For a robust performance, Shipping costs planning in festive seasons is given the input data as free from trends, season-of-year effects etc. Seasonal forecasting for supply-chain planning with past few years of similar data impact shipping costs. Additionally, during a festive season of the year, unbiased and accurate prediction of shipment load plays a major role in bringing up sales. Time-series forecasting methods can be useful to remove traditional fluctuations due to gap in months-of-year of festivals. We describe exponential smoothing techniques and trend fitting methods and compare the predictive accuracy. The accuracy is compared using root-mean square error and median absolute deviation. The exponential smoothing shows changing behavior with increased data size and data item values. The data is compared with and without tuning the seasonal effects due to festive season.
For a robust performance, Shipping costs planning in festive seasons is given the input data as free from trends, season-of-year effects etc. Seasonal forecasting for supplychain planning with past few years of similar data impact shipping costs. Additionally, during a festive season of the year, unbiased and accurate prediction of shipment load plays a major role in bringing up sales. Time-series forecasting methods can be useful to remove traditional fluctuations due to gap in months-of-year of festivals. We describe exponential smoothing techniques and trend fitting methods and compare the predictive accuracy. The accuracy is compared using rootmean square error and median absolute deviation. The exponential smoothing shows changing behavior with increased data size and data item values. The data is compared with and without tuning the seasonal effects due to festive season.