July 23, 2026
A leading global energy management and automation company wanted to modernize its sales forecasting capabilities and improve the accuracy of business planning across regions, business units, and activities. The organization relied heavily on historical sales data and required a scalable, data-driven solution that could incorporate additional business signals and provide a longer forecasting horizon.
THE CHALLENGE
The company’s existing sales forecasting system relied primarily on historical sales data and had little linkage to other relevant business datasets, limiting its ability to capture broader demand drivers. Forecasting was performed using only three machine learning models with fixed weightages, which reduced adaptability and constrained forecast accuracy as market conditions changed. Data quality issues and the lack of granular forecasts across regions, business units, and activities further impacted planning and decision-making. In addition, existing dashboards offered limited KPIs and interactivity, making it difficult for stakeholders to gain deeper insights into sales performance and trends.
To address these challenges, the organization needed an analytics partner to assess the current forecasting environment, identify gaps, integrate additional data sources, develop more accurate forecasting models, and build enhanced dashboards with detailed KPIs and actionable insights.
THE SOLUTION
The Prescience team assessed the existing forecasting system and identified opportunities to combine multiple datasets with sales data. An extensive exploratory data analysis (EDA) was conducted to analyze correlations between sales and additional datasets, and key data quality issues were identified and addressed.
A dual forecasting approach was implemented, combining sales-only forecasting with integrated-data forecasting. Multiple machine learning models, including ARIMA, SARIMAX, XGBoost, Random Forest, and Prophet, were trained and evaluated across different data combinations. To further improve performance, the team introduced a dynamic weighting mechanism that continuously adjusted model contributions over time instead of relying on static weights.
The solution also included opportunity prediction models (win/loss classification) and comprehensive Tableau dashboards with enhanced KPIs, interactivity, and drill-down capabilities. The new framework enabled granular forecasting across multiple business dimensions and extended the forecasting horizon to 15 months.
Technologies used:
- Python
- ARIMA
- SARIMAX
- XGBoost
- Random Forest
- Prophet
- Tableau
THE IMPACT
The new forecasting solution significantly improved forecast accuracy and adaptability by leveraging multiple machine learning models with dynamically weighted combinations. Stakeholders gained granular visibility into sales performance through interactive dashboards and enhanced KPIs, enabling more informed planning and decision-making. The scalable framework also supports the integration of new data sources and provides a 15-month forecasting horizon to strengthen budgeting and resource planning
