For buyers
Intelligent matching algorithm
Our ultimate goal is to pair buyers of drawing parts with the right manufacturing companies.
To that end, we have developed a system that classifies and standardizes individual parts, part families, or components as well as the suppliers' manufacturing competencies. This lets us know which part can be manufactured on which machine. With each submitted quote and each new drawing part, our algorithm learns which companies go well together and enables ideal partnerships.
Identification of matching suppliers
Manufacturing competencies
Our intelligent algorithm extracts the following from a buyer's needs at the individual part level: - Material - Component geometry and - the requested quantities. Using this information, the system can detect the machines that can manufacture what the buyer needs. This data is used to generate a shortlist of suppliers that are able to meet the technical requirements of the buyer's need.
Manufactured drawing parts
In the next step, an analysis is conducted to determine how many similar parts the suppliers in question have already manufactured. This analysis' data is based on the parts library in the manufacturing company's profile. Suppliers with many similar parts are displayed with priority in the shortlist.
Submitted quotes
Another sorting criterion is the classification of the manufacturing companies' historic readiness to provide a quote. Suppliers that have previously submitted quotes for similar parts thereby prove their production competency.
Profile quality
To provide buyers with as detailed of an overview of potential partners' competencies as possible, the individual components of supplier profiles undergo review. Profiles with more in-depth information are ranked higher on the shortlist.
From the idea to the core of the platform
„Our basic idea in the year 2000 was: "Each part has its matching machine - now where are they?" The answer to that is the central core of our matching algorithm. Workpieces are classified according to parametrical similarity criteria and combined with historical data. This allows us to create comparability in a field where "no two parts are the same.„
Techpilot
Frank Sattler
CEO
