Forecasting and inventory model with Vertex (IA) to increase textile demand fulfillment

Abstract

Textile companies require engineering models that allow them to improve demand planning to reduce inventory breakage. The textile sector represents 8% of the participation of the manufacturing sector, which represents 12.5% of GDP, with the demand for textile garments standing out. Likewise, the article contributes to companies dedicated to the textile sector by implementing engineering and technology tools that improve logistics processes to avoid the risk of inventory breakage and non-compliance with textile demand. The research work was developed in a textile factory incorporating standardized work, forecast model, inventory model and Vertex Artificial Intelligence to achieve the reduction of root causes (non-compliance with the dispatch of raw materials, errors in the registration of inventory and its low levels of inputs). The results of the diagnosis were obtained by the implementation of qualitative (VSM) and quantitative tools (Control limit) of which 53%, 21% and 26% represented. This is expected to demonstrate the viability of engineering tools complemented by predictive technologies to improve the company’s logistics process. The proposed model will be validated by the systematic review of the literature of cases related to inventory and predictive tools supported by AI with the aim of being referenced in the expected goals such as the increase in stock.