Making forecasting self-learning and adaptive -- Pilot forecasting rack
Shaun D'Souza Dheeraj Shah Amareshwar Allati Parikshit Soni
Retail sales and price projections are typically based on time series
forecasting. For some product categories, the accuracy of demand forecasts
achieved is low, negatively impacting inventory, transport, and replenishment
planning. This paper presents our findings based on a proactive pilot exercise
to explore ways to help retailers to improve forecast accuracy for such product
categories.
  We evaluated opportunities for algorithmic interventions to improve forecast
accuracy based on a sample product category, Knitwear. The Knitwear product
category has a current demand forecast accuracy from non-AI models in the range
of 60%. We explored how to improve the forecast accuracy using a rack approach.
To generate forecasts, our decision model dynamically selects the best
algorithm from an algorithm rack based on performance for a given state and
context. Outcomes from our AI/ML forecasting model built using advanced feature
engineering show an increase in the accuracy of demand forecast for Knitwear
product category by 20%, taking the overall accuracy to 80%. Because our rack
comprises algorithms that cater to a range of customer data sets, the
forecasting model can be easily tailored for specific customer contexts.