R&D Efficiency Analysis: From Metric Definition to a Digital Decision‑Making System

Enterprise pressure to reduce costs and increase efficiency has made R&D effectiveness a hot topic; Baidu’s R&D efficiency platform evolves from simple metric dashboards to a value‑based digital decision system.

Definition of R&D efficiency : "Higher efficiency, higher quality, higher reliability, sustainable delivery of superior business value." The formula is expressed as per‑person output = demand throughput × value‑ratio × value/cost, highlighting three core factors: doing the right (high‑value) work, doing work correctly, and ensuring sustainable smooth delivery.

Analysis framework : Using GQM, the article builds quantitative models for key scenarios, then moves from measurement to diagnosis and decision‑making. It emphasizes problem‑driven analysis—identifying abnormal indicators, drilling down to root causes, and proposing concrete improvement actions.

Case study 1 – Human efficiency (testing staff) : By analyzing time utilization, bug detection efficiency, and defect‑rate, the study shows many testers are under‑utilized and many bugs are found in low‑value tasks. Metrics such as OLE = utilization × efficiency × (1‑leakage) are introduced, and visualizations reveal saturation gaps and uneven workload distribution. Recommendations include pooling test resources across teams to balance load.

Case study 2 – Code Review (CR) process : Metrics reveal that 70% of teams have low effective review counts and high “quick‑pass” rates, indicating high effort with low output. Analysis of review participation, saturation, and senior‑level involvement leads to suggestions such as faster review turnaround, better reviewer selection, and limiting large change submissions.

Case study 3 – End‑to‑end workflow bottlenecks : By separating explicit waiting periods (bright lines) from implicit delays (dark lines), the study identifies integration, testing, and deployment waiting as major contributors. Detailed drill‑downs pinpoint causes like temporary demand spikes and uneven test scheduling, prompting process‑level controls (e.g., stricter temporary‑demand admission, balanced test‑task timing).

Digital platform implementation : The solution consists of a low‑cost data lake, configurable ingestion, high‑performance query layer, a decision engine with metadata service, algorithm center, model management, and multi‑system notification. It supports business owners, line managers, and front‑line engineers with reports, online dashboards, and automated alerts.

Technical challenges and solutions : Data volume and freshness, limited analytical talent, and lack of automated action propagation are addressed by building scalable storage, providing reusable analysis algorithms, and integrating with task‑generation services.

Conclusion : The article summarizes five key takeaways—core efficiency factors, human‑testing analysis, CR inefficiencies, workflow bottlenecks, and the digital platform architecture—demonstrating how data‑driven diagnostics can drive real business value.