Stylianos Kavadias

Dynamic Portfolio Selection of NPD Programs Using Marginal Returns

Selecting program portfolios within a budget constraint is an important challenge in the management of new product development (NPD). Optimal portfolios are difficult to define because of the combinatorial complexity of project combinations. However, at the aggregate level of the strategic allocation of resources across product lines, investment in a program is not an all-or-nothing decision, but can beadjusted, resulting in a higher or lower program benefit (e.g., higher or lower quality). In some cases, resources can be adjusted even for individual projects.With this insight, one can usemarginal analysis to optimally allocate the scarce budget. This article develops a dynamic model of resource allocation, taking into account multiple interacting factors, such as independent or correlated, uncertain market payoffs that change over time, increasing or decreasing returns from the NPD investment, carry-over of the investment benefit over multiple periods, and interactions across market segments. We characterize optimal policies in closed form and derive qualitative decision rules for managers.

A Theoretical Framework for Managing the New Product Development Portfolio: When and How to Use Strategic Buckets

Developing the “right” new products is critical to firm success and is often cited as a key competitive dimension. This paper explores new product development (NPD) portfolio strategy and the balance between incremental and radical innovation. We characterize innovative effort through a normative theoretical framework that addresses a popular practice in NPD portfolio management: the use of strategic buckets. Strategic buckets encourage the division of the overall NPD resource budget into smaller, more focused budgets that are defined by the type of innovative effort (incremental or radical). We show that time commitment determines the balance between incremental and radical innovation. When managers execute this balance, they are often confounded by (i) environmental complexity, defined as the number of unknown interdependencies among technology and market parameters that determine product performance; and (ii) environmental instability, the probability of changes to the underlying performance functions. Although both of these factors confound managers, we find that they have completely opposite effects on the NPD portfolio balance. Environmental complexity shifts the balance toward radical innovation. Conversely, environmental instability shifts the balance toward incremental innovation. Risk considerations and implications for theory and practice are also discussed.

The Effects of Problem Structure and Team Diversity on Brainstorming Effectiveness

Since Osborns Applied Imagination book in 1953 (Osborn, A. F. 1953. Applied Imagination: Principles and Procedures of Creative Thinking. Charles Scribners Sons, New York), the effectiveness of brainstorming has been widely debated. While some researchers and practitioners consider it the standard idea generation and problem-solving method in organizations, part of the social science literature has argued in favor of nominal groups, i.e., the same number of individuals generating solutions in isolation. In this paper, we revisit this debate, and we explore the implications that the underlying problem structure and the team diversity have on the quality of the best solution as obtained by the different group configurations. We build on the normative search literature of new product development, and we show that no group configuration dominates. Therefore, nominal groups perform better in specialized problems, even when the factors that affect the solution quality exhibit complex interactions (problem complexity). In cross-functional problems, the brainstorming group exploits the competence diversity of its participants to attain better solutions. However, their advantage vanishes for extremely complex problems.

Revenue Driven Resource Allocation: Funding Authority, Incentives, and New Product Development Portfolio Management

The first step in transforming strategy from a hopeful statement about the future into an operational reality is to allocate resources to innovation and new product development (NPD) programs in a portfolio. Resource allocation and NPD portfolio decisions often span multiple levels of the organizations hierarchy, leading to questions about how much authority to bestow on managers and how to structure incentives for NPD. In this study, we explore how funding authority and incentives affect a managers allocation of resources between existing product improvement (relatively incremental projects) and new product development (more radical projects). Funding may be either fixed or variable depending on the extent to which the manager has the authority to use revenue derived from existing product sales to fund NPD efforts. We find that the use of variable funding drives higher effort toward improving existing products and developing new products. However, variable funding has a subtle side effect: it induces the manager to focus on existing product improvement to a greater degree than new product development, and the relative balance in the NPD portfolio shifts toward incremental innovation. In addition, we highlight a substitution effect between explicit incentives (compensation parameters) and implicit incentives (career concerns). Explicit incentives are reduced as career concerns become more salient.

Introduction of New Technologies to Competing Industrial Customers

Motivated by several examples from industry, such as the introduction of a biotechnology-based process innovation in nylon manufacturing, we consider a technology provider that develops and introduces innovations to a market of industrial customers---original equipment manufacturers (OEMs). The technology employed by these OEMs determines the performance quality of the end product they manufacture, which in turn forms the basis of competition among them. Within this context of downstream competition, we examine the technology providers introduction strategies when improving technologies are introduced sequentially. We develop a two-period game-theoretic framework to account for the strategic considerations of the parties involved (i.e., the technology provider and the OEMs). Our main result indicates that the technology provider may find it beneficial to induce partial adoption of the new technology, depending on the technological progress the provider intends to offer in the future. We analyze many technology-specific and market-related characteristics---such as volume-based pricing for new component technologies, upgrade prices, and OEMs with differing capabilities---that correspond to various business settings. Our key result (i.e., partial adoption) proves to be a robust phenomenon. We also develop additional insights regarding the interactions between adoption and OEM capabilities.

Sequential Testing of Product Designs: Implications for Learning

Past research in new product development (NPD) has conceptualized prototyping as a “design-build-test-analyze” cycle to emphasize the importance of the analysis of test results in guiding the decisions made during the experimentation process. New product designs often involve complex architectures and incorporate numerous components, and this makes the ex ante assessment of their performance difficult. Still, design teams often learn from test outcomes during iterative test cycles enabling them to infer valuable information about the performances of (as yet) untested designs. We conceptualize the extent of useful learning from analysis of a test outcome as depending on two key structural characteristics of the design space, namely whether the set of designs are “close” to each other (i.e., the designs are similar on an attribute level) and whether the design attributes exhibit nontrivial interactions (i.e., the performance function is complex). This study explicitly considers the design space structure and the resulting correlations among design performances, and examines their implications for learning. We derive the optimal dynamic testing policy, and we analyze its qualitative properties. Our results suggest optimal continuation only when the previous test outcomes lie between two thresholds. Outcomes below the lower threshold indicate an overall low performing design space and, consequently, continued testing is suboptimal. Test outcomes above the upper threshold, on the other hand, merit termination because they signal to the design team that the likelihood of obtaining a design with a still higher performance (given the experimentation cost) is low. We find that accounting for the design space structure splits the experimentation process into two phases: the initial exploration phase, in which the design team focuses on obtaining information about the design space, and the subsequent exploitation phase in which the design team, given their understanding of the design space, focuses on obtaining a “good enough” configuration. Our analysis also provides useful contingency-based guidelines for managerial action as information gets revealed through the testing cycle. Finally, we extend the optimal policy to account for design spaces that contain distinct design subclasses.

Strategic Resource Allocation: Top-Down, Bottom-Up, and the Value of Strategic Buckets

When senior managers make the critical decision of whether to assign resources to a strategic initiative, they have less precise initiative-specific information than project managers who execute such initiatives. Senior management chooses between a decision process that dictates the resource level top-down and one that delegates the resource decision and gives up control in favor of more precise information bottom-up. We investigate this choice and vary the amount of information asymmetry between stakeholders, the “penalty for failure” imposed upon project managers, and how challenging the initiative is for the firm. We find that no single decision process is the “best.” Bottom-up processes are beneficial for more challenging initiatives. Increased organizational penalties may prompt the firm to choose a narrower scope and deter the approval of profitable initiatives. Such penalties, however, enable an effective decision process known as “strategic buckets” that holds the potential to achieve first-best resource allocation levels. This paper was accepted by Kamalini Ramdas, entrepreneurship and innovation.

Dynamic Knowledge Transfer and Knowledge Development for Product and Process Design Teams

We consider a manager who invests in knowledge development of a product and a process design team as well as knowledge transfer between teams throughout a new product development NPD project. Knowledge development at a particular time e.g., prototyping and experimentation increases a teams level of knowledge at that time. In contrast, the recipients benefits from knowledge transfer may be lagged because of the difficulties in articulating and documenting knowledge as well as the challenges regarding its interpretation and application. Over time, as each team embeds knowledge in the NPD project, the levels of product and process performance increase, thereby increasing the net revenue earned at the product launch time. In a key contribution to the literature, analytic conditions are given that characterize the dynamic rates at which knowledge development and knowledge transfer occur throughout the project. We show that the investment in knowledge development for each team and knowledge transfer between teams may be constant, front-loaded, back-loaded, U-shaped, or the peak rate may be delayed over time. As such, we show how concurrent engineering is optimally pursued throughout the NPD project.

R&D Intensity and the New Product Development Portfolio

A key metric for assessing innovative activity at the firm level is R&D intensity. R&D intensity is the ratio of a firms R&D investment to its revenue (the percentage of revenue that is reinvested in R&D). Empirical and anecdotal evidence suggests that R&D intensity within an industry tends to be remarkably consistent. Despite this consistency in R&D spending, however, firms differ with respect to their new product development (NPD) portfolio strategy and overall performance. This paper seeks to explain how R&D intensity can be so consistent at the aggregate level, while NPD portfolio strategies and firm performance are so varied at the firm level. We develop a model that considers firm level factors, such as the NPD portfolio composition and risk levels, as well as industry level factors, such as competition intensity and environmental stability. We show how a simple evolutionary process links aggregate R&D intensity and firm level portfolio choices. Our results highlight that R&D intensity alone does not explain firm performance. Rather, it is the proper alignment between R&D intensity (how much the firm invests) and an NPD portfolio strategy (how the firm invests the money) that drives profitability.

R&D Intensity and the NPD Portfolio

A key metric for the assessment of innovative activity at the firm level is R&D intensity. R&D intensity is the ratio of a firms R&D investment to its revenue (the percentage of revenue that is reinvested in R&D). Empirical and anecdotal evidence suggests that R&D intensity within an industry is remarkably consistent. Despite this consistency in R&D spending, firms tend to be differentiated with respect to their NPD portfolio strategy and overall performance. This study aims to explain the observed consistency in R&D intensity for firms within an industry, despite the varying choices in terms of how much the firm invests in R&D and how resources are allocated among projects in a portfolio. We consider the implications of firm level factors, such as NPD portfolio composition, as well as industry level factors, such as competition intensity and environmental stability. We find that R&D intensity alone does not explain firm performance. Rather, it is the proper alignment between R&D intensity (how much the firm invests) and NPD portfolio strategy (how the firm invests the money) that drives profitability. More importantly, the proper alignment critically depends on two industry factors - competition intensity and environmental stability.