Accelerated intelligent enterprise including timely vendor spend analytics
An intelligent enterprise system and method is disclosed. The system and method include an input/output (IO) interface configured to receive information related to accounting information, a processor interconnected with a memory configured to store received information from the IO interface, and analyze the information to provide timely vendor spend analytics, and a display device configured to display the provided timely vendor spend analytics.
1 . An intelligent enterprise system in a company, said system comprising:
an input/output (IO) interface configured to receive information related to accounting information;
a processor interconnected with a memory configured to store received information from the IO interface, perform a matching algorithm to find matching vendors within the information and analyze the information to provide timely vendor spend analytics, the timely vendor spend analytics configured from the spending records of the company and identifying a plurality of spend categories with associated direction and size of the spend per category; and
a display device configured to display the provided timely vendor spend analytics.
2 . The system of claim 1 , wherein the received information includes spending by category.
3 . The system of claim 1 , wherein the received information includes spending by vendor.
4 . The system of claim 1 , wherein the received information includes spending over a time period.
5 . The system of claim 4 , wherein the time period is a selected fiscal year.
6 . The system of claim 1 , wherein performing the matching algorithm includes using nearest neighbor's propensity scoring to find exact matches.
7 . The system of claim 1 , wherein performing the matching algorithm includes fuzzy matching algorithm to identify matches.
8 . The system of claim 1 , wherein performing the matching algorithm includes at least two fuzzy matching algorithms to identify matches.
9 . The system of claim 1 , wherein the matching is based on a cutoff score.
10 . The system of claim 1 , wherein parent company information is identified for at least one vendor.
11 . The system of claim 1 , wherein a textual tokenization is performed.
12 . The system of claim 1 , wherein an OEM is learned based on the received information.
13 . The system of claim 1 , wherein a taxonomy category is determined based on the received information.
14 . The system of claim 13 , wherein at least one of a hybrid model combining a Support Vector Machine (SVM), a Random Forest (RF), a neural network (NN), and an expert system (ES) is utilized.
15 . The system of claim 1 , wherein the display device is configured to select vendor true spend, peer comparison, market insight and prescriptive insight aspects.
16 . A method to provide vendor spend analytics in a company, said method comprising:
receiving information related to accounting information via an input/output (IO) interface;
storing the received information via a processor interconnected with a memory;
performing a matching algorithm to find matching vendors with the information;
analyzing the information to provide timely vendor spend analytics, the timely vendor spend analytics configured from the spending records of the company and identifying a plurality of spend categories with associated direction and size of the spend per category; and
displaying via a display device the provided timely vendor spend analytics.
17 . The method of claim 16 , wherein the received information includes at least one of spending by category and spending by vendor.
18 . The method of claim 16 , wherein the matching algorithm includes at least one of processing by the processor using nearest neighbor's propensity scoring to find exact matches and processing by the processor using fuzzy matching algorithm to identify matches.
19 . The method of claim 16 , wherein an OEM is learned based on the received information and a taxonomy category is determined based on the received information.
20 . The method of claim 16 , wherein at least one of a hybrid model combining a Support Vector Machine (SVM), a Random Forest (RF), a neural network (NN), and an expert system (ES) is utilized in the analyzing.