Full-Chain Efficiency: How Structural Restructuring Delivered 30%+ Cost Reduction Without Layoffs

The article opens with a Deming quote: true efficiency gains come from restructuring the system, not patching processes. The author, a former JD Retail senior director and large-promotion commander, recounts how in 2018 their team presented the industry-standard "Golden Triangle" of R&D efficiency — platform, measurement, practices — to the CEO and were rejected as thinking too small. Optimizing tools and workflows for tens of thousands of engineers might yield only 5% gains, which doesn't reach CXO-level attention.

Reframing the Problem

The core insight: "Local optimum ≠ global optimum" (SAFe's "Optimize the Whole"). The team shifted from "R&D efficiency" to "full-chain efficiency," spanning demand source → business-product-R&D → front/mid/back-office → upstream/downstream engineering → post-launch value verification.

Front-End Source Layer: Project & Product Portfolio Governance

Project tiering & resource matching: Using strategic focus and alignment (referencing a prior article), projects were classified as strategic/non-strategic, business/product-R&D/maintenance. Non-strategic B/C-grade or maintenance projects in the PMP system were shut down or redirected.

Concrete cost example: A minimal 3-person unit consumes ~60 person-days/month. At ~¥4,000/person-day (fully loaded), that's ~¥150K/month, ~¥1.8M/year per project. Many projects couldn't articulate why they still existed.

Product portfolio rationalization: Via product panorama or CMDB, every product/tool was categorized, graded, and assessed for utility and traffic. The author states 30–40% of products/internal tools in large companies are unused, zero-traffic, duplicate, or purpose-unknown. A single tech-committee-level review (a "No.1 project") can retire hundreds of products, reclaiming resources — "a product easily carries hundreds of thousands to millions in headcount cost."

Principle: "Strategic focus, resource matching, value verification" must be rigidly executed, not compromised.

Upstream & Downstream Engineering: Coding Is <20% of Cycle

JD Retail data (2026 JDD summit, Hu Xi): B-end teams pushed AI coding to 55% of code output, yet overall demand throughput rose only 2.1%.

Root cause: Real software engineering spends <20% on coding; >80% goes to coordination — requirements analysis, system design, solution reviews ("upstream engineering").

Implication: The bottleneck is organizational productivity, not individual coding speed. AI coding without knowledge bases, tech docs, engineering context, and continuous distillation loops produces buggy demo code. The moat lies in enterprise-specific context, collaboration systems, and continuous learning loops (Model → Context → Learning → Loop).

Post-Launch: Value Verification Closed Loop

"Only birth, no nurturing" — projects deliver and close, but no one checks if promised benefits materialized. This wastes prior source-layer governance.

Third-party benefit approval: Commitment metrics at initiation are reviewed by independent business analysis ("jingfen"), not the requestor.

Penalty-backed commitments: Unmet targets incur multiplicative cost penalties (up to 10× observed). Purpose: make sponsors "hands shake" when writing numbers. Result: double-digit percentage-point improvement in benefit achievement rate.

Monthly verification cadence: Not year-end; fixed monthly batches of launched projects verified, forcing business owners to stay accountable post-launch.

Two Definitions of "Full-Chain Efficiency" Contrasted

Common view: Platform, measurement, practices across R&D flow — optimizes flow .

Author's view: Top-leader mandate; from demand source (strategy, BRD, PMP) through biz-prod-R&D, front/mid/back, upstream/downstream, to post-launch verification — find the true bottleneck, apply resources, remove redundancy. Optimizes direction and structure .

"Former makes known processes run smoother; latter first confirms we're doing the right things."

Three Weekly Self-Checks

Can you pull a full project list and product panorama? If not, you're only doing R&D process optimization.

Has the No.1 leader personally killed a project or retired a product this quarter? If not, the mechanism is idling.

Are launched projects reviewed for actual benefits? If not, saved resources leak back into next planning cycle.

The author notes each sub-optimization at platform-scale companies holds 10%+ room; anything under ¥500M impact is thinking too small.