- switched '??_Parts.py' to 'Parts _ ??.py'
- Parts_Demand.py -> changed 'LeadTime' to 'TopLevel_LeadTime' Parts_Demand.py -> changed 'Lead' to 'Item_Lead'
- CatalogTracker.py < Catalog/PTS Equip Standarsization Tracker.xlsx
- DataFrameBOMs.py < Inventory Lead Time/BOMs/
- CostedMaterials.py < Costing/ZCOST_ACC.xlsx
- P90LeadTimes.py < Inventory Lead Time/Plant 90 Leadtimes - 10.24.19.csv
- P90MFGTimes.py < Catalog/P90MFGTimes.csv
- PTSDemand.py < Inventory Lead Time/GlenwoodProduction_clean.xlsx
- Parts_???.py - are function files
- Results_____.py - output files to excel
- % Analysis %.py are end result files
- use get_final(), get_partslist(), and get_demand() as base data for the analysis
- get_final() yields 3 DataFrames:
- final[0] - returns the parts that appear in every bom for the product line
- final[1] - returns the parts that appear in at least X% of boms, specified or default of 75%
- final[2] - returns all parts that appear in the product line
- get_partslist() yields 2 data frames
- parts[0] or df_cost - returns the estimated cost of the parts that need to be inventoried at given lead times specified in weeks
- parts[1] or df_list - returns the part numbers that need to be inventoried at the given lead times, specified in weeks
- get_demand() - yields a DataFrame containing historical demand for a given product line
- get_final() yields 3 DataFrames:
There is a jupyter file Inventory Analysis 1.0.ipynb that starts to outline the data and looks at some graphs.