Powering Your Digital Supply Chain: Forecasting Returns – Part 4
Have you ever ordered something online and then decided to return it? You’re not alone. While returns for in-store purchases average in the single digits, returns from e-commerce sales average 30%, with even higher rates in certain categories. This creates an interesting problem for both retailers and manufacturers that sell online: in addition to predicting a customer’s original order, manufacturers and retailers need to be able to forecast expected returns.
Why has this been a difficult problem to solve?
Streamlined returns management starts with an advanced ability to forecast returns, which is not an easy problem to solve. Forecasting returns effectively is a two-step process: first, you must predict the sale itself, and next, you must predict that the sale will be reversed in the near future. The second event cannot occur if the first doesn’t take place. Given the proliferation of new product introductions, ever-changing customer expectations and differences across markets and channels, this can easily become a mind-boggling problem to solve.