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New product development (NPD) projects are typically managed through a series of screens, or gates, where ideas compete for resources. Ideas are carved into projects, and these projects are reviewed, and approved or terminated through the screening process so that only the best performing projects continue to subsequent stages of design, development and testing, and are released into the market place (Krishnan and Ulrich 2001;Terwiesch and Ulrich 2009). Most large innovative organizations deal with more than one NPD project at a time and typically engage in product pipeline management (PPM), where a set of active projects are evaluated together while they traverse through a sequence of such screens. Key decisions in a R&D pipeline are: screen thresholds, complexity of projects, resource allocation and capacity adjustment biases. We explore the impact of structural and behavioral aspects of these decisions through a simulation based analysis of a pharmaceutical dataset. Results establish concave relationships between value created at the end of pipeline and the resource allocation and complexity allocation biases, indicating optimizability and a limit for front loading practices.Keywords: product development, ideas, projects, product pipeline management, development funnel, stage/gate, screening. risk, and the resolution of uncertainty. Wheelwright and Clark (1992) describe typical decision levers in this setting: resource allocation (allocating workers), selection of task complexity (defining number, size and relations between tasks), capacity utilization (work intensity), and the level of threshold (minimum quality or expected value) for passing through a screen and the frequency of screening. Resource allocation dictates the types and amounts of resources available for executing tasks before a project, or a cohort of projects, goes through a screen. Complexity selection defines the nature of these tasks, and the amount of resources it takes to complete these tasks. Even though the level of complexity at each stage is predetermined to a certain degree by the existence of a minimum number of tasks to be performed, and their sequence, it is fair to assume that there is considerable freedom to managers while deciding project activities. For example, Thomke and Fujimoto (2000) and Khurana and Rosenthal (1998) recommend the front loading of activities in a project, i.e. the increase in complexity and activities early in the development process, as a way of reducing uncertainty and the amount of rework or new work to be done later.Capacity utilization affects the tradeoff between output quality (and thus the value created), and throughput. The total amount of resources available for allocation across stages is determined by a budgeting exercise (Chao et al., 2009) and is divided among the stages, so that each stage receives a fraction of the total. The selection of average complexity in any one stage, on the other hand, is not subject to such a global constraint. Product portfolio management deals with the p...

Overcommitment of development capacity or development resource deficiencies are important problems in new product development (NPD). Existing approaches to development resource planning have largely neglected the issue of resource magnitude required for NPD. This research aims to fill the void by developing a simple higher-level aggregate model based on an intuitive idea: The number of new product families that a firm can effectively undertake is bound by the complexity of its products or systems and the total amount of resources allocated to NPD. This study examines three manufacturing companies to verify the proposed model. The empirical results confirm the study's initial hypothesis: The more complex the product family, the smaller the number of product families that are launched per unit of revenue. Several suggestions and implications for managing NPD resources are discussed, such as how this study's model can establish an upper limit for the capacity to develop and launch new product families.

PurposeThis empirical and exploratory study analyzed the role of interaction with the innovation environment and of the organizational learning capacity (OLC) development stage in startups in Northeast Brazil based on the perception of managers of these companies.Design/methodology/approachThis was a quantitative study. Questionnaires were sent electronically to the managers of startups in the nine states of Northeast Brazil. A total of 54 managers participated, composing a non-probabilistic sample. The data collected were analyzed by multiple linear regressions.FindingsThe results obtained seek to evidence whether the interaction of startups in Northeast Brazil with the startup ecosystem and the development stage in which these companies are found are associated with OLC. The results confirmed the hypothesis that higher startup development stages are positively associated with higher levels of OLC in the sample. A negative association was identified between the manufacturing startup type and the OLC level, and the level of interaction with the innovation environment was still infrequent.Research limitations/implicationsThis study contributes to the literature on the determinants of organizational learning and to startup managers who wish to more effectively promote this learning. Implications of the findings are discussed.Practical implicationsThis study contributes to the literature on the determinants of organizational learning and to startup managers who wish to more effectively promote this learning. Implications of the findings are discussed.Originality/valueStudies on Brazilian startups are still relatively scarce, especially studies that focus on learning capacity. No other studies addressing the hypotheses tested here were found.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. IntroductionResearch results relating motivation and work have encouraged organizations to efficiently value its workers to maintain a prominent place in today's highly competitive market (Macintosh, & Doherty, 2010;Stringer et al., 2011). Even in a training context, motivation can influence the willingness of an employee to participate in a program and put in practice the best way to apply what has been learned (Maurer, & Tarulli, 1994;Noe, & Wilk, 1993).Although this is a relatively well investigated topic, the study of the factors that motivate workers and, consequently, organizations to achieve good performance still demands the accumulation of observations obtained in different countries, whose workers are governed by specific institutional norms linked to work and are immersed in different cultures, as well as in companies of varied size, markets, work organization, skills and workforce skills (Di Cesare, & Sadri, 2003).Another factor highly interdependent with the performance of the industrial company is maintenance, defined by Pintelon & Van Puyvelde ( 2006) as a combination of all the technical and administrative activities necessary to keep equipment, installations and other assets in the desired operating condition or restore them