Compliance management in business operations is often addressed through fragmented procedures that are difficult to coordinate and evaluate in a consistent manner. This study develops a structured compliance management framework grounded in a system engineering perspective, with the aim of linking regulatory requirements to operational processes in a coherent way. The framework is constructed by organizing compliance activities into a set of interrelated components, including regulatory interpretation, process integration, monitoring mechanisms, and feedback loops. On this basis, an evaluation scheme is established to examine the consistency and effectiveness of compliance implementation across operational stages. Particular attention is given to the identification of critical control points and the interaction between compliance measures and routine business processes. The proposed framework is examined through its application to typical organizational settings, where it allows a more transparent mapping between compliance requirements and operational execution. The analysis shows that a system-based structure supports clearer identification of process dependencies and facilitates more consistent evaluation outcomes. The study provides a structured basis for understanding compliance as an integrated operational system rather than a set of isolated practices, and offers a foundation for more informed decision-making in compliance management.
Effective maintenance planning in high-performance mechanical systems requires a structured approach to identifying and prioritizing potential failure modes under multiple, often conflicting criteria. Conventional Failure Mode and Effects Analysis (FMEA) relies heavily on subjective judgment, which can limit consistency and transparency in decision-making. To address this limitation, this study develops a decision-oriented framework that integrates Shannon entropy-based weighting with three Multi-Criteria Decision-Making (MCDM) methods, namely SAW, TOPSIS, and VIKOR. The framework is applied to a representative high-performance mechanical system, in which maintenance-related factors, including failure probability, detection capability, economic impact, repair time, and resource availability, are evaluated in a unified structure. Entropy weighting is employed to derive criterion importance directly from data, reducing reliance on expert bias. The combined use of multiple MCDM techniques enables cross-validation of ranking outcomes and improves the robustness of the prioritization process. The results show a high degree of consistency among the three methods (Spearman’s $\rho>0.80$), indicating stable identification of critical failure modes. The proposed framework provides a transparent basis for risk-informed maintenance planning and supports more effective allocation of inspection and repair resources. From an engineering management perspective, the approach facilitates the transition from experience-driven decisions to data-supported strategies, contributing to improved system reliability and operational efficiency. Although demonstrated in a specific application context, the framework can be extended to other engineering systems where structured failure prioritization is required.
Construction projects frequently encounter field constraints that affect cost, schedule, and quality performance. When delays arise, contractors often adopt overtime work as an acceleration strategy using the existing workforce. However, such practices may lead to concerns regarding productivity decline. This study investigates the impact of overtime work on construction labor productivity based on the Five-Minute Rating method, focusing on plastering and skim coating activities in a residential project in Palangka Raya, Indonesia. A systematic work sampling approach was employed, comprising 1,296 observations collected over six days, with comparisons made between regular working hours and overtime periods. The results indicate distinct productivity responses across work types. Plastering exhibited only a marginal reduction in Labor Utilization Rate (LUR) of approximately 1%, whereas skim coating showed a more pronounced decline of about 6.5% during overtime. Effective activities decreased by approximately 6% under overtime conditions. In contrast, volume-based analysis suggests that output increased during overtime, with gains of 28% for plastering and 49% for skim coating. Statistical analysis suggested a significant difference in productivity for skim coating (p = 0.031), while no statistically meaningful difference was observed for plastering (p = 0.109) at the 95% confidence level. Despite the observed increase in output, the achieved productivity levels remain below standard unit price analysis benchmarks.
Sustainable transformation within food and beverage (F&B) small and medium-sized industries (SMIs) in developing countries continues to be constrained by high levels of food loss and waste (FLW), inefficient resource utilization, limited technological capability, and weak organizational preparedness for digital and circular transition. Although the integration of Circular Economy (CE) principles and Industry 4.0 (I4.0) technologies has increasingly been recognized as a strategic pathway toward sustainable industrial development, limited empirical attention has been devoted to the assessment of organizational readiness for such transformation, particularly within resource-constrained SMIs. In response to this gap, a data-driven readiness assessment framework was developed to evaluate the preparedness of F&B SMIs for CE and I4.0 adoption. Survey data obtained from 150 F&B SMIs were analysed through a method integrating Principal Component Analysis (PCA), CRITIC method, and the TOPSIS. PCA was employed to identify latent readiness dimensions and reduce indicator redundancy, while the CRITIC method was utilized to derive objective indicator weights based on contrast intensity and inter-criteria conflict. Subsequently, the TOPSIS was applied to calculate composite readiness scores and classify firms according to their readiness levels. Eleven readiness dimensions were identified, among which sustainable sourcing and circular procurement, environmental value internalization, and human-centric managerial capability were found to exert the strongest influence on readiness performance. The findings further revealed that most participating SMIs were positioned within the developing readiness category, indicating that sustainability-oriented and digital transformation practices have been initiated but remain insufficiently institutionalized and operationally integrated. The results suggest that readiness for CE and I4.0 adoption is shaped not solely by technological infrastructure, but also by organizational culture, strategic procurement practices, managerial orientation, and workforce-related capabilities. The proposed framework contributes to the sustainability and industrial transformation literature by providing a robust and transferable decision-support instrument capable of supporting evidence-based managerial interventions and policy formulation aimed at accelerating sustainable industrial transition within F&B SMIs in developing economies.
The research analyzes whether monitoring system design, calibration management, timestamp consistency, data traceability, and verification procedures relate to the risk control of the financial aspects of methane-abatement engineering projects. An analytical case study based on a single project and involving a before-and-after comparison of the implementation of an monitoring, reporting, and verification (MRV) regime was conducted under fixed engineering and accounting conditions. This design allows the comparison to focus on differences in MRV evidence management conditions rather than on changes in physical mitigation technology. Conservative issuability was estimated using the low-confidence adjustment metric (LCAM). This analytical metric scales engineering emission reductions by evidence-related factors without supplanting registry rules or verifier judgment. With the enhanced MRV regime, the conservatively supportable fraction was 77.0% to 91.3%, while the realized price wedge declined from 0.30 to 0.12. The monitoring-to-issuance period was also shortened by 50 days.
Building occupancy certification is a key mechanism for managing post-construction compliance, covering building safety, functional readiness, and legal operability. In Indonesia, this function is carried by the SLF (Sertifikat Laik Fungsi, Certificate of Functional Worthiness), yet fewer than 10% of buildings nationwide hold one. This study asks what drives participation in SLF certification, looking at both behavioural and system-level factors within the country’s digital building certification system. Using a sequential explanatory mixed-methods design, we analysed 270 valid survey responses from Semarang, Sidoarjo, and Bandung with partial least squares–structural equation modelling (PLS-SEM), then drew on focus group discussions (FGDs) with government officials, consultants, technical experts, and business associations to interpret the results. The quantitative results show that intention to obtain an SLF is the strongest predictor of participation, supported by knowledge and perceived ease of use of the SIMBG (Sistem Informasi Manajemen Bangunan Gedung, Building Management Information System) platform. Technical and bureaucratic barriers did not show a statistically significant negative effect in the expected direction. However, the qualitative findings reveal that high consultant costs, weak document validation, inconsistent local requirements, limited technical staff capacity, and unclear institutional coordination remain important obstacles in the certification workflow. The study contributes to engineering management by repositioning SLF participation as part of a digital building compliance management process rather than merely an administrative or public service issue. The findings indicate that improving SLF participation requires not only awareness campaigns, but also workflow-level interventions, including document pre-checking, standardised technical submission templates, cost estimation tools, application tracking, and clearer coordination between central platform managers and local technical agencies.
Healthcare supply chains face increasing challenges related to counterfeit products, fragmented information flows, limited traceability, and insufficient coordination among distributed stakeholders. Existing centralized and partially decentralized approaches still encounter difficulties in maintaining immutable records, real-time verification, and trusted operational transparency across the pharmaceutical distribution process. This study investigates a distributed medical supply chain framework that improves traceability, compliance control, and operational reliability in healthcare logistics. A blockchain-enabled architecture was developed by integrating dynamic quick response (QR)-based identification, customizable smart contracts, and a hybrid consensus mechanism combining Proof-of-Work (PoW) and Proof-of-Stake (PoS). The framework assigned a unique cryptographic identity to each medicine unit and supported end-to-end verification through blockchain-linked QR validation. Smart contracts were designed to automate ownership transfer, compliance checking, and counterfeit detection throughout the supply chain workflow. The framework was implemented and evaluated in a simulated distributed environment using pharmaceutical transaction scenarios. The experimental results showed that the proposed approach achieved average validation accuracy of approximately 98.1%, maintained transaction throughput between 150 and 320 transactions per second (TPS), and reduced consensus delay through adaptive PoW–PoS coordination. The system also demonstrated strong resistance to forgery attempts and stable operational performance across repeated validation experiments. The results indicate that integrating blockchain governance mechanisms with QR-enabled authentication can improve transparency, trust, and traceability in distributed healthcare supply chains. The proposed framework provides a scalable systems engineering solution for pharmaceutical logistics management and offers a practical foundation for compliance-oriented digital transformation in healthcare supply networks.
The psychological well-being of truck drivers is a distinct concern in occupational psychology and engineering management. Job isolation, work insecurity, and poor working conditions are the factors that contribute to elevated stress and turnover among truck drivers. However, limited studies could be found with these factors that ultimately influence truck drivers’ psychological well‑being. Underpinned by balance theory, this study examines the relationships between working environment, social support, economic domains and truck drivers’ psychological well-being, with the mediating effect of quality of life (QoL). A primary quantitative approach was administered to 403 truck drivers. The data were then analyzed using partial least squares–structural equation modeling (PLS–SEM) approach and mediation analysis with bootstrapping procedures. The results indicate that working environment, social support, and economic domains significantly influenced overall QoL. Crucially, overall QoL fully mediated the relationships between working environment, social support, economic conditions, and psychological well-being. However, the direct paths from these occupational factors to psychological well-being were statistically non-significant. This research would strengthen occupational psychology and engineering management fields theoretically as overall QoL is used as a key mediator between occupational stressors and psychological well-being of the truck drivers. These findings may help logistics managers identify relevant areas for economic, social, and organizational intervention. Practically, these results emphasize the need for holistic interventions to create fair compensation and ergonomic work design to socially support and enhance truck drivers’ well-being.
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