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.

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.

‘'Project Portfolio Risk Management” is approached through a bibliometry and collaboration networks study determining its dynamics and development as a formal domain that links Project, Risk Management and Portfolio concepts. To facilitate replicability, a scientometric study under a PRISMA structure is carried out: i) Identification or domain structuring, as well as keywording accuracy; ii) Screening: Search string refinement and outputs review; iii) Eligibility: Several criteria applied to a content analysis, and iv) Inclusion: Consolidation of domain analytics through bibliometry and collaboration networks. Assessing the field as a formal knowledge domain is novel, contributing to a synthesis of its trends and evolution: For first time, descriptive statistics show increasing attention based on the growing citation scores, participation, H index and productivity of its main journals. Project Portfolio Selection is established as hot topic, the main authors are identified, as well as key concepts such as optimization, mathematical programming, multi-objective optimization, stochastic programming, and robust optimization. Three main research themes are obtained: Incorporation of Risk Assessment into Project Portfolio Selection problem, Risk Management as a Project Portfolio Management process, and Risk Analysis considering social and environmental issues. An accurate match is found in the contrast of the domain's behavior with some bibliometric and linguistic laws. Theoretical richness is achieved in the conjunction of the three terms, presenting dynamics and tendencies and thus contributing to focus related research processes on a unified field for the use of both scholars' and practitioners’ perspectives.

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.

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.