Why Attributes Are Critical to PLM Part Search
In fiction, a composite character can be comprised of two or more real life individuals; for example, the character Peter Brand in Moneyball is a composite of real world people. To streamline the story in Black Hawk Down, several real-life soldiers, who were involved in the event, composed the character Matt Eversmann.
In the piece below, several real world customers, who had questions regarding the value of attributes to their PLM implementation, compose the customer represented in this blog. Readers will get extra points for offering their thoughts on attributes as well as articulating the difference between the use of “comprise” versus “compose.”
Customer: What is an attribute and how do they impact item numbers and categories for items, request types and change types?
Arena: Attributes help you to define and find data and drive analysis from the data. For example, it might be good to have an attribute labeled “Product Line” across your Requests, Changes and Quality Processes. Adding attributes gives more ability to analyze your processes over time. For instance, using an attribute for a Product Line would allow you to see that Product Line X had 10 changes while Product Line Y had 60 changes.
Customer: Can attributes help to drive analytics, and the ability to search and find out more information?
Arena: Yes, they can also help from an item perspective to drive knowledge. Instead of putting intelligence into a part number that gives you the screw type, length, head type, you put that information into attributes so you can search your PLM system for all screws of all types. Attributes help users of your PLM system to find the lowest level component available, or determine if a new part number needs to be created.
Customer: Can attributes drive economical reuse of parts into your product instead of having to create a new part number every time?
Arena: Exactly. Before using a PLM system, companies had no easy way to search or find if they had an existing part. So they created new part numbers each time because searching for a specific part was difficult. And in their system they might have 50 resistors, capacitors or screws that are all the same part, but they did not adhere to any kind of best practices. So companies needing 10 of the same screw would purchase in quantities of 5,000, 8,000, 10,000 or 12,000. If the company had a better way to aggregate part usage across product lines, they could have negotiated a better price if they had purchased a much larger quantity in their initial single order. All are aware of the hard and fast rule: the more you buy, the lower the unit price.
Customer: So what’s the ultimate advantage of attributes?
Arena: We would say the big reason is the more metadata you have about your product data, the easier it is to find that data. It’s like tagging a photo on Flickr.
It also enhances your capacity for smart, complex searches. Like, you may want to search for all resistors. Or maybe you want to search for all discrete components that have a 0603 footprint, or a +/- 5% tolerance. Basically, the more information you provide about your items, the easier it is to find them and your search results exhibit more precision.
If you accurately and diligently update a custom attribute like Product Line then you can slice the analytics by that attribute. And ultimately, you could link it to Arena Analytics, to extract maximum benefit as you view the KPIs across all your product data.
To learn more about how Arena PLM helps with attributes click here.