AI-Supply-Chain
Repository for common AI use cases in supply chain, procurement
v1.0 - Added project to normalize legal entity names of vendors/suppliers. For example,
[DELL FINANCIAL SERVICES, DELL MARKETING LP, DELL NV, DELLEMC, DMI DELL CORP BUS] Becomes DELL
[ORACLE, ORACLE AMERICA INC, ORACLE CORPORATION, ORACLE FINANCIAL SERVICES, ORACLE USA INC] becomes ORACLE
You can utilize your spend data or simply choose a custom vendor dataset to perform vendor name normalization.
Requirements
fuzzywuzzy library (For fuzzy matching of string) - https://pypi.org/project/fuzzywuzzy/ levenshtein library (For faster computation of Levenshtein Distance) - https://pypi.org/project/python-Levenshtein/
Datasets
a) Goverment of California's 2012-2015 Purchase Orders - https://data.ca.gov/dataset/purchase-order-data/resource/bb82edc5-9c78-44e2-8947-68ece26197c5
b) Custom dataset - Refer sample vendor_data.csv available
All input to the program requires data as per format prescribed in above two links. Additionally, you can customize the settings.py file to accomodate data in your own format.