SmartPlant Instrumentation (SPI) — Enterprise Platform Modernisation

SmartPlant Instrumentation (SPI) is a 30-year-old enterprise engineering platform used by EPCs and owner-operators across Oil & Gas, Chemicals, Power, and Industrial megaprojects to manage instrumentation design, loop diagrams, wiring, I/O data, specifications, and deliverables. With decades of extensions and customisations, SPI has one of the most complex engineering data models in the industry. My role was to modernise key modules, reduce manual work, improve data accuracy, and enhance workflows that thousands of engineers rely on — balancing backward compatibility with meaningful upgrades.

Four key pain points drove the modernisation programme: loop drawing generation was slow, error-prone, and based on legacy templates with brittle mappings; large EPC projects in remote locations struggled with constant server connections, blocking the offline engineering model; the specification module had rigid templates, poor validation, and excessive manual entry with limited data reuse; and the platform had stability and performance issues under large datasets and multi-user conditions.

Modernisation across four areas while preserving compatibility. Loop Drawing Generation: redesigned generation logic for more predictable output, improved template handling and mappings, added pre-generation validation, and enhanced preview workflow with clearer error messaging. Offline Engineering Model: defined offline workflow requirements, designed conflict-resolution on sync, reduced dependency on live server connections, and ensured compatibility with large engineering databases. Specification Module: introduced more flexible templates, field-level validations, stronger instrument-to-datasheet mappings, data reuse to reduce manual entry, and revision tracking for change traceability. Performance and Stability: identified and resolved query bottlenecks, improved multi-user performance, and maintained backward compatibility across patch and hotfix releases.

Senior Product Owner across multiple releases. Defined scope, workflows, and acceptance criteria for each module. Coordinated engineering, QA, UX, and customer stakeholders across complex, multi-dependency delivery cycles. Balanced legacy behaviours with modern UX and validation improvements — ensuring no regressions for a global enterprise customer base.

More consistent and accurate loop drawings with fewer failed generations and faster QC cycles. Engineers can work fully offline in remote and brownfield environments. Faster and more consistent specification creation with fewer data entry errors. Improved stability and performance on large datasets — with measurably higher customer satisfaction across key accounts.