JOURNAL ARTICLE

Improving Holistic Business Intelligence with Artificial Intelligence for Demand Forecasting.

  • Published In: Journal of Multiple-Valued Logic & Soft Computing, 2024, v. 42, n. 1-3. P. 241 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: ALFURHOOD, BADRIA SULAIMAN; ALONAZI, WADI B.; ARUNKUMAR, K.; SANTHI, S.; TAWFEQ, JAMAL FADHIL; RASHEED, TARIQ; POOVENDRAN, PARTHASARATHY 3 of 3

Abstract

Business Intelligence Model (BIM) plays a vital role in forming a strategy and taking correct data-based steps in a modern generation to achieve a better demand forecasting result. An inevitable resolution support structure that helps the organization conduct data analyses throughout the business process has been considered a significant challenge. The prediction of potential demands for businesses is predicted with the help of artificial intelligence has been introduced in this research. Based on the intelligence technique, demand estimation is considered one of the company's major decision-making activities focused on Improving Holistic Business Intelligence Model (IHBIM). For predictions of demand, first raw data from the market is gathered, and then potential demand for sales/products is predicted according to requirements using IHBIM. This forecast is based on data obtained from multiple sources. Further, Artificial intelligence conducts data from various modules and calculates the goods/products' demands regularly, monthly, and quarterly has been integrated into IHBIM. The simulation results show that the accuracy of the demand forecast is non-compromising. Furthermore, the model's performance is validated by combining the projected results with accurate data and calculating the percentage error. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Multiple-Valued Logic & Soft Computing. 2024/01, Vol. 42, Issue 1-3, p241
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:15423980
  • Accession Number:175399149
  • Copyright Statement:Copyright of Journal of Multiple-Valued Logic & Soft Computing is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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