Abstract not found

This study analyzes the value created by so-called ‘‘toolkits for user innovation and design,’ ’ a new method of integrating customers into new product development and design. Toolkits allow customers to create their own product, which in turn is produced by the manufacturer. In the present study, questions asked were (1) if customers actually make use of the solution space offered by toolkits, and, if so, (2) how much value the self-design actually creates. In this study, a relatively simple, design-focused toolkit was used for a set of four experiments with a total of 717 participants, 267 of whom actually created their own watches. The heterogeneity of the resulting design solutions was calculated using the entropy concept, and willingness to pay (WTP) was measured by the contingent valuation method and Vickrey auctions. Entropy coefficients showed that self-designed watches vary quite widely. On the other hand, significant patterns still are visible despite this high level of entropy, meaning that customer preferences are highly heterogeneous and diverse in style but not completely random. It also was found that consumers are willing to pay a considerable price premium. Their WTP for a self-designed watch exceeds the WTP for standard watches by far, even for the best-selling standard watches of the same technical quality. On average, a 100 % value increment was found for watches designed by users with the help of the toolkit. Taken together, these findings suggest that the toolkit’s ability to allow customers to customize products to suit their individual preferences creates value for them in a business-to-consumer (B2C) setting even when only a simple toolkit is employed. Alternative explanations, implications, and necessary future research are discussed.

Traditional idea generation techniques based on customer input usually collect information on new product needs from a random or typical set of customers. The “lead user process ” takes a different approach. It collects information about both needs and solutions from users at the leading edges of the target market, as well as from users in other markets that face similar problems in a more extreme form. This paper reports on a natural experiment conducted within the 3M Company on the effect of the lead user idea generation process relative to more traditional methods. 3M is known for its innovation capabilities – and we find that the lead user process appears to improve upon those capabilities. Annual sales of lead user product ideas generated for the average lead user project at 3M are conservatively projected to be $146 million after 5 years- more than eight times higher than sales for the average contemporaneously-conducted “traditional ” project. Each funded lead user project created a new major product line for a 3M division. As a direct result, divisions funding lead user project ideas experienced their highest rate of major product line generation in the past 50 years.

Abstract. Handshaking between product management and R&D is key to the success of product development projects. Traditional requirements engineering processes build on good quality requirements specifications, which typically are not achievable in practical circumstances, especially not in distributed development where daily communication cannot easily be achieved to support the understanding of the specification and tacit knowledge cannot easily be spread. Projects thus risk misunderstanding requirements and are likely to deliver inadequate solutions. This paper presents an approach that uses downstream engineering artifacts, design decisions, to improve upstream information, a project’s requirements. During its preliminary validation, the approach yielded promising results. It is well suited for distributed software projects, where the negotiation on requirements and solution design need to be made explicit and potential problems and misunderstandings caught at early stages. 1

This article discusses a promising entrepreneurial opportunity: toolkits for user innovation and design. This innovative method of new product development (NDP) shifts the design task to the customer by making use of recent developments in IT, media, and production technologies. The customer, in turn, gets a product that perfectly suits his/her needs. This new approach challenges the time-consuming and costly traditional approach of screening the market for new product needs which are then converted into novel or adapted products. Our analysis shows that there are basically two different ways to exploit this opportunity. First, by means of high-end toolkits, radically new products can be gathered. Second, lowend toolkits can be used efficiently to further exploit seemingly mature markets. We argue that in particular startups might be in the best position to take advantage of these entrepreneurial opportunities. They can either enter the arena of manufacturers or they can act as new intermediaries between manufacturers and users.

Speed-to-Market is cited as being vital in today’s competitive, uncertain and turbulent environments. Scholars and industry professionals alike assert that companies can achieve competitive advantages by launching their product faster than their competitors. However, this paper presents a slightly different perspective on speed-to-market by considering another aspect of the speed equation-speed-to-learn or fast learning in new product development (NPD) teams. We assert that although speed-to-market can increase the probability of new product success, speed-to-learn is one of the critical factors that allows teams to get to market rapidly and be more successful. In this study, we propose a model for fast team learning in new product development based on constructs borrowed from accelerated learning models or suggestopedy in the individual learning scholarship. We then empirically test the model on 171 new product teams. We argue that 1) fastlearning teams launch new products quicker with an increased probability of success. And 2) specific mechanisms that are within the teams ’ control can help teams learn faster. Mechanisms uncovered include: vision clarity, learning from customer and competitor, and information coding.

There are two ways for a business to succeed at new products: doing projects right, and doing the right projects. Most new product prescriptions focus on the first route – for example on effective project management, using cross-functional teams, and building in the voice of the customer. Portfolio management, the topic of this chapter, focuses on the second route, namely on doing the right projects. A vital question in product innovation management is this: How should the corporation most effectively invest its R&D and new product resources? That is what portfolio management is all about: resource allocation to achieve corporate new product objectives. Much like a stock market portfolio manager, those senior executives who manage to optimize their R&D investments – to define the right new product strategy for the firm, select the winning new product projects, and achieve the ideal balance of projects – will win in the long run 1. A Roadmap for the Chapter This chapter first outlines the four goals in portfolio management together with the various tools and techniques for achieving each goal: � The first goal is to maximize the value of the portfolio for a given resource expenditure,

This article helps newcomers decide how to approach time to market. It first addresses some common fears about development acceleration, such as increased project expense and lapses in quality. An effective program overcomes these concerns, but it must do much more. It must stem from a statement, clear to all developers, explaining how faster development is tied directly to improved competitiveness and thus to higher profits for the company. A general corporate goal of a 50% across-the-board cut in cycle time will not do, nor will an underlying hope that faster development will improve developer productivity

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informs ® doi 10.1287/inte.1040.0099 ©2004 INFORMS Dow AgroSciences has improved its bottom line by using simulation-based optimization to reduce the time and expense of moving a portfolio of products to market. We examined mathematical programming approaches for scheduling new-product development but found that the solution of these formulations is intractable for problems of practical size that include resource constraints. We developed a simulation-based framework for generating feasible solutions, which can be used with any heuristic optimization approach (for example, tabu search or genetic algorithms). The heuristic optimization algorithms operate on a solution space consisting of the precedence relationships among activities in the new-product development process. The firm implemented this approach via a Six Sigma project. From 1998 to 2004, it saved several million dollars based on the schedules determined using simulation-based heuristic optimization.