OnePart
Decrease Risks with Product Part Sourcing & Standardization Intelligence
Streamline Component Sourcing and Standardization, from Designers to Suppliers
NETVIBES OnePart artificial intelligence-driven applications help users classify company assets, identify master parts for reuse, and ensure Engineering selects the preferred part, while monitoring over time the execution of company policy. Even more, Sourcing and Procurement professionals leverage these applications to optimize orders by finding the right supplier, grouping components or selecting the right technically viable alternatives. Decrease risks by enforcing company standards and introducing more flexibility into the supply chain.
OnePart for Standardization
Helps Businesses to Simplify, Organize and Optimize Parts
Thanks to machine learning, it has never been easier to identify similar parts and compare technical information, helping complexity managers classify and identify the standard part to be reused by the design office.
From the entire legacy of company assets, quality and method engineers are able to classify parts. Based on 3D shape similarity and/or semantic criteria, each part is associated with the closest one to form a cluster that will eventually be classified into a family and sub-family. Once all the parts in the family are classified, specific parts can be tagged as either the master, alternate, or obsolete. This categorization of part families and sub-families, as well as the associated tags, is massively propagated into OnePart to guide part selection by designers and engineers and can be easily imported into the PDM and ERP systems.
It also prevents deviation and the proliferation of the same part references already optimized in the catalog. Receive a new part creation alert with a proposed classification or identification of existing similar parts to be reused.
OnePart for Sourcing & Purchasing
Makes It Easy to Search and Compare Parts In-house and Across Countries
Applying the power of automation and analytics to industrial parts enhances a company’s purchasing strategy by revealing major savings opportunities for next-gen design-to-value programs. Its unique 3D CAD Similarity and machine learning technologies enable cross-linking of Engineering and Purchasing data to quickly reveal similar or identical parts with different reference numbers and prices, facilitating quantity discounts and optimizing the selection of preferred suppliers.
One doesn’t need to master 3D software in order to benefit from OnePart. Procurement professionals can better:
- Detect possible inconsistencies between 3D parts, prices and suppliers
- Estimate potential savings and determine how to secure these savings
- Negotiate with suppliers
OnePart for Design & Engineering
Helps You Find and Reuse Existing Parts, 2D/3D Designs and Related Documentation
NETVIBES OnePart helps designers and engineers reuse existing parts instead of recreating existing ones, wasting time or simply not finding the right part. With just a few clicks, users explore legacy parts and related 2D/3D designs, gaining immediate insight into vast amounts of previously hidden data. They can further refine their search with side-by-side comparisons to identify the right part for reuse. When a similar part is discovered, the user can also analyze important related documentation like specifications, testing, validation, and certification material in multiple formats (such as .xls and .pdf) to facilitate optimal reuse and a complete view of the situation.
The OnePart integrated search experience is uniquely capable of combining similarity, metadata, and semantic-linked documents and related information with shape search capabilities, all on a single page.
Start Your Journey
We’ve got ideas about what components should cost, but we also know what our actuals are and we have all that information in one place. We know which parts we can reuse, but just as importantly, we know when our supply chain doesn’t have the appropriate capacity to do that. Just knowing that and being able to scenario-plan around it is absolutely transformational.
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