@article{
 title = {Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry},
 type = {article},
 year = {2019},
 pages = {81-87},
 volume = {15},
 websites = {http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87},
 month = {10},
 day = {25},
 id = {dee4938a-034c-3c20-8ae3-bc1beba7bcc1},
 created = {2022-01-26T03:00:28.976Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:00:28.976Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {L.Lorente-Leyva2019},
 private_publication = {false},
 abstract = {This research presents an application of artificial neural networks in demand forecasting by using MATLAB Software. Keeping in mind that in any planning process forecasts play a fundamental role, being one of the bases for; planning, organizing and controlling production. It gives priority to the most critical nodes and their key activities, so that, the decisions made about them will generate the greatest possible positive impact. The methodology applied demonstrates the quality of the solutions found which are compared with traditional statistical methods to demonstrate the value of the solution proposed. When the results show that the minimum quadratic error is reached with the application of artificial neural networks, a better performance is obtained. Therefore, a suitable horizon is established for the planification and decision making in the metal-mechanical industry for the use of artificial intelligence in the production processes.},
 bibtype = {article},
 author = {L. Lorente-Leyva, Leandro and R. Patino-Alarcon, Delio and Montero-Santos, Yakcleem and D. Herrera-Granda, Israel and H. Peluffo-Ordonez, Diego and M. Lastre-Aleaga, Arlys and Cordoves-Garcia, Alexis},
 doi = {10.36478/jeasci.2020.81.87},
 journal = {Journal of Engineering and Applied Sciences},
 number = {1}
}

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