AI DRIVEN SUPPLIER SELECTION AND TAM ALLOCATION
A machine learning (ML) module that can continuously learn from the market data and historical orders to dynamically recommend an optimum supplier portfolio to the manufacturer for a specific product. Using artificial intelligence, this analytical tool automates the supplier selection process based on evaluation of each supplier against a number of business features. The limitations of a manual selection of suppliers are substantially alleviated when each supplier is rigorously and automatically evaluated against a well-designed set of business features. For each supplier, the ML module generates a set of feature-specific scores for the business features used in evaluating the supplier. All scores are then combined to generate a supplier-specific final score for each supplier. The ML module uses the supplier scores to dynamically allocate Total Available Material (TAM) percentages to a pre-defined number of top-ranked suppliers to assist the manufacturer in the selection of best suppliers for the desired product.
1 . A method comprising:
receiving, by a computing system, a Material Requisition Plan (MRP) identifying a product to be procured by a manufacturer;
selecting, by the computing system, a list of suppliers of the product based on the MRP; and
using, by the computing system, a machine learning (ML) module to identify a pre-defined number of top-ranked suppliers from the list of suppliers based on evaluation of each supplier in the list against a plurality of business features.
2 . The method of claim 1 , wherein the pre-defined number is specified by a user.
3 . The method of claim 1 , wherein the plurality of business features includes:
a supply efficiency feature indicating how efficiently a supplier can supply the product;
a change flexibility feature indicating how flexible a supplier is as to quantity, quality, and delivery of the product;
a supplier strength feature indicating financial and innovation strength of a supplier;
a cost feature indicating overall cost competitiveness of a supplier;
a time feature indicating an average time a supplier takes to deliver the product; and
a risk feature indicating an overall product delivery risk associated with a supplier.
4 . The method of claim 1 , further comprising:
further receiving, by the computing system, a list of preferred suppliers of the product, wherein the list of preferred suppliers is a subset of the list of suppliers of the product, and
wherein the pre-defined number indicates a maximum number of suppliers that can be identified by the ML module from the list of preferred suppliers.
5 . The method of claim 1 , wherein selecting the list of suppliers comprises:
grouping, by the computing system, a plurality of suppliers associated with the manufacturer into a plurality of product-specific groups, wherein each group contains a roster of one or more suppliers from the plurality of suppliers that are qualified to supply a corresponding product to the manufacturer;
storing, by the computing system, the plurality of product-specific groups in a database;
identifying, by the computing system, the product specified in the MRP; and
accessing, by the computing system, the database to select one of the plurality of product-specific groups corresponding to the product specified in the MRP as the list of suppliers.
6 . The method of claim 1 , wherein using the ML module comprises:
evaluating, by the computing system, each supplier in the list against the plurality of business features using the ML module to generate a plurality of feature-specific scores for each supplier in the list;
for each supplier in the list, combining, by the computing system, all feature-specific scores of the supplier to generate a supplier-specific final score; and
ranking, by the computing system, each supplier in the list based on the supplier-specific final score.
7 . The method of claim 6 , further comprising:
using the ML module, by the computing system, to assign a supplier-specific Total Available Material (TAM) percentage for the MRP to each of the pre-defined number of top-ranked suppliers in the list.
8 . The method of claim 6 , further comprising:
using, by the computing system, the ML module to analyze each feature-specific score for each supplier in the list;
further using, by the computing system, the ML module to provide a plurality of feature-specific predictions for each supplier in the list based on the analysis of each feature-specific score for the corresponding supplier; and
further using, by the computing system, the ML module to assign a supplier-specific Total Available Material (TAM) percentage for the MRP to at least one supplier in the list based on the plurality of feature-specific predictions for the at least one supplier.
9 . The method of claim 1 , further comprising:
receiving, by the computing system, historical data containing a plurality of order-specific performance datasets, wherein each dataset in the plurality of performance datasets provides information about a corresponding past product order of the manufacturer, a portfolio of suppliers associated with the product order, and a profit associated with the product order; and
training, by the computing system, the ML module with the historical data to generate a trained version of the ML module;
wherein using the ML module includes:
using the trained version of the ML module to identify the pre-defined number of top-ranked suppliers.
10 . A computing system comprising:
a memory storing program instructions; and
a processing unit coupled to the memory and operable to execute the program instructions, which, when executed by the processing unit, cause the computing system to:
receive a Material Requisition Plan (MRP) identifying a product to be procured by a manufacturer;
select a list of suppliers of the product based on the MRP; and
use a machine learning (ML) module to identify a pre-defined number of top-ranked suppliers from the list of suppliers based on evaluation of each supplier in the list against a plurality of business features.
11 . The computing system of claim 10 , wherein the program instructions, upon execution by the processing unit, cause the computing system to:
further receive a list of preferred suppliers of the product, wherein the list of preferred suppliers is a subset of the list of suppliers of the product, and wherein the pre-defined number indicates a maximum number of suppliers that can be identified by the ML module from the list of preferred suppliers.
12 . The computing system of claim 10 , wherein the program instructions, upon execution by the processing unit, cause the computing system to:
group a plurality of suppliers associated with the manufacturer into a plurality of product-specific groups, wherein each group contains a roster of one or more suppliers from the plurality of suppliers that are qualified to supply a corresponding product to the manufacturer;
store the plurality of product-specific groups in a database;
identify the product specified in the MRP; and
access the database to select one of the plurality of product-specific groups corresponding to the product specified in the MRP as the list of suppliers.
13 . The computing system of claim 10 , wherein the program instructions, upon execution by the processing unit, cause the computing system to:
evaluate each supplier in the list against the plurality of business features using the ML module to generate a plurality of feature-specific scores for each supplier in the list;
for each supplier in the list, combine all feature-specific scores of the supplier to generate a supplier-specific final score; and
rank each supplier in the list based on the supplier-specific final score.
14 . The computing system of claim 13 , wherein the program instructions, upon execution by the processing unit, cause the computing system to:
use the ML module to assign a supplier-specific Total Available Material (TAM) percentage for the MRP to each of the pre-defined number of top-ranked suppliers in the list.
15 . The computing system of claim 10 , wherein the program instructions, upon execution by the processing unit, cause the computing system to:
receive historical data containing a plurality of order-specific performance datasets, wherein each dataset in the plurality of performance datasets provides information about a corresponding past product order of the manufacturer, a portfolio of suppliers associated with the product order, and a profit associated with the product order;
train the ML module with the historical data to generate a trained version of the ML module; and
use the trained version of the ML module to identify the pre-defined number of top-ranked suppliers.
16 . The computing system of claim 10 , wherein the plurality of business features includes:
a supply efficiency feature indicating how efficiently a supplier can supply the product;
a change flexibility feature indicating how flexible a supplier is as to quantity, quality, and delivery of the product;
a supplier strength feature indicating financial and innovation strength of a supplier;
a cost feature indicating overall cost competitiveness of a supplier;
a time feature indicating an average time a supplier takes to deliver the product; and
a risk feature indicating an overall product delivery risk associated with a supplier.
17 . A computer program product comprising a non-transitory computer-usable medium having computer-readable program code embodied therein, the computer-readable program code adapted to be executed by a computing system to implement a method comprising:
receiving a Material Requisition Plan (MRP) identifying a product to be procured by a manufacturer;
selecting a list of suppliers of the product based on the MRP; and
using a machine learning (ML) module to identify a pre-defined number of top-ranked suppliers from the list of suppliers based on evaluation of each supplier in the list against a plurality of business features.
18 . The computer program product of claim 17 , wherein the method further comprises:
further receiving a list of preferred suppliers of the product, wherein the list of preferred suppliers is a subset of the list of suppliers of the product, and wherein the pre-defined number indicates a maximum number of suppliers that can be identified by the ML module from the list of preferred suppliers.
19 . The computer program product of claim 17 , wherein the method further comprises:
evaluating each supplier in the list against the plurality of business features using the ML module to generate a plurality of feature-specific scores for each supplier in the list;
for each supplier in the list, combining all feature-specific scores of the supplier to generate a supplier-specific final score;
ranking each supplier in the list based on the supplier-specific final score; and
using the ML module to assign a supplier-specific Total Available Material (TAM) percentage for the MRP to each of the pre-defined number of top-ranked suppliers in the list.
20 . The computer program product of claim 17 , wherein the method further comprises:
receiving historical data containing a plurality of order-specific performance datasets, wherein each dataset in the plurality of performance datasets provides information about a corresponding past product order of the manufacturer, a portfolio of suppliers associated with the product order, and a profit associated with the product order;
training the ML module with the historical data to generate a trained version of the ML module; and
using the trained version of the ML module to identify the pre-defined number of top-ranked suppliers.