Aug 2026· Management Systems in Production Engineering· Vol 34, pp. 333 - 347· 0 citations· 35 references
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.
Manufacturing systems increasingly require real-time performance monitoring and data-driven optimization to reduce downtime, stabilize quality, and support flexible production. Although Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies have been widely discussed in smart manufacturing, existing studies often treat sensing, key performance indicators (KPIs), analytics, and decision support as separate concerns. This paper presents a structured literature review and conceptual synthesis of IoT-enabled performance monitoring and optimization in manufacturing systems, with emphasis on recent work in IIoT architectures, edge and cloud analytics, digital twins, predictive maintenance, and manufacturing KPIs. The main contribution is an integrated five-layer conceptual framework that connects physical sensing and data acquisition, edge computing and connectivity, data management and integration, analytics and intelligence, and application-level decision support. The framework clarifies how shop-floor data can be transformed into KPI-oriented insights and optimization actions while accounting for cybersecurity, interoperability, data governance, scalability, and human-in-the-loop decision-making. An illustrative automotive parts/CNC manufacturing scenario demonstrates the framework's potential application; however, no simulation, pilot deployment, or quantitative validation is claimed. The review concludes by outlining implementation considerations and a validation roadmap for future empirical studies, including digital-twin simulation, pilot testing, baseline KPI comparison after implementation, and cost-benefit assessment.
Sami Gazem Abdullah Thabet, M. Amrani· 2026 6th International Confe...· 0 citations
Backgroung: The confluence of the Internet of Things (IoT) and the Physical Internet (PI) is a major accelerant of Logistics 4.0, which can provide significant upsides to supply chain performance. While prior reviews mainly concentrated on technological advancement and applications, limited consideration has been given to the mechanisms through which the PI-IoT system integration generates benefits, namely operational, economic, and environmental benefits. Methods: To fill this gap, this study undertakes a systematic literature review of 43 peer-reviewed studies guided by PRISMA. This study uses the Context, Intervention, Mechanism, Outcome (CIMO) framework to automatically conduct a mechanism-based synthesis of PI-IoT integration, unlike previous reviews. Results: The results show that enhanced operational and economic efficiency, as well as environmentally sustainable performance, can be achieved by using real-time visibility, predictive decision-making, collaborative resource optimization, intelligent automation, and adaptive system responsiveness. Conclusion: This review also proposes a conceptual framework, linking contextual conditions, technological interventions, mechanisms, and outcomes, while also identifying important research gaps and future research directions. This study offers essential findings that offer practical insights for researchers, logistics managers, and policymakers intending to implement collaborative, data-driven, and sustainable PI-IoT-enabled logistics systems.
The construction industry in Abuja continues to experience persistent challenges, including project delays, cost overruns, and inadequate safety performance, which significantly affect project delivery outcomes. This paper examines the use of Building Information Model (BIM) and Internet of Things (IoT) in construction project management in Abuja, Nigeria. Mixed methods with an explanatory sequential approach were used in this research work. The primary data was gathered through the use of a structured questionnaire and an advanced audit of HSE and Equipment Logs at an active construction project in Abuja. The data analysis used in the research included descriptive statistics and Key Performance Indicator (KPI) mapping. There were moderate levels of awareness among professionals (Mean=3.31). However, the logbooks exhibited significant utility of technology in terms of risk mitigation and achieved 847,861 man-hours without Lost Time Injury (LTI). Idle monitoring of IoT assets worth more than ₦1,000,000 in the form of cranes was undertaken in order to increase Return on Investment (ROI). The findings revealed that the technical infrastructure supporting IoT implementation exists among the participating firms in Abuja, although a stronger managerial and policy framework are required to maximise its benefits. The study recommends government-mandated BIM-IoT standards and usage-based maintenance protocols for construction equipment to improve project sustainability and safety metrics.
Anna Omata, O. Mogbo· Nile Journal of Engineering...· 0 citations
In the era of Industry 4.0, the Internet of Things (IoT) has emerged as a transformative technology with the potential to enhance organizational performance in the manufacturing sector significantly. The primary aim of this paper is to review previous studies on how the manufacturing industry can improve its organizational performance by implementing IoT Smart Decision-Making Processes. Despite the numerous benefits associated with IoT, including real-time monitoring, predictive maintenance, and process automation, many firms face significant challenges such as high implementation costs, limited technical expertise, cybersecurity risks, and uncertain returns on investment. The paper addresses critical questions regarding which IoT Smart Decision-Making processes are instrumental in enhancing organizational performance, how these processes drive improvements in manufacturing outcomes, and what essential elements constitute an effective IoT Smart Decision-Making process in the manufacturing industry. By bridging the gap between the theoretical potential of IoT and its practical application in developing economies such as Malaysia, this paper proposes an IoT smart decision-making process for industry practitioners, informs policy formulation, and contributes to the academic discourse on smart manufacturing and Industry 4.0.
Siti Nur Nabilah Isnin, Nurul Farhaini Razali, N. A. A. Aziz et al.· International journal of res...· 0 citations
Supply chain disruptions in LPG manufacturing can significantly affect procurement, production, and distribution, requiring a proactive risk management approach. This study aims to identify supply chain risks, prioritize their root causes, and develop preventive mitigation strategies by integrating the Supply Chain Operations Reference (SCOR) model and the House of Risk (HOR) method. A quantitative case study was conducted using interviews, focus group discussions, and expert judgment involving five managers from procurement, operations, terminal, marine, and sales functions. The SCOR model identified 22 Risk Events and 41 Risk Agents across six supply chain processes. HOR Phase 1 prioritized seven Risk Agents, accounting for 55.59% of the total Aggregate Risk Potential, with procurement- and logistics-related risks emerging as the most critical. HOR Phase 2 proposed 13 preventive actions, including supplier performance evaluation, unloading schedule optimization, preventive maintenance, cross-functional coordination, and real-time shipment monitoring. The integration of SCOR and HOR provides a systematic framework for prioritizing critical Risk Agents and developing proactive mitigation strategies, thereby improving supply chain resilience and operational reliability in LPG manufacturing.
Delays in the construction industry continue to be a challenge in India, which has been mainly caused by a lack of proper planning, ill-defined decision-making and inability to adapt to technological advancements. The proposed research work aims to analyze the impact of the use of specific, measurable, achievable, relevant and time-bound (SMART) project management techniques, in partnership with Internet of Things (IoT), to improve delay management in the Indian construction industry.
The approach was qualitative and expert-driven. Sixteen dimensions of delay, identified from the literature using a political, economic, social, technological, environmental and legal (PESTEL) analysis, were verified by eight senior experts from industry, government, technology supply and academia. Total interpretive structural modeling (TISM) was performed to generate the relationship between identified variables. Fuzzy Matrixy Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis was performed to classify variables based on their driving power and dependence into independent, dependent, linkage and autonomous categories.
Issues of disputes, negotiations, lack of communication, planning, lack of clear contract procedures and scope creep were identified as the most probable delay causes. The adoption of smart philosophy in monitoring allows better coordination, decision-making and provides a clear avenue for eliminating process inefficiencies.
This study integrates SMART project management, IoT and a PESTEL–TISM–Fuzzy MICMAC framework to analyze construction delays in India. It advances prior research by modeling interdependencies and prioritizing delay drivers based on systemic influence, linking macro-environmental factors with organizational practices to inform managerial and policy-level interventions for improved project performance.
Rakesh Acharya, Vikas Thakur, Mayank Yadav et al.· Built Environment Project an...· 0 citations
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