A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches
Abstract Companies in the fashion industry are struggling with forecasting demand due to the short-selling season, long lead times between the operations, huge product variety and ambiguity of demand information. The forecasting process is becoming more complicated by virtue of evolvi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/136919 |
| _version_ | 1826206868998979584 |
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| author | Kharfan, Majd Chan, Vicky W. K. Firdolas Efendigil, Tugba |
| author_facet | Kharfan, Majd Chan, Vicky W. K. Firdolas Efendigil, Tugba |
| author_sort | Kharfan, Majd |
| collection | MIT |
| description | Abstract
Companies in the fashion industry are struggling with forecasting demand due to the short-selling season, long lead times between the operations, huge product variety and ambiguity of demand information. The forecasting process is becoming more complicated by virtue of evolving retail technology trends. Demand volatility and speed are highly affected by e-commerce strategies as well as social media usage regards to varying customer preferences, short product lifecycles, obsolescence of the retail calendar, and lack of information for newly launched seasonal items. Consumers have become more demanding and less predictable in their purchasing behavior that expects high quality, guaranteed availability and fast delivery. Meeting high expectations of customers’ initiates with proper demand management. This study focuses on demand prediction with a data-driven perspective by both leveraging machine learning techniques and identifying significant predictor variables to help fashion retailers achieve better forecast accuracy. Prediction results obtained were compared to present the benefits of machine learning approaches. The proposed approach was applied by a leading fashion retail company to forecast the demand of newly launched seasonal products without historical data. |
| first_indexed | 2024-09-23T13:39:50Z |
| format | Article |
| id | mit-1721.1/136919 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:39:50Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1369192021-11-02T03:19:18Z A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches Kharfan, Majd Chan, Vicky W. K. Firdolas Efendigil, Tugba Abstract Companies in the fashion industry are struggling with forecasting demand due to the short-selling season, long lead times between the operations, huge product variety and ambiguity of demand information. The forecasting process is becoming more complicated by virtue of evolving retail technology trends. Demand volatility and speed are highly affected by e-commerce strategies as well as social media usage regards to varying customer preferences, short product lifecycles, obsolescence of the retail calendar, and lack of information for newly launched seasonal items. Consumers have become more demanding and less predictable in their purchasing behavior that expects high quality, guaranteed availability and fast delivery. Meeting high expectations of customers’ initiates with proper demand management. This study focuses on demand prediction with a data-driven perspective by both leveraging machine learning techniques and identifying significant predictor variables to help fashion retailers achieve better forecast accuracy. Prediction results obtained were compared to present the benefits of machine learning approaches. The proposed approach was applied by a leading fashion retail company to forecast the demand of newly launched seasonal products without historical data. 2021-11-01T14:34:11Z 2021-11-01T14:34:11Z 2020-06-23 2021-07-21T03:26:53Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136919 en https://doi.org/10.1007/s10479-020-03666-w Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Science+Business Media, LLC, part of Springer Nature application/pdf Springer US Springer US |
| spellingShingle | Kharfan, Majd Chan, Vicky W. K. Firdolas Efendigil, Tugba A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches |
| title | A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches |
| title_full | A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches |
| title_fullStr | A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches |
| title_full_unstemmed | A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches |
| title_short | A data-driven forecasting approach for newly launched seasonal products by leveraging machine-learning approaches |
| title_sort | data driven forecasting approach for newly launched seasonal products by leveraging machine learning approaches |
| url | https://hdl.handle.net/1721.1/136919 |
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