Thanks to globalization, the environment in which companies operate today has changed. Whereas a few decades ago, the same government generated protectionist policies, today with the opening of markets, competition is not only national but also international, so companies, regardless of their size, must make changes in their business model, aiming at the internationalization of markets both for the acquisition of materials and goods and for the marketing of their products, being more agile in responding to their customers in terms of product quality, expected delivery time and cost. It is in this scenario where logistics plays a fundamental role as a tool for competitiveness and, therefore, the following work aims to highlight the impact of logistics on SMEs and the need to aim for continuous improvement of their logistics processes. By means of a qualitative/descriptive methodology, relevant aspects are reviewed, finding as a final result essential components that SMEs must incorporate to attend a worldclass market.

Customer´s segmentation is used as a marketing differentiation tool which allows organizations to understand their customers and build differentiated strategies. This research focuses on a database from the SMEs sector in Colombia, the CRISP-DM methodology was applied for the Data Mining process. The analysis was made based on the PFM model (Presence, Frequency, Monetary Value), and the following grouping algorithms were applied on this model: k-means, k-medoids, and Self-Organizing Maps (SOM). For validating the result of the grouping algorithms and selecting the one that provides the best quality groups, the cascade evaluation technique has been used applying a classification algorithm. Finally, the Apriori algorithm was used to find associations between products for each group of customers, so determining association according to loyalty.

For reducing the degree of uncertainty caused by constant change in the environment, large, medium or small, private or public organizations must support their decisions in something more than experience or intuition; they must be supported by the development of accurate and reliable forecasts in order to meet the needs in the organization planning tasks. This case study presents a growing company dedicated to the storage of perishable products and incorporates time series forecasting techniques to estimate the volume of storage to foresee the requirements of additional facilities, personnel and materials needed for product mobility.

No abstract

The prices of products belonging to the basic family basket are an important component in the income of producers and consumer spending; its excessive variations constitute a source of uncertainty and risk that affects producers, since it prevents the realization of long-term investment plans, and can refuse lenders to grant them credit. His study to identify these variations, as well as to detect their sources, is then of great importance. The analysis of the variations of the prices of the basic products over time, include seasonal patterns, annual fluctuations, trends, cycles and volatility. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of massive sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in basic agricultural products, considering seasonal factors.