Effect of Industry 4.0 on Education Systems: An Outlook

Congreso Universitario de Innovación Educativa En las Enseñanzas Técnicas, CUIEET (26º. 2018. Gijón

Measuring the efficiency of large pharmaceutical companies: an industry analysis

This paper evaluates the relative efficiency of a sample of 37 large pharmaceutical laboratories in the period 2008-2013 using a Data Envelopment Analysis (DEA) approach. We describe in detail the procedure followed to select and construct relevant inputs and outputs that characterize the production and innovation activity of these pharmaceutical firms. Models are estimated with financial information from Datastream, including R&D investment, and the number of new drugs authorized by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) considering the time effect. The relative performances of these firms –taking into consideration the strategic importance of R&D– suggest that the pharmaceutical industry is a highly competitive sector given that there are many laboratories at the efficient frontier and many inefficient laboratories close to this border. Additionally, we use data from S&P Capital IQ to analyze 2,071 financial transactions announced by our sample of laboratories as an alternative way to gain access to new drugs, and we link these transactions with R&D investment and DEA efficiency. We find that efficient laboratories make on average more financial transactions, and the relative size of each transaction is larger. However, pharmaceutical companies that simultaneously are more efficient and invest more internally in R&D announce smaller transactions relative to total assets

Intelligent decision support system for real-time water demand management

Environmental and demographic pressures have led to the current importance of Water Demand Management (WDM), where the concepts of efficiency and sustainability now play a key role. Water must be conveyed to where it is needed, in the right quantity, at the required pressure, and at the right time using the fewest resources. This paper shows how modern Artificial Intelligence (AI) techniques can be applied on this issue from a holistic perspective. More specifically, the multi-agent methodology has been used in order to design an Intelligent Decision Support System (IDSS) for real-time WDM. It determines the optimal pumping quantity from the storage reservoirs to the points-of-consumption in an hourly basis. This application integrates advanced forecasting techniques, such as Artificial Neural Networks (ANNs), and other components within the overall aim of minimizing WDM costs. In the tests we have performed, the system achieves a large reduction in these costs. Moreover, the multi-agent environment has demonstrated to propose an appropriate framework to tackle this issue

Exploring the interaction of inventory policies across the supply chain: An agent-based approach

The Bullwhip Effect, which refers to the increasing variability of orders traveling upstream the supply chain, has shown to be a severe problem for many industries. The inventory policy of the various nodes is an important contributory factor to this phenomenon, and hence it significantly impacts on their financial performance. This fact has led to a large amount of research on replenishment and forecasting methods aimed at exploring their suitability depending on a range of environmental factors, e.g. the demand pattern and the lead time. This research work approaches this issue by seeing the whole picture of the supply chain. We study the interaction between four widely used inventory models in five different contexts depending on the customer demand variability and the safety stock. We show that the concurrence of distinct inventory models in the supply chain, which is a common situation in practice, may alleviate the generation of inefficiencies derived from the Bullwhip Effect. In this sense, we demonstrate that the performance of each policy depends not only upon the external environment but also upon the position within the system and upon the decisions of the other nodes. The experiments have been carried out via an agent-based system whose agents simulate the behavior of the different supply chain actors. This technique proves to offer a powerful and risk-free approach for business exploration and transformation

Holism versus reductionism in supply chain management: An economic analysis

Since supply chains are increasingly built on complex interdependences, concerns to adopt new managerial approaches based on collaboration have surged. Nonetheless, implementing an efficient collaborative solution is a wide process where several obstacles must be faced. This work explores the key role of experimentation as a model-driven decision support system for managers in the convoluted decision-making process required to evolve from a reductionist approach (where the overall strategy is the sum of individual strategies) to a holistic approach (where global optimization is sought through collaboration). We simulate a four-echelon supply chain within a large noise scenario, while a fractional factorial design of experiments (DoE) with eleven factors was used to explore cause-effect relationships. By providing evidence in a wide range of conditions of the superiority of the holistic approach, supply chain participants can be certain to move away from their natural reductionist behavior. Thereupon, practitioners focus on implementing the solution. The theory of constraints (TOC) defines an appropriate framework, where the Drum–Buffer–Rope (DBR) method integrates supply chain processes and synchronizes decisions. In addition, this work provides evidence of the need for aligning incentives in order to eliminate the risk to deviate. Modeling and simulation, especially agent-based techniques, allows practitioners to develop awareness of complex organizational problems. Hence, these prototypes can be interpreted as forceful laboratories for decision making and business transformation

Real-time water demand forecasting system through an agent-based architecture

Water policies have evolved enormously since the Rio Earth Summit (1992). These changes have led to the strategic importance of water demand management. The aim is to provide water where and when it is required using the fewest resources. A key variable in this process is the demand forecasting. It is not sufficient to have long term forecasts, as the current context requires the continuous availability of reliable hourly predictions. This paper incorporates artificial intelligence to the subject, through an agent-based system, whose basis are complex forecasting methods (Box-Jenkins, Holt-Winters, multi-layer perceptron networks and radial basis function networks). The prediction system also includes data mining, oriented to the pre and post processing of data and to the knowledge discovery, and other agents. Thereby, the system is capable of choosing at every moment the most appropriate forecast, reaching very low errors. It significantly improves the results of the different methods separately

Applying Goldratt's theory of constraints to reduce the Bullwhip Effect through agent-based modeling

In the current environment, Supply Chain Management (SCM) is a major concern for businesses. The Bullwhip Effect is a proven cause of significant inefficiencies in SCM. This paper applies Goldratt’s Theory of Constraints (TOC) to reduce it. KAOS methodology has been used to devise the conceptual model for a multi-agent system, which is used to experiment with the well known ‘Beer Game’ supply chain exercise. Our work brings evidence that TOC, with its bottleneck management strategy through the Drum-Buffer-Rope (DBR) methodology, induces significant improvements. Opposed to traditional management policies, linked to the mass production paradigm, TOC systemic approach generates large operational and financial advantages for each node in the supply chain, without any undesirable collateral effect

Bullwhip effect reduction through artificial intelligence-based techniques

Tesis con mención internacional. Tesis doctoral por el sistema de compendio de publicacionesLa globalización ha revolucionado el contexto empresarial. El incremento de la oferta en bienes y servicios, los constantes cambios en los gustos de los consumidores y la expansión geográfica de las redes de distribución, entre otros factores, han trazado un nuevo entorno competitivo marcado por la intensidad, la complejidad y el dinamismo. Éste ha enfatizado el concepto de cadena de suministro. En los procesos, en las relaciones y en las interdependencias de la cadena de suministro se esconde una fuente clave de ventajas competitivas para las organizaciones que, sin embargo, es muy compleja de captar. Una de las razones de ello es la generación del denominado Efecto Bullwhip, que ha de entenderse como una fuente clave de ineficiencias en la cadena de suministro. Este fenómeno se refiere a la amplificación de la variabilidad de las órdenes transmitidas a lo largo del sistema. Los capítulos 1 a 3 del presente trabajo exploran el papel de la inteligencia artificial en el desarrollo de mecanismos de previsión orientados a mejorar la gestión de la cadena de suministro. Se han utilizado redes neuronales artificiales (artificial neural networks, ANNs), bajo arquitecturas del tipo perceptron multi-capa (multi-layer perceptron, MLP) y funciones de base radial (RBF), junto a métodos estadístico dentro de una estructura multi-agente. Ante demandas con tendencia y estacionalidad, el sistema —que escoge en cada momento la previsión más adecuada— obtiene un gran rendimiento en la reducción del Efecto Bullwhip desde una perspectiva local. Asimismo, se muestra cómo este sistema se podría integrar con facilidad en un sistema de mayor alcance, lo cual representa una de las principales ventajas de esta aproximación. Los capítulos 4 a 6, que representan la principal línea de investigación dentro de este trabajo, tratan esta problemática desde una perspectiva sistémica. En este sentido, se pretende contribuir al despliegue de esta perspectiva dentro de las cadenas de suministro; el cual entendemos como el gran reto de las cadenas de suministro en el siglo XXI. Con este objetivo, se desarrolla un marco integrador para la gestión colaborativa de sistemas de producción y distribución basado en el Modelo de los Sistemas Viables de Beer (Viable System Model, VSM) y la Teoría de las Restricciones de Goldratt (Theory of Constraints, TOC). Sobre este marco, se explora la implementación de la solución mediante herramientas de modelado y simulación. Más en concreto, se utiliza la metodología Drum-Buffer-Rope (DBR) para proponer un motor operativo para la cadena de suministro y demuestra su eficacia, en comparación con alternativas tradicionales basadas en la producción en masa, tanto en términos operacionales (donde se engloba el Efecto Bullwhip) como en términos económicos. No obstante, el trabajo subraya que la integración de procesos es sólo una de las áreas clave para el diseño de soluciones colaborativas. La transparencia en la información relevante, la sincronización y distribución en la toma de decisiones, y el diseño de un sistema de rendimiento global han de entenderse igualmente como condiciones sine qua non para la implementación exitosa de la colaboración en las cadenas de suministro. La alineación de incentivos también es esencial. Los riesgos y los beneficios han de ser compartidos adecuadamente con el objetivo de reducir la amenaza de comportamientos oportunistas. Los cinco campos mencionados se han considerado en la propuesta de una solución colaborativa viable y beneficiosa para todos los miembros; dado que este esquema nos permite comprender por qué solo un pequeño porcentaje de las cadenas de suministro reales son capaces de crear valor a través de la colaboración. Esta Tesis Doctoral también pretende resaltar las técnicas de modelado y simulación como poderosos laboratorios de ensayo para el estudio de grandes problemas organizacionales que serían complejos de estudiar de otra forma. Este hecho subraya el enorme potencial del desarrollo de prototipos como metodología para el apoyo a la toma de decisiones y la transformación empresarial, especialmente en torno al complejo proceso de transición de una aproximación reduccionista (basada en la optimización local) a una holista (basada en la optimización global) en la cadena de suministro. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Globalization has utterly changed the economic landscape. The increase in the supply of goods and services, the constant evolution in customer preferences, and the geographical expansion of distribution networks, among other factors, have set up a new competitive environment—marked by intensity, complexity, and dynamism—that has put a greater emphasis on the concept of supply chain. Supply chain processes, relationships, and interdependencies can be a key source of competitive advantages. However, these advantages are difficult to capture. One of the reasons behind it is the generation of the so-called Bullwhip Effect, a major source of inefficiencies within supply chains. It refers to the amplification of the variability of orders throughout the system. Chapters 1 to 3 in the present dissertation explore the role of artificial intelligence in the development of forecasting mechanisms that improve the management of the supply chain. We employ artificial neural networks (ANNs), both under multi-layer perceptron (MLP) and radial basis function (RBF) architectures, together with statistical methods within a multi-agent structure. Facing demand series with trend and seasonality, the system—that selects the most suitable forecast for every moment—greatly mitigates the generation of the Bullwhip Effect from a local perspective. In addition, we show how this system could be easily integrated in a system with a larger scope, which represents one of the main benefits of this approach. Chapters 4 to 6, which represent the main research stream of this research work, analyze this issue from a systemic perspective. In this sense, we aim to add to the deployment of this view throughout supply chains; which we understand as a major challenge for 21st-century supply chains. To this end, we develop an integrative framework for the collaborative management of production and distribution systems based on the Beer’s Viable System Model (VMS) and on Goldratt’s Theory of Constraints (TOC). Building upon this framework, we investigate the implementation of this solution though modelling and simulation techniques. Specifically, we design a Drum-Buffer-Rope (DBR) mechanism to act as the operational engine for the supply chain. We show its effectiveness in comparison with traditional alternatives based on the mass production paradigm both in operational (including the Bullwhip Effect) and financial terms. Notwithstanding the foregoing, the present dissertation also underscores that process integration is only one of the key fields within the development of collaborative solutions for supply chains. Transparency in the relevant information, synchronization and allocation in the decision making must also be understood as conditions sine qua non for the successful implementation of collaboration across the system. Aligning incentives is also essential. In this regard, risks and benefits must be shared appropriately to reduce the menace of opportunistic behaviors. We carefully take into consideration all these fields in order to make the collaborative solution viable and profitable for every node, since we believe that this five-edge scheme makes it easier to understand why only a small percentage of real supply chains are capable of adding value through collaboration. In this research, modelling and simulation techniques appear as powerful laboratories for the study of large organizational problems that would be difficult to study otherwise. This fact emphasizes the great potential of prototype development as a methodology for the support of decision making and business transformation, especially around the complex transition process from reductionism (based on local optimization) to holism (based on global optimization) in supply chains

The Bullwhip effect in water demand management: taming it through an artificial neural networks-based system

The Bullwhip Effect refers to the amplification of the variance of orders and inventories along the supply chain as they move away from the customer. This is considered as the main cause of inefficiencies in the management of a traditional supply chain. However, the Bullwhip Effect is not relevant in the classic system of water distribution, based on long-term supply management. Nevertheless, current circumstances have drawn a new context, which has introduced the concept of Water Demand Management (WDM), in which efficiency and sustainability are of great importance. Then, the time horizon of management has decreased enormously and the supply time takes on an important role. Therefore, the Bullwhip Effect must be considered, as it significantly raises the costs of management. On the one hand, this paper brings evidence that Bullwhip Effect appears in a system of real-time management of water demand. On the other hand, it proposes the application of Artificial Intelligence techniques for its reduction. More specifically, an advanced forecasting system based on Artificial Neural Networks (ANNs) has been used. The Bullwhip Effect is heavily damped.Severo Ochoa. Ref BP13011