Data Cleansing and Outlier Correction

The SCM planning solutions market is getting pretty crowded these days - almost everyone has a solution on the cloud for Demand, Supply, S&OP and analytics.

As we examined the different solutions out there, we realized many of the solutions are quickly cooked up with a few basic forecast models & a replenishment planning module.

One of the key criteria is the ability of the Demand Forecasting Solutions to do automatic cleansing of the data including outlier correction.

A few tools lack even the basic ability to correct outliers that are based on ex-post forecast. With the advent of machine learning and AI, now we have advanced capabilities for the engine to scan, detect and correct outliers with very little interventions by the planner.

And the tools need to distinguish between outliers and seasonal dips and peaks - otherwise, important information may be lost.

Do you adopt best practices in data cleansing and management?