6. Application of Constraints
We integrate real operational constraints, such as maximum production capacity or budgets, into the statistical forecast to obtain a realistic and immediately actionable execution plan.
5. Deployment in Production
We put the system into production, automating the flow of generating, updating, and distributing forecasts through integrated dashboards and reports.
4. Application of Constraints
We develop and validate the forecasting model, rigorously measuring its performance against an industry benchmark to ensure its superiority and operational reliability.
3. Variable Selection
We analyze the predictive power of each collected data, selecting only statistically significant variables and creating new features to maximize prediction accuracy.
2. Potential Exogenous Data Collection
We identify and integrate datasets of all potentially influential variables, such as marketing campaigns, events, and inventory data, to enrich the model's information context.
1. Historical Data Acquisition
We retrieve and clean the historical data of the KPI to be forecasted, such as turnover, forming the fundamental time series on which our predictive model will be built and trained.
Forecast
Olivia combines historical data and artificial intelligence to offer accurate forecasts and strategic advice. From forecasting for new openings to financial analysis, it helps companies and retailers optimize choices and resources with data-driven tools.
Proprietary algorithms are based on database and variable analysis, ensuring reliable forecasts. Integration with external data and collaboration with large companies demonstrate the adaptability of the solution to different business contexts, to support strategic decisions in food, retail, finance and much more.
Case study
Olivia worked with Mondadori to develop advanced predictive models, combining internal and external data. The goal was to improve strategic decisions through trend analysis and data-driven opportunity identification.
The project used machine learning and business intelligence to process complex variables, such as historical data, market indicators, and socioeconomic factors. This approach allowed for more accurate forecasts and personalized business strategies.
Thanks to the implemented models, Mondadori was able to reduce uncertainty in decisions, anticipate future scenarios and optimize resources. Data-driven analysis supported a more agile and informed decision-making process, strengthening competitiveness in the publishing market.
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