A planning and lifecycle cost model for industrial Internet of Things systems

The Industrial Internet of Things (IIoT) is a pillar for Industry 4.0 and 5.0, yet the profitability is critically dependent on choosing appropriate architecture and estimating infrastructure costs. This paper addresses these challenges by introducing a comprehensive 3-phase framework for IIoT system planning and lifecycle cost estimation, designed as a decision-support tool for strategic investment analysis. A primary barrier to IIoT adoption remains the financial justification, with uncertain return on investment and high implementation expenses frequently cited as major concerns. To provide a structured approach, this paper describes the methodological framework through three phases: strategic architecture selection, to identify the most suitable architecture based on business and technical constraints, system planning and data gathering, to map the architecture to the specific process and define key cost drivers; and detailed economic modeling, applying a parametric lifecycle cost model to quantify capital and operational expenditures, integrating quantitative sub-models for financial risks and strategic expenses. This approach ensures that the economic estimation is grounded in a sound strategic and technical foundation. This methodology facilitates an economic comparison of alternative solutions, allowing the selection of the optimal configuration for data acquisition and transmission based on specific application requirements. The framework’s usefulness is demonstrated through a case study of a predictive maintenance application, which compares different architectures and highlights the trade-offs between cost, scalability, and risk exposure. The proposed framework is presented as a convenient tool for engineers and strategic planners navigating the economic complexities of investments in the ongoing digital and sustainable industrial transition.