While doing automation and business consulting in Planning, it became clear that the time had come for advanced ML-enabled forecasting solutions to replace widely used statistical methods.
This led to the creation of the Augmento business unit which employs data scientists, data engineers and business process consultants.
We have a very specific work domain. We develop forecasting models using machine learning algorithms in the area of demand and promotion forecasting, as well as help adjust your business process to accommodate a ML-enabled forecast.
Machine Learning has opened up a new era of forecasting. It gears businesses up for management by exception when time and resources are focused on what makes a real difference.
Forecast based on a single factor – historical sales
Highly dependent on expertise and availability of responsible people
Process any available internal and external data sources to find the best correlations and build an optimal forecast
Managing by exception when it comes to forecasting
Less manual work:
Higher forecast accuracy => more reliable P&L => higher quality management decisions
More accurate long term forecast => more sustainable production facilities utilization => higher quality CapEx decisions
Higher customer satisfaction: better contract terms, less penalties, stronger negotiation power
Less out of stock:
‘Based on an internal assessment by Advanced
Why is an ML forecast better than a statistical one?
How do you prove that an ML-based forecast is better than the existing one?
Which are the key requirements for data to build and run an ML model?
What do you mean by “demand forecasting”?
What is a forecast horizon in an ML model?
Can a model be based only on a single data input apart from historical sales?
Which business teams will benefit from the solution?
Which IT tool do you use to create and maintain an ML model?
Will we need to roll-out an IT planning solution in order to manage our newly launched ML forecast?
Does an ML model have any limitations?
What do you mean by Manage by exception?
How long does it take to launch the model in full?
What is specific in your approach to one of the big consulting firms like Accenture, SAAS and others have?
What involvement from the business is required?