Accelerate demand planning and analytics

Leveraging trained Artificial Intelligence (AI) modeling to streamline HR annual operations planning based on net sales forecasts

Time series-based machine learning (ML)

Modeling to optimize staffing and reduce operations

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The need

Complex financial planning and inaccurate staffing forecasts

To optimize operational costs incurred at a store level, one of the largest Fortune 100 retailers required a net sales per annum forecasting solution with five-year historical data available at the store-department level. Accuracy in forecasts defined as Mean Absolute Percentage Error (MAPE) < 5% & Proximity < 2% was crucial to the success of gauging human resource and operational needs for each store such as the number of check-out counters or in-store staff required in the stores at a given point in time. Findings through data would be analyzed and incorporated in the Annual Operating Plan for the company.  

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The Solution

Predictive machine learning modeling

A team of certified data scientists and qualified machine learning engineers came together to employ time series-based machine learning models to address the need at hand. Sales data from the past 5 years was used to train, evaluate and improve the models to achieve an accuracy of above 95% in the forecasts. Anomalies in data such as missing information and holiday-driven outliers were taken into account through meticulous feature engineering and exploratory data analysis. To ensure sustainable growth prospects, modeling was also performed to allow for scalability in the future.

the solution image

The Solution

Predictive machine learning modeling

A team of certified data scientists and qualified machine learning engineers came together to employ time series-based machine learning models to address the need at hand. Sales data from the past 5 years was used to train, evaluate and improve the models to achieve an accuracy of above 95% in the forecasts. Anomalies in data such as missing information and holiday-driven outliers were taken into account through meticulous feature engineering and exploratory data analysis. To ensure sustainable growth prospects, modeling was also performed to allow for scalability in the future.

The outcome

Net sales forecasting to unlock cost-efficiency

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Sue McMohon