This data can also be taken into account in the demand forecasting process to further optimize replenishment strategies and inventory-allocation decisions.
With demand forecasting, there is no one-size-fits-all solution. At DSLab we've developed a forecasting solution tailored exclusively for perishable goods. It takes into account all the specificities of fresh food and associated demand patterns such as seasonality, price dynamics, storage conditions, hourly stock on hand, next delivery date, advertising, public holidays, and even weather. Bayesian methods of machine learning at the core of our solution align with business requirements and allow retailers to assess the potential influence of out-of-stocks and write-offs on key business metrics and find the balance between on-shelf availability and stock levels depending on business goals and KPIs.
To learn more about our approach and technologies behind DSLab demand forecasting for fresh food, read the article Fresh food forecasting. Challenges of demand forecasting for fresh and ultra-fresh assortments