Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

Multiobjective strategies for New Product Development in the pharmaceutical industry

New Product Development (NPD) constitutes a challenging problem in the pharmaceutical industry, due to the characteristics of the development pipeline. Formally, the NPD problem can be stated as follows: select a set of R&D projects from a pool of candidate projects in order to satisfy several criteria (economic profitability, time to market) while coping with the uncertain nature of the projects. More precisely, the recurrent key issues are to determine the projects to develop once target molecules have been identified, their order and the level of resources to assign. In this context, the proposed approach combines discrete event stochastic simulation (Monte Carlo approach) with multiobjective genetic algorithms (NSGAII type, Non-Sorted Genetic Algorithm II) to optimize the highly combinatorial portfolio management problem. In that context, Genetic Algorithms (GAs) are particularly attractive for treating this kind of problem, due to their ability to directly lead to the so-called Pareto front and to account for the combinatorial aspect. This work is illustrated with a study case involving nine interdependent new product candidates targeting three diseases. An analysis is performed for this test bench on the different pairs of criteria both for the bi- and tricriteria optimization: large portfolios cause resource queues and delays time to launch and are eliminated by the bi- and tricriteria optimization strategy. The optimization strategy is thus interesting to detect the sequence candidates. Time is an important criterion to consider simultaneously with NPV and risk criteria. The order in which drugs are released in the pipeline is of great importance as with scheduling problems

The Increasing Multifunctionality of Agricultural Raw Materials: Three Dilemmas for Innovation and Adoption

Bio-economy, industry convergence, renewables, disruptive innovation, multifunctionality, Agribusiness, Agricultural and Food Policy, Demand and Price Analysis, Q10, Q27, Q42, Q47,

Empirical Tests Of Optimal Cognitive Distance

This article provides empirical tests of the hypothesis of ‘optimal cognitive distance’, proposed by Nooteboom (1999, 2000), in two distinct empirical settings. Variety of cognition, needed for learning, has two dimensions: the number of agents with different cognition, and differences in cognition between them (cognitive distance). The hypothesis is that in interfirm relationships optimal learning entails a trade-off between the advantage of increased cognitive distance for a higher novelty value of a partner’s knowledge, and the disadvantage of less mutual understanding. If the value of learning is the mathematical product of novelty value and understandability, it has an inverse-U shaped relation with cognitive distance, with an optimum level that yields maximal value of learning. With auxiliary hypotheses, the hypothesis is tested on interfirm agreements between pharmaceutical companies and biotech companies, as well as on interfirm agreements in ICT industries.innovation;organizational learning;ICT;biotechnology;alliances

Introducing Inventiveness into the Patent System: Submission to the Review of the National Innovation System

Because of the potential impact of the patent system on innovation diffusion, particularly on continuous and/or incremental innovation, patent policy should be of central importance to the review of the national innovation system. Substantial empirical evidence shows that most industrial innovations are not induced by the patent system. Even in very large markets, such as the USA, only a minority of patents are likely to be induced by the patent system. To the extent that patents do induce innovations, it is the inventiveness of the innovation which gives rise to possible social benefits (externalities, mainly in the form of knowledge spillovers) which may offset the costs of a patent system and thus give rise to a net economic benefit. On the basis of this evidence about the inducement effect of the patent system, and evidence on the current very low inventiveness standard for patent grant, policy proposals are put forward to re-introduce inventiveness into the patent system, thus making it potentially welfare-enhancing. These proposed changes would also have a major impact in ameliorating the negative impact of the patent system on continuous/incremental innovation

Markets for Technology and Their Implications for Corporate Strategy.

Although market transactions for technologies, ideas, knowledge or information are limited by several well-known imperfections, there is evidence that they have become more common than in the past. In this paper we analyze how the presence of markets for technology conditions the technology and corporate strategy of firms. The first and most obvious implication is that markets for technology increase the strategy space: firms can choose to license in the technology instead of developing it in-house or they can choose to license out their technology instead of (or in addition to) investing in the downstream assets needed to manufacture and commercialize the goods. The implications for management include more proactive management of intellectual property, greater attention to external monitoring of technologies, and organizational changes to support technology licensing, joint-ventures and acquisition of external technology. For entrepreneurial startups, markets for technology make a focused business model more attractive. At the industry level, markets for technology may lower barriers to entry and increase competition, with important implications for the firms' broader strategy as well.

Global supply chains of high value low volume products

Imperial Users onl

National institutions and technological innovation : a case study of Japanese biotechnology

Includes bibliographical references.Steven W. Collins