Part Numbers are hard. How to think about data first?

One of the topics that usually raises a lot of debates is Part Numbers. One of my first takes on the complexity of Part Numbers was here – PDM, Part Numbers and the Future of Identification. Ed Lopategui reminded me about that topic in his GrabCAD post – Intelligent Numbering: What’s the Great Part Number Debate? few days ago. He speaks about four aspects related to handling of Part Numbers – creation, readability, uniqueness and interpretation. The conclusion is complex as well as the topic itself. Here is the passage, which outlines the conclusion Ed made.

Balancing all these diverse factors is difficult, because no solution is optimal for every company. Here are some final tips to help you make prudent decisions: 1/ Understand your PDM/PLM system part number generation capabilities; 2/ Understand the limitations of any other systems that interact with your parts; 3/ Go through every activity that requires interpreting part numbers and understand what system access is available, and how the interfaces work. This will provide a good basis for your interpretation cost; 4/ Understand how easy/difficult it is for a new employee to interpret a part number.

These tips made me think again about Part Numbering, data and different data and process management tools involved into the process of Part Numbers handling. Most of approaches are focusing on systems and functionality to handle part identification and classification. What we do is trying to align our need to identify and classify parts with what multiple systems can do. The hardest part is to find Part Numbers that will make all systems involved into the process (CAD, PDM, PLM, ERP, SCM, etc.) to work smooth. Honestly it is too complex and too costly.

So, how to manage that complexity? Is there a reasonable way to resolve the complexity of Part Numbering and made everybody happy? Thinking about that I came to conclusion that companies should start thinking about data first. From the longevity standpoint, data must have much higher priority compared to any data management system. In some industries companies are obliged to keep data for decades. Thinking about that, I want to outline some principles that will help you to do so and will allow to create some standardization around parts and data identification.

1- Disconnect Part Numbers and classification from specific applications. PN should not be dependent on requirements and capabilities of data and process management systems. Data has much longer lifespan compared to applications and systems. By defining PN independently you will keep data and processes in your company clean and well organized.

2- Generate PN based on classification, business needs and processes.  Develop independent service to make it happen. This service is most probably should be independent from existing data management systems and converted in some sort of URI based notation.

3- Use  independent service to convert independent PN into system specific identification. You can convert for any system you have at your disposal – PDM, PLM, ERP, SCM… What is important is to be able to control the process of conversion and adapt it each time data and/or process management system changes.

What is my conclusion? Product data is one of the most expensive assets in manufacturing companies. It represents your company IP and it is a real foundation of every manufacturing business. Think about data first. It will help you to develop strategy that organize data for longer lifecycle and minimize the cost of bringing new systems and manage changes in existing systems. I think, some services should be developed to make the process of part numbering easier for manufacturing companies. As manufacturing is getting global to maintain part numbering systems becomes a huge problem. Just my thoughts…

Best, Oleg

Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital network-based platform that manages product data and connects manufacturers and their supply chain networks. My opinion can be unintentionally biased.