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 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.
Agile decision-making tool
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.
Partial data not a blocking point
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% 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 optimised the original data set to allow running the algorithm to identify printability.
“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,” – Mohammed Yahyai – 3D Scoping workstream Lead, Lead Rotating Equipment Engineer.
Funnel approach
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 parts descriptions and identifies part names that are earmarked for further printability analysis. In parallel, DigiPART precisely defines the functional specifications of a part. For example, having recognised an Impeller, DigiPART automatically associated more than 10 functional specifications relevant to PDO’s equipment and operating environment for a particular application (e.g. operating temperature, fluid service, corrosion levels, pressure, wear, resistance, etc.). This level of automation and precision is the key to efficiently identify the right AM material and print method like Selective Laser Melting (SLM), rapid casting or material deposition on large amount of data. 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.
“PDO now has access to an agile decision tool covering over 60,000 spare parts. Customised business cases filters enable PDO to select the most relevant parts to print and install or switch from physical to digital inventory,” Commented Paul Guillaumot, CEO of Spare Parts 3D.
In-Country Value initiative
An important benefit of the analysis is the ability for PDO to understand the potential to print in Oman. SP3D regrouped the outcome to show the AM solutions and materials combinations with most potential to increase local added value, including expected annual demand in US dollars. This enables PDO to provide input to local investment decisions and support the localisation strategy based on facts, not estimates.
“The ability to provide valorisation on AM potential is a first in Oman and of great support to accelerate In-Country Value initiatives and build our Industry 4.0 ecosystem in the sultanate,” said Sulaiman Ruqaishi, PDO In-Country Value Business Development Lead. The partnership allows PDO to accelerate its AM journey based on an 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.
“Our fundamental issue was to logically and quickly screen and identify 3D printing candidates to define PDO AM scope and move forward. With the support of our partner SP3D PDO is now able to make informed decisions, accelerate deployment and support stakeholders,” said Philippe Dupont, Head of Material Procurement & Inventory Management, PDO.
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