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Magic Clusters: Uniting Components and Supplier Management

Posted by Convergence Data Team on Jun 1, 2021 9:59:46 AM

Quite often, manufacturing companies' engineering and procurement organizations don't work together as closely as they should. We've seen that, as a company grows, its new part requests can increase, making it challenging for procurement to keep up with an effective supplier sourcing process. The issues often start when engineers, experiencing difficulties finding an existing part to reuse for a new design, find themselves creating a new part in order to save time. Procurement can become inundated with these new part requests. They may not know which are the best suppliers that can currently provide similar parts. When this happens, procurement may get a price from a new supplier and not know if this new supplier is the best option.

    • 25% of new parts requests are unnecessary - typically existing parts would meet the requirement, yet engineers cannot find them due to poor data.
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Topics: Classification, Product Analytics, PLM, Part cost, convergence data, Part Preparation, DFR, Cost Reduction, Spend Analysis, Clusters, Loading Data, Cost Savings, Part Standardization, Duplicate Analysis, data normalization, categories, classification structure, reclassify, smartclass, supplier pricing

Top Part Classification Blogs 2020

Posted by Richard Turner on Jan 6, 2021 10:55:00 AM

It’s that time of year again when we send out a list of our most popular blogs and our most popular community group videos. We would like to thank everyone of our subscribers for their interest in our blogs and community group meetings - 2020 was an exciting year for us! 

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Topics: Cleansing Data, Classification, Product Analytics, Teamcenter, Part Cleansing, 2019 Blogs

Component & Supplier Data Management Top 7 Issues to Avoid

Posted by Convergence Data Team on Dec 11, 2020 11:57:07 AM

This video outlines the most frequent issues we have seen our customers experience and ways we have been able to help them. 

This webinar includes the new DFR v10.5 demo!

Key Learning Points:

  • Mitigating high supplier pricing
  • Misclassifications of spend
  • Too many vendors by category or sole sourcing
  • Integrating technical and commercial data

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Topics: Classification, convergence data, Cost Reduction, Cost Savings, classification structure, supplier pricing

7 Best Practices for a Parts Classification Project

Posted by Convergence Data Team on Nov 19, 2020 11:35:07 AM

When we talk to companies who are just starting their classification journey, we provide them with the best practice framework focused on obtaining the most value from their classification project. All too often this work is done in only one division or it becomes an “engineering searchability” project which limits the value of the project.

When making an investment in classification, the budget for this program should come from these sources:

  • Person responsible for direct material spend – VP of Operations, CFO, or CPO
  • ERP or PLM Deployment budget

7 Best Practices for a Parts Classification Project

    1. Target high value parts for classification – high spend, proliferation, too many vendors, shared across programs, etc. Start with the 10 top categories using this criterion.
    2. Pull all the parts from each organization for each category – this is the biggest bang for the buck approach.
    3. Assign one (1) Engineering SME and one (1) Purchasing Category owner for each commodity – they will approve structure and data. You don’t want too many cooks in the kitchen.
    4. For each part – provide part number, part description,
      MFG Part Number and MFG Name, drawing or specification.
    5. Obtain the commercial data for each part – provide pricing,
      supplier names, buying org and volumes.
    6. Clustering – once each category is complete, look for clustering opportunities – this will expose cost savings for procurement spend rationalization. Clustering will expose parts with higher prices compared to other similar parts with lower pricing. 
    7. Load classification data to PLM - make sure to validate the data against the PLM systems rules. This saves time dealing with load issues.
The advantage of taking this approach is that you only go into each category once. You don’t want to have to go into each category multiple times. This will allow you to have a much better chance of finding cost savings. Lastly you will be in a better position to prepare the data for your global PLM system. You don’t want to have to keep updating your PLM system with changes. The video below describes the process to prepare your classification data for cost savings and sourcing cost savings.

Here are some useful links and videos:

     • 15 min PLM Classification Process video:

     • 5 min Clustering video
     • 1 min Explainer video

     • DVA Case Studies
     • CDS Brochure

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Topics: Classification, Product Analytics, PLM, Part cost, convergence data, Part Preparation, DFR, Cost Reduction, Spend Analysis, Loading Data, Cost Savings, Part Standardization, Duplicate Analysis, data normalization, categories, classification structure, reclassify, smartclass

New Multi-Select Structure Editing

Posted by Convergence Data Team on Oct 23, 2020 12:34:41 PM

We are excited to announce our 10.1 release and promote our new data classification multi-select editing capabilities.  Now DFR users can select multiple categories at once and perform different types of edits – copy/paste or prune/graft.  What this means is users can make significant data model changes when building and iterating your classification structure; that much easier to do.  Along with the categories – all the associated attributes get moved in one single operation.

If you have parts assigned to categories that you want to move – don’t worry – our multi-select prune and graft allows DFR users to select multiple categories and move them along with the attributes and the parts – all in one single operation.  These changes will save our users lots of time and hopefully give them the structure they desire with less effort.

For more details check out the short video or contact us today for a demonstration –

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Topics: Classification, Data Cleansing, convergence data, DFRv10, DFR, categories, classification structure, Category Editing, prune

Top 10 Benefits of using DFR for PLM/PIM

Posted by Convergence Data Team on Oct 19, 2020 3:04:29 PM

Convergence Data is a Software as a Service (SAAS) company.  This means that we use our software every day along with our customers. Doing so keeps us on top of the refinements and enhancements we need to make to make DFR the best classification software it can be.  We have mitigated the challenges other systems like PIM and PLM can have with managing this information.

We have summarized what we think are the top 10 areas in which we provide the maximum benefit in a classification project for your PIM or PLM system deployment.

  1. Classification structure can be imported from different sources - PLM, excel then fine-tuned to global standards while adhering to best practices in DFR.
  2. Classification changes are simplified with DFR's multi-select capabilities - copy and paste multiple categories at once or prune and graft to move parts and categories all in one operation
  3. Convergence provides best practices in classification category development to ensure your structure is optimal for your business mitigating common mistakes based on our 20 years experience
  4. Classification projects are highly collaborative and may involve a lot of people working on structure and classify parts - DFR's workflow and task tracking process lets you know where you are each step of the journey
  5. Bulk data cleansing tools allows users to update values in seconds to adhere to approved lists, description templates, and approved units of measure
  6. DFR allows any type of data to be loaded – even if it’s not correct – then points out the errors and makes it easy to fix.  It’s very forgiving and makes fixing errors easy.
  7. DFR's Validation tools allows a user to select the error type to check or check all errors and run on an entire category – exposes errors and what needs to be fix for every item.
  8. Data Exporting - DFR Policy manager contains all the rules needed to validate exports to insure publishing to other systems is as efficient as possible.  DFR export tool supports exporting item data, classification structure, lists of values, in different formats – CSV, excel, XML and custom formats.
  9. DFR includes analytical tools used to spot trends in the data – e.g. what part characteristics is driving part proliferation for a category; its clustering capabilities can expose duplicate and near duplicate parts for rationalization.  DFR integrates supplier pricing to expose real cost saving opportunities.
  10. Role based security ensures only the right person can make changes to structure or data or approve and/or reject changes.
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Topics: Classification, Data Cleansing, convergence data, Cost Reduction, Cost Savings, Part Standardization, Duplicate Analysis, categories, classification structure, DFR PLM Integration

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