Urban routing applications increasingly generate operational records that can support sustainability-oriented decision support in shared urban road networks. This paper develops a descriptive and diagnostic routing analytics framework for route-level observability in sustainable last-mile decision support. The framework structures route alternatives, selected options, route-cost components, CO2 indicators, air-quality context, recommendation metadata, communicated benefits, and application-interaction indicators into anonymised and data-minimised KPI outputs. The empirical component is presented as a passenger-mobility proof of concept rather than freight-routing validation. It uses anonymised aggregate data from a Thessaloniki mobile routing and recommendation application, comprising 46 route-choice records from 8 route-active users over the period 24 April 2026 to 4 June 2026. Among 40 voluntary personalised-routing records, the selected option was the lowest-CO2 alternative in 18 cases, the fastest alternative in 17 cases, and the financially cheapest alternative in 26 cases. When an active option was displayed, it was selected in 7 of 13 cases. The results illustrate how heterogeneous application records can be converted into transparent route-level KPIs and how the same observability logic could inform future freight, parcel, service, and platform-based last-mile pilots. The paper does not estimate population-level behaviour, causal recommendation effects, network-level environmental outcomes, or freight-operational performance. Its contribution is methodological: it defines a reproducible indicator architecture, clarifies its evidence boundaries, and identifies the additional freight-specific constraints required for future validation.
Simulation is widely used in supply chain management because it can represent uncertainty and complex network dynamics. Inventory management plays a central role in maintaining high service levels while reducing total supply chain cost. However, optimizing inventory policies is computationally intensive because it typically requires a large number of simulation runs. This paper addresses the problem of identifying satisfactory inventory policies that minimize total supply chain cost in a multi-echelon distribution network subject to target service level and target order frequency constraints. To address this problem, this study proposes a scalable multi-echelon inventory optimization framework that integrates learned constraints into the optimization model, enabling simultaneous consideration of multiple operational requirements without substantially increasing computational effort. First, simulation is used to generate sample points for the multi-echelon distribution network. Next, a machine learning model trained on the generated data is embedded within a mathematical optimization framework. Finally, the (s,S) inventory policy parameters for each location are determined by solving a mixed-integer programming (MIP) model. Computational experiments on three synthetic divergent distribution networks demonstrate the effectiveness of the proposed framework. Among the evaluated machine learning models, gradient boosting achieves the best performance. Compared with an evolutionary optimization approach, the proposed method reduces computational time by approximately 29%, 81%, and 77% for the two-, three-, and four-echelon networks, respectively. Furthermore, sensitivity analysis provides insights into the impact of order-frequency and service-level targets on total costs.
Accurate demand forecasting is essential for supply chain planning, yet conventional forecasting models are generally optimized for statistical accuracy without explicitly considering downstream operational requirements. This study proposes Decision-SCINN, a supply-chain-informed neural framework that integrates demand forecasting with operational constraints and decision objectives. Inspired by the broader principle of embedding domain knowledge into neural learning, the framework formulates inventory balance, capacity limits, non-negativity, stockout avoidance, and service level targets within a penalty based constrained learning approach. Decision-SCINN combines forecasting losses with operational penalty terms and a projected decision layer to jointly support predictive accuracy and operational feasibility.The framework is evaluated using a public retail demand forecasting benchmark covering the period 2013-2017. Inventory, replenishment, capacity, service-level, and cost variables are generated through a common recursive simulation applied consistently to all evaluated models. Decision-SCINN is compared with LSTM and Transformer forecasting models and with forecast then optimize baselines based on clipping and order up to policies. The results show that Decision-SCINN achieves an MAE of 6.39, RMSE of 8.33, MAPE of 12.33%, and R2 of 0.933. Under the simulated test policy, it obtains the lowest total operational cost of 7.211 million cost units, maintains a 100% service level, and records no stockout, capacity, non-negativity, or inventory-balance violations in the selected main test run. Although order-up-to baselines also achieve full service and eliminate hard violations, they produce higher operational costs. These findings indicate that integrated constraint-aware learning can provide a more favorable balance between forecasting accuracy, service performance, feasibility, and simulated operational cost than sequential forecast-then-optimize approaches.
Despite substantial advances in artificial intelligence (AI), forecasting systems in practice often remain anchored in traditional, siloed approaches, leaving managers with unreliable and inconsistent signals for decision-making. Such forecasts tend to exacerbate the bullwhip effect, inflate safety stocks and generate significant inefficiencies in working capital and logistics costs. In fast-moving consumer goods (FMCG) supply chains, forecasts that fail to reach a minimum level of reliability are of limited value for operational planning, whereas sufficiently accurate forecasts provide a dependable foundation for managerial decisions. This practice-oriented study examines whether joint AI-based forecasting, combining downstream retailer data with advanced machine-learning techniques, enables firms to consistently achieve forecast reliability levels that are suitable for planning. Drawing on a unique dataset from a Central European manufacturer-retailer partnership, we compare three forecasting scenarios: a Manufacturer baseline forecast based on traditional statistical methods (ARIMA) applied to shipment data; a Manufacturer AI forecast using machine-learning models (XGBoost) trained exclusively on shipment data; and a Joint AI forecast in which machine-learning models (XGBoost) are trained on retailer warehouse outbound data. The analysis shows that Joint AI reduces total absolute error by 44.7% relative to Manufacturer AI on the test-data basis (cluster-bootstrap 95% CI: 27.8–57.3%). Under the stricter common-demand benchmark, the reduction relative to Manufacturer AI remains statistically robust at 21.2% (95% CI: 4.4–35.7%). The advantage over the traditional Manufacturer baseline is positive in point estimate but not statistically established on the common-demand benchmark. Taken together, the results indicate that, within the scope of the dyad and the two algorithmic approaches examined, collaborative data access shifts forecast errors most where their operational and financial consequences are largest, concentrating the gains of AI-based forecasting in the high-volume core of the product portfolio.
The integration of Data Envelopment Analysis (DEA) into supply chain management and logistics has produced a large and rapidly expanding literature, yet no prior study has comprehensively mapped this research across both domains, and existing reviews remain limited in scope, database coverage, and analytical depth. This study addresses that gap through a dual-database bibliometric analysis of 2,764 publications retrieved from Web of Science and Scopus (1996–2025). Combining performance analysis with scientific mapping, including co-authorship, keyword co-occurrence, thematic clustering, and temporal evolution analysis, it maps the intellectual structure, developmental trajectory, and emerging frontiers of the field. The analysis identifies four developmental phases, progressing from an early conceptual foundation to the current phase of sustainability, resiliency, and technological transformation, and reveals seven thematic clusters organized around a stable methodological core that has expanded into sustainability, risk, and intelligent-optimization research. The study identifies machine-learning integration, blockchain-enabled assessment, circular-economy efficiency modeling, sustainable logistics benchmarking, and multimodal performance analytics as priority directions for future inquiry, and derives theoretical, managerial, and policy implications from the findings, offering the first comprehensive scientific map of DEA research in supply chain and logistics.
Ineffective inventory classification compromises supply chain resilience when criteria selection methods conflate statistical prominence with decision utility. Standard techniques, specifically Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), and Mutual Information (MI), risk inducing projection loss by excising structurally critical variables. We apply the Topological-Structural Axiomatic Validation (T-SAV) framework, which models criteria sets as simplicial complexes (structures built from points and their higher-order connections) and maps the decision axioms of completeness and non-redundancy to computable topological invariants. Using empirical data from a telecommunications infrastructure provider, we benchmark T-SAV against PCA, RFE, and MI. Results indicate distinct failure modes: PCA fragments the decision manifold into 14 disconnected components (consensus bias), while RFE discards control variables lacking predictive correlation (target dependency). T-SAV instead retains orthogonal keystone criteria that bridge financial and physical operational dimensions. T-SAV achieves a 99.63% variation capture ratio, against PCA (99.24%), MI (99.28%), and RFE (97.39%). Bootstrap resampling (B = 10,000) indicates these differences are statistically significant (p < 0.001), though the margin over PCA and MI is modest and the principal distinction is structural, since T-SAV alone satisfies both topological axioms. T-SAV also shows the lowest resampling variance (SD = 0.26), against markedly greater instability in RFE (SD = 1.84). In this empirical setting, topological validation can reveal structural weaknesses that statistical covariance and predictive error minimisation may miss. Practically, the framework lets managers check, before deployment, whether a criteria set holds together, lowering the risk of discarding criteria that are statistically quiet but operationally critical.
Geopolitical disruptions have become a source of systemic risk for global supply chains across interconnected transportation, energy, fertilizer, and food systems. Conventional descriptive and predictive analytics are often inadequate under deep uncertainty because geopolitical crises are characterized by ambiguity, limited historical observations, and cascading cross-sector effects. This study proposes an integrated fuzzy prescriptive analytics framework for resilience-oriented supply chain decision-making under geopolitical uncertainty. The framework combines historical evidence, expert elicitation, a Mamdani fuzzy inference system, a weighted aggregation model for food-system stress assessment, and mixed-integer linear programming (MILP). The fuzzy inference system evaluates five disruption drivers—Geopolitical Tension, Maritime Accessibility, Energy Supply Shock, Logistics Delay, and Fertilizer Input Risk—to estimate the Supply Chain Disruption Risk Index (SCDRI). The Food System Stress Index (FSSI) is then estimated through an expert-informed weighted aggregation of the SCDRI, Energy Supply Shock, and Fertilizer Input Risk. These disruption indicators are translated into deterministic optimization parameters that adjust transportation costs and shortage penalties. The Strait of Hormuz serves as a numerical case study. A MATLAB-based MILP model determines supplier activation, shipment allocation, inventory deployment, emergency procurement, and demand allocation. The results demonstrate that increasing disruption severity raises operating costs and food-system stress while promoting supplier diversification, inventory deployment, transportation reconfiguration, and emergency procurement to maintain supply continuity. The proposed framework provides a transparent, explainable, and reproducible prescriptive analytics methodology that transforms qualitative geopolitical intelligence into quantitative operational decisions, extending supply chain resilience research toward decision support under geopolitical uncertainty.
The steel industry, a cornerstone of global supply chains, is under growing pressure to improve sustainability and resource efficiency. This study develops a system dynamics (SD) model to explore how circular economy strategies can transform the steel supply chain by reducing waste, emissions, and energy consumption. Three key interventions are evaluated: increasing steel scrap utilization by 50%, implementing government policies such as carbon taxation and recycling subsidies, and adopting hydrogen-based steelmaking technologies. Simulation results over a 100-period horizon show that higher recycling adoption reduces total waste by 40%, energy use by 10%, production costs by 12%, and CO₂ emissions by 18%. Policy interventions lead to a 45% decrease in waste, 15% drop in energy consumption, and 32% reduction in CO emissions, despite a short-term 8% rise in costs. The hydrogen-based scenario, while involving an initial 20% cost increase, delivers the greatest sustainability benefits: CO₂ emissions fall by 60%, energy use by 50%, and waste by 55%. These findings demonstrate that integrating circular economy principles through targeted technological and policy actions can significantly improve the environmental and economic performance of the steel industry, supporting long-term resilience and alignment with global climate goals.
Humanitarian Logistics (HL) require the coordinated planning of facility allocation, relief distribution evacuation and debris cleaning under uncertainty. However, most existing studies treat these decision independently. They do not capture the hierarchical structure in the decision making of HL. This study develops a fully grey- uncertain, multi-level, multi-objective, multi-product decision making model integrating simultaneously facility location, relief commodity distribution, evacuation of affected populations and debris removal. The decision maker at the first level minimizes the total operational and transportational cost, at the second level minimizes the total time while at the third level maximizes the satisfaction as well as minimizes the required humanitarian force. Grey uncertainty is used to model uncertain parameters such as cost, demand, time, capacities and supplies. The model is validated using real-life data from the 2018 Kerala floods in India, involving eight suppliers, two central distribution hubs, thirteen temporary disaster relief hubs, ten disaster-affected zones, ten essential commodities, and debris collection points. The model coded in AMPL gives compromise solutions remaining close to the ideal solutions with deviations of approximately 17% for cost and 20% for delivery time. The model supports the evacuation of over one million affected individuals, delivery of over two million units of relief supplies and co-ordinated debris removal. Robustness checks under the scenario of increased demand and reduced relief hub capacity shows the solution feasibility and flexibility under operational disruptions. The proposed study can support authorities in converting post-disaster information into co-ordinated and implementable decisions on relief hub activation, commodity allocation, evacuation, workforce deployment and debris removal.
Pharmaceutical cold chains face strict temperature limits, time-sensitive deliveries, and uncertain demand and travel times. This study formulates a mixed-integer nonlinear model for a three-echelon stochastic open location-routing problem with time windows in pharmaceutical cold chains. The model integrates home delivery and pickup-point service. It represents demand and travel-time uncertainty through finite scenarios. A hybrid Whale Optimization Algorithm with Adaptive Large Neighbourhood Search (HWOA-ALNS) is developed. Its main features are binary encoding, feasibility repair, adaptive operator selection, problem-specific local search, penalty-based reliability handling, and dual stopping criteria. HWOA-ALNS is compared with Hybrid Genetic Algorithm (HGA), Genetic Algorithm (GA), Simulated Annealing (SA), Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Imperialist Competitive Algorithm with Variable Neighbourhood Search (ICA-VNS) on 30 self-generated instances containing 80–2000 customers. It obtains the lowest expected total cost in 26 of 30 instances. The result also records the highest demand-weighted customer-satisfaction value in 27 instances. The remaining exceptions are reported explicitly. Sensitivity and uncertainty analyses show the cost-service effects of time-window flexibility and higher variability.
Accurate forecasting plays a pivotal role in supply chain management (SCM), especially within capacity-constrained cold storage systems where pricing, procurement, production, and distribution decisions are highly interdependent. In such contexts, warehouse occupancy both affects and is affected by upstream supply processes, such as replenishment and production planning, as well as downstream operations, including order fulfillment and distribution flows.This study develops a hierarchical forecasting framework combining behavioral time-series profiling, profile-based model comparison, forecast reconciliation, and decision-oriented evaluation. The framework is applied to six years of weekly stock data covering 70 products grouped into nine operational families. Candidate models are evaluated through rolling-origin out-of-sample validation, and family-level forecasts are reconciled with total warehouse occupancy using alternative hierarchical methods. The results show that forecasting performance varies across behavioral profiles and that MinT diagonal provides the lowest observed aggregate error measures, although bootstrap intervals indicate overlapping performance across reconciliation methods. Statistical rankings also do not fully coincide with tariff-oriented and scenario-based economic outcomes. These findings show that occupancy forecasts should be evaluated jointly in terms of accuracy, hierarchical coherence, uncertainty, and operational consequences.By integrating behavioral segmentation, hierarchical forecasting, and decision-oriented evaluation, this study demonstrates the importance of assessing forecasting systems based on their impact on interconnected supply chain decisions rather than predictive accuracy alone. The framework provides both methodological contributions and managerial insights for managing capacity-constrained Cold storage supply chain.
This study addresses the transparency paradox in organic food supply chains, where substantial investments in traceability technologies often fail to yield linear increases in consumer trust. Moving beyond a purely behavioral lens, this research evaluates the downstream efficacy of two distinct supply chain visibility configurations: Data-Driven Transparency (derived from traceability infrastructure like Blockchain, IoT) versus Relational Transparency (derived from Short Food Supply Chain network designs). Employing a novel dual-analytic methodology of Partial Least Squares – Structural Equation Modelling (PLS-SEM) and eXplainable AI (XAI), we analyzed data from active organic consumers in India. The PLS-SEM results indicate that Relational Transparency and not Data-Driven Transparency, is the primary driver for collapsing the psychological distance between farm and fork. Conversely, the XAI analysis reveals that data-driven mechanisms function as a non-linear hygiene factor, essential for satisfying the validation thresholds of high-identity consumers but insufficient to drive purchase intention alone. The findings offer a strategic roadmap for behavioral supply chain management, suggesting that firms must decouple their transparency strategy, utilizing technological traceability for market entry (verification) while optimizing network design for relational proximity to drive market share (connection).
Sustainable supply chains require analytical methods that explain how firms adapt supplier relationships under disruption while balancing resilience, resource efficiency, and environmental exposure. This study develops an agent-based network simulation and supply network analytics model to examine how selective supplier rewiring may shape sustainable supply network resilience. The model represents firms as autonomous agents embedded in a directed logistics supply network. Agents adjust sourcing ties under disruption using decision rules based on supplier dependency, relational embeddedness, supplier reliability, capacity, network proximity, and supplier sustainability performance. The simulation is used as a theory-elaborating computational experiment rather than as an empirical validation design. Under the specified model assumptions, selective rewiring generates patterns of more balanced structural adaptation than random or unconstrained supplier switching. Local sourcing decisions produce measurable changes in density, clustering, modularity, path length, recovery, and sustainability exposure. The sustainability-constrained scenario illustrates how supplier replacement rules are associated with a higher simulated sustainability profile of active ties while preserving recovery performance under the specified model assumptions. The study contributes to supply chain analytics by linking micro-level supplier adaptation rules with macro-level network transformation. The results should be interpreted as simulation-based pattern-generation outcomes that require future empirical validation using real-world longitudinal supply network data.
Fuzzy inference systems (FISs) have been widely applied to support decision-making and operational functionalities in supply chain management (SCM), leveraging highly interpretable fuzzy inference rules (FIRs) expressed through linguistic terms to enable transparent and flexible nonlinear modeling, planning, control, and optimization. However, in many practical settings, multiple FIRs may be activated simultaneously with varying degrees of relevance, creating ambiguity for decision-makers, similar to interpretability challenges observed in ensemble models such as random forests. To address this issue, this study integrates explainable artificial intelligence (XAI) techniques into FIS-based SCM applications to enhance interpretability and user trust, employing visualization-driven methods such as partial dependence plots (PDP), SHAP analysis, and locally interpretable model-agnostic explanations (LIME) to provide insight into inference behavior. Building on these approaches, an FIS-based incremental interpretation framework is proposed to systematically reduce ambiguity and support human-centric decision-making. The proposed methodology is validated through a real-world supply chain case study, evaluated through expert-based assessment, and further examined using statistical hypothesis testing to establish its effectiveness and practical relevance.
Reducing carbon emissions has become increasingly critical in response to growing legislative pressure and heightened consumer awareness. In this context, organizations face significant challenges in achieving meaningful emission reductions within complex logistics networks and a volatile economic environment. This study focuses on the environmental sustainability in forward logistics network design, aiming to identify opportunities to reduce emissions while minimizing deviations from existing operations. A multi-objective mixed-integer programming model is then developed and solved using an exact branch-and-bound algorithm. Machine learning techniques are employed to estimate missing data, enhancing model realism. Experiments conducted on a real-world dataset show that moderate reductions in emissions, costs, and lead times can be achieved by simply rerouting flows without altering processed volumes. Furthermore, substantial improvements are attainable with as little as a 5% reduction in the network’s existing processing volume. The findings provide both theoretical and practical insights for sustainable supply chain design and offer a scalable framework for data-driven decarbonization.
Agricultural innovation increasingly relies on sustainable financing, supply chain mechanisms and smallholder involvement. The literature on sustainable supply chain finance (SCF) remains fragmented, with limited synthesis of how financial instruments, digital technologies, and institutional arrangements support inclusive agricultural transformation (AT). This study conducts a systematic bibliometric review of 58 peer-reviewed articles published between 2015 and 2025 using negative binomial regression to examine the intellectual structure and determinants of scholarly impact in SCF research. Bibliometric mapping reveals co-authorship networks, keyword patterns, and thematic clusters. Regression analysis was used to assess the effects of collaboration intensity, journal quality, and publication age on citation performance. The findings identify three dominant trajectories: (i) the convergence of finance, risk management, and sustainability as the core of SCF research; (ii) digital financial platforms, such as blockchain, IoT, and AI, as operational enablers; and (iii) increasing inclusion of SMEs and smallholders within SCF frameworks. The regression results show that multi-author collaboration and publication in high-quartile (Q1) journals enhance scholarly impact, while international collaboration has a limited effect. This study integrates bibliometric mapping with statistical modeling to identify structural gaps in SCF research. The study highlights three underexplored areas: the weak linkage between SCF instruments and measurable agricultural innovation outcomes, insufficient socio-technical alignment of digital tools with smallholder realities, and the marginal role of policy frameworks. By synthesizing these insights, this study proposes a structured research agenda that advances SCF scholarship and guides future research at the intersection of sustainable finance, supply chain integration, and smallholder innovation.
Exploratory factor analysis (EFA) is widely used in supply chain management, yet a considerable gap has emerged since the last focused review in this area. As a result, scholars may rely on outdated practices and may also draw inaccurate inferences about the latent constructs underlying their indicators. Recent studies have emphasized the need for field-specific EFA guidelines, particularly in the interpretation of factor loadings, which remain unavailable for supply chain management without a contemporary review. To address these issues, this study presents a systematic literature review of 441 articles and 914 EFAs published in eight leading supply chain management journals since 2000. The findings reveal that researchers frequently apply obsolete factor retention techniques and rotation methods, such as the Kaiser criterion and varimax rotations, and researchers show substantial inconsistency in the selection of factor loading cutoffs. These practices raise concerns about the interpretability and stability of EFA results in supply chain management research, as analytical decisions are often not aligned with current methodological recommendations. In response, we offer practical recommendations informed by contemporary methodological guidance for conducting robust EFAs, wherein we direct readers to applicable resources. We also produce tailored recommendations for interpreting factor loadings based on percentile distributions observed in our review, enabling the more consistent and theoretically grounded measurement practices in supply chain management.
This study integrates circular economy principles and Industry 5.0 dimensions into the supplier selection problem, introducing the concept of "Circular Supplier 5.0" selection within an analytics-driven supply chain context. A novel multi-criteria decision-making approach is developed by integrating the general best-worst method (GBWM), interval numbers, and a Supermatrix structure. Six main factors (resiliency, human-centricity, economic, circular, social, and Industry 4.0), along with 27 sub-factors, are proposed to evaluate Circular Supplier 5.0 performance in the construction industry. In the proposed framework, interval GBWM is used to determine the independent weights of factors and sub-factors, capture interdependencies among factors, and assess the relative performance of suppliers across sub-factors. A Supermatrix-based structure is then employed to compute the final dependent weights and overall supplier rankings. To demonstrate applicability, six suppliers from a Malaysian construction company are evaluated. The results show that, compared with traditional approaches that overlook Industry 5.0 and circularity considerations, the proposed model highlights the importance of incorporating these dimensions into supplier evaluation. The findings indicate that security, cost, quality, capability, information sharing, and traceability are the most influential sub-factors. Expert validation further supports the robustness of the results and confirms the practical relevance of the proposed framework for real-world supply chain analytics and supplier selection decisions.
As supply chain networks grow in structural and computational complexity, organisations increasingly rely on artificial intelligence (AI)-enabled optimisation systems to generate prescriptive recommendations. When these systems are embedded within supply chain digital twin environments-where real-time scenario evaluation and rapid disruption response are expected-the gap between algorithmic output and human comprehension becomes a barrier to effective, accountable decision-making. This paper proposes a surrogate-based explainability framework with semantic abstraction that enables structured, anticipatory characterisation of how an optimisation system's decision logic shifts across operating conditions. The framework is model-agnostic with respect to the optimisation layer and operates in three stages. A surrogate-based explanatory layer approximates the optimisation response surface and produces feature-level attributions using Shapleybased decomposition. A semantic abstraction layer aggregates these attributions into operational concepts computing normalised concept relevance, scenario variability, and concept interaction metrics that bridge numerical explanation and managerial reasoning. Evaluated on a supply chain case study comprising 7,500 instances solved by a mixed-integer routing-assignment-scheduling model under baseline and supply-disrupted conditions, the framework reveals that demand and transport concepts form a stable explanatory hierarchy robust to disruption, while processing constraints-though not directly perturbed-nearly double their relative explanatory weight, emerging as the primary differentiator among feasible solutions under stress. The study identifies a distinction between operational importance and explanatory salience, demonstrating that a disrupted parameter can decrease in explanatory weight when uniform perturbation compresses cross-instance variance. By providing decision-makers with structured, scenario-comparable explanatory profiles prior to operations, the framework supports the anticipatory preparedness that contemporary supply chain resilience demands - advancing transparent and accountable prescriptive analytics for human-centred decision-making.
Sustainable Industrial Supply Chain Management (SISCM) has emerged as a strategic imperative for industries aiming to reconcile economic performance with environmental governance and social responsibility. Traditional Industrial Supply Chains (ISCs) models often fall short in delivering key determinants required for sustainability. However, despite the growing literature body on the individual contributions of many technologies, their integration and interoperability for SISCM remain insufficiently explored. To this end, this paper proposes an integrated conceptual framework that enables synergistic integration and interoperability among IoT, Blockchain, and BDA within SISCM. The framework identifies their functional roles, interactions, and complementarities in enhancing sustainability key determinants. It highlights the potential to target significant performance benchmarks, with expected results including a projected 20%–30% improvement in resource efficiency and a 15%–25% reduction in carbon emissions. Furthermore, the architecture aims to achieve a 30%–40% enhancement in information transparency and trust across the ISC stakeholders. The proposed framework contributes both theoretical grounding and practical guidance for building intelligent, transparent, and sustainable industrial networks.
