A House of Risk-Based Framework for Managing Risks in IoT-Enabled Social Manufacturing Systems
TL;DR
The most effective actions include more careful production planning, improved coordination with SMRs, early identification of production bottlenecks, comprehensive recording of customer requests, and selective SMR qualification.
Abstract
Abstract This study proposes a risk management framework for an IoT-based Social Manufacturing system using the House of Risk (HOR) method. In a social manufacturing system, all distributed and collaborative production activities are highly dependent on real-time internet connectivity. In this study, 27 risk events and 22 risk agents were identified through observation and interviews, as well as SCOR-based process mapping. The method used is HOR Phase 1 and HOR Phase 2 analysis. In HOR Phase 1, which calculates the Aggregate Risk Potential (ARP), it was found that 12 risk agents predominantly contributed 78.52% of the total system risk, with the highest ARP values related to production planning errors (1872), production delays (1813), and inaccurate customer order identification (1148). In HOR Phase 2, 21 preventive actions were evaluated using the Effectiveness-to-Difficulty Ratio (ETD), yielding a priority-based mitigation sequence. The most effective actions include more careful production planning, improved coordination with SMRs, early identification of production bottlenecks, comprehensive recording of customer requests, and selective SMR qualification. This research contributes to the risk management literature by applying HOR to Industry 4.0 and provides practical guidance for risk mitigation in IoT-based social manufacturing systems.