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

Influence of Portfolio Management in Decision-Making

Purpose: Today’s manufacturing facilities are challenged by highly customized products and just in time manufacturing and delivery of these products. In this study, a batch scheduling problem has been addressed to enable on-time completion of customer orders in a lean manufacturing environment. The problem is optimizing the partitioning of product components into batches and scheduling of the resulting batches where each customer order is received as a set of products made of various components. Design/methodology/approach: Three different mathematical models for minimization of total earliness and tardiness of customer orders are developed to provide on-time completion of customer orders and also, to avoid excess final product inventory. The first model is a non-linear integer programming model whereas the second is a linearized version of the first. Finally, to solve larger sized instances of the problem, an alternative linear integer model is presented. Findings: Computational study using a suit set of test instances showed that the alternative linear integer model is able to solve all test instances in varying sizes within quite shorter computer times compared to the other two models. It has also been showed that the alternative model is able to solve moderate sized real-world problems. Originality/value: The problem under study differentiates from existing batch scheduling problems in the literature owing to the inclusion of new circumstances that are present in real-world applications. Those are: customer orders consisting of multi-products made of multi-parts, processing of all parts of the same product from different orders in the same batch, and delivering the orders only when all related products are completed. This research also contributes to the literature of batch scheduling problem by presenting new optimization models.Peer Reviewe

The Potential of Artificial Intelligence in IT Project Portfolio Selection

The rapid growth of innovative technologies and the complexity of IT projects lead to the change in the tools and competency required for organization management and project management. Also, the scope of an IT product is no longer within a single project and team but requires the collaboration among multiple projects, teams and the alignment with the organization’s strategies. Therefore, project portfolio selection becomes a challenging process due to the complexity and uncertainty of various factors and risks. In the IT industry, the emergence of artificial intelligence (AI) could bring opportunities to organizations to address different challenges including challenges in project portfolio selection. In this paper, we have discussed the current challenges in IT project portfolio selection, the available methods and tools and their limitations. Then an overview of the potential applications of AI in IT project portfolio selection is explored. Finally, we conclude the paper by providing future research directions

Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection

A multiobjective binary integer programming model for R&D project portfolio selection with competing objectives is developed when problem coefficients in both objective functions and constraints are uncertain. Robust optimization is used in dealing with uncertainty while an interactive procedure is used in making tradeoffs among the multiple objectives. Robust nondominated solutions are generated by solving the linearized counterpart of the robust augmented weighted Tchebycheff programs. A decision maker’s most preferred solution is identified in the interactive robust weighted Tchebycheff procedure by progressively eliciting and incorporating the decision maker’s preference information into the solution process. An example is presented to illustrate the solution approach and performance. The developed approach can also be applied to general multiobjective mixed integer programming problems

A FUZZY BI-LEVEL PROJECT PORTFOLIO PLANNING CONSIDERING THE DECENTRALIZED STRUCTURE OF PHARMACY HOLDINGS

Research and development (R&D) in the pharmaceutical industry requires proper and optimal planning and management because of its critical role in public health. Taking into account a decentralized decision-making structure in R&D management in pharmaceutical holding companies, this study introduces a new fuzzy bi-level multi-follower mathematical optimization model to address budget allocation and project portfolio planning. Specifically, the holding company's head office, as the leader, and the subsidiaries, as followers, make strategic and operational decisions concerning important issues such as budget allocation and portfolio selection and scheduling. Since the lower level represents multiple mixed-integer programming problems with uncooperative reference relationships between followers, solving the resulting bi-level model is challenging. Therefore, our model is based on an effective hybrid solution methodology, which converts the bi-level model, including multiple followers, into a single-level model. In order to validate the proposed model, we conducted a case study and analyzed the strategies of each actor within the conglomerate. Based on the results of experiments, it is evident that a strategy that focuses on one level of operations profoundly affects decisions at the other level

PORTFOLIO MANAGEMENT OF INNOVATION FIELDS : APPLYING CK DESIGN THEORY IN CROSS INDUSTRY EXPLORATORY PARTNERSHIP

Our paper refers to an industrial practice based on an integrated theoretical framework of design, CK design theory (Hatchuel and Weil, 2002, Hatchuel and Weil, 2003, Hatchuel and Weil, 2008), to support people in management of innovation fields. This study is based on an empirical case in a new form of R&D partnerships, the Cross Industry Exploratory Partnerships. MINATEC IDEAs Laboratory® is composed of a broad scope of partners 2 which aims to co-explore opportunities of micronanotechnologies. The paper deals with a strategic design tool, OPERA, which has been experimented since 2007 and involved participation of design team work and powerholders. During two years, creative insights and projects of the two laboratory's major innovation fields have been collected and structured within CK theory. This tool permits power-holders to drive innovation projects by giving an overview of explored concepts (and still not explored), activation and production of competencies and knowledge.CK theory; innovative design; innovation partnership; OPERA; design theory; management of innovation

Project Portfolio Selection Using Interactive Approach

AbstractIn the paper project portfolio selection problem is considered. Both researchers, as well as practitioners agree that various criteria, including both quantitative and qualitative ones, should be taken into account when the project portfolio is constructed. Various decision aiding techniques dedicated for project portfolio selection problems are proposed in literature. Most of them assume that the information about the decision-maker's preferences is collected before starting the calculation procedure. Several criticisms have been expressed against such approach. The assessment of the sufficient a priori preference information is inconvenient and time consuming. Moreover, as the decision maker is not employed in the second phase of the procedure, when the final solution is generated, so he/she may feel excluded from the important part of the analysis and put little confidence in a final result. In the paper a concept of a new methodology based on interactive approach is presented. It assumes, that a single portfolio is proposed to the decision maker in each iteration. The decision maker evaluates the proposal, thus indicating how to improve the solution. A simple example is presented to explain how the dialog with the decision maker can be carried out

Projects selection and prioritization: a Portuguese Navy pilot model

In the face of rapid technological changes, short product cycles and strong global competition, it is vital that organizations know how to optimize their scarce resources and thus profit from investments with the goal of obtaining the expected benefits and successes. One of the great difficulties facing organizations is the large number of projects that they usually have in their portfolio. Therefore, it is necessary to select and prioritize which projects become essential, to guarantee the maximum return on investments and the sustainability of the organization. Although there are several approaches to analyzing and selecting projects, there is no unanimity about which methodologies to apply. When analyzed in more detail, all approaches presented advantages and disadvantages which need to be considered. Project selection also depends on the nature and profile of the managers and on the techniques, that best fit the organization’s environment. This study analyzes and establishes the link between the academic literature and a pilot model of selection and prioritization of projects developed by the Portuguese Navy. The project was carried out to improve the support and allocation of the necessary resources and forces for the accomplishment of the Navy’s missions in the context of Portugal’s National Defense. The results obtained ensure the necessary alignment with the academic literature and reinforces the credibility of the proposal model for the selection and prioritization process. .info:eu-repo/semantics/publishedVersio

A real options based support system to open innovation

Pharmaceutical R&D process (PR&DP) has been deeply investigated by different streams of literature; the interest is due to the strategic implication of the related decisions undertaken. The PR&DP has been revolutionised by the biotech advent and as a consequence R&D managers cannot avoid to consider Open Innovation paradigm during this decision process. Starting from a Real Option optimization model available in literature, the paper aims at proposing a decision support system (DSS) able to suggest the candidate products to be included in the best R&D portfolio varying input parameters (resilient products), to provide a products Pareto analysis that aims at individuating the products for which it is worthwhile to acquire a deeper input parameters knowledge and to draw what if rules. The proposed DSS has been applied to a numerical example available in literature and research findings show interesting managerial and academic implications