Comparing
Now that our forecasting is complete, we can compare the forecasts with IBP and get the difference in accuracy and errors.
Now that our forecasting is complete, we can compare the forecasts with IBP and get the difference in accuracy and errors.
The dashboard for presenting the results of this project was made programmatically using the Tremor framework of React Javascript. The web app was made using the NextJS framework and TypeScript.
Another page was added to the Sales Forecasting dashboard for Smart Supply.
Delivery data
The data required for this was the forecast data of both my forecasts and IBP forecasts from earlier, and the current inventory data which was fetched with the SQL query:
Choosing a good evaluation metric for our problem is important. How accurate our estimate is depends on what metric we are using, and depending on the situation, different metrics can be the most suitable.
After cleaning and preprocessing the data, we created the relevant and important features for forecasting.
Now that we have the cleaned dataset with the appropriate features, we can finally perform forecasting. This forecasting is just regression, with the exception that train data must be sequential upto a date and test data must be the sequence following that.
This project was about optimally managing the inventory of various products, particularly Crop Protection (Insecticides, Fungicides, Herbicides, etc).
This was a project started as an experiment to see whether using covariates like weather and economic indicators provide better forecasts than simply using historical data.
Now that demand has been forecasted and is better than IBP forecasts for a significant number of products in many countries/regions, I decided to make use of it to further recommend how inventory should be managed.
There are several steps followed for preprocessing the data.
For every plant, its forecast and IBP forecast are summed for the given number of months and compared to the total inventory.
When the forecast accuracy got from our method is compared to IBP forecasts, we get some brands for which we have significantly higher accuracy/lower errors. We would like to improve the accuracy even further for these brands.