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Management of a product development pipeline involves starting and steering several promising projects through a sequence of screens known as stages/gates. Only projects with payoffs above a predetermined threshold survive each screen. We model a two-stage product development pipeline as an aging chain with a co-flow. The co-flow structure tracks t...
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Citations
... The present study steers away from analyzing conventional stage-gate processes that do not screen out items, and instead focuses on the funnels (ZAPATA; CANTÚ, 2008; JUGEND; SILVA, 2012). As was mentioned in Figueiredo & Loiola (2012) and also in Figueiredo and Joglekar (2007), there are three key structures endogenous to the process of screening, namely 1) Capacity adjustment (how the throughput of projects will be adjusted), 2) Type of screening (minimum or maximum values can be selected) and 3) Relation between co-flow attribute (the risk-adjusted Net Present Value or NPV, a measure of financial performance) and throughput. This basic measure of performance is proposed by Cooper et al. (1998). ...
... For instance, by selecting only the best performing projects, managers increase the average value of the surviving population of projects in a NPD pipeline. All the equations for the model can be found in Figueiredo and Joglekar (2007) and Figueiredo and Loiola (2012). The behavior of such model is studied, aiming to determine how the presence of screening changes the presence and intensity of the bullwhip effect in the chain. ...
In many situations, System Dynamics modelers have to capture attributes of items tracked in an aging chain by means of a co-flow. This study presents an application of co-flows in aging chains: A co-flow that enables the process of screening, i.e. the process of either terminating or approving projects. The article is the fifth in a series of articles about the model. An application to Product Pipeline Management (PPM) is developed. A two-stage product development pipeline was modeled with a co-flow structure that tracks the number of projects and the related net present value of payoff. Managers at each stage must decide on capacity utilization and thresholds for minimum value of projects. Simulation results illustrate that screening can eliminate the backlog bullwhip effect in the pipeline. Bullwhip. Decision Biases. Product Development. Screening. Stage/Gate. Product portfolio management.
... The basic structure and logic of the model are simple. We can find this in Figueiredo and Joglekar (2007) and Figueiredo and Loiola (2012), so only a summary is given here. It is important to point out that although there are a few key variables involved here, we will use only two variables for the experiment: the average complexity of projects (in terms of man-hours per project) and the proportion of total resources (people) allocated to stage 1. ...
... Figure 2 shows the only loop found in the model. Figueiredo and Joglekar (2007) and Figueiredo and Loiola (2012) developed a model which they calibrated to the Novartis Innovation Pipeline (Reyck et al., 2004). This case study has all the data necessary for calibration, including NPV values at each stage, flows, complexity and resources. ...
... There is wide recognition of the contribution of computational models for investigating the impact of different organizational models and theories about adaptive agents (Lant 1994, Carley 1995, Carley & Gasser 1999, Carley & Svoboda 1996. In this study, we created a managerial game based on Figueiredo and Joglekar's (2007) and Figueiredo and Loiola´s (2012) dynamic model of the product development pipeline. A key insight generated from the model is that there is a concave relationship between the decision variables (average task complexity of projects and fraction of total resources) and total value created (NPV). ...
The objective of this study is to understand the nature and role of complexity in simulation performance. In order to do this, a system dynamics model of the product development pipeline was developed, and an online game based on that model was created. An experiment was run where subject made a series of decisions on one or two variables for many periods, with different levels of information available to them (attribute complexity). Hypotheses were proposed based on the literature. The results confirmed the hypotheses relating degree of difficulty to distance from optimality. Two factors adversely affected the subjects’ performance: dealing with more complex information on performance (more than one attribute) and having to make more than one simultaneous decision, i.e. more than one decision per period. The latter condition was more detrimental to performance than the former.
Assuming a fixed total R&D budget, the product pipeline management (PPM) problem has two parts: (1) Which and how many projects should be initiated? (2) Which projects should continue to be invested in or terminated? We use a dynamic model calibrated to a pharmaceutical company to study PPM, focusing on three types of heuristics — gradual increase or decrease, random-normal choice, and target-based search — to evaluate the impact of the introduction of innovation projects in the pipeline on the performance in R&D. We find that a gradual decrease of project introduction rates results in convergence, but the size of the adjustments and delays in the pipeline can limit the precision of the results. A random choice is detrimental to performance even when the average value is the optimal. A target-based search results in oscillation. The results of our analysis show that the specific problem of choosing the project introduction rate can be significantly improved by using an adequate rule of thumb or heuristic.
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.
