IMPROVING PROJECT LIFECYCLE MANAGEMENT (PLM) EFFICIENCY WITH CLOUD ARCHITECTURES AND CAD INTEGRATION AN EMPIRICAL STUDY USING INDUSTRIAL CAD REPOSITORIES AND CLOUD‑NATIVE WORKFLOWS

Abstract

This empirical quantitative study examined how cloud architectures and CAD–PLM integration mechanisms related to Project Lifecycle Management (PLM) efficiency using trace evidence from industrial CAD repositories and cloud-native workflows. A retrospective longitudinal, multi-case design was applied to linked event logs spanning CAD artifact histories, PLM change and release records, and workflow telemetry. The final analytic dataset comprised 12 projects, 4,860 engineering change objects, 1,140 release packages, 98,420 CAD artifacts, and 312,775 workflow job instances (numerical findings as summarized in the descriptive results). PLM efficiency was operationalized with governance-aligned event boundaries and distribution-focused metrics, including engineering change lead time, release cycle time, workflow throughput, workflow success rate, tail-delay prevalence, and rework proxies derived from reopened changes, repeated short-window edits, repeated packaging attempts, and retry cascades. Descriptive results indicated heavy-tailed timing behavior: engineering change lead time exhibited a median of 14.6 days with a 90th percentile of 42.0 days, and release cycle time exhibited a median of 6.2 days with a 90th percentile of 18.5 days. Workflow operations showed a mean throughput of 1,360 jobs/day and an overall workflow success rate of 93.8%, while exception clusters were concentrated in translation and packaging stages. Rework signals were nontrivial, with reopened changes averaging 8.4% and repeated edits within short windows averaging 20.2% across change cases. These findings supported a measurement-based interpretation of PLM efficiency as a coupled outcome shaped by propagation timeliness, integrity preservation, execution reliability, product structure complexity, and collaboration concurrency, with tail delays and rework emerging as central stability risks alongside average speed.