SCENARIO
In recent years, technological evolution has led companies to become more and more Data-Driven and to invest part of their resources for the implementation of complex architectures for data analysis.
Thanks to new technologies, faster, scalable and performing, the use of machine learning algorithms has become an important means for achieving company objectives for both medium and large companies.
However, there are many challenges to be faced for the implementation and real production of an ML model in general and of a Forecasting model in particular.
Traditional sales forecasts are often inaccurate, leading to missed sales targets and budget deficits. Collaboration between business stakeholders is required to use new features offered by technology such as predictive analytics based on artificial intelligence to improve the accuracy of sales forecasts.
To date, over 40% of organizations continue to rely on intuition rather than data analysis, and over 80% do not use analytical processes that leverage automation, AI and ML. With consequent inaccurate forecasts, sales pipeline and achievement of quotas.
BBI for FORECASTING
Thanks to a heterogeneous team with statistical and IT expertise, Blue BI has developed a solution for Forecasting, which thanks to a hybrid, statistical and ML approach, generates reliable forecasts based on data that differentiate the evolution of companies on the market, overcoming the risk of the analytical divide.
Forecast process
The BBI solution for Forecasting responds to these needs, providing a solution that follows a complete forecasting process through the following steps:
- Detection of the demand through all possible collection points
- Accurate predictions for continuous series also considering random events and factors
- Forecasts of non-continuous series
- Handling of exceptions
- Monitoring of performance and deviations from plans
Strengths
The BBI solution for Forecasting allows:
- Automatic generation of forecasts
- Forecasts made at each hierarchical level
- Ability to work at a single SKU level or appropriate aggregations
- Reconciliation of forecasts (Top-Down, Middle-Out, Bottom-Up)
- GUI to generate forecasts without the use of code, visualizations and captivating interactions, obviously with the possibility of integrated overwriting
- Inclusion of external variables and events
- Ability to simulate, demand sensing and what-if scenario
- Interactive analysis and exploration of time series
- Performance, high computing
- A platform that orchestrates the entire analysis life cycle
- A fully digitized process
- Analytical approach, full spectrum AI and ML usage capabilities
- Integration with management processes and related platforms
- Integration with Open Source: R, Spark, Python, etc.
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