5 Key Steps to Developing an Effective Parts and Data Classification Program

Written by: Nick Samardzija

Read Time: 5 min

(This blog was co-written with Richard Turner, President of Convergence Data Services.)

Often, organizations overlook the effect of having duplicated pieces of data within a PLM system. One survey found engineers spend 36% of their time on non-value-add work and nearly a quarter of that time searching for information.  Without effective data classification, your engineers waste critical time and effort searching for parts, creating duplicate parts, and struggling to find a common language for cross-departmental collaboration and reporting. Prioritizing your classification strategy can eliminate inefficiencies, encourage teamwork and higher productivity, and ultimately create bandwidth for your team to focus on innovation.

Developing an effective part and data classification strategy is also critical given supply chain disruptions, higher than anticipated material costs, labor shortages, and product complexity. Incorporating parts and data classification into your product development process can help operations optimize for cost and part availability, as well as simplify designs for manufacturing and service. 

Understanding Types of Data Classification and How to Get Started

Part and data classification is the process of organizing your products, parts, and documents to drive efficiencies in searchability and productivity. An effective classification strategy includes:

  • Creating an initial description of each part 
  • Adding additional detailed attributes to the descriptions, making it easier to break down parts by category
  • Tying each part to supplier management information that helps engineers quickly understand where alternative parts can be sourced, while also offering insights into important issues such as level of preference, region, availability, cost, and compliance.

Despite the value that parts classification strategies create, the idea of classifying potentially millions of parts and uniting stakeholders across your organization during the process can be daunting. A phased approach can be an effective solution. In our experience, successful companies begin their parts and data classification with commodity parts and then over time expand to encompass engineered parts. 

Best Practices for Implementing Parts and Data Classification

Take some of the pain out of developing a classification strategy by following best practices and utilizing today’s advanced PLM solutions. Let’s explore how you can get started.

1. Establish a Parts Governance Strategy

Support the long-term success of your parts classification initiative by establishing a governance strategy at the very beginning. A governance strategy offers a framework for how to manage new part creation during the new product introduction (NPI) process that will evolve over time. By setting up a formal parts governance strategy, you’ll be laying the framework for reducing duplicate parts and allowing your company to effectively launch a parts reuse initiative. The first objective of your governance strategy will be to assign a Classification Administrator.

2. Assign a New Role of Classification Administrator to Own New Part Creation

Consider developing a new role of Classification Administrator or Librarian to own new part creation. Critically, this approach ensures that a person who fully understands the system can act as a centralized resource for your company. Many successful companies with robust parts reuse programs employ an engineer in this role. Not only can they act as a valuable SME for searches and reporting, but they’ll play a critical role in ensuring that your strategy evolves with the changing needs of your industry, customers, and broader business landscape.

3. Establish the Classification Taxonomy with Supporting Attributes

Defining your classification taxonomy – the organizational structure of your data – should be the next step on your journey. A strong classification taxonomy makes it easier to find parts and get your engineers back to work designing solutions. The most effective information structures are as streamlined as possible; if the organizational structure is too complex, the data may not be truly useful for your end users. Start with the goal in mind, by clearly defining your organization’s reporting and search needs before you get started. Often, companies begin this process by focusing on their commodity parts and then rolling out the process to include in-house and outsourced engineered parts over time.

What steps do companies go through to establish a taxonomy? Building consensus is crucial. One of the challenges companies face is that there’s no one agreed upon industry standard. Instead, companies find partial solutions for commodity parts such as fasteners or electronics, and then use that as the basis to further develop their own classification structure.  For example, when you’re looking for a window screen at HomeDepot.com, you can search the branches of the structure they created (Hardware ->Window Hardware -> Screens) and from there you can even go further by adding dimensions and materials, etc. This works well for Home Depot customers. Be sure to organize a group of subject matter experts who understand the needs of your engineering/supply chain community. They will come together to review the proposed classification structure and refine it to something that will work for everyone.

Once the underlying classification structure has been identified, it’s time to augment your data with attribute details. For example, machine screws aren’t simply listed within your PLM system as screws. Each unique part in your inventory may contain information on attributes such as thread, head type, length, material, finish, and preferred part status for specific products. Deeper detail makes it easier for engineers to find the parts they need, minimizing the chance they’ll design something that’s already available. As you outline your classification structure and search/reporting needs, you should identify which attributes are key to data extraction. 

During this process, you can use a staging environment as your technological working environment. A staging area allows for search and analysis so you can perform detailed tests on retrieving information within that structure and refine it over time. Staging areas can also help you more easily complete complex projects, such as merging the classification systems of two organizations together.

4. Align Taxonomy with Important Attributes for Purchasing and the Supply Chain

Your data classification strategy can go beyond basic parts attributes and capture additional details that drive key business decisions. Aligning your taxonomy with important attributes for purchasing and the supply chain can provide greater insights for your engineers. The attribute data included might encompass areas such as materials used, availability of materials, and supplier classification. For example, if an engineer is searching for an alternate part for a product that must adhere to specific sustainability requirements, supplier classifications can help ensure that any substitutions meet those criteria.

5. Normalize, Validate, and Export the Data

Once the structure is in place and the attributes have been harvested, now it is vital to streamline and prepare your data so it’s ready for broader use. 

  • Normalize – Ensure you have reviewed the data in the attributes and normalized them to your standard nomenclature. Otherwise, your data will not show accurately in reports or be returned in relevant searches.
  • Validate – Review the data quality and ensure it is ready for use by reviewing the units of measure and field lengths on the attribute fields. This can be seen specifically in those fields that are limited in a PLM system like PTC’s Windchill.
  • De-Duplicate – Inevitably there will be duplicate parts within your inventory.  At this point, the parts are cleaned and attributes are populated. It is much easier to identify those duplicates and develop an actionable plan for parts reuse.

Whether your primary goal is to drive down costs by reusing as much as possible or to improve your team’s ability to collaborate concurrently, a part and data classification strategy gives you the centralized source of truth for efficient parts management. Developing a strategy doesn’t have to be daunting. Following best practices, working with specialized service providers, and accessing the tools available as part of today’s best PLM solutions can reduce the pain and help you quickly capture important gains.

For more ideas on how to classify and better manage your parts, visit Parts Classification and Duplicate Parts Avoidance.


About Convergence Data
Convergence Data is a partner with PTC. Manufacturers with chaotic or incomplete data trust Convergence Data to scrub that information into an organized, efficient structure. The company specializes in: 

  • Minimizing part duplication
  • Cultivating part standardization and re-use
  • Industry standard taxonomies
  • Data Services team based in India
  • DVA – Data Value Analysis – based on Clustering
  • PLM/ERP migrations

Convergence Data enables customers to manage data in a variety of industries, including Aerospace and Defense, Appliances, HVAC, Automotive, Electronics, Industrial Manufacturing, and Oilfield Services. To learn more about Convergence Data and receive a Data Value Analysis (DVA) go to: www.convergencedata.com.





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About the Author

Nick Samardzija

Nick is a Product Manager for PTC's core Windchill solution. His areas of management span from search, project management, document management, collaboration packages, parts classification, integration of embedded software, & supplier management. He is extremely passionate in home improvement, interior design, simulation racing, skiing, & Formula 1.