Global supply chains of high value low volume products

Imperial Users onl

THE IMPACT OF PROJECT INTRODUCTION HEURISTICS ON RESEARCH AND DEVELOPMENT PERFORMANCE

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

A robust R&D project portfolio optimization model for pharmaceutical contract research organizations

Pharmaceutical drug Research and Development (R&D) outsourcing to contract research organizations (CROs) has experienced a significant growth in recent decades and the trend is expected to continue. A key question for CROs and firms in similar environments is which projects should be included in the firm?s portfolio of projects. As a distinctive contribution to the literature this paper develops and evaluates a business support tool to help a CRO decide on clinical R&D project opportunities and revise its portfolio of R&D projects given the existing constraints, and financial and resource capabilities. A new mathematical programming model in the form of a capital budgeting problem is developed to help revising and rescheduling of the project portfolio. The uncertainty of pharmaceutical R&D cost estimates in drug development stages is captured to mimic a more realistic representation of pharmaceutical R&D projects, and a robust optimization approach is used to tackle the uncertain formulation. An illustrative example is presented to demonstrate the proposed approach

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

Pharmaceutical R & D pipeline management under trial duration uncertainty

We consider a pharmaceutical Research & Development (R & D) pipeline management problem under two significant uncertainties: the outcomes of clinical trials and their durations. We present an Approximate Dynamic Programming (ADP) approach to solve the problem efficiently. Given an initial list of potential drug candidates, ADP derives a policy that suggests the trials to be performed at each decision point and state. For the classical R&D pipeline planning problem with deterministic trial durations, we compare our ADP approach with other methods from the literature, and find that it can find better solutions more quickly in particular for larger problem instances. For the case with stochastic trial durations, we compare the ADP algorithm with a myopic approach and show that the expected net profit obtained by the derived ADP policy is higher (almost 20% for a 10-drug portfolio)

Quantitative Models in Life Science Business

This open access book explores the field of life science business from a multidisciplinary perspective. Applying statistical, mathematical, game-theoretic, and data science tools to pharmaceutical and biotechnology business endeavors, the book describes value creation, value maintenance, and value realization in the life sciences as a sequence of processes using the quantitative language of applied mathematics. Written by experts from a variety of fields, the contributions illustrate the shift from a deterministic to a stochastic view of the processes involved, offering a new perspective on life sciences economics. The book covers topics such as valuing and managing intellectual property in life science, licensing in the pharmaceutical business, outsourcing pharmaceutical R&D, and stochastic modelling of a pharmaceutical supply chain. The book will appeal to scholars of economics and the life sciences, as well as to professionals in chemical and pharmaceutical industries