ML forecastingNavigation by topic
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.
- Machined Learning-enabled demand and promotion (volume and budget) forecasting models
- Technical support of the model after go-live (business as usual)
- Business process adjustment in line with the new technology
- Forecast accuracy growth
- Forecast bias decrease
- Less resource intensive forecasting
- Multivariate Machine Learning algorithms:
- Promo investments
- Baseline sales
A NEW ERA OF FORECASTING
Forecast based on a single factor – historical sales
Highly dependent on expertise and availability of responsible people
Machine Learning algorithms
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
FORECAST BASED ON BIG DATA
Other data to feed
CORE STAGES OF LAUNCHING AN ML FORECAST
discovery & preparation~ 4-7 weeks
building~ 8-15 weeks
and go live~ 3-5 weeks
HOW TO FURTHER WORK WITH THE ML FORECAST
ACHIEVABLE RESULTS AFTER LAUNCH
CORE STAGES OF LAUNCHING THE MODEL
Data & Process discovery
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ACHIEVABLE ROI OF THE PROJECT
If Forecast accuracy grows by + 10%
Less manual work:
- Less time to collect data
- Less errors and time spent to correct them
- Less IT involvement
- Less time for forecasting
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:
- Higher service level + 3%’
- Higher revenue +1%’ (a conservative estimation for highly efficient business)
- Optimized stocks – 25%’
- Less write-offs- 17%’
- Higher OEE (by up to 11% due to a more forecastable production schedule’)
- More sustainable and efficient labor management
‘Based on an internal assessment by Advanced
CUSTOMER SUCCESS STORIES
FREQUENTLY ASKED QUESTIONS
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”?
-Primary sales forecast (shipments)
-Baseline sales forecast
-Promo sales forecast
-Secondary sales forecast (off shelf)
-Promo investments forecast and optimization
-New products sales forecast
-Optimal shelf price forecast
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?
-Teams in charge of demand planning and sales forecasting: Demand planning in Supply chain and Sales departments
-Teams in charge of promo sales forecasting and promo investments management: Trade and customer marketing, Sales Finance
-Teams in charge of pricing: Finance, Business intelligence, Trade and customer marketing
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?
–No touch – this is a forecast (e.g. a sales forecast), which does not require review by forecast specialists and is handed over directly to supply planning/production or sales teams depending on the forecast type
–Light touch – a forecast which is recommended to be double-checked by experts
Heavy touch – a forecast which has to be created manually. For example, when it comes to a sales forecast, there are products which have no historical data like innovations and special promo packs.
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?