Demand Prediction and Apparel Production Management Using AI-Based Decision Tree Article Swipe
Iqbal Haqiqi Ariyanto
,
Ahmad Abdul Chamid
,
Rina Fiati
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.32877/bt.v8i1.2325
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.32877/bt.v8i1.2325
The apparel industry faces significant challenges in demand forecasting due to market volatility, rapid changes in fashion trends, and diverse consumer behavior, especially within e-commerce environments. Traditional forecasting methods such as linear regression and time series models often fall short in addressing the complex dynamics of the modern fashion market. This study presents a novel integration of demand forecasting and size recommendation into a unified AI-based system utilizing the Decision Tree algorithm. The system is designed to predict product demand while also providing personalized clothing size recommendations based on user attributes such as body measurements, style preferences, and seasonal trends. The system was developed using a structured data processing and predictive modeling approach, incorporating user profiles and trend sentiment derived from social media. The evaluation results show that the system achieved an accuracy rate of 87.5% in demand forecasting and 84% user satisfaction for size recommendations. It demonstrated better adaptability and performance compared to traditional methods such as ARIMA. A functional prototype was implemented, allowing users to interactively input data and receive real-time predictions. This study confirms the potential of Decision Tree-based AI models to enhance the shopping experience, reduce product return rates, and optimize inventory management. Future improvements may involve integrating real-time data and advanced technologies such as 3D body scanning to further increase prediction accuracy and personalization in digital fashion retail.
Related Topics
Concepts
Computer science
Demand forecasting
Autoregressive integrated moving average
Clothing
Adaptability
Decision tree
Tree (set theory)
Personalization
Product (mathematics)
Time series
Industrial engineering
Machine learning
Data mining
Operations research
Engineering
Ecology
Biology
Archaeology
Geometry
History
Mathematics
World Wide Web
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32877/bt.v8i1.2325
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413210241
All OpenAlex metadata
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https://openalex.org/W4413210241Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32877/bt.v8i1.2325Digital Object Identifier
- Title
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Demand Prediction and Apparel Production Management Using AI-Based Decision TreeWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-10Full publication date if available
- Authors
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Iqbal Haqiqi Ariyanto, Ahmad Abdul Chamid, Rina FiatiList of authors in order
- Landing page
-
https://doi.org/10.32877/bt.v8i1.2325Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.32877/bt.v8i1.2325Direct OA link when available
- Concepts
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Computer science, Demand forecasting, Autoregressive integrated moving average, Clothing, Adaptability, Decision tree, Tree (set theory), Personalization, Product (mathematics), Time series, Industrial engineering, Machine learning, Data mining, Operations research, Engineering, Ecology, Biology, Archaeology, Geometry, History, Mathematics, World Wide Web, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.system | 66, 73, 101, 129 |
| abstract_inverted_index.within | 23 |
| abstract_inverted_index.apparel | 1 |
| abstract_inverted_index.changes | 14 |
| abstract_inverted_index.complex | 43 |
| abstract_inverted_index.derived | 119 |
| abstract_inverted_index.digital | 220 |
| abstract_inverted_index.diverse | 19 |
| abstract_inverted_index.enhance | 185 |
| abstract_inverted_index.fashion | 16, 48, 221 |
| abstract_inverted_index.further | 213 |
| abstract_inverted_index.involve | 200 |
| abstract_inverted_index.market. | 49 |
| abstract_inverted_index.methods | 28, 155 |
| abstract_inverted_index.predict | 77 |
| abstract_inverted_index.product | 78, 190 |
| abstract_inverted_index.receive | 171 |
| abstract_inverted_index.results | 125 |
| abstract_inverted_index.retail. | 222 |
| abstract_inverted_index.trends, | 17 |
| abstract_inverted_index.trends. | 99 |
| abstract_inverted_index.unified | 64 |
| abstract_inverted_index.AI-based | 65 |
| abstract_inverted_index.Decision | 69, 180 |
| abstract_inverted_index.accuracy | 132, 216 |
| abstract_inverted_index.achieved | 130 |
| abstract_inverted_index.advanced | 205 |
| abstract_inverted_index.allowing | 164 |
| abstract_inverted_index.clothing | 84 |
| abstract_inverted_index.compared | 152 |
| abstract_inverted_index.confirms | 176 |
| abstract_inverted_index.consumer | 20 |
| abstract_inverted_index.designed | 75 |
| abstract_inverted_index.dynamics | 44 |
| abstract_inverted_index.increase | 214 |
| abstract_inverted_index.industry | 2 |
| abstract_inverted_index.modeling | 111 |
| abstract_inverted_index.optimize | 194 |
| abstract_inverted_index.presents | 52 |
| abstract_inverted_index.profiles | 115 |
| abstract_inverted_index.scanning | 211 |
| abstract_inverted_index.seasonal | 98 |
| abstract_inverted_index.shopping | 187 |
| abstract_inverted_index.approach, | 112 |
| abstract_inverted_index.behavior, | 21 |
| abstract_inverted_index.developed | 103 |
| abstract_inverted_index.inventory | 195 |
| abstract_inverted_index.potential | 178 |
| abstract_inverted_index.prototype | 161 |
| abstract_inverted_index.providing | 82 |
| abstract_inverted_index.real-time | 172, 202 |
| abstract_inverted_index.sentiment | 118 |
| abstract_inverted_index.utilizing | 67 |
| abstract_inverted_index.Tree-based | 181 |
| abstract_inverted_index.addressing | 41 |
| abstract_inverted_index.algorithm. | 71 |
| abstract_inverted_index.attributes | 90 |
| abstract_inverted_index.challenges | 5 |
| abstract_inverted_index.e-commerce | 24 |
| abstract_inverted_index.especially | 22 |
| abstract_inverted_index.evaluation | 124 |
| abstract_inverted_index.functional | 160 |
| abstract_inverted_index.prediction | 215 |
| abstract_inverted_index.predictive | 110 |
| abstract_inverted_index.processing | 108 |
| abstract_inverted_index.regression | 32 |
| abstract_inverted_index.structured | 106 |
| abstract_inverted_index.Traditional | 26 |
| abstract_inverted_index.experience, | 188 |
| abstract_inverted_index.forecasting | 8, 27, 58, 138 |
| abstract_inverted_index.integrating | 201 |
| abstract_inverted_index.integration | 55 |
| abstract_inverted_index.management. | 196 |
| abstract_inverted_index.performance | 151 |
| abstract_inverted_index.significant | 4 |
| abstract_inverted_index.traditional | 154 |
| abstract_inverted_index.volatility, | 12 |
| abstract_inverted_index.adaptability | 149 |
| abstract_inverted_index.demonstrated | 147 |
| abstract_inverted_index.implemented, | 163 |
| abstract_inverted_index.improvements | 198 |
| abstract_inverted_index.personalized | 83 |
| abstract_inverted_index.predictions. | 173 |
| abstract_inverted_index.preferences, | 96 |
| abstract_inverted_index.satisfaction | 142 |
| abstract_inverted_index.technologies | 206 |
| abstract_inverted_index.environments. | 25 |
| abstract_inverted_index.incorporating | 113 |
| abstract_inverted_index.interactively | 167 |
| abstract_inverted_index.measurements, | 94 |
| abstract_inverted_index.recommendation | 61 |
| abstract_inverted_index.personalization | 218 |
| abstract_inverted_index.recommendations | 86 |
| abstract_inverted_index.recommendations. | 145 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.44953379 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |