Are you using the best technology available today to forecast demand?
Whether you’re working with dozens or millions of SKUs, successfully predicting future demand in Retail is challenging. Leading retailers are adopting AI/ML to build demand forecasts that outperform traditional methods. In this on-demand webinar OneClick.ai Co-founder and CTO Ning Jiang presents 10 demand forecasting challenges in retail and how AI, specifically Automated Deep Learning (AutoDL), solves them. He then provides a short demonstration of how to build automated forecasting models using retail time series data with OneClick.ai.
Top Ten Challenges: Retail Demand Forecasting
- Public holidays and planned sales events may affect sales before the events begin.
- The aftermath of a sales event may include lowered sales because demand was over-fulfilled.
- Sales cycles may affect the order size as salespeople push to meet their quota before the end of the cycle.
- Sales may be deferred to the next cycle if sales quota has been met in the current cycle.
- Competitors’ sales events may impact the sales of relevant products.
- Promotions may impact sales of products similar to those on sale.
- Products might have similarities based on historical sales patterns & intrinsic characteristics.
- Determining if a change in sales volume is due to a seasonal pattern or an anomaly is difficult without historical data.
- A new product may be going through similar growth patterns as related products, but exact sales volume may vary due to different launch times, intrinsic features, etc.
- The sales of one product have been increasing, but predicting how long the trend will last is difficult.
Ning Jiang | Co-founder and CTO @ OneClick.ai | LinkedIn