Turning tacit knowledge into explicit advantage, and opening the lab door
Tacit knowledge is the skill, intuition and "feel" inside an expert’s head. Explicit knowledge is that same experience written down, coded into data, software or a standard procedure — so anyone can apply it, not just the expert.
Decades of experience let a Takumi hear or feel a defect in a part — a skill that normally retires when the person does.
Engineers studied that sense and encoded it into automated defect sensors (Jidoka) and a line-wide alert system (Andon) — so the machine now works at the Takumi’s precision, system-wide.
Recognizing extreme weather in multispectral satellite imagery used to depend entirely on an experienced forecaster’s trained eye.
Experts label thousands of images, turning personal judgment into a structured training set. A model learns from it and becomes a deployable forecasting tool, usable at global scale.
Closed innovation keeps research inside one lab, start to finish. Open innovation deliberately lets ideas, technology and people cross the company’s boundary in both directions — bringing outside ideas in, and sending unused internal technology out.
Siri began as a spin-out of SRI International. FaceID traces back to the acquisition of PrimeSense. Apple rarely invents the core sensor technology itself.
Apple’s advantage is turning acquired technology into a polished feature through UI/UX design and hardware optimization — and the App Store itself is open innovation, letting millions of outside developers create value Apple’s own R&D never could alone.
Design partnership with Pininfarina (Italy); chassis technology from BMW; solid-state battery co-development with ProLogium (Taiwan) — open innovation used to skip a knowledge-building timeline that normally takes decades.
Buying the technology is not the hard part. Integrating components from many different vendors into one coherent software and hardware system, without conflicts, is — the real constraint is the company’s capacity to absorb, not to acquire.
High-performance computing processes multispectral data from satellites like Sentinel or Himawari to run complex climate models, and combine with AI to optimize how renewable energy is dispatched onto the grid using real-time forecasts.
Global logistics’ biggest bottleneck is an information blind spot and slow response. IoT sensor networks with edge AI monitor cargo condition and auto-reroute the moment a disruption happens.
Instead of linear planning, organizations simulate spatial interactions in a logistics ecosystem or disease spread, “stress-testing” systemic risk and building contingency scenarios with mathematical confidence.
Between building macro remote-sensing processing power (HPC) and building a micro ground-level sensor network (IoT), which direction creates a more durable defensive moat against disruption for today’s global supply chains — and why?