Extreme learning machine for business sales forecasts: a systematic review

Abstract

Technology in business is vital, in recent decades technology has optimized the way they are managed making operations faster and more efficient, so we can say that companies need technology to stay in the market. This systematic review aims to determine to what extent an Extreme Learning Machine (ELM) system helps sales forecasts (SF) of companies, based on the scientific literature of the last 17 years. For the methodology, the systematic search for keywords began in the repositories of Google Scholar, Scielo, Redalyc, among others. Documents were collected between 2002 and 2019 and organized according to an eligibility protocol defined by the author. As an inclusion criteria, the sources in which their conclusions contributed to deepening the investigation were taken and those that did not contribute were excluded. Each of the results represented in graphs was discussed. The main limitation was the little information on the subject because it is a new topic. In conclusion, an ELM system makes use of both internal and external data to develop a more precise SF, which can be used not only by the sales and finance area but also to coordinate with the production area a more exact batch to be produced; this has a great impact on the communication and dynamism of companies to reduce costs and increase profits.