A blog by Oleg Shilovitsky
Information & Comments about Engineering and Manufacturing Software

Intelligent part numbers and organizational alpha-male and alpha-female space

Intelligent part numbers and organizational alpha-male and alpha-female space
Oleg
Oleg
4 November, 2016 | 3 min for reading

dominant-behvaior-part-number

I was taking Uber ride from the hotel in Plymouth, Michigan to Detroit airport yesterday coming back home from CIMdata PLM Roadmap. For my surprise, I found an interesting topic to discuss with Uber driver. We discussed Part Numbers. Without revealing specific details, I can share that we’ve been talking about Gaskets, variations of Part Numbers and how some people in organization can memorize Part Numbers. It was very fascinating and interesting conversation that took me back to think about why to use intelligent part numbers in 21st century.

Part Numbers is one of these topics that can keep PLM people engaged into discussions for very long time. Some of these articles can be republished and they will look fresh and relevant even after 5-10 years. My attention was caught by few of these “reposts” done by Zero-wait state blog. Navigate to read – The PLM State TBT: “Make My Day”, The Pro’s and Con’s of Hybrid and Random Part Numbering and The PLM State TBT: The Good, the Bad and the Ugly of Intelligent Part Numbering.

Here is my favorite passage with arguments against non-intelligent Part number.

There are some valid arguments against Random part numbers. Probably the most notable is the inability to understand what the part is based on the number alone. Without additional attribute information it is impossible to tell the form, function, etc… of a given part. CAD designers like the fact that they can search for a specific number based on the schema and determine if the part exists by the number alone. In order to produce the same functionality, you not only have to set up the system to capture the attributes but it also requires users configure these and set/input the values in order to make an object intelligible. Accordingly, the random part number is only useful if the associated attributes are available. If the system is down, then it’s difficult at best to decipher the part based on the number alone. Also where random part numbing systems rely heavily on the description, it gives rise to user error based on inputs.

I found the argument about “what if system is down” interesting. You can hardly believe about how such mission critical systems like ERP, PLM or procurement can be taking down in an organization for a long time. So, why people are worry anyway? I see it connected to those inefficient rituals that potential can exist in many organizations around Part numbers, BOM management and supply chain. What if a problem of Part Numbers is actually human problem? Then PLM attempt to get rid of intelligent or semi-intelligent part numbers is actually an anthropological problem. Somebody in organization who is capable to demonstrate his (or her) alpha-male (alpha-female) capabilities can be pissed off. Somebody who can memorize 1000s of part numbers can be removed from his (or her) job by PLM system. It is a very dangerous position for PLM system capable to remove the significance of a human presence.

What is my conclusion? Part Numbers can be a problem related to some company cultural rituals. An attempt to change it can jeopardize PLM implementation. Those significant individuals will fight PLM project. So, technology is easy (what can be easier than automatic code generation?), but people are really hard. PLM strategists need to think how future engineering and data management technologies can avoid a conflict with manufacturing alpha-males (females) holding organizational Part Numbers in their heads. Just my thoughts…

Best, Oleg

Want to learn more about PLM? Check out my new PLM Book website.

Disclaimer: I’m co-founder and CEO of openBoM developing cloud based bill of materials and inventory management tool for manufacturing companies, hardware startups and supply chain. My opinion can be unintentionally biased.

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