Cleaning up the Supply Chain

Fingerprinting Part Numbers

Mergers and Acquisitions: A Supply Chain Challenge

Mergers and acquisitions (M&A) in the manufacturing industry can significantly complicate supply chain management. One major issue is the merging of different part numbering systems. This can lead to:

  • Duplicate Part Numbers: The same component may have multiple identifiers, causing confusion and inefficiencies.
  • Changed Part Identifiers: Part numbers and manufacturer names may change, disrupting tracking and comparison.
  • Data Management Challenges: Integrating legacy data can lead to data quality issues and the need for robust master data management (MDM) strategies.
  • Operational Disruptions: Supply chain disruptions, increased resource allocation, and challenges in comparing parts by number can impact efficiency.

innoLab Fingerprints: A Solution for Diverse Industries

innoLab Fingerprints can help address these challenges by analyzing and identifying patterns in alpha-numeric codes. This technology can be applied to various industries, including:

  • Pharmaceuticals: Verifying drug codes, batch numbers, and clinical trial identifiers.
  • Financial Services: Validating transaction IDs, security identifiers, and account codes.
  • Manufacturing: Verifying part numbers, serial numbers, and model numbers across merged companies and product lines.
  • Logistics and Transportation: Validating tracking numbers, container IDs, and booking reference codes.
  • Healthcare: Verifying medical device identifiers, patient IDs, and insurance claim numbers.
  • Automotive: Verifying VIN numbers, part numbers, and diagnostic trouble codes.
  • Telecommunications: Validating equipment serial numbers, network element IDs, and service order numbers.
  • Energy: Validating meter IDs, well or field identifiers, and power plant unit designations.

How innoLab Fingerprints Work

  1. Analysis: Known valid part numbers are analyzed to identify common patterns and characteristics.
  2. Fingerprint Generation: A unique fingerprint is created based on these patterns.
  3. Comparison: Unknown part numbers can be compared against the fingerprint to determine similarity and potential origins.

    4. innoLab Fingerprints is a powerful tool for any industry that relies on alpha-numeric codes for identification and management. By identifying patterns and similarities, this technology can help streamline processes, reduce errors, and improve overall efficiency.

Try it out!

The web interface is constrained to 100 entries. Any more than that will be truncated. A more generous API is available to registered users.

The system will look for token separators in the following order: [SPACE], [TAB], [NEW LINE].

Need some numbers to try?

...copy these and play around with them.

CXD905 ALD1095M CXD1044 CXD967 CXD866 CXD1363 CXD1095 CXD1184 CXD340A ALD1044M ALD1592M CXD1084 ALD1326M CXD1108 CXD1107A CXD2135 ALD1327M ALD1439M ALD1421M ALD1037M CXD537 CXD1327 ALD866M ALD1084M CXD1375A CXD1679 ALD1593M ALD1212M ALD1596M CXD1326 ALD1157M CXD815 CXD1156 ALD1454AM ALD1275M CXD465A ALD1432M ALD1543M ALD1544M CXD1086