Abstract:
A leading entity in the food manufacturing industry in Sri Lanka, encounters
substantial challenges in inventory management and forecasting. The reliance on basic internal
forecasting methods, due to the high cost of Salesforce’s forecasting extension, has led to
inaccuracies in inventory forecasting. These inaccuracies result in oversupply, which ties up
capital and increases storage costs, and undersupply, which causes stockouts, lost sales, and
disrupted cash flow. These issues impact the financial department, inventory management team,
and overall business operations, especially during seasonal periods.
The aim of this project was to develop a more accurate and efficient forecasting model to improve
inventory management, reduce financial strain, and enhance operational efficiency at this
Company. The methodology involved incorporating external factors into a time series model,
including Sri Lankan coconut prices, coconut oil selling prices and the Colombo Consumer Price
Index.
Both a basic LSTM model and an SARIMAX model with feature engineering were developed to
determine the more accurate model. The forecasting models were implemented using Python and
trained on pre-processed historic data to predict coconut sales quantities for 3 months. The
performance of both models was assessed using evaluation metrics, and the LSTM model was
selected as the more accurate model, with a Mean Absolute Error (MAE) of 18.70, a Root Mean
Squared Error (RMSE) of 23.99, and an R² Score of 0.56. The final model output was integrated
into a Microsoft Power BI dashboard, providing insights to support production management
decision-making and detailing sales performance.
The final results indicate that incorporating a systematic forecasting model with external
influencing factors outperforms traditional methods. The conclusions drawn from this project
suggest that advanced forecasting models can effectively address inventory management
challenges, reduce financial strain, and enhance overall operational efficiency at this Company.
These observations highlight the importance of accurate sales forecasting in optimizing inventory
levels and supporting strategic business decisions.
