System for automated part-number mapping

• US 7,512,553 B2
• Filed: 12/05/2003
• Issued: 03/31/2009
• Est. Priority Date: 12/05/2003
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Patek, Michael D., Chidiac, Ghassan, Singh, Moninder, Wong, Yuk Wah, White, Fabio Dennis, Verma, Sudhir, Kalagnanam, Jayant R.
• Primary Examiner(s): Zeender; F.
• Assistant Examiner(s): BUCHANAN, CHRISTOPHER R
• Application Number:
  US10/727,978
• Publication Number:
  US 20050125311A1
• Time in Patent Office: 1,943 Days
• Field of Search: None
• US Class Current: 705/28
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 10/0875 Itemisation or classification of parts, supplies or services, e.g. bill of materials
• CPC Class Codes: G06Q 20/203 Inventory monitoring

Abstract

Automated mapping of part numbers associated with parts in a bill of materials (BOM) to internal part numbers assigned by a BOM receiver. Historical data on bills of materials, known part number mappings, and parametric data describing the parts associated with bills of materials re received. Prediction models, for predicting BOM receiver internal part numbers, are learned from the historical BOM data, known part number mappings data and parametric data. The learned models are applied to unmapped BOM originator part numbers to assign BOM receiver internal part numbers.

Claims

1. A system for automated mapping of part numbers associated with parts in bills of materials (BOMs), submitted by a plurality of BOM originators to a BOM receiver, to the BOM receiver'"'"'s internal part numbers, the system comprising a computer having a processor, a memory operatively coupled to the processor, wherein the memory stores instructions that when executed by the processor perform operations comprising:

receiving historical BOM data describing BOMs received by the BOM receiver, from a plurality of BOM originators, over a time history;

receiving known mapping data defining historical mappings between the BOM receiver'"'"'s internal assigned part numbers and the BOM originators'"'"' various assigned part numbers;

receiving part description parameters describing a plurality of parts to which the BOM receiver has assigned internal part numbers;

learning by computer methods mapping prediction models for predicting BOM internal part numbers based on received BOMs from the plurality BOM originators, based on the historical BOM data, mapping data and part parametric data, wherein said computer methods include feature extraction for tokenizing a textual description of parts according to a token scheme and generating a corresponding list of parametric features based on the extracted tokens, wherein said computer methods form said learned mapping prediction models according to a multi-level taxonomy arranged for a hierarchical prediction mapping, including initially predicting a class of an unmapped part based on received information, and traversing down levels of the taxonomy, predicting the sub-class of the unmapped part at each subsequent level and, at a leaf level of the multi-level taxonomy, classifying the unmapped part to a BOM receiver internal part number based on the parametric features extracted from the BOM originator textual description of the unmapped part;

learning said prediction models from said historical BOM data, known mapping data, and part description parameters, using said methods of learning, and generating resulting learned prediction models;

receiving a BOM from a BOM originator, said BOM describing parts according to BOM originator assigned part numbers, wherein at least one of said BOM originator assigned part numbers is an unmapped part number not within said historical mapping data;

predicting a BOM receiver internal part number associated with at least one part described by said BOM having an unmapped part number, said predicting including applying at least one of said learned prediction models to said received BOM bill of materials; and

generating a release data having said predicted BOM receiver internal part number.