Abstract:
"
Retail Modular furniture business segment of ABC Company has been experiencing excess
stock, out of stock, material shortages, production inefficiencies which are typical supply
chain issues mainly caused by inadequate accuracy level of forecasting. Current forecasting
models of Modular furniture sales is dominated by traditional statistical methods like
ARIMA combined with judgmental techniques. In this research, to improve the existing
forecasting methods of ABC Company, advanced ML techniques-based forecasting
models are developed, which can consume external variables. In addition to the sales
quantity of historical time steps, macro environment variables and marketing mix variables
which influence sales are considered as external variables. As potential advanced ML
techniques, ANN, DNN, Vanilla LSTM and Stacked LSTM are used to develop forecasting
models. Out of those four models, for each furniture product, best model is selected by
comparing performances based on MSE and MAE which are two popular error metrics in
advanced ML space. As sample of Modular furniture, six top selling wardrobes are
subjected to development of new forecasting models. Those selected advanced ML
techniques respond differently based on the product type but each of them performed better
than ARIMA model which is taken as the industry benchmark. Finally, advanced ML
techniques-based forecasting models which consume external variables are recommended
to ABC Company to carry out future forecasting of the retail Modular furniture sales as it
may help to smoothen the supply chain operations."
