Intermitent Demand Forecast for spare parts usage.

The job is to forecast the usage for next month, 3 months, 6 months and one year for 6 different spare-parts code with intermittent demand.
The data is in a CSV format file containing the 1861 records of stock outputs for 6 different spare-parts between June 2018 and October 2022.
The detail of the records is as follows:
- CodeA 851 stock movements
- CodeB 825 stock movements
- CodeC 100 stock movements
- CodeD 50 stock movements
- CodeE 25 stock movements
- CodeF 10 stock movements
The forecast should be done for each spare-part code and not for the sum total of all spare-parts.
From the state of the art that I have studied I have seen that there are several methods such as Moving Average Forescats (MA), Exponential Smoothing (ES), Cronston (CR), Syntetos-Boylan (SBA), Teunter-Syntetos-Babai (TSB) that can be used.
The solution can also be a combination of several techniques since some of the codes have a very low number of records during the 4 years records.
I wish the prediction could be done from the file even if the number of spare-parts codes incresases.