kHUB All Member Forum

• Shortened upfront strategy and analysis: Strategy and concept work are compressed, with fewer long exploratory cycles and earlier locking of target segments, price points, and feature sets. This shifts learning into the development and launch phases rather than front‑loaded analysis [2,8].
• Platform and architecture reuse: Modular product platforms (e.g., EV skateboard architectures, common electronics and software stacks) allow many derivatives to be spun off quickly with limited new engineering. This dramatically reduces design time and validation effort [7,9].   
• Minimum viable product mindset: Teams emphasize good‑enough configurations that satisfy core needs for large segments, consciously avoiding niche variants and frequent engineering changes. Enhancements come later via fast follow‑on releases or software updates [2,8].   

• Parallel, overlapping stages: Concept, engineering, industrialization, and even marketing preparation proceed in parallel, with its rapid iterations and interim builds (build-and-test) and early supplier involvement. This increases rework risk but slashes calendar time [2,10,11]. 
• Lean, hierarchical decision-making: R&D and procurement operate in lean structures with clear end‑to‑end accountability and top‑down calls, reducing cross‑functional debate and escalation delays. Leaders accept higher decision risk in exchange for speed [7,8].             
• Tight OEM–supplier integration: There are fewer but key suppliers that act as system partners, taking responsibility for whole subsystems and co‑developing on shared roadmaps. This removes many interface negotiations typical of Western programs and accelerates both design and changes [7,12].   

• AI-driven consumer insight and concept development: AI hubs in China (e.g., Unilever Shanghai) mine social, livestream, and e‑commerce data in real time to surface micro‑trends and auto-generate concepts, claims, and packaging options in hours instead of months. Teams co‑design with consumers using AR/VR and instant feedback loops [13,14]. 
• Simulation, digital twins, and rapid prototyping: High‑performance computing plus modelling software allow virtual experimentation with formulations, structures, and manufacturability before physical builds, shortening test–learn cycles. Auto companies like NIO, Zeekr, and BYD are already 30% faster in vehicle development [15]: instead of spending years building physical prototypes, many Chinese companies now rely almost entirely on digital simulations and virtual twins. Factory‑floor data integrated with AI creates closed loops between design and manufacturing to stabilize ramp‑up quickly [13,16].
• Software‑led product evolution: In sectors like automotive, software and vehicle OS (Operating Systems) set the pace; new features and updates are pushed out online, decoupling a portion of innovation from hardware refresh cycles. This enables frequent, post‑launch enhancements without full redesign [6,8].  

• State-backed lab‑to‑market pathways: Reforms to agencies (e.g., National Medical Products Administration) have introduced priority review and faster approval tracks for promising technologies, explicitly targeting reduced time from lab to market. State‑owned enterprises often act as early testbeds, absorbing early risk and creating reference customers [6,17,18].
• Market formation and demand shaping: Government uses subsidies, public procurement, and regulations (e.g., for New Energy Vehicles, NEVs) to quickly create volume markets where new technologies can scale and iterate. This demand certainty encourages firms to invest aggressively in rapid development and deployment [17,19].
• Technology diffusion and indigenous innovation: Policy has moved from importing technology to aggressively adapting, improving, and localizing it, shortening learning cycles and building capabilities to innovate atop global platforms [9,19].         

• Tolerance for fast imitation and incrementalism: Firms iterate quickly on existing designs and competitor products, using rapid cycles of imitation, adaptation, and improvement rather than long, original blank sheet programs. The emphasis is on commercial execution speed more than novelty for its own sake [9,20].
• Cost–speed dual focus: Product teams work under stringent cost‑out models at the same time as speed targets, using bulk purchasing, automated assembly, and logistics optimization to keep aggressive price points while moving fast [2,8].
• Talent and AI scale-up: Chinese companies are ramping AI investment, with most large firms expecting their AI development to proceed faster than initially planned, reinforcing these speed advantages in both white‑ and blue‑collar workflows [14,21].  

Accelerating product development is no longer optional—it’s a matter of survival. In today’s fast moving markets, Chinese innovators are proving that speed itself can be a weapon. By combining disciplined portfolio and Stage Gate thinking with newer, AI enabled and China speed practices—such as platform reuse, parallel experimentation, and data driven decision making—firms can compress time to market without sacrificing quality or increasing risk beyond acceptable bounds.

Does this mean we should throw our existing new product process? Not at all! Next generation Stage‑Gate thinking and China speed methods are converging on the same underlying idea: use disciplined governance, but drive development through rapid, iterative build‑test‑learn cycles. Modern Stage Gate provides the needed governance—investment decision points (gates with teeth) and risk management; and it offers a common language and a structure—a roadmap for the project team—while still allowing variable length, experimentation driven iterations inside stages.

Many of these methods may sound familiar, such as parallel processing, rapid build-and-test iterations, Agile-Stage-Gate, and AI-powered NPD. Indeed, many were developed in the West and some were part of 5th Gen Stage-Gate as far back as 2017 [22]. But more recently, Chinese firms appeared to have embraced and deployed these much methods more aggressively than in the West. For example, as of 2024, only about 18% of US firms had adopted AI for any application in NPD, about half the Chinese adoption rate [23]!

Management leadership is needed: The opportunity is to fuse modern Stage‑Gate discipline with rapid, China‑style iteration—treating speed as a managed capability—so the firm can move fast, but on strategy and with acceptable risk [24]. Over time, businesses that treat speed as an innovation capability—supported by governance, metrics, talent, and technology—will be best positioned to out innovate slower rivals and turn rapid learning into a durable competitive advantage.

The Author