Optimal diversification for R&amp;D project portfolios

Luo, Lieh-Ming

doi:10.1007/s11192-011-0537-0

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…Other promising approaches for studying drug portfolio management and/or quantifying innovation risk may be based on simulation (Blau et al, 2004;Perez-Escobedo et al, 2011;Yu, 2012;Perez-Escobedo et al, 2012;Rosiello et al. 2013), optimization (George and Farid 2008a;2008b;Colvin and Maravelias, 2011;Laínez et al, 2012;Luo, 2012;Gibbert et al, 2014), real options (Bergemann and Hege, 1998;Ewens and Fons-Rosen, 2013;Ewens et al, 2014;Kerr and Nanda, 2015) and a combination of fuzzy and real options (Carlsson et al, 2007;Lo Nigro et al, 2016;Guo et al, 2018) or other related methodologies, sometimes mixing more than one methodology at a time. Irrespective of the actual methodology(ies) used, it is interesting to study the strategic choices faced by large pharmaceutical laboratories when trying to manage innovation risk and compete with both large and small laboratories.…”

Section: Discussionmentioning

confidence: 99%

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

Puente

Gascón

Ponte

et al. 2019

Artificial Intelligence in Medicine

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Structured abstractObjectives. We develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment.Through three structured fuzzy inference systems (FIS), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of innovation risk.Methods and Materials. Three FIS, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to the strategic choices they face.Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base were agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and validated the model. Results.In an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008-2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret the results. We have also run regressions to better understand the implications of our results.ii Conclusions. The main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of the system, which may be easily applied to other settings. KeywordsFuzzy inference systems; 2-tuple-based method; innovation risk; R&D; Pharmaceutical laboratories Highlights -Consensual fuzzy sets and rules are used to model innovation risk in large pharmaceutical laboratories, clarifying the system for pharmaceutical decision-makers and stakeholders.-With the aid of experts, we have combined knowledge with large and diverse datasets and embedded it into the fuzzy inference systems.-The fuzzy evaluation model captures both the financial side and the pipeline side of innovation risk of pharmaceutical companies operating in uncertain environments.-The fuzzy evaluation model we develop in this work has been applied to evaluate innovation risk in a sample of 31 large pharmaceutical laboratories covering the period from 2008 to 2013. Acronyms ATCAnatomical, Therapeutic, Chemical (classification system) CFO Chief Financi...

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Section: Discussionmentioning

confidence: 99%

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

Puente

Gascón

Ponte

et al. 2019

Artificial Intelligence in Medicine

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“…Researchers have identified important similarities between PSP and PPSP (e.g., Boasson, Cheng, & Boasson, 2012;Luo, 2012;Sefair et al, 2016). Table 1 summarizes these similarities distinguished in four concepts, i.e.…”

Section: Applying Mpt In Ppspmentioning

confidence: 99%

“…Having considered the similar concepts between PSP and PPSP such as return, risk, correlation and the available budget, researchers tried to apply MPT into the risk-return optimization of PPSP (e.g., Esfahani, Sobhiyah, & Yousefi, 2016;Luo, 2012;Sefair et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Customizing Modern Portfolio Theory for the Project Portfolio Selection Problem

Merikhi

Zwikael

2017

Proceedings

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As organization's performance depends on the projects it implements, selecting the most appropriate set of projects given limited resources is a crucial decision. In such project portfolio selection decision, the combined analysis of portfolios' returns and risks, i.e. risk-return optimization, is essential since a project portfolio with a high attractive expected return might also expose the organization to a large loss. Furthermore, as these two variables are influenced by some external threats and opportunities that may affect the returns of one or more projects simultaneously, it is crucial to incorporate such effects into the optimization model. However, the literature is underdeveloped in such critical incorporation. Inspired by "modern portfolio theory", an effective approach in (financial) portfolio selection problem, in this paper, we propose a new approach to solve the project portfolio selection problem, which comprehensively considers the effects of threats and opportunities around projects in the risk-return optimization model. To demonstrate how to apply the proposed new approach, we employ a numerical example and report the results.

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“…Minimizing risk is central to all explicit treatments of corporate R&D portfolios we have examined and diversification is key to dealing with situations of high risk. While risk is simplified as the variance on return in purely financial approaches (Markowitz 1952), it can also refer to various concerns for R&D firms, related to uncertainty in market returns or to the inherent possibility of failure of a given technology, for example (Luo 2011).…”

Section: Scholarly Work On Corporate Randd Portfoliosmentioning

confidence: 99%

Research Portfolio Analysis in Science Policy: Moving from Financial Returns to Societal Benefits

Wallace

Ràfols

2015

Minerva

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Wallace, M.; Rafols García, I. (2015). Research portfolios in science policy: moving from financial returns to societal benefits. Minerva. 53 (2) AbstractFunding agencies and large public scientific institutions are increasingly using the term "research portfolio" as a means of characterising their research. While portfolios have long been used as a heuristic for managing corporate R&D (i.e., R&D aimed at gaining tangible economic benefits), they remain ill-defined in a science policy context where research is aimed at achieving societal outcomes. In this article we analyze the discursive uses of the term "research portfolio" and propose some general considerations for their application in science policy. We explore the use of the term in private R&D and related scholarly literature in existing science policy practices, and seek insight in relevant literature in science policy scholarship. While the financial analogy can in some instances be instructive, a simple transposition from the world of finance or of corporate R&D to public research is problematic. However, we do identify potentially fruitful uses of portfolio analysis in science policy. In particular, our review suggests that the concept of research portfolio can indeed be a useful analytical instrument for tackling complex societal challenges. Specifically, the strands of scholarship identified suggest that the use of research portfolio should: i) recognize the diversity of research lines relevant for a given societal challenge, given the uncertainty and ambiguity of research outcomes; ii) examine the relationships between research options of a portfolio and the expected societal outcomes; and iii) adopt a systemic perspective to research portfolios -i.e., examine a portfolio as a functional whole, rather than as the sum of the its parts. We argue that with these considerations, portfolio-driven approaches may foster social inclusion in science policy decisions, help deliberation between "alternative" portfolios to tackle complex societal challenges, as well as promote cost-effectiveness and transparency.

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Optimal diversification for R&D project portfolios

Cited by 10 publications

References 18 publications

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

Customizing Modern Portfolio Theory for the Project Portfolio Selection Problem

Research Portfolio Analysis in Science Policy: Moving from Financial Returns to Societal Benefits

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