What It Does:
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Accurately forecast demands of products and SKUs at granular and aggregated levels of markets (for ex, town, sales areas, district, state) and time periods (for ex, hour, day, week, month, quarter, year)
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Predict point forecasts and confidence intervals of forecasts
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Dynamically revise forecasts based on updated data (for ex, daily, weekly, monthly)
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Equip demand planning, sales and marketing, and suppply chain teams to take analytical forecasts and collaboratively decide final demand plans.
Analytical Models and Techniques
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Machine Learning models: Linear Regression, Polynomial Regression, Logistic Regression and Classification, Random Forest, XG Boost, and others
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Statistical Time-Series models: Moving Average, Simple Exponential Smoothing, Double Exponential Smoothing with Trend (Holt), Triple Exponential Smoothing with Trend and Seasonality (Holt-Winters), Exponential Smoothing for Intermittent Demand Products (Croston), ARIMA, SARIMA
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Algorithms for rigorous for training, validation and selection of models
Tools and Technologies
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Analytical models with Python
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Input data interface with Java
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Dashboards for reports and collaborative platform with Power BI and Java
Case Studies
Our Clients
