Modelling an End to End Supply Chain system Using Simulation

Within the current uncertain environment industries are predominantly faced with various challenges resulting in greater need for skilled management and adequate technique as well as tools to manage Supply Chains (SC) efficiently. Derived from this observation is the need to develop a generic/reusable modelling framework that would allow firms to analyse their operational performance over time (Mackulak and Lawrence 1998, Beamon and Chen 2001, Petrovic 2001, Lau et al. 2008, Khilwani et al. 2011, Cigollini et al. 2014). However for this to be effectively managed the simulation modelling efforts should be directed towards identifying the scope of the SC and the key processes performed between players. Purpose: The research attempts to analyse trends in the field of supply chain modelling using simulation and provide directions for future research by reviewing existing Operations Research/Operations Management (OR/OM) literature. Structural and operational complexities as well as different business processes within various industries are often limiting factors during modelling efforts. Successively, this calls for the end to end (E2E) SC modelling framework where the generic processes, related policies and techniques could be captured and supported by the powerful capabilities of simulation. Research Approach: Following Mitroff’s (1974) scientific inquiry model and Sargent (2011) this research will adopt simulation methodology and focus on systematic literature review in order to establish generic OR processes and differentiate them from those which are specific to certain industries. The aim of the research is provide a clear and informed overview of the existing literature in the area of supply chain simulation. Therefore through a profound examination of the selected studies a conceptual model will be design based on the selection of the most commonly used SC Processes and simulation techniques used within those processes. The description of individual elements that make up SC processes (Hermann and Pundoor 2006) will be defined using building blocks, which are also known as Process Categories. Findings and Originality: This paper presents an E2E SC simulation conceptual model realised through means of systematic literature review. Practitioners have adopted the term E2E SC while this is not extensively featured within academic literature. The existing SC studies lack generality in regards to capturing the entire SC within one methodological framework, which this study aims to address. Research Impact: A systematic review of the supply chain and simulation literature takes an integrated and holistic assessment of an E2E SC, from market-demand scenarios through order management and planning processes, and on to manufacturing and physical distribution. Thus by providing significant advances in understanding of the theory, methods used and applicability of supply chain simulation, this paper will further develop a body of knowledge within this subject area. Practical Impact: The paper will empower practitioners’ knowledge and understanding of the supply chain processes characteristics that can be modelled using simulation. Moreover it will facilitate a selection of specific data required for the simulation in accordance to the individual needs of the industry

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

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