Case Study: Optimising Inventory for an online Retailer
A fast growing online retailer with hundreds of suppliers and thousands of products, trying to figure out what to order and when.
Shelf Planner’s Automated Replenishment for WooCommerce
A Danish cosmetics and organic beauty products retailer.
Generate Order Proposals and automate replenishment for hundreds of products in the midst of Covid19.
A Danish retailer, specialized in organic beauty products, that was already experiencing rapid growth found it accentuated by the COVID-19 pandemic of 2020.
They believed advanced demand forecasting would be of great benefit to their operations and solve two core problems: Lengthy lead times and volatile demand. This combination had proved to be a challenge for traditional forecasting methods, leading to a large number of products that were overstocked with more than 26 weeks of supply.
Shelf Planner was engaged to connect the client’s data into the forecasting engine in the Shelf Planner AI platform, with the goal of forecasting sales up to 6 months in advance. Starting with a small sample of SKUs to prove the technology, the Shelf Planner team worked to include covariate data such as COVID-19 cases, mobility data, government actions such as shelter-in-place orders, and online traffic.
Once we became familiar with the client’s product range, it became clear we needed to split products into 2 buckets: those with short lead times and those with longer lead times, as different forecasting approaches and data streams were relevant for each bucket.
Sales Forecasting is a particularly powerful tool for ecommerce businesses because the space allows for access to a plethora of data and data streams – fantastic for some of the data hungry algorithms in the Shelf Planner AI Forecasting Engine.
In the case of this client, a combination of quality data, powerful technology, and a strong project team produced the accuracy results that the client needed to succeed.