Capturing the Value — Veridian Systems and the Data-Layer Race

A four-round, advanced innovation-strategy simulation set inside Veridian Systems SAS, a Lyon-based EUR 280M manufacturer of connected industrial sensors whose value is migrating from the device it sells once to the data and predictive-maintenance analytics its 25,000 installed machines stream every day. At the Hannover trade fair a cash-rich German automation giant has just unveiled PlantSense AI — a EUR 90/machine/month predictive-maintenance subscription with no hardware margin and a 6x marketing budget — a near-replica of the platform Veridian has prototyped for 14 months but never launched. The board meets in 120 days and wants a value-capture strategy, a committed go-to-market date, a build-buy-partner decision on the analytics platform, and a defendable answer to 'what stops PlantSense from eating us alive?'. Playing the Chief Strategy Officer with a EUR 40M two-year investment envelope, you (1) diagnose where in the value chain economic value is created versus where Veridian actually captures it, naming the data/analytics layer as the defensible one and the layer most exposed to imitation; (2) decide where to innovate and how to source the capability — Build (EUR 32M, 18 months, full IP), Buy (a Paris analytics start-up, EUR 28M, 9 months, integration risk) or Partner (white-label a cloud vendor, EUR 9M plus a 35% revenue share, 5 months but cedes the data relationship) — under the envelope and the deadline; (3) design the value-capture architecture: pricing model (one-time, subscription, outcome/pay-per-uptime) and the isolating mechanisms that defend margin — patents, data network effects, switching costs, exclusive integration, brand, speed — matching each mechanism to the appropriability regime; and (4) commit a GTM date, align the sales incentives to the new model, and defend an imitation-adjusted innovation ROI to the board. The math rewards innovating where value is captured, choosing a sourcing path that is fast enough to beat imitation while keeping the data relationship, building a compounding data moat rather than over-relying on slow patents, and shipping an interim move before the 18-month build — and it punishes the six classic errors. Final KPIs track Value Captured, Defensibility, Innovation ROI (imitation-adjusted) and Investment used against the EUR 40M envelope.