This study addresses the strategic engineering management challenge of balancing short-term farm economic profitability with long-term soil carrying capacity restoration in staple crop systems. It aims to evaluate policy trade-offs and inter-agency coordination mechanisms across the Triple Bottom Line (TBL) dimensions—People, Planet, and Profit—to enhance regional rice food security in East Java, Indonesia. A system dynamics (SD) simulation model was engineered using STELLA software, integrating demographic demand, biophysical soil degradation/regeneration loops, farmer financial investment flows, and policy execution dynamics. Model parameters were calibrated using empirical historical data (2021–2025) and stakeholder interviews ($N$ = 45). Structural validity was evaluated through dimensional consistency, extreme-condition tests, and mean relative error (E1), defined as the average percentage deviation between simulated and historical behavior (E1 $\leq$ 4.73%). Four policy intervention scenarios were simulated over a 2026–2030 horizon: Baseline, Economic Intervention, Environmental Intervention, and a Combined Policy Intervention. The Baseline scenario experiences saturation after 2028, with the Sustainable Rice Food Security Index (SFSI) declining to 70.41 by 2030 due to unmitigated soil health degradation. The Economic Intervention scenario enhances immediate farmer profitability (SFSI = 75.43), but performance levels off over time due to ongoing biophysical soil depletion. The Environmental Interventio scenario restricting subsidized chemical fertilizer to 1,034,209 tons/year and transitioning to organic inputs exhibits steady ecological resilience (SFSI = 77.61). The Combined Policy Intervention scenario achieves the highest projected performance (SFSI = 81.12), buffering short-term financial transition shocks while supporting soil carrying capacity restoration. Overall, the simulation results suggest that biophysical soil health restoration constitutes a primary structural leverage point preventing limits-to-growth system degradation. Successful policy execution requires an integrated inter-agency roadmap: the National Food Agency must align price floors, the Ministry of Agriculture must reallocate chemical subsidies toward bio-organic supply chains and extension training, and the Ministry of Environment and Forestry must enforce regional soil organic monitoring.
This study develops and validates a data-driven model based on an artificial neural network (ANN) to predict the effective shear strength parameters (effective angle of internal friction and effective cohesion) of granular soils from basic index properties. A database of 1,105 secondary records, obtained from standardized geotechnical tests performed at Peruvian universities, was used for training and internal validation. External verification was carried out with 39 independent samples tested by consolidated-drained direct shear in accordance with ASTM International D3080/D3080M and Peruvian Technical Standard (Norma Técnica Peruana, NTP) 339.171. The network, with three hidden layers, dropout, and batch normalization, uses particle size distribution, Atterberg limits, dry density, and moisture content as input variables. In the external test, the model achieved a coefficient of determination ($R^2$) of 0.86 for the effective angle of internal friction (mean absolute error [MAE] = 1.21°; root mean square error [RMSE] = 1.75°) and 0.77 for the effective cohesion (MAE = 1.09 kPa; RMSE = 2.14 kPa). Unlike previous studies based on limited or heterogeneous datasets, this work uses a broad database of Peruvian soils, which facilitates regional calibration. Gradient and sensitivity analyses indicated that particle size distribution is the most influential factor on shear strength, whereas moisture content has a comparatively smaller influence. The results suggest that the proposed network may serve as a preliminary estimation tool for the shear strength of Peruvian granular soils when basic index properties are available. However, given the small external test set and the regional scope of the data, its extension to other soil types or regions requires further validation.
The integration of artificial intelligence (AI) into engineering practice is bringing new dimensions to the role of a leader, including professional judgment, collaboration, and responsibility for decisions made with AI’s support. However, desirable personal leadership orientations do not necessarily correlate with desirable work conditions. This study examines this perceived capability–enablement divide among engineers in Iraq. Survey data were collected from 144 engineers working in Iraq using 39 Likert-scale questions. The primary within-respondent comparison used bootstrap estimation, the Wilcoxon signed-rank test, and a cumulative ordinal fixed-effects model covering 5,616 item responses. Adjusted associations were examined using ordinary least squares regression with heteroskedasticity-consistent type 3 (HC3) standard errors and complete-profile permutation tests. Individual Leadership Orientation (ILO) was rated more favorably ($M$ = 3.789, standard deviation [SD] = 0.435) than Organizational Enablement (OE) ($M$ = 3.479, SD = 0.848). The mean paired difference was 0.309 points (95% bootstrap confidence interval [CI] [0.187, 0.443], $p$ $<$ .001; Cohen’s d$_z$ = 0.394). Using the ordinal model, and controlling for respondent and item differences, the same directionality was found; that is, endorsement of Organizational Enablement was associated with lower odds (odds ratio [OR] = 0.450, 95% CI [0.380, 0.534], $p$ $<$ .001). AI-use frequency was associated with ILO, but its association did not differ significantly from that with OE ($p$ = .242). Education was associated much more strongly with individual orientation than enablement. The results suggest that there is a perceived gap in capability and enablement among the engineers participating, rather than any broad opposition to AI. The results indicate that leadership orientation and perceived organizational support are separate factors to consider when designing responsible AI implementation for engineering organizations.
Background: Reconfigurable manufacturing systems (RMSs) enable rapid adaptation of machines, tools, and control software to produce different part families. Objectives: This paper addressed a Multiple Part Family Production (MPFP) problem selecting the best reconfiguration policy (RP) when switching production between part families. RPs are represented as sequences of reconfiguration steps and evaluated by number of setups, total reconfiguration cost, and total reconfiguration time. This work presented a conceptual digital twin (DT) based framework for forming RP applicable to reconfigurable flow line (RFL). Method: This framework describes how a DT can model candidate reconfiguration sequences by using real or historical data to estimate time and cost, and support virtually in what if analyses for policy comparison prior to physical changes. The manuscript focuses on framework design, required data flows, and decision logic rather than on implementation or experimental deployment. Results: This work provides a structured DT architecture which acts as reconfiguration decision making tool for engineering managers and a formal representation of reconfiguration policies and evaluation metrics, and a roadmap for integrating data sources, simulation components, and validation procedures. A worked example is also presented to demonstrate how the proposed framework supports a management decision. Illustrative results show that RP$_1$ and RP$_3$ require only one reconfiguration, whereas RP$_2$ incurs an additional change at Stage 3. Consequently, RP$_1$ is the most efficient policy, followed by RP$_3$, while RP$_2$ imposes the highest reconfiguration burden. Conclusion: This work proposes a novel DT-driven RP for MPFP, applicable to an RFL, integrating real-time data analytics into RMS to manage reconfigurable machine tool (RMT) configurations across four production stages. The selected RP is intended to improve delivery performance, capacity utilization, production continuity, and system availability by minimizing setups and reconfiguration time/cost, thereby reducing downtime and maintaining a smoother production flow.
The fruit-processing agroindustry plays a strategic role in reducing post-harvest losses and enhancing agricultural value creation in developing countries. However, small and medium-sized enterprises (SMEs) in this sector face multidimensional operational challenges, necessitating a holistic and structural evaluation of their performance system rather than fragmented, financially oriented assessments. This study aimed to analyze the multidimensional performance and uncover the hierarchical interdependencies of performance indicators within fruit-processing agroindustry SMEs. The methodology employed a sequential mixed-method design integrating the balanced scorecard (BSC) and interpretive structural modeling (ISM) combined with cross-impact matrix multiplication applied to classification (MICMAC, from the French “matrice d’impacts croisés multiplication appliquée à un classement”) analysis. The framework evaluated two representative product typologies in Malang Raya, Indonesia: a dry-processing case (fruit chips) and a liquid-processing case (fruit juice). The comparative BSC results revealed distinct performance profiles; the dry-processing case demonstrated stronger financial performance, whereas the liquid-processing case excelled in customer satisfaction and asset turnover. Concurrently, both typologies exhibited a significant drop in quality control, characterized by negative scores on the number of defective products, driven by scaling production volume over capacity boundaries. The ISM analysis structured these cross-perspective metrics into a robust nine-level hierarchy, identifying the number of machines as the ultimate foundational driver possessing the highest driving power (DP) of 17 and minimal dependence power (D). The MICMAC matrix further isolated eight independent driving variables, nine dependent outcome variables, and two autonomous variables, with no linkage variables present. These findings indicate that long-term enterprise competitiveness is structurally anchored to basic machinery availability and workforce competency development rather than downstream marketing actions. Theoretically, this study extends the BSC into a structurally informed paradigm by mapping structural influence pathways under resource constraints. Practically, it provides managers and extension institutions with a systematic diagnostic sequence to prioritize infrastructure investments over uniformly distributed improvement efforts.
Despite the rapid expansion of simulation-driven design in discrete manufacturing, the governance of computer-aided engineering (CAE) data remains largely disconnected from enterprise product lifecycle management (PLM) systems, creating configuration traceability gaps, limiting validated model reuse, and impeding reproducibility. While simulation process and data management (SPDM) frameworks have been proposed architecturally, limited empirical evidence exists on end-to-end implementations reporting measurable outcomes across contrasting industrial domains. This paper presents PLM for CAE, a framework embedding simulation lifecycle management natively within the PLM data model and process engine. The architecture extends the PLM product structure with simulation-specific object types and integrates high-performance computing (HPC) environments through boundary-layer agents that enforce governed inputs and harvest execution metadata. Mandatory workflow review gates govern simulation progression from request to archival, establishing a continuous digital thread between design revision and simulation outcome. Evaluation across two industrial deployments—a global railway equipment manufacturer prioritizing regulatory compliance and multi-site traceability, and a global consumer goods manufacturer focused on model reuse velocity—showed observed improvements over a twelve-month post-implementation period. The deployments reported design-to-simulation traceability of 85–95%, model reuse rates of 30–65%, preparation time reductions of 20–40%, and a 50–75% reduction in simulation retrieval time. These results provide preliminary empirical support for the applicability of PLM-native simulation governance in large engineering enterprises, while recognizing that the observed improvements may also reflect concurrent organizational and process changes.
Owners, contractors, and consultants are among the stakeholders most vulnerable to high risk due to their intricate interactions over project phases. This study aims to identify and categorize the primary and secondary risk factors associated with these three parties and to examine the causal relationships among them to inform risk mitigation. A hybrid approach combining fuzzy theory and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was adopted to analyze 32 secondary risk factors grouped under 3 primary risk factors. Nine experts, each with at least fifteen-year experience in risk management in engineering organizations, were consulted to identify and examine the risk factors. Based on the fuzzy DEMATEL (FD) model, risks were analyzed through a weighted impact matrix, and all risks were prioritized by identifying the causes leading to their effects. Owner risk (Degree of Dispatching ($D$) $-$ Degree of Receiving ($R$) = +0.739) and contractor risk ($D - R$ = +0.108) were classified as causes, whereas consultant risk ($D - R$ = -0.84) was classified as an effect. The risk factors were coded RC1–RC32. From the consultant side, the second most prominent sub-factor ($D + R$ = 2.635) was regulatory non-compliance (RC15), which affected approval timelines and coordination with the consultant. As regards the owner, poor communication (RC5, $D + R$ = 5.756) was a causal sub-factor. For the contractor, regulatory non-compliance (RC26, $D + R$ = 2.690) was the most prominent causal sub-factor. The analysis revealed divergent risk perceptions among stakeholders, thus highlighting the importance of a collaborative risk management framework.
This study develops a life-cycle engineering-management framework for intelligent systems in industrial settings and examines its applicability through a descriptive comparison of four publicly documented cases: Siemens, Atlas Copco, Vallourec, and thyssenkrupp Materials Services. Peer-reviewed studies published in 2020–2025 and official corporate disclosures published in 2019–2025 were screened using explicit relevance and traceability criteria. A structured extraction matrix recorded the industrial context, technology, deployment area, life-cycle stage, responsible actors, intended function, and availability of performance data. Inferential statistics and author-generated estimates were not used because the public sources did not provide replicated observations, consistent baselines, or common denominators. The cases document heterogeneous systems: a generative artificial-intelligence assistant for industrial engineering, connected compressor monitoring, digital traceability and operational support for tubular products, and artificial-intelligence-supported materials logistics. The evidence supports comparison of disclosed functions and management requirements, but it does not support causal claims or rankings based on return on investment, downtime, quality, energy, emissions, or workforce outcomes. The resulting framework links planning, design, integration, operation, and upgrade or retirement to a management decision, systems-engineering task, responsible actor, indicator, and implementation risk. It provides a reproducible basis for future plant-level evaluation while keeping conclusions within the limits of public secondary data.
The growing adoption of remote and hybrid work has transformed organizational leadership practices, creating new challenges for trust, communication, and employee engagement. Despite increasing scholarly attention, limited research has examined how employees in high power-distance cultural contexts perceive leadership in remote work environments or how these perceptions contribute to Sustainable Development Goal (SDG) 8: Decent Work and Economic Growth. This study explores how employees in Indonesian organizations interpret leadership behaviors in remote settings and how these interpretations influence psychological safety and employee voice. Using an interpretive qualitative approach grounded in constructivist epistemology, semi-structured interviews were conducted with 48 employees from the technology, financial services, higher education, government, and professional services sectors across Indonesia. Data were analyzed using reflexive thematic analysis (RTA). Four empirical themes were developed: behavioral consistency as the foundation of remote trust; the relational weight of supervisory disclosure; the cultural renegotiation of hierarchy; and the communicative significance of digital micro-behaviors. These themes support three theoretical mechanisms within one integrated model. Digital micro-behaviors provide the signals employees interpret; trust develops through asymmetric accumulation; and cultural permission structures develop partly in parallel, with trust and permission jointly determining whether psychological safety and voice become available. By specifying the conditions under which remote employees can participate meaningfully in organizational decision-making and receive fair and dignified treatment, the findings demonstrate how remote leadership practices can advance SDG 8 through employee participation, psychological safety, inclusive leadership, fairness, and quality of working life. The study offers implications for leadership development, organizational communication design, and inclusive remote-work practices.
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