Convergence Data Team
Apr 17, 2024
Classification, PLM, Duplicate Parts, Manufacturer Parts, Taxonomy, Parts Classification, Data Classification, Findability, Electrical Parts, Repair Parts, Electronic Parts, Service Parts, Aftermarket Parts, Supplier Management, Sourcing, Component Data, Purchased Parts, Match and Merge, Data Integration, Digital Thread, Design Parts, Part Obsolescence, Shape-Based Search

Parts Search vs. Shape-Based Search: What To Know

We get asked frequently about the difference between parts-based search and shape-based searching. Let's break it down! 👇

 

Many enterprises kick off projects with the running assumption that utilizing a geometric search capability will solve their Engineering team's findability issues. In this article, we explore why geometric-based search is not a "silver bullet" enabling findability and other ways that this can be achieved.

 

👉 Let's start with the basics. For most organizations, a better search experience for end users is paramount with key outcomes being:

  • Part findability
  • Usability of data associated with a part and, ultimately;
  • Part reuse ⚙️

To find parts (whether they be purchased/COTS parts or design parts), the creation of a data structure is critical. This is where taxonomy, as a foundational element, comes in. A well-designed parts taxonomy and supporting metadata should:

  • Enable dynamic filtering across a population of parts - this includes purchase parts, design parts, and service parts for Aftermarket and eCommerce 🔎

  • Facilitate the identification of the part - the "isness" and "aboutness", as our taxonomists would say 🍃

  • Allow for efficient reuse of the parts ♻️

  • Stay in step with an enterprise's inventory of items - be they purchased parts or design parts. ⚙️

  • Support scalability of data as the business grows and acquires other companies

From the standpoint of geometric search, there are several key gaps with leveraging this approach and solution to find and reuse parts. Each of these shortcomings also highlights the opportunity to harness a far deeper value proposition by looking at more complete attribution afforded through parts classification:

  • Inability to Rationalize Duplicates and Supplier Spend: The data profile generated by geometric search tools tends to be quite limited. Other attributes, such as materials, performance or those leveraged by Supplier Management and Sourcing, are not normally encompassed within a shape-based profile. This ultimately hampers an enterprise's efforts to analyze and rationalize duplicates and spend globally across individual regions.
  • Inability to Drive Revenue: Geometric-based tools also lack critical attribution needed for divisions such as Aftermarket and eCommerce to create a sellable SKU. Focusing efforts on shape-based search tools also means forgoing the opportunity to share vital metadata downstream with divisions responsible for revenue generation.
  • Limited to Design Parts: For enterprises with large populations of COTS or purchased parts, the geometry-based tools typically fall short, as they are not designed to support large volumes of parts across broader commodity groups. In this scenario, parts classification would be a better approach and enable deeper attribution and, therefore, better part filtering and findability.
For teams looking to solve the challenges of findability, it's important to also consider:

In summary, geometric search tooling will only produce partial results for an Engineering team and the broader organization. Instead, enterprises should begin focusing on the most strategic uses for their technical data, with an emphasis on sharing data across the enterprise via a digital thread approach and a proper governance program geared to standardizing how data is created and handled throughout the lifecycle.

 

✍️ Contact us today to learn more about the benefits of leveraging parts data to drive better findability and parts reuse across the enterprise!