https://ria.asturias.es/RIA/handle/123456789/6528 Fri, 19 Dec 2025 10:13:14 GMT 2025-12-19T10:13:14Z https://ria.asturias.es/RIA/handle/123456789/7629 Title: Systemic approach to supply chain management through the viable system model and the theory of constraints Authors: Puche, Julio; Ponte, Borja; Costas, Jose; Pino, Raul; De la Fuente, David Abstract: In today’s environment, Supply Chain Management (SCM) takes a key role in business strategy. A major challenge is achieving high customer service level under a reasonable operating expense and investment. The traditional approach to SCM, based on local optimisation, is a proven cause of meaningful inefficiencies – e.g. the Bullwhip Effect – that obstruct the throughput. The systemic (holistic) approach, based on global optimisation, has been shown to perform significantly better. Nevertheless, it is not widely expanded, since the implementation of an efficient solution requires a suitable scheme. Under these circumstances, this paper proposes an integrative framework for supply chain collaboration aimed at increasing its efficiency. This is based on the combined application of the Beer’s Viable System Model (VSM) and the Goldratt’s Theory of Constraints (TOC). VSM defines the systemic structure of the supply chain and orchestrates the collaboration, while TOC implements the systemic behaviour – i.e. integrate processes – and define performance measures. To support this proposal, we detail its application to the widely used Beer Game scenario. In addition, we discuss its implementation in real supply chains, highlighting the key points that must be considered. Fri, 01 Jan 2016 00:00:00 GMT https://ria.asturias.es/RIA/handle/123456789/7629 2016-01-01T00:00:00Z https://ria.asturias.es/RIA/handle/123456789/6549 Title: Real-Time Water Demand Forecasting System through an Agent-Based Architecture Authors: Ponte, Borja; De la Fuente, David; Pino, Raúl; Rosillo, Rafael Abstract: 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 wa-ter 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 arti-ficial intelligence to the subject, through an agent-based system, whose basis are complex fore-casting methods (Box-Jenkins, Holt-Winters, Multi-Layer Perceptron Networks and Radial Ba-sis 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 Thu, 01 Jan 2015 00:00:00 GMT https://ria.asturias.es/RIA/handle/123456789/6549 2015-01-01T00:00:00Z https://ria.asturias.es/RIA/handle/123456789/7625 Title: Intelligent Decision Support System for Real-Time Water Demand Management Authors: Ponte, Borja; De la Fuente, David; Parreño, Jose; Pino, Raul Abstract: 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. Fri, 01 Jan 2016 00:00:00 GMT https://ria.asturias.es/RIA/handle/123456789/7625 2016-01-01T00:00:00Z https://ria.asturias.es/RIA/handle/123456789/7630 Title: Holism versus Reductionism in Supply Chain Management: An Economic Analysis Authors: Ponte, Borja; Costas, José; Puche, Julio; De la Fuente, David; Pino, Raúl Abstract: 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. Fri, 01 Jan 2016 00:00:00 GMT https://ria.asturias.es/RIA/handle/123456789/7630 2016-01-01T00:00:00Z