Nisum developed a framework utilizing an AI-driven approach to improve forecast accuracy.
The client has seen improvement in forecast accuracy, resulting in:
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25%
reduction in execution time for forecast processing
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25%
increase in accuracy for estimated delivery date (EDD)
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Business Challenge
A Fortune 500 premium goods retailer had a manual process for forecasting orders which provided an inaccurate quantity of orders, leading to:
- Poor resource planning due to inaccurate forecasts, leading to:
- The inability to meet peak holiday capacity
- Inaccurate order delivery dates due to unavailable seasonal forecasts
Solution
Nisum developed a framework for effective resource optimization using KPIs such as Site Availability, Service Violations, and Shift Coverage. Nisum also used an AI-driven approach to handle data; a high-accuracy iterative model was developed, tested, and fitted, leading to:
- Faster forecast processing with improved downtime tracking and enhanced availability across the ecosystem by consolidating historical data as well as additional information for effective forecasting
- Using Exploratory Data Analysis (EDA) for missing values and outlier treatment
- Improved EDD with improved forecasting of resources using feature engineering. They derived features with impact on response variables to create trend, seasonal, and cyclic variations of forecasts for optimal simulations
- Increased forecast accuracy by tracking forecast accuracy and model performance periodically using accuracy metrics such as MAPE, MSE, RMSE, and R-Square
Feel free to contact us for more information on how Nisum can drive results for your company.