Long-term demand forecasting applied to a retail company using Prophet
DOI:
https://doi.org/10.47456/bjpe.v10i3.45146Keywords:
Supply Chain Management, Demand Forecast, Time Series, ProphetAbstract
The spread of data-driven decision making has been driven by the abundance of information and the increase in computer processing capacity. To support this decision-making process, it is feasible to extract knowledge and make forecasts using Data Science. Within Supply Chain Management, a common challenge is to forecast demand using historical data. Forecasting a company's sales volume is complex. Overestimating demand leads to wasted stock, while underestimating causes stock-outs. In this case study, long-term demand forecasting (30 weeks) will be carried out for two different sales channels of a company in the industry and retail segment of the Cosmetic, Fragrance and Toiletry market. The Prophet algorithm was used. After implementing the methodology, the results showed that week 30 had a WAPE of 4% and 5% for the Store and Direct Sales channels, respectively. When analyzing the error in the thirtieth week for the three most marketed product categories, the highest error recorded was 9.36%. This result suggests that the methodology employed achieved satisfactory performance.
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