3Business & industry

• 3.1e-business & digital business models

  Covers digital business models—EC, the long tail, omnichannel, the sharing economy, platform business (two-sided markets and network effects), fintech, and subscription—as tools an IT strategist uses to judge "how the firm enters which market." The point is not term memorization but discerning which model fits according to the source of value (selling one's own product vs connecting two user groups).

• 3.2IoT, embedded applications & engineering systems

  Covers the business use of IoT, production-management methods (MRP, JIT / kanban, cell production), the smart factory (Industrie 4.0), CAD/CAM/CAE, and predictive maintenance, from the viewpoint of how an IT strategist judges manufacturing efficiency. A key point is trade-off judgment—such as whether to favor JIT or safety (buffer) stock under demand-variability constraints.

• 3.3Industry solutions & enterprise systems

  Covers SCM (optimizing the whole supply chain), CRM (deepening customer relationships), ERP (integrating core operations on a single foundation), SFA (supporting sales), industry-specific industry solutions (finance, distribution, manufacturing, public), and demand forecasting, from the viewpoint of an IT strategist choosing against the firm's strategy. A key point is judging whether fit-to-standard (fitting operations to ERP standard functions) or bespoke customization suits the firm's differentiation strategy.

• 3.4Business use of AI & data

  Covers data-driven management (deciding by data, not hunch or experience), the judgment of which business problems to apply AI/machine learning to so as to create real business value, the use of big data, the digital twin that mirrors the physical into virtual space, and data utilization and value creation that turns data itself into value. The point is to invest AI where it links to a concrete management indicator (KPI), separating it from hype—not doing AI "just because it is AI."