Customer
One of the largest international logistics companies.
Challenge
Implement a model for forecasting the warehouse stock volume in all departments of the logistics company to increase the accuracy of transport resources planning.
Project Results
ENFINT has developed a set of time series models using various approaches (STL + SARIMA, ARIMA+Fourier, TBATS, Prophet, LSTM). The SARIMA model has shown the best results; median weighted percentage error is ~28%. The accuracy of the developed planning algorithm has allowed the planning accuracy to be increased by more than 5% compared to the previously used algorithm.
Business Value
Best performing model (SARIMA) model has shown the median weighted percentage error around ~28%. This has allowed to increase planning accuracy by more than 5% compared to the algorithm used previously.
Tech Stack
Python (Pandas, TensorFlow).
One of the largest international logistics companies.
Challenge
Implement a model for forecasting the warehouse stock volume in all departments of the logistics company to increase the accuracy of transport resources planning.
Project Results
ENFINT has developed a set of time series models using various approaches (STL + SARIMA, ARIMA+Fourier, TBATS, Prophet, LSTM). The SARIMA model has shown the best results; median weighted percentage error is ~28%. The accuracy of the developed planning algorithm has allowed the planning accuracy to be increased by more than 5% compared to the previously used algorithm.
Business Value
Best performing model (SARIMA) model has shown the median weighted percentage error around ~28%. This has allowed to increase planning accuracy by more than 5% compared to the algorithm used previously.
Tech Stack
Python (Pandas, TensorFlow).