MASTERING ITEM NUMBERS FOR DIGITAL SUPPLY CHAINS
Anthony G. Tarantino, PhD
Industry 4.0, Supply Chain, and Continuous Improvement SME and Consultant
CONTINUOUS IMPROVEMENT WITH TONY
Newsletter, Volume 10, July 1, 2024
Example of a Descriptive Part Number for a Microcontroller.[i]
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CONTINUOUS IMPROVEMENT WITH TONY
Newsletter, Volume 10, July 1, 2024
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Mastering Item Numbers for
Digital Supply Chains
Before the advent of computer databases and Enterprise Resource Planning (ERP) systems many if not most manufacturing organizations applied significance or intelligence to part or item numbering systems. When I began implementing ERP systems some 40 years ago, our implementation teams had to overcome internal resistance to converting to non-significant numbering systems that relied on a variety of descriptor fields in relational data bases to classify and identify items. ?Here are some examples of what descriptive part numbers look like:
Screw Part Number:
?? - Manufacturer A: "4-40-3/4-pan-phil"
?? - Manufacturer B: "100-440-0.750-3434-A"
?? - Manufacturer C: "TSR-1002" [ii]
Manufacturer-Defined Resistor Part Number:
?? - Bourns Manufacturer: "CRT0805-DY-1R00ELF" (for a specific 1 Ohm resistor) ?
Part or item numbers, supplier numbers, and customer numbers fall under structured data. Descriptive flexfields enhance relational databases by allowing custom attributes. These attributes enable you to define validation rules and display properties. ?The advent of relational databases makes the use of significant part numbers unnecessary and an obstacle to creating digital supply chains. Here is a summary of the major benefits in using non-significant part/item numbers.
?Simplicity and Consistency: Numerical part numbers are straightforward and easy to manage. They follow a consistent format (e.g., sequential numbers) that simplifies data entry and reduces the chance of errors. Employees can quickly identify and reference parts without needing to interpret complex codes or descriptions.
Scalability: As an organization grows, maintaining a simple numbering system becomes more critical. Numeric part numbers scale well because they don't rely on specific meanings or patterns. Adding new parts doesn't require extensive planning or adjustments to existing numbering schemes.
Reduced Cognitive Load: Employees don't need to memorize intricate part codes or understand their significance. This reduces cognitive load and speeds up processes. Training new staff on numeric part numbers is straightforward.
Vendor Independence: ?Numeric part numbers are vendor-agnostic. They don't reveal supplier information or tie the part to a specific manufacturer. This flexibility allows organizations to switch suppliers without reconfiguring their part numbering system.
Data Security: Non-significant part numbers protect sensitive information. For example, using a part number like "12345" doesn't reveal proprietary details about the part. In contrast, intelligent part numbers might inadvertently leak information about the product's function, materials, or design.
Compatibility with Systems and Databases: Numeric part numbers work well with databases, ERP systems, and inventory management software. They avoid conflicts with reserved keywords or special characters that could disrupt data integration.
It might seem that Generative AI will be able to overcome the problems that significant part numbers present, but the reality is that they continue to be troublesome, especially in the digitization of physical operations. Here are some considerations:
Interpretation Complexity: Significant part numbers often encode information about the part's function, material, or other attributes. When transitioning to digital systems, interpreting these codes becomes cumbersome. Automated processes struggle to extract relevant details from complex part numbers.
Data Entry Errors: Manually entering significant part numbers into digital systems increases the risk of errors. Typos or misinterpretations can lead to incorrect inventory management, procurement, or assembly.
Integration with Digital Platforms: Digital platforms (such as ERP systems, inventory databases, and e-commerce platforms) work best with non-significant part numbers. These systems rely on sequential or random numeric codes for efficient data processing.
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Vendor Independence: Non-significant part numbers avoid revealing supplier-specific information. In a digital ecosystem, maintaining vendor independence is crucial for flexibility and scalability.
Legacy Systems and Compatibility: ?Organizations with existing significant part numbering systems may face challenges integrating them with modern digital tools. Legacy systems might not align with current best practices.
Standardization and Globalization:? Non-significant part numbers facilitate global operations by avoiding language-specific meanings. Standardized numeric codes streamline communication across borders.
In summary, while significant part numbers have historical context, transitioning to non-significant systems enhances efficiency, accuracy, and compatibility in the digital era. Organizations should carefully evaluate their needs and consider migrating to simpler, numeric part numbering for smoother operations.
Prediction
Many large manufacturing companies still use significant part/item numbers which will be an obstacle in creating a true digital twin of physical operations that creates one source of truth. I have worked with engineers for over 40 years and predict the resistance to converting to non-significant numbering systems will be major challenge. ?????
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Anthony Tarantino, PhD
Six Sigma Master Black Belt, CPM (ISM), CPIM (APICS)
Adjunct Professor, Santa Clara University – Smart Mfg. & Industry 4.0
Author of Wiley's Smart Manufacturing, the Lean Six Sigma Way Amazon Links
Senior Advisor to IM Republic, ?https://imrepublic.com
?(562) 818-3275?? ?[email protected] ? ?Anthony Tarantino
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[i] Wikipedia, https://en.wikipedia.org/wiki/Part_number
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[ii] PLMadvisors – PLM and Configuration Management Best Practices: Part Numbers. https://plmadvisors.com/plm-and-configuration-management-best-practices-part-numbers/
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Product Environmental Compliance Consultant
4 个月Anthony G. Tarantino, PhD I couldn't agree more! A significant problem has been that the PLM systems (e.g., Agile, Arena, etc.) where these numbers are created and assigned drive the adoption of intelligent/semi-intelligent numbering schemes because they lack capabilities that would obviate the apparent need for intelligent part numbers. The ability to create and enter meaningful parametric and functional properties, then perform search/mathematical functions on them is missing.