While doing planning automation, it became clear that the time had come for advanced AI-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 the most progressive machine learning algorithms. These include demand forecasting, replenishment, innovations planning, promotions planning, as well as assistance in adjusting your current planning and S&OP processes 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.
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
How do you prove that a ML-based forecast is better than the existing one?
Which are the key requirements for data to build and run the model?
Does the model create a total forecast or a baseline forecast?
What is the forecast horizon which comes out of the model?
Can the model be based only on a single causal?
What do you mean by “manage by exception?”
Does the model have any limitations?
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 BCG, SAAS have?
What involvement from the business you need?