Why Convergence Data

Why You Should Consider Convergence Data Services

Ten critical capabilities of part classification systems

  1. Part classification systems need to be flexible and easy to use; a new acquisition can drive significant rework of your existing classification structure. Changing your classification structure and reclassifying your parts needs to be easy to do and require minimal testing
  2. Bulk data migration, data integration and custom export capabilities where specific validation rules and diagnostic tools are required for migrating data between systems and search tools. Category properties control search application data presentation
  3. Custom workflow and managing concurrency between users are critical to managing data modeling and data cleansing activities, where users work on items based on status, assignment and role. Assignments implemented by working batch, category and/or user
  4. Global data cleansing tools facilitate data changes across many parts, categories and batches including allowed values management capability for string attribute values, managed at both a global level and local level, by attribute, by category and by batch
  5. Data analysis clustering tools identify duplicate parts by part characteristics exposing non-standard configurations that drive direct material costs. Input to market basket direct material sourcing
  6. Easy to administer security model that scales with the part classification structure via inheritance properties, insuring only approved users can make taxonomy changes. Each category can have its own approvers and approvers can inherit to other sub-categories
  7. 3rd Party content integration tools that facilitates data refresh of lifecycle attribute values for electronic parts e.g. YTEOL, material compliance updates supporting RoHS or REACH requirements and punch outs to additional reference content e.g. part data sheets
  8. Item relationships management allows items to be mapped to other items supporting substitute parts, part to document relationships, and where used queries by program or site
  9. Attribute management capabilities supporting global properties for each attribute instance or local to a category. Custom attribute management capabilities enabling multiple values, auto-generation, ranges, units of measure and custom validations for data cleansing. Robust part number name space capability enabling complex item number schemes
  10. Project management capabilities supporting assignment by user, batch, and category and tracking status by batch, project, category, user, status, and validation errors

Ten 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

Ten 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

Ten 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

Ten 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

Ten 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

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