A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry
The imperative of accurate forecasting spans diverse industrial sectors, notably impacting the tent manufacturing industry. This study embarks on a rigorous examination and development of novel forecasting models, specifically tailored for this sector. We introduce and juxtapose two distinct approac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302400152X |
| _version_ | 1797247677255122944 |
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| author | George Rumbe Mohammad Hamasha Sahar Al Mashaqbeh |
| author_facet | George Rumbe Mohammad Hamasha Sahar Al Mashaqbeh |
| author_sort | George Rumbe |
| collection | DOAJ |
| description | The imperative of accurate forecasting spans diverse industrial sectors, notably impacting the tent manufacturing industry. This study embarks on a rigorous examination and development of novel forecasting models, specifically tailored for this sector. We introduce and juxtapose two distinct approaches: the Holt-Winters method and Artificial Neural Networks (ANN). Our analysis is grounded in a case study of a tent manufacturing company, delving into the dynamics of demand variation, particularly under seasonal influences. Through meticulous comparison, we demonstrate the efficacy of the ANN model, highlighting its superior accuracy in forecasting, especially for the Elite and Party Canopy tent models, albeit with a noted prediction error of 15% for the Vista tents. The paper also explores the broader supply chain context of the tent industry, examining influential factors affecting commercial tent sales and identifying key supply chain players. Our findings underscore the nuanced capabilities of ANN in capturing intricate demand patterns, offering a promising direction for refining forecasting practices in the tent manufacturing industry. |
| first_indexed | 2024-03-08T00:09:04Z |
| format | Article |
| id | doaj.art-550ffa424dc349f7b2d43f08cae620ba |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-24T20:02:30Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-550ffa424dc349f7b2d43f08cae620ba2024-03-24T07:01:06ZengElsevierResults in Engineering2590-12302024-03-0121101899A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industryGeorge Rumbe0Mohammad Hamasha1Sahar Al Mashaqbeh2Process Developer, IKEA Houston, Texas 77024 USA; Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, JordanCorresponding author.; Department of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, JordanDepartment of Industrial Engineering, Faculty of Engineering, The Hashemite University, Zarqa, 13133, JordanThe imperative of accurate forecasting spans diverse industrial sectors, notably impacting the tent manufacturing industry. This study embarks on a rigorous examination and development of novel forecasting models, specifically tailored for this sector. We introduce and juxtapose two distinct approaches: the Holt-Winters method and Artificial Neural Networks (ANN). Our analysis is grounded in a case study of a tent manufacturing company, delving into the dynamics of demand variation, particularly under seasonal influences. Through meticulous comparison, we demonstrate the efficacy of the ANN model, highlighting its superior accuracy in forecasting, especially for the Elite and Party Canopy tent models, albeit with a noted prediction error of 15% for the Vista tents. The paper also explores the broader supply chain context of the tent industry, examining influential factors affecting commercial tent sales and identifying key supply chain players. Our findings underscore the nuanced capabilities of ANN in capturing intricate demand patterns, offering a promising direction for refining forecasting practices in the tent manufacturing industry.http://www.sciencedirect.com/science/article/pii/S259012302400152XForecasting techniquesArtificial neural networks (ANNs)Holts-winter modelTent manufacturing industryAnd supply chain management |
| spellingShingle | George Rumbe Mohammad Hamasha Sahar Al Mashaqbeh A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry Results in Engineering Forecasting techniques Artificial neural networks (ANNs) Holts-winter model Tent manufacturing industry And supply chain management |
| title | A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry |
| title_full | A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry |
| title_fullStr | A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry |
| title_full_unstemmed | A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry |
| title_short | A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry |
| title_sort | comparison of holts winter and artificial neural network approach in forecasting a case study for tent manufacturing industry |
| topic | Forecasting techniques Artificial neural networks (ANNs) Holts-winter model Tent manufacturing industry And supply chain management |
| url | http://www.sciencedirect.com/science/article/pii/S259012302400152X |
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