance of a group as the quality of the best ideas identified. Prior research has defined performance as the average quality of ideas or the number of ideas generated, ignoring what most organizations seek, a few great ideas. We build a theory that relates organizational phenomena to four different variables that govern the quality of the best ideas identified: (1) the average quality of ideas generated, (2) the number of ideas generated, (3) the variance in the quality of ideas generated, and (4) the ability of the group to discern the quality of the ideas. We test this theory with an experiment. We find that groups organized in the hybrid structure are able to generate more ideas, to generate better ideas, and to better discern the quality of the ideas they generate. Moreover, we find that the frequently recommended brainstorming technique of building on others ideas is counterproductive; teams exhibiting such buildup neither create more ideas, nor are the ideas that build on previous ideas better.

Learning and process improvement during production ramp-up

Abstract Rapid product lifecycles and high development costs pressure manufacturing firms to cut not only their development times (time-to-market), but also the time to reach full capacity utilization (time-to-volume). The period between completion of development and full capacity utilization is known as production ramp-up. During that time, the new production process is ill understood, which causes low yields and low production rates. This paper analyzes the interactions among capacity utilization, yields, and process improvement (learning). We model learning in the form of deliberate experiments, which reduce capacity in the short run. This creates a trade-off between experiments and production. High selling prices during ramp-up raise the opportunity cost of experiments, yet early learning is more valuable than later learning. We formalize the resulting intertemporal trade-off between the short-term opportunity cost of capacity and the long term value of learning as a dynamic program. The paper also examines the tradeoff between production speed and yield/quality, where faster production rates lead to more defects. Finally, we show what happens if managers misunderstand the sources of learning.

Measuring the Frictional Costs of Online Transactions: The Case of a Name-Your-Own-Price Channel

We study the offers submitted by consumers to a large Name-Your-Own-Price (NYOP) online retailer. A distinctive feature of this retailer is that it allows consumers to repeatedly submit offers on one and the same product. While consumers could identify the threshold price (the minimum price for which the retailer is willing to sell) by incrementing their offer in small steps in each consecutive round, such a strategy would require them to go through many additional online transactions. We define frictional cost as the disutility that the consumer experiences when conducting an online transaction, such as submitting an offer. Thus, in our setting, consumers trade off a direct financial value (lower price) for frictional costs. Based on a consumer choice model capturing this trade-off, we use the observed consumer behavior to reconstruct the frictional cost parameters for the consumers in our sample. We show that, perhaps contrary to the general wisdom, frictional costs in electronic markets are substantial, with median values ranging from EUR 3.54 for a portable digital music player (MP3) to EUR 6.08 for a personal digital assistant (PDA). We find that consumers who have gathered experience with the NYOP channel in previous transactions exhibit lower frictional costs than consumers who use the channel for the first time. Surprisingly, sociodemographic variables do not help to explain the variation in frictional costs.

Impact of Workload on Service Time and Patient Safety: An Econometric Analysis of Hospital Operations

Much of prior work in the area of service operations management has assumed service rates to be exogenous to the level of load on the system. Using operational data from patient transport services and cardiothoracic surgery---two vastly different health-care delivery services---we show that the processing speed of service workers is influenced by the system load. We find that workers accelerate the service rate as load increases. In particular, a 10% increase in load reduces length of stay by two days for cardiothoracic surgery patients, whereas a 20% increase in the load for patient transporters reduces the transport time by 30 seconds. Moreover, we show that such acceleration may not be sustainable. Long periods of increased load (overwork) have the effect of decreasing the service rate. In cardiothoracic surgery, an increase in overwork by 1% increases length of stay by six hours. Consistent with prior studies in the medical literature, we also find that overwork is associated with a reduction in quality of care in cardiothoracic surgery---an increase in overwork by 10% is associated with an increase in likelihood of mortality by 2%. We also find that load is associated with an early discharge of patients, which is in turn correlated with a small increase in mortality rate.

Parallel and Sequential Testing of Design Alternatives

An important managerial problem in product design in the extent to which testing activities are carried out in parallel or in series. Parallel testing has the advantage of proceeding more rapidly than serial testing but does not take advantage of the potential for learning between tests, thus resulting in a larger number of tests. We model this trade-off in the form of a dynamic program and derive the optimal testing strategy (or mix of parallel and serial testing) that minimizes both the total cost and time of testing. We derive the optimal testing strategy as a function of testing cost, prior knowledge, and testing lead time. Using information theory to measure the test efficiency, we further show that in the case of imperfect testing (due to noise or simulated test conditions), the attractiveness of parallel strategies decreases. Finally, we analyze the relationship between testing strategies and the structure of design hierarchy. We show that a key benefit of modular product architecture lies in the reduction of testing cost.

Managing the Process of Engineering Change Orders: The Case of the Climate Control System in Automobile Development

Abstract Engineering change orders (ECOs) are part of almost every development process, consuming a significant part of engineering capacity and contributing heavily to development and tool costs. Many companies use a support process to administer ECOs, which fundamentally determines ECO costs. This administrative process encompasses the emergence of a change (e.g., a problem or a market-driven feature change), the management approval of the change, up to the change’s final implementation. Despite the tremendous time pressure in development projects in general and in the ECO process in particular, this process can consume several weeks, several months, and in extreme cases even over 1 year. Based on an in-depth case study of the climate control system development in a vehicle, we identify five key contributors to long ECO lead times: a complex approval process, snowballing changes, scarce capacity and congestion, setups and batching, and organizational issues. Based on the case observations, we outline a number of improvement strategies an organization can follow to reduce its ECO lead times.

Valuing R&D Projects in a Portfolio: Evidence from the Pharmaceutical Industry

Understanding the value of a product development project is central to a firms choice of project portfolio. The value of a project to a firm depends not only on its properties but also on the other projects being developed by the firm. This is due to interactions with the other projects that address the same consumer need and require the same development resources. In this study, we empirically investigate the structure and significance of these portfolio-level project interactions. Using a self-developed pharmaceutical industry data set, we conduct an event study around the failure of phase III clinical trials and their effect on the market valuation of the firm. The study exploits the natural experiment of a product development failure to give us a measure of the value of a drug development project to a firm. We then explain the variance in the value of projects based on interactions with other projects in the firms portfolio. We find that the presence of other projects targeting the same market and a build-up of projects that require the same development resources reduce the value of a development project. In addition to providing evidence on the significance and structure of these portfolio-level project interactions, the empirical model estimated in this paper also provides a data-driven approach to valuing projects that may be relevant to licensing transactions.

Managing Demand and Sales Dynamics in New Product Diffusion Under Supply Constraint

The Bass diffusion model is a well-known parametric approach to estimating new product demand trajectory over time. This paper generalizes the Bass model by allowing for a supply constraint. In the presence of a supply constraint, potential customers who are not able to obtain the new product join the waiting queue, generating backorders and potentially reversing their adoption decision, resulting in lost sales. Consequently, they do not generate the positive word-of-mouth that is typically assumed in the Bass model, leading to significant changes in the new product diffusion dynamics. n nWe study how a firm should manage its supply processes in a new product diffusion environment with backorders and lost sales. We consider a make-to-stock production environment and use optimal control theory to establish that it is never optimal to delay demand fulfillment. This result is interesting because immediate fulfillment may accelerate the diffusion process and thereby result in a greater loss of customers in the future. Using this result, we derive closed-form expressions for the resulting demand and sales dynamics over the product life cycle. We then use these expressions to investigate how the firm should determine the size of its capacity and the time to market its new product. We show that delaying a product launch to build up an initial inventory may be optimal and can be used as a substitute for capacity. Also, the optimal time to market and capacity increase with the coefficients of innovation and imitation in the adoption population. We compare our optimal capacity and time to market policies with those resulting from exogeneous demand forecasts in order to quantify the value of endogenizing demand.

Retail Assortment Planning in the Presence of Consumer Search

Consumers often know what kind of product they wish to purchase, but do not know which specific variant best fits their needs. As a result, a consumer may find an acceptable product in one retailer but nevertheless purchase nothing, opting to search other retailers for an even better product. We study several models of retail assortment planning, some of which explicitly account for consumer search and one that does not, which we call the no-search model. Even though the no-search model never includes an unprofitable variant in the assortment, in the presence of consumer search, it may indeed be optimal to include an unprofitable variant. Furthermore, we find that the no-search model can lead to an assortment with an expected total profit that is significantly less than optimal. In the extreme, the no-search model may recommend closing down a category (i.e., carry no variants) even if a profitable assortment exists (a 100% profit loss). We conclude that failing to incorporate consumer search into an assortment planning process may cause a retailer to underestimate the substantial value a broad assortment has in preventing consumer search. We discuss how the insights from our stylized models may apply to actual assortment planning processes.