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Why You Should Consider Convergence Data Services

Top 10 reasons not to use intelligent part numbers

1. Intelligent part numbers work for experienced people only – new people will not know how to use it
2. Intelligent part numbers become a problem when a company merges with another company. How can it be reconciled?
3. Intelligent part numbers can create a fixed framework that does not allow companies to work in a different way. It is a legacy they will be carrying on for the rest of their existence
4. Most manufacturers want multiple or at least more than one vendor, for the same part
5. Error prone – a slight keystroke error and instead of getting a part from company A, you get company B if the part numbers are very close in appearance
6. Intelligent part numbers only provide limited information on a part vs. attributes which provide more detail
7. Finding part substitutes including preferred alternatives can be a challenge if the part number scheme is different
8. Doesn’t support corporate re-use or enterprise search requirements
9. Not flexible enough to support the New Part Introduction process, managing multiple part number schemas
10. Will not meet the requirements of industries like Aerospace that require cage-codes and org-id’s in their part numbers

Top 10 reasons why a company needs part meta-data

1. Enable re-use of an existing approved part versus recreating a new part
2. Properly define a part so it can be re-used
3. Manage the lifecycle of a part including providing current part status
4. Source more parts at once, enabling similar part groupings or sourcing clusters
5. Identify standard part configurations facilitating part standardization and rationalization
6. Properly migrate a part to mitigate migration issues
7. Find part substitutes including preferred alternatives
8. Parts preferencing to help prioritize part usage
9. Find associated data about a part including where used, documents, programs, etc.
10. Pre and Post merger acquisition - consolidate parts and sourcing

Top 10 capabilities of a part meta-data management system

1. Agnostic meta-data system that manages data amongst multiple systems
2. Multi-user collaborative capabilities to easily manage concurrency
3. Project management including: data life-cycles, approval workflow and status reporting
4. Database for one version of the truth (no spreadsheets or access files)
5. Data cleansing tools to clean data that can support outsourced data engineering
6. Data validations that are easy to set-up, identify and fix data errors
7. Flexible taxonomy management for managing change promoting low maintenance
8. Robust security model to manage access via taxonomy
9. Intuitive application to make easy to use for engineers not IT
10. Scalable to handle millions of parts and 100's of concurrent users

Top 10 criteria for implementing a successful part meta-data program

1. Subject matter experts must own taxonomy not an IT responsibility
2. Part taxonomy must support multiple business needs not just engineering
3. Search engines must support classification and attributes meta-data searching
4. Integrate with other systems including PLM and ERP
5. Cross-functional executive sponsorship serving multiple business objectives
6. Data model changes as the business changes, maintaining relevance
7. Support key business initiatives: re-use, rationalization and sourcing
8. Meta-data system must be agnostic not an enterprise solution side offering
9. Accessible to the entire organization, not a point solution
10. Configurable to meet cross industry business needs

Top 10 common failures with part meta-data initiatives

1. Meta-data architecture too rigid to manage change
2. Relying on a PLM system to manage meta-data
3. IT driven program versus business drive creates lack of ownership
4. Single user system versus collaborative
5. Meta-data system set up for a one time use, not supporting iterative process
6. Not supporting multi-functional business needs
7. Addressing meta-data issues post migration, resulting in data sync issues
8. Not an agnostic meta data system architecture
9. Meta-data structure lacks detail to support re-use, generic commodity categories
10. Meta-data architecture lacks standard structure for common purchased parts