Classification and Part Standardization

The Opportunity

The goal of parts standardization is to improve operational readiness and reduce lifecycle costs by promoting the use of common, available, cost effective, and reliable parts.

Studies on this subject vary, however the conclusion does not. Proliferation of parts is very common and very expensive! Decisions made early in the design cycle drive the lifecycle cost, with 70% of the product’s cost established in the conceptual design phase.

Many companies are aware of the cost of their parts. For example: A major manufacturer in Europe concluded that a purchased part costs $2,450 per new part where as a self-constructed company part costs $4,500 per new part. Furthermore, based on a US DLA Parts Standardization and Management Committee study, “Reduced Program Cost Thru Parts Mgmt.”, there is a cost avoidance of $20K per part by reusing an existing standard part.



An efficient parts standardization program has far reaching benefits

  • Reductions in annual cost to maintain a supplier: $6100-$16,000
  • Reductions in annual cost to maintain a part: $3000-$6000
  • Reduce “near duplicate” designs
  • Better Product Quality – Increasing re-use of known “good” parts can reduce warranty costs
  • Reductions in Engineer’s time searching for parts, up to 25%
  • Reduction in new product re-designs by avoiding obsolete, long lead-time, or non-compliant parts

Summarizing, a standard parts library is an important part of an efficient PLM system and encourage part re-use. Standard parts are of higher quality and more cost effective

Required Capabilities

The journey to an efficient parts standardization system has proven to be a difficult and time consuming undertaking:

  • First, you must agree on a structure or hierarchy for the library. Without any automated tools to analyze the existing data or to evaluate groups of similar products, the effort turns into a long and tedious part by part process.
  • Second, a set of attributes must be determined for each part class. A one part at a time analysis of attributes is inefficient. A method of collecting and grouping similar parts and their attributes must be found to enable class wide decisions regarding attribution.
  • Third, the data must be evaluated and edited to ensure “cleanliness” before loading. Loading duplicate or “dirty” part data must be avoided.
  • Fourth, the data must be loaded into the library.

The Solution

Fortunately, advances in shape-based classification technology can have a dramatic effect on this process. A shape based searchable part library can be created in months versus years.

How does this work? Using our powerful shape similarity algorithm, Bingo! automatically organizes and groups part information based on shape. A predetermined hierarchy can be used prior to indexing or, once indexed, a classification can be developed from the grouped data. The process is dramatically improved by the automatic grouping of part data. Attributes are extracted during the indexing process so that attribute comparisons can be done at the group level and commonality is easily identified and standardized. Furthermore, attributes from external sources can also be linked to the data, thereby producing a fully described and attributed library very quickly.

Shape-based classification and search is nameless. Spelling, language, and file naming rules are not required. The shape search technology of Bingo! is simply the most ubiquitous, accurate, and intuitive search key for product data. Bingo! will enhance your part standardization initiative by enabling shape-based part similarity grouping, shape and attribute analysis and linking geometric data with attribute data from CAD and non-CAD systems to provide a central repository for similarity based search and re-use decisions.

Bingo! capabilities include:

  • Identifying duplicate and similar parts
  • Automatically organizing and classifying part data into useful catalogs using shape based technology.
  • Providing powerful and intuitive shape search capabilities
  • Analyzing and reporting on the data to enable effective decision making
  • Integration to external attribute based systems for additional filtering and consolidation of data in a single, similarity based system