Catarina Moreira Marques

Abstract
The chemical-pharmaceutical sector is facing an unprecedented fast-changing environment, with new market and technological trends impacting the companies' operational strategies. Managing the pharmaceutical supply chain (PSC) operations is, therefore, ever more complex and challenging. The goal of this work is to present a comprehensive overview of the current state of the industry and research developments; and then, to develop a new decision-making reference framework to assist in the creation of optimization-based decision support models. This will be achieved through a multi-perspective analysis that encompasses strategic and tactical planning decision-making, in the current and future business context of the chemical-pharmaceutical industry. The findings reveal a lack of research addressing the most prominent trends currently driving this sector, such as patient centricity or new technological developments, thus highlighting the disruptive nature of the expected changes in a highly conservative industry.

Abstract
This study presents an assessment of the main research problems addressed in the literature on New Product Development (NPD) and its methodologies, for the pharmaceutical industry. The work is particularly focused on the establishment of an evolutionary perspective of the relevant modelling approaches, and on identifying the main current research challenges, considering the fast-changing business context of the industry. Main findings suggest a generalized misalignment of recent studies with today's technological and market trends, highlighting the need for new modelling strategies. © 2019 Elsevier B.V.

Abstract

Abstract

Abstract
This paper presents an innovative approach to solve the product-launch planning problem in the pharmaceutical industry, with uncertainty on the product demand and on clinical trials. A mixed integer linear programming (MILP) model, incorporating Monte Carlo simulation (MCS), was developed for optimizing the process design (process-unit allocation and scale-up decisions) and for capacity planning (acquisition of new units), considering the products that still require development, and the products that are already in commercialization. MCS is performed in a two-step procedure, based on Normal and Bernoulli distributions, in order to capture the effects of demand variability and trials pass-fail uncertainty, respectively. Product-launch decisions are made taking into account the probability distributions of alternative process designs, of new capacity requirements, and of the coefficients of the objective function. The applicability of the proposed solution approach is demonstrated in an illustrative case study. © 2016 Elsevier B.V.