When it comes to part classification, a common challenge we see stems from product complexity. The more intricate a product is, the more parts it’s going to have, and the dominos begin to fall:
- Your team slowly stops using the governance strategy you implemented.
- Duplicate parts are frequently introduced.
- Engineers begin having a difficult time finding what they need.
This roadblock can slow progress, reduce efficiencies, and increase costs across your organization. So, how can you avoid these pitfalls? The answer is simple—enhance your part classification strategy.
Read on for five best practices our team of technical data experts swear by.
1. Establish a Data Governance Strategy
A data governance strategy offers a framework for how to manage new part creation during the new product introduction (NPI) process that will evolve as time goes on. Establishing a structure for data governance at the very beginning will help support the long-term success of your classification.
The first step in developing a strategy is to cleanse and normalize data for similar parts. This means ensuring they share the same naming conventions, attribute profiles, and attribute value lists.
Once completed, you can confirm that all similar parts are described in the same way. For example, material attributes will have the same list of materials for each part and the descriptions will have the same format.
2. Always Have the End Goal in Mind
It’s important to think of the bigger picture at all times. What will my part classification look like when I’m done? How will I ensure it operates smoothly day in and day out?
One of the greatest and simplest ways to create a seamless structure from beginning to end is by limiting the number of people involved in making classification decisions. Consider assigning a new role of classification administrator to own part classification—this will remove any further debates on what your structure should look like.
There’s no point in reinventing the wheel—these industry-standard structures used by consortiums may work just the same for your organization:
3. Define a Classification Taxonomy
Once you’ve identified which attributes are the most critical, your next action item should be implementing a classification taxonomy. Not sure what that is? It’s essentially the organizational structure of your data.
Defining a strong taxonomy will make it easier to find parts and keep your engineers on track to be efficient when designing solutions.
You want to ensure your classification taxonomy is as streamlined as possible—when it becomes too complex, the data starts to become less useful for you and your end-users. Follow these steps to establish your taxonomy:
- Refine a structure that will work for everyone on your team.
- Enhance your data with attribute details (the more detailed, the easier your parts will be found).
- Identify which attributes are key to data extraction.
4. Identify the Most Critical Attributes
It’s very easy to get carried away and want to have an attribute for every characteristic of a part. However, the more attributes you use, the more difficult your data enrichment, normalization and cleansing efforts will be.
To help you be the most efficient, determine which attributes are the most critical and make the less important attributes optional.
So, how do you accomplish this?
Start by thinking about what the minimal information is that you need to find a part or raw material. If you cross-reference a drawing, users can always look at that drawing to find out more detailed information on that part.
The parts and materials shared by products across multiple business groups are typically the most critical. They can also be the parts that represent the biggest spend within your organization. Starting with those parts first will help bring the most visibility to initiatives, such as data cleansing, allowing it to have a big impact right away.
5. Leverage Existing Resources
There are many techniques, tools, and resources you can leverage to help expedite data cleansing that you may want to consider. For example:
- Smart Part Numbers = Unique identifiers that provide indications as to the type and specifications of the part and where it might be classified.
- Description Fields = Short or long descriptions that may contain specifications, features and other info that help guide classification.
- Commodity Codes = Classification codes that assist resources in classifying the parts and may also be used to automate classification.
- Suppliers = The provider or source for acquiring the part.
- Content Providers = In-house or third-party providers that source content (e.g. technical data sheets) that provides context and detail to aid in classification and attribution.
Additional Support For Your Part Classification
If you’re still looking for ways to enhance your part classification, we’ve got you covered! Download our 10 Critical Requirements of a Classification System eBook—this will help you identify what you’re doing right and what your classification may be missing.
*Editor’s note: This post was originally published in 2015, and has been updated for comprehensiveness.