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Publicação
Automated time series demand forecast for luxury fashion online retail company
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| 2.67 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
Demand forecasting for a retail company in luxury fashion is a challenging process due to the
highly complex and demanding customer profile. As the company keep growing, more and more
partners are demanding the expected volume of orders for better operational capacity planning
and to justify the return of their investment. This project aims to create an automatic and
scalable forecasting process to ensure customer experience and partnership profitability. By
studying decomposition time series forecasting taking in consideration the customer behavior,
a machine learning process can be applied for parameters tuning depending on customer
clusters based on geolocation and marketing events. The proposed process has shown forecast
accuracy number up to 90% for non-sale season and 84% for sale season periods, reducing the
forecasting time in 88% versus the previous forecast process and increasing the partner
coverage from 20% to 100%. Acknowledging that this forecast process is a continuous learning
process, the foundation of a robust supply chain planning was created building trust in the
organization and adding value to the partners.
Descrição
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Palavras-chave
Decomposition Time Series Scalable Marketing Geolocation Trend Error Seasonality Cross Validation Parameter Tuning Machine Learning Continuous Improvement Clustering Forecast Accuracy