Research Note: Fuzzy Supplier Selection by Use of Weighted Indices

Since supplier selection is an important part in management fields, this research focuses on weighted non-hierarchical fuzzy model to increase supply chain management performance. Supplier selection researches have significantly increased but most of methods have been focused on hierarchical determination of indices. This article by use of a multiple objective function tried to present a method that can consider non-hierarchical determination of indices in specific conditions. This research by use of deliverable indices of supply chain management tries to select the best suppliers. In this paper it is assumed that all suppliers have the ability to supply needed items but client can only make a product they provide. Quality of supply chain deliverable, supply chain reliability and supply chain visibility names indices have been selected to increase efficiency in the supply chain. This approach presents local optimal solutions by use of a heuristic logic in supply chain management. These indices are used as fuzzy to select the appropriate suppliers. By this fuzzy method, appropriate supplier can be set for each of the items. The presented approach have been introduced a weighted indices to determine best supplier in specific conditions. In this research weighted non-hierarchical fuzzy sets have been used to select appropriate suppliers. This method is useful for supplier selection problems

Combining field data analysis and simulation to evaluate an alternative Just-In-Time clinical trial supply strategy

This paper combines recurrence analysis of field data from clinical trial supply chain (CTSC) with a proof-of-concept inventory profile simulation to evaluate an alternative packing capability that supports just-in-time (JIT) manufacturing and distribution of investigational medicinal products (IMP). Assumptions for JIT packing supply capabilities and expedite quality release were taken from a detailed design prototype recently commissioned by a leading pharmaceutical consortium. The suggested technological intervention is assessed in its ability to reduce finished good inventory while adequately responding to the dynamics of uncertain patient recruitment and required service levels. The proposed combination of field data analysis and simulation enables practitioners to consider the possibilities for a more economically viable adaptive clinical trial supply based on JIT technologies and near real-time product utilisation information across multiple locations

Supply chain hybrid simulation: From Big Data to distributions and approaches comparison

The uncertainty and variability of Supply Chains paves the way for simulation to be employed to mitigate such risks. Due to the amounts of data generated by the systems used to manage relevant Supply Chain processes, it is widely recognized that Big Data technologies may bring benefits to Supply Chain simulation models. Nevertheless, a simulation model should also consider statistical distributions, which allow it to be used for purposes such as testing risk scenarios or for prediction. However, when Supply Chains are complex and of huge-scale, performing distribution fitting may not be feasible, which often results in users focusing on subsets of problems or selecting samples of elements, such as suppliers or materials. This paper proposed a hybrid simulation model that runs using data stored in a Big Data Warehouse, statistical distributions or a combination of both approaches. The results show that the former approach brings benefits to the simulations and is essential when setting the model to run based on statistical distributions. Furthermore, this paper also compared these approaches, emphasizing the pros and cons of each, as well as their differences in computational requirements, hence establishing a milestone for future researches in this domain.This work has been supported by national funds through FCT -Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2019 and by the Doctoral scholarship PDE/BDE/114566/2016 funded by FCT, the Portuguese Ministry of Science, Technology and Higher Education, through national funds, and co-financed by the European Social Fund (ESF) through the Operational Programme for Human Capital (POCH)

PLANNING PROCESS OF PILOT BATCH PRODUCTION OF AN INNOVATIVE DRUG FOR CLINICAL TRIAL IN A PHARMACEUTICAL INDUSTRY

Purpose – Clinical trials are the most critical step in the process of drug development and evaluation to bring a new drug to market. The purpose of this article was to show an approach of the planning and production of a new innovative drug for a clinical study, based on the presentation of decision-making process in a national pharmaceutical industry. Design / methodology / approach - Through a case-study methodology, it was described the pilot batch production planning of a new drug for clinical trial, focusing on how the company evaluates the adequacy of the available systems at the manufacturing plant and how they use them in drug production planning process. Findings – A previous planning for clinical trial supplies production is determinant to decide the order, amount and timing of the products to be produced when the manufacturing plant is shared with the production of commercial products. Also, even in a small pilot batch production, there is a substantial waste of supplies during the process. Research Limitations / Implications – This study showed the production planning process of one investigational product with the recruitment of few patients for the clinical trial. However, the number of patients enrolled can reach thousands in many clinical trials, and it does need a more complex production planning to avoid wastes and try to reduce the process costs. Practical Implications - This article provides a picture of the production planning of clinical trial supplies chain under uncertainty and the decisions that affect the large-scale production of commercial drugs and the pilot batch production of experimental drugs. Originality / value - Although the results of clinical trials is the most significant source of uncertainty in the development process of any new drug, a good clinical supply planning and processes management can avoid or attenuate the imminent risk of process failure.

A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded vs. unblinded, sponsor CRO selection, enrollment quarter, and enrollment country values to predict patient enrollment characteristics in clinical trials. The model was tested using a dataset consisting of 5,000 data points and yielded a high level of accuracy. This development in patient enrollment prediction will optimize portfolio demand planning and help avoid costs associated with inaccurate patient enrollment forecasting

Modelo basado en escenarios para la determinación de tamaño y frecuencia de envío de medicamentos oncológicos. Caso aplicado al sector farmacéutico en la ciudad de Medellín

Introducción: El presente trabajo ayuda a mejorar la eficiencia de las actividades llevadas a cabo en la gestión de compra de medicamentos oncológicos. Su importancia radica en organizar, planificar y controlar el conjunto de medicamentos oncológicos en las entidades sanitarias con el fin de brindar un servicio de atención eficiente a los usuarios, a través de una buena gestión de la cadena de suministro de medicamentos. En particular se tiene en cuenta la diferencia entre demanda prevista en un determinado horizonte y la demanda real, cuya reducción es clave para evitar generación de inventarios en exceso y/o alcanzar los niveles de servicio de los clientes que son determinantes en el éxito de las empresas (Menac et al, 2006). Dentro del contexto de las organizaciones sanitarias se deben adoptar nuevas estrategias para gestionar de un modo más eficiente u actividad logística, optimizando de este modo los niveles de existencias, las rutas de reparto y la dimensión requerida por los almacenes hospitalarios, propios o subcontratados que pueden estar dispersos geográficamente (Kim, 2005). Al mismo tiempo con proveedores en zonas alejadas provocando plazos de entrega más dilatados. Por lo tanto, las empresas deben esforzarse por hacer que sus productos y servicios estén a disposición de sus consumidores, así como mantener a los médicos bien informados sobre los nuevos medicamentos (Whittemore y Division, 2004). Este trabajo se enmarca dentro del proyecto de COLCIENCIAS llamado: Propuesta metodológica para la definición de políticas, reglas de negociación y coordinación en la gestión de abastecimiento de los medicamentos oncológicos en Colombia. Código: 1101-521-28420. Investigador Principal: Wilson Adarme Jaimes. A través de la caracterización del servicio oncológico y sus hallazgos se puede proponer una metodología para mejorar las provisiones de medicamentos tanto en los laboratorios como en las IPSs, de tal manera que permita mejorar el desempeño de las operaciones logísticas y realizar procesos basados en diferentes escenarios en el control de la variabilidad de la demanda (Kovács et al, 2013). Ya que los lineamientos de evaluación del manejo de inventario permite hacer una mejor determinación de necesidades y mejorar los niveles de inventarios en la gestión de la cadena de suministros (Arango, 2010). En este trabajo se presentan los elementos más relevantes sobre el control de inventarios y la administración de la cadena de abastecimiento de medicamentos oncológicos como trabajo de grado para optar al título de Magister en Ingeniería Administrativa. El objetivo general de este trabajo es determinar el tamaño y frecuencias de pedidos de medicamentos considerando los diferentes escenarios que pueden surgir en la gestión de los procesos de compra en una IPS oncológica en la ciudad de Medellín. El análisis de los diferentes 10 escenarios se realizará a través de la programación estocástica bajo la modelación de escenarios con parámetros de incertidumbre.Maestrí

Integrated management of chemical processes in a competitive environment

El objetivo general de esta Tesis es mejorar el proceso de la toma de decisiones en la gestión de cadenas de suministro, tomando en cuenta principalmente dos diferencias: ser competitivo considerando las decisiones propias de la cadena de suministro, y ser competitivo dentro de un entorno global. La estructura de ésta tesis se divide en 4 partes principales: La Parte I consiste en una introducción general de los temas cubiertos en esta Tesis (Capítulo 1). Una revisión de la literatura, que nos permite identificar las problemáticas asociadas al proceso de toma de decisiones (Capítulo 2). El Capítulo 3 presenta una introducción de las técnicas y métodos de optimización utilizados para resolver los problemas propuestos en esta Tesis. La Parte II se enfoca en la integración de los niveles de decisión, buscando mejorar la toma de decisiones de la propia cadena de suministro. El Capítulo 4 presenta una formulación matemática que integra las decisiones de síntesis de procesos y las decisiones operacionales. Además, este capítulo presenta un modelo integrado para la toma de decisiones operacionales incluyendo las características del control de procesos. El Capítulo 5 muestra la integración de las decisiones del nivel táctico y el operacional, dicha propuesta está basada en el conocimiento adquirido capturando la información relacionada al nivel operacional. Una vez obtenida esta información se incluye en la toma de decisiones a nivel táctico. Finalmente en el capítulo 6 se desarrolla un modelo simplificado para integrar múltiples cadenas de suministro. El modelo propuesto incluye la información detallada de las entidades presentes en una cadena de suministro (suministradores, plantas de producción, distribuidores y mercados) introduciéndola en un modelo matemático para su coordinación. La Parte III propone la integración explicita de múltiples cadenas de suministro que tienen que enfrentar numerosas situaciones propias de un mercado global. Asimismo, esta parte presenta una nueva herramienta de optimización basada en el uso integrado de métodos de programación matemática y conceptos relacionados a la Teoría de Juegos. En el Capítulo 7 analiza múltiples cadenas de suministro que cooperan o compiten por la demanda global del mercado. El Capítulo 8 incluye una comparación entre el problema resuelto en el Capítulo anterior y un modelo estocástico, los resultados obtenidos nos permiten situar el comportamiento de los competidores como fuente exógena de la incertidumbre típicamente asociada la demanda del mercado. Además, los resultados de ambos Capítulos muestran una mejora sustancial en el coste total de las cadenas de suministro asociada al hecho de cooperar para atender de forma conjunta la demanda disponible. Es por esto, que el Capítulo 9 presenta una nueva herramienta de negociación, basada en la resolución del mismo problema (Capítulo 7) bajo un análisis multiobjetivo. Finalmente, la parte IV presenta las conclusiones finales y una descripción general del trabajo futuro.This Thesis aims to enhance the decision making process in the SCM, remarking the difference between optimizing the SC to be competitive by its own, and to be competitive in a global market in cooperative and competitive environments. The structure of this work has been divided in four main parts: Part I: consists in a general introduction of the main topics covered in this manuscript (Chapter I); a review of the State of the Art that allows us to identify new open issues in the PSE (Chapter 2). Finally, Chapter 3 introduces the main optimization techniques and methods used in this contribution. Part II focuses on the integration of decision making levels in order to improve the decision making of a single SC: Chapter 4 presents a novel formulation to integrate synthesis and scheduling decision making models, additionally, this chapter also shows an integrated operational and control decision making model for distributed generations systems (EGS). Chapter 5 shows the integration of tactical and operational decision making levels. In this chapter a knowledge based approach has been developed capturing the information related to the operational decision making level. Then, this information has been included in the tactical decision making model. In Chapter 6 a simplified approach for integrated SCs is developed, the detailed information of the typical production‐distribution SC echelons has been introduced in a coordinated SC model. Part III proposes the explicit integration of several SC’s decision making in order to face several real market situations. As well, a novel formulation is developed using an MILP model and Game Theory (GT) as a decision making tool. Chapter 7 includes the tactical and operational analysis of several SC’s cooperating or competing for the global market demand. Moreover, Chapter 8 includes a comparison, based on the previous results (MILP‐GT optimization tool) and a two stage stochastic optimization model. Results from both Chapters show how cooperating for the global demand represent an improvement of the overall total cost. Consequently, Chapter 9 presents a bargaining tool obtained by the Multiobjective (MO) resolution of the model presented in Chapter 7. Finally, final conclusions and further work have been provided in Part IV.Postprint (published version

REAL-TIME DATA MINING FOR PROCESS OPERATIONS USING GRAPHICS PROCESSING UNIT (GPU)-BASED HIGH PERFORMANCE COMPUTING

Ph.DDOCTOR OF PHILOSOPH

Gestão de Riscos Logísticos em Cadeias de Suprimentos: Otimização via Metamodelo de Simulação.

Alguns tipos de riscos podem causar danos às cadeias de suprimentos, provocando rupturas nos fluxos de materiais e produtos acabados. Riscos logísticos se relacionam às falhas nos processos de transporte, armazenagem, produção e vendas. A gestão adequada desses riscos é fator crítico para a integração dos fluxos sob a responsabilidade da logística e operações, cujas atividades são frequentemente realizadas por provedores de serviços logísticos. Entretanto, observou-se a falta de procedimentos sistemáticos focados na gestão de riscos logísticos que melhor aproveitasse as vantagens da integração entre métodos de simulação e otimização. A pesquisa foi realizada em uma cadeia de suprimentos do segmento automotivo português, a partir de dados secundários disponíveis na literatura. Os problemas desse estudo foram: (a) quais os impactos dos riscos logísticos sobre o desempenho dessa cadeia? (b) sob a influência desses riscos, que ajustes no sistema logístico poderiam melhorar a resposta do arranjo aos impactos? Para solucionar tais questões, definiu-se como objetivo, mitigar os efeitos desses riscos a partir de um metamodelo de simulação para a otimização de parâmetros críticos. As atividades logísticas desempenhadas na cadeia de suprimentos foram escolhidas como objeto de estudo. Essa pesquisa foi classificada como aplicada, quantitativa e exploratória normativa, considerando, respectivamente, a sua natureza, a abordagem do problema e os objetivos. A simulação a eventos discretos, elaborada no ambiente Arena®, foi utilizada como método de pesquisa. A otimização Black Box, realizada através do software OptQuest®, foi empregada para projetar os parâmetros adequados para o sistema logístico. Um metamodelo de regressão baseado no método OLS foi desenvolvido a partir da projeção e implantação de experimentos, servindo para integrar as saídas do modelo de simulação às entradas do modelo de otimização. Inúmeras técnicas de verificação e validação foram empregadas para calibrar o modelo de otimização via simulação, tais como: implantação modular e análise de sensibilidade. Uma sistemática metodológica fundamentada na abordagem DMAIC foi elaborada para relacionar as etapas de gestão dos riscos logísticos e conduzir aos resultados dessa pesquisa, incluindo a identificação (Definir), avaliação (Mensuração), gestão (Melhoria e análise) e monitoramento (Controle) do risco logístico. Um evento de risco logístico foi inserido no modelo com o fim de reproduzir rupturas no fluxo físico de distribuição e permitir a avaliação dos seus impactos sobre o desempenho da cadeia. Os impactos foram medidos por meio do custo logístico total, do risco de ruptura e da taxa de nível de serviço. Estratégias de mitigação do risco logístico de transporte, como redundância e flexibilidade, foram testadas para minimizar simultaneamente custo e risco e maximizar a taxa de entrega. A solução sugerida pelo modelo multiobjetivo de otimização via simulação mostrou ser adequada e eficaz já que os ajustes no sistema logístico bloquearam as consequências da ruptura. A principal contribuição da pesquisa foi desenvolver procedimentos sistemáticos para melhorar a gestão de riscos logísticos no âmbito de cadeias de suprimentos a partir do uso combinado entre métodos de simulação e otimização