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