Commercial and AI Visual Presentation AI03: Streamlining supply chain contracting through Industry Standard Datasheet Definitions (ISDDs)

Commercial and AI Visual Presentation AI03 In complex, asset-intensive industries such as energy, exchanging requirements for supply chain contracting using standard datasheets remains challenging. While Industry Standard Datasheets (ISDs) exist, often as spreadsheets, they are rarely shared in a machine-readable format. Current practices are labour-intensive, error-prone, and incomplete, complicating equipment selection, evaluation of manufacturer offerings, and supplier commitment management. Missing or incomplete information during contracting and handover can cascade into later asset lifecycle phases, affecting installation, commissioning, and operations and maintenance, where accurate data is critical. This generates recurring costs and increases the risk of supply chain misalignment. This paper presents Industry Standard Data Dictionaries (ISDDs) as a solution. ISDDs capture ISDs as machine-readable, reusable, and extensible business objects, defining property sets for functional locations, models, and uniquely identified assets. They map to a shared reference data library, enabling interoperable data exchange across suppliers, engineering teams, and operational systems. ISDDs extract high-value properties from trusted datasheets published by associations such as ISA, API, ASME, and PIP. Demonstrations from the Oil and Gas Interoperability pilot show how functional requirements embedded in process and instrumentation diagrams can be shared digitally via ISDDs, streamlining requirement sharing, vendor responses, and purchasing decisions, while supporting downstream processes including installation, commissioning, operations, and maintenance. Embedding ISDDs into contracting workflows provides a practical pathway to efficiency, transparency, and resilience, advancing digital transformation across energy sector supply chains. To access the Visual Presentation click on 'Supplementary data' below. To read the full paper click here