Oscar Rubiano-Ovalle

Objetivo: Optimizar recolección de pedidos en los centros de distribución (CD), una tarea que implica tiempo, mano de obra y costos significativos. Metodología: Se utilizó un modelo matemático basado en la programación lineal entera mixta (MILP) para minimizar la distancia total recorrida durante la recolección de pedidos. Este modelo se aplicó a un caso colombiano y se complementó con un modelo de simulación para evaluar escenarios de mejora. Resultados: El modelo matemático generó cuatro rutas óptimas para la recolección de pedidos. Dos de las rutas utilizaron un enrutamiento en forma de S, una utilizó el enrutamiento con retorno y otra combinó ambas políticas, reduciendo así la distancia total en un 5% para completar el proceso de recogida de pedidos. Posteriormente, el modelo de simulación se utilizó para evaluar tres escenarios de mejora: i) aumentar la capacidad de los carros de picking, ii) aumentar el número de carros de picking, y iii) aumentar ambos parámetros simultáneamente. Conclusiones: El mejor resultado se obtuvo al aumentar la capacidad de los carros de picking en un 33%, lo que redujo la distancia recorrida en un 49,5% y tuvo un impacto positivo en otros indicadores operativos definidos. Este innovador enfoque combinado del problema de las rutas puede utilizarse para explorar nuevas mejoras.

Colombia has positioned itself as a significant actor in Hass avocado production, capitalizing on the rising global demand and its suitable climate for the crop. These conditions have led to considerable investments from farmers. In this business environment, this research presents a systematic simulation and methodological approach for the evaluation of crops focusing on the Hass avocado and its extension to small-scale producers in Colombia. The initial phase involved a comprehensive analysis of key variables in the Hass avocado production chain, assessing productivity and viability. Subsequently, a dynamic model was developed to explore four scenarios spanning 13 years. The first scenario evaluated the production chain’s current behavior, while the second scrutinized the impact of credit accessibility. The third scenario analyzed the associativity among producers’ effects, and the fourth assessed the impact of government support. The results underscore that the implementation of each strategy improves the economic returns. Integrating all three strategies is the most effective method. These scenarios serve as proactive tools for investors, equipping them valuable insights and enabling informed decisions. Moreover, the study advocates for the promotion of rural economic development through strategic agricultural interventions.

The selection of organizational strategies should be approached with a systemic process that interrelates the decision elements. The objective of this paper is to present an organizational decision framework that incorporates the key elements in a decision-making process to select the best strategic option, considering the interdependence among all of them. A large Colombian company in the manufacturing sector is used as a case study. The Strengths, Weaknesses, Opportunities, Threats and Opportunities (SWOT) and the Analytical Network Process (ANP) approaches were combined for this purpose. The network integrates SWOT, Porter's generic strategies, corporate strategies, and, as a new addition, key success factors (KSF). The results include the simultaneous ranking of the generic strategies and associated corporate strategies, as well as the ranking of the influence the key criteria clusters on the prioritized strategies.

Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better.Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) statistical model and a Neural Network-ARMAX (NN-ARMAX) hybrid model for forecasting. Later, we compared both to a standard univariate statistical model to forecast the demand for electrical products in a Colombian manufacturing company.Findings: The outcomes demonstrated that the NN-ARMAX model outperformed the other two. Indeed, demand management improved with the reduction of overstock and out-of-stock products.Research limitations/implications: The findings and conclusions in this work are limited to Colombian manufacturing companies that sell electrical products to the construction industry. Moreover, the experts from the company that provided us with the data also selected the external factors based on their own experiences, i.e., we might have disregarded potential factors.Practical implications: This work suggests that a model using neural networks and including exogenous variables can improve demand forecasting accuracy, promoting this approach in manufacturing companies dealing with demand planning issues.Originality/value: The findings in this work demonstrate the convenience of using the proposed hybrid model to improve demand forecasting accuracy and thus provide a reliable basis for its implementation in supply chain planning for the electrical/construction sector in Colombian manufacturing companies.

Avocado is an important export and consumption product in Colombia, and its economic importance is expected to increase in the coming years. With its vast potential territory for avocado cultivation, the department of Cauca is a crucial area for producing this variety. However, small producers in the region often need more knowledge of the most suitable locations for planting. This study seeks to determine the ideal areas for Hass avocado cultivation in Cauca using geographic information tools and multi-criteria decision analysis, using a set of official data from different governmental entities and the hierarchical analytical process that allows determining the intensity of the interrelation of factors in the cultivation of Hass avocado. The results indicate that the municipalities near the Popayán plateau have the most significant potential for Hass avocado production, using the analytical hierarchy process. Approximately 9.2% of the administrative territory of the region is classified as highly suitable for Hass avocado cultivation, and an additional 14.2% is considered moderately suitable, constituting about 700,000 hectares of arable land. This research provides decision-makers and producers with valuable knowledge to support and improve Hass avocado agriculture in the region by implementing agricultural engineering practices.

In recent years, agriculture has become an essential activity in Colombia, despite the challenges faced by farmers due to low yields and insufficient resources to improve their main activities, such as irrigation systems, agricultural practices, and industrial machinery. This Hass avocado approach has been addressed in previous research considering system dynamics simulation to evaluate farmers’ behavior strategies and improve their competitiveness. However, these studies typically examine a single strategy effect and avoid multiple integrated strategies. Other studies focused on the complex interactions between different factors in the production chain and their feedback effects on farmers’ productivity and cash flow. For these reasons, this research provides a comprehensively dynamic model and evaluates long-term strategies and their effects on supporting and improving small farmers’ productivity and profitability. A system dynamics methodology was used to model complex systems processing Hass avocado farmer association data and explore their effects on competitiveness for long-term sustainable and profitable agriculture. This research proposes optimal scenarios for small farmers, including strategies such as low-interest credit access, logistics practices, and government technical support. The scenarios provide a proactive tool for decision makers and promote rural farmers’ development, aligning high-quality fresh product supply and demand.

Objetivo: Este trabajo de investigación propone y evalúa un modelo para la prestación de servicios de rehabilitación física apoyados en ayudas tecnológicas tele operadas, utilizando Internet como canal de comunicación para el cuidado de la población rural, y tomando como región de estudio, una subregión del occidente de Colombia alrededor el municipio de Santiago de Cali- Colombia. Metodología: A partir de la dinámica generada por la interacción entre el sistema de servicios y los usuarios, caracterizada por la ocurrencia de eventos, se construyó un modelo formal, utilizando una Red Petri Coloreada. Para ello, se propuso una metodología de modelado basada en un enfoque top-Down. Resultados: Se desarrolló y evaluó un modelo del sistema de atención utilizando redes de Petri, a partir de un caso de estudio consistente en el modelo de atención actual que ofrece un proveedor de servicios de salud típico de Cali denominado "Red de salud La Ladera ESE". Las mejoras ponderadas a través del sistema propuesto fueron: mayor objetividad de trazabilidad, rapidez de comunicación, mejor calidad de rehabilitación, así como la capacidad del nuevo sistema para contribuir a una mayor precisión en el diagnóstico inicial y posterior evaluación periódica de recuperación. Conclusiones: El protocolo propuesto basado en el modelo, permitió atender de manera concurrente y precisa el diagnóstico de hasta tres pacientes, mediante la implementación de ayudas tecnológicas para mejorar el soporte diagnóstico y permitir una posterior evaluación periódica de la recuperación del paciente.

This study proposes an architecture based on multiple agents through a Colored Petri Net model (CPN), which enables the integration of supply chain links and information technology to speed up the production process and services. The methodology was developed based on three fundamental concepts: CPN as design tool, Supply Chain (SC) Architecture and Multi-Agent Systems (MAS). As a case study, a typical supply chain in the Colombian production system was considered. As a case study, a typical supply chain in the Colombian production system was considered. For this, the study developed a modeling procedure to transform its supply chain architecture from a typical structure to a multi-agent structure. This procedure consists of seven phases. Initially, the case study is characterized as a supply chain, then the information is collected, next the limitations of the supply chain are identified, then the variables and their interactions are defined. In the following two phases, three models are developed: the structural model, representing the physical distribution of the plant; the conceptual model, that relates the system components and their interactions; and the functional model, which integrates the previous models and associates them with the behavior of the variables during the process ow. Finally, a qualitative and quantitative validation of the models are performed, simulating different scenarios. The results show that a distributed supply chain structure, using multi-agent architecture, first, offers robust information and physical flows, and secondly, contributes to supply time reduction, communication quickness, efficiency and quality of information for operational decision making. The results could also motivate organizations to integrate the supply chains considering MASs in resource management.