1. The application of machine learning offers visibility into the underlying factors that impact ... more 1. The application of machine learning offers visibility into the underlying factors that impact demand with an illustration of their significance. 2. Machine learning allows using, processing and delivering value out of categorical variables, which represent a significant chunk of the fashion industry’s data. 3. Customizing the demand forecasting process based on product characteristics deliver better results in comparison to a general forecasting approach. By: Majd Kharfan and Vicky Wing Kei Chan Advisor: Tugba Efendigil Topic Areas: Forecasting, Demand Planning, Machine Learning
Companies in the fashion industry are struggling with forecasting demand due to the short-selling... more 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.
1. The application of machine learning offers visibility into the underlying factors that impact ... more 1. The application of machine learning offers visibility into the underlying factors that impact demand with an illustration of their significance. 2. Machine learning allows using, processing and delivering value out of categorical variables, which represent a significant chunk of the fashion industry’s data. 3. Customizing the demand forecasting process based on product characteristics deliver better results in comparison to a general forecasting approach. By: Majd Kharfan and Vicky Wing Kei Chan Advisor: Tugba Efendigil Topic Areas: Forecasting, Demand Planning, Machine Learning
Companies in the fashion industry are struggling with forecasting demand due to the short-selling... more 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.
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