International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 4 July-August 2024 Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 4
July-August 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Machine Learning for Demand Forecasting in Manufacturing

Author(s) Sai Mani Krishna Sistla, Gowrisankar Krishnamoorthy, Jawaharbabu Jeyaraman, Bhargav Kumar Konidena
Country United States
Abstract This research paper investigates the application of machine learning (ML) techniques in demand forecasting within the manufacturing sector. By analyzing case studies, practical examples, and comparative studies, we explore the effectiveness and challenges of ML-driven demand forecasting. The paper discusses various ML techniques, including regression models, time series forecasting methods, neural networks, and ensemble methods, highlighting their strengths and limitations. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are examined to assess forecasting accuracy. Additionally, challenges such as data quality, model interpretability, computational resources, and overfitting are discussed, along with recommendations for addressing these challenges. The paper concludes with recommendations for practitioners and suggestions for future research directions, emphasizing the importance of data quality improvement, model interpretability enhancement, and ethical considerations in ML-based demand forecasting.
Keywords Machine learning, demand forecasting, manufacturing, regression models, time series forecasting, neural networks, ensemble methods, evaluation metrics, challenges, recommendations
Field Engineering
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-25
Cite This Machine Learning for Demand Forecasting in Manufacturing - Sai Mani Krishna Sistla, Gowrisankar Krishnamoorthy, Jawaharbabu Jeyaraman, Bhargav Kumar Konidena - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.14204
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.14204
Short DOI https://doi.org/gtjts6
View / Download PDF File

Share this


E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 4 Cover Page Vol 6 Isu 3 Cover Page Vol 6 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com