Long-term demand forecasting applied to a retail company using Prophet - Sousa, Ana Clara Chaves; Barbosa, Yuri de Almeida Malheiros; Silva Filho, Telmo de Menezes e; Rêgo, Thaís Gaudencio do (Brazilian Journal of Production Engineering, 2024)
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.
Keywords: Supply Chain Management; Demand Forecast; Time Series; Prophet