Petroleum Development Oman (PDO), the national oil company for Oman, has partnered with the French startup, Spare Parts 3D (SP3D), to assess the 3D printability of 150,000 unique spare parts.
In six months, the team processed 150,000 coded spare parts to define a 3D printing adoption matrix based on technical feasibility and economical attractiveness.
This was made possible thanks to SP3D’s data-driven funnel approach methodology, its unique machine-learning-based software DigiPART, the availability of material coding information records and the commitment of a multidisciplinary PDO team.
PDO now has an agile decision-making tool to enable its 3D printing deployment roadmap, speeding up Additive Manufacturing (AM) deployment by an estimated two years, and helping the company to remain at the forefront of innovation in the Middle East.
Despite having extensive master data records, PDO did not have the ideal set of data available containing all technical and economic data points, technical drawings, 3D files in one place and readily accessible. For example, it was a challenge to scope the AM journey given that circa 80 percent of the parts lacked dimension and weight information.
The information provided was extracted from PDO ERP (Inventory management, Purchase Orders and Material Coding) limiting the time required to collect data on such a large number of parts.
Thanks to DigiPART’s semantic recognition algorithms combined with extensive AM databases the team optimized the original data set to allow running the algorithm to identify printability.
Paul Guillaumot CEO – PDO
“PDO now has access to an agile decision tool covering over 60,000 spare parts. Customized business case filters enable PDO to select the most relevant parts to print and install or switch from physical to digital inventory.”
“After having selected non-suitable parts, ourselves, we contracted SP3D for their ability to enrich our partial dataset. This helped to select the right spare parts to focus on for PDO’s AM journey,” said Mr. Mohammed Yahyai – 3D Scoping workstream Lead, Lead Rotating Equipment Engineer.
To efficiently sift through 150,000 coded parts, DigiPART runs a number of algorithms among which is the Semantic Recognition Algorithm (SRA). The SRA reads through the descriptions of the parts and identifies part names that are earmarked for further printability analysis. In parallel, DigiPART precisely defines the functional specifications of a part.
This iterative funnel approach resulted in a first 200 opportunity list. PDO then asked warehousing teams to verify the assumptions made during the Identification stage, which is manageable for 200 parts. The data-driven outputs for these opportunities will support the 3D printing deployment roadmap.
The partnership allows PDO to accelerate its AM journey based on extensive and comprehensive analysis. Enriching PDO’s existing data via the use of algorithms enabled the creation of a 3D printing parts selection matrix including both technical and commercial feasibility considerations.
