Background: Integrating additive manufacturing (AM) into manufacturing small and medium-sized enterprises (SMEs) requires addressing technological, economic, organizational, environmental, and institutional conditions whose interrelationships remain insufficiently understood. Methods: This study proposes a conceptual framework for the sustainable integration of AM by combining Systematic Literature Mapping (SLM) with Fuzzy DEMATEL. The SLM covered publications indexed in Scopus, Web of Science Core Collection, and EI Compendex from January 2015 to May 2026. Among 1123 identified records, 72 studies met the inclusion criteria, leading to eight critical factors across political, economic, social, and environmental dimensions. These factors were subsequently evaluated by a panel of 15 experts to determine their interdependencies and cause–effect structure. Results: Government incentives (F1; D − R = 0.928) and digital infrastructure (F2; D − R = 0.469) emerged as the main causal drivers, while implementation costs (F4; D + R = 22.690) showed the highest prominence. The resulting framework comprises four levels: strategic activation, implementation enablement, operational integration and feasibility, and sustainable outcomes. Conclusions: Sustainable AM integration follows a hierarchical causal structure in which institutional and digital conditions initiate adoption, financial and regulatory mechanisms support implementation, and organizational and environmental benefits emerge as subsequent outcomes, providing SMEs with a structured basis for implementation decisions.
Background: Port access traffic combines pronounced calendar structure with substantial day-to-day variability, raising the question of whether temporal heterogeneity should condition a common forecasting configuration or support model specialization. Methods: Daily car and freight-truck arrivals at an urban non-containerized port were forecast at 1-, 7-, and 14-day horizons using statistical, machine-learning, and composite models under a leakage-free rolling-origin protocol. A unified calendar-conditioned ensemble was compared with a prespecified regime-based model assignment, with development, test, and temporal stress-test periods kept distinct. Results: The unified ensemble achieved next-day R2 values of 0.75 for trucks and 0.80 for cars on the held-out 2018 test period and 0.73 and 0.75, respectively, under the 2019 temporal stress-test. Model preference varied jointly with temporal regime, forecast horizon, and evaluation period. Although fixed assignment improved car accuracy on the 2018 test period, the model–regime dominance required to justify it was not sufficiently stable across horizons and evaluation periods. Conclusions: Calendar conditioning provides a robust strategy for the present forecasting problem. More generally, fixed regime-based specialization should be supported by sufficiently stable model–regime dominance rather than inferred from identifiable temporal heterogeneity alone.
Background: Planning power distribution networks is crucial in contemporary infrastructure development. Current distribution paradigms require not only cost minimization but also reliability and fault tolerance. However, designing meshed network topologies is a computationally demanding combinatorial optimization problem, especially for large instances. Methods: We reframe this problem as a multi-depot vehicle routing problem in which electrical substations act as depots and power lines represent routes. We develop a four-phase Large Neighborhood Search (LNS) that combines geographically-based destroy operators with a topology-specific MILP repair operator. Each repair subproblem is solved to optimality under the adopted topological, flow conservation, and line capacity constraints. Results: Experiments on realistic medium-voltage distribution network instances with up to 1150 nodes show that the proposed method handles cases that are beyond the reach of exact global optimization. Compared with a greedy constructive heuristic, the best LNS solution achieves an average cost reduction of 28.5%. Ablation and sensitivity analyses support the algorithmic design and show stable behavior under reasonable parameter variations. AC power flow analyses on the largest instance confirm electrical consistency under the tested single-branch outage scenarios, with a maximum voltage deviation of 5.1%. Conclusions: The proposed optimal-repair LNS provides a scalable approach for planning large fault-tolerant distribution networks under topology and line capacity constraints.
Background: The transformation of businesses resulting from the implementation of artificial intelligence (AI) is currently underway. A research gap lies in the absence of research examining the impact of AI on logistics companies from the perspective of its effect on their business models. Methods: Logistics companies are described, using PRISMA methodology, through an analysis of 151 open-access scientific studies indexed in the Web of Science and Scopus databases, using the Business Model Canvas (BMC) framework and its nine blocks. This research focuses on identifying how AI might influence individual BMC blocks. The research follows the PRISMA methodology and is based on an analysis of 108 open-access scientific studies indexed in the Web of Science and Scopus databases. Results: The findings suggest that AI is transforming the business models of logistics companies across all nine of its blocks. This transformation is taking the form of AI-supported, data-driven ecosystems. Across all BMC blocks, the importance of data analytics, automation, predictive management, service personalization, and digital interoperability is growing. Conclusions: The competitive advantage of logistics companies is increasingly based on the ability to process data, coordinate processes in real time, and create adaptive logistics systems, thereby broadening the interpretation of how logistics companies operate.
Background: Cruise and passenger vessel traffic is an important component of coastal transportation, but increasing activity can intensify logistical complexity, traffic interactions, and environmental pressure. Understanding vessel behavior across ports and sailing corridors is therefore necessary to support safer and more efficient transport planning. Methods: A reproducible AIS-based framework is proposed to integrate port- and route-level analysis. Port calls are detected using predefined port geofences and minimum dwell time criteria; operational states are classified from vessel location and speed; AIS-valid study-network voyages are reconstructed between consecutive qualifying calls; and modeled CO2 is estimated separately for hoteling and transit using representative engine power assumptions and speed-dependent load factors. The framework is applied to 2023–2024 AIS data from ten Norwegian ports. Results: The analysis identified 5546 port calls and 3323 AIS-valid voyages in 2023 and 5768 calls and 3794 voyages in 2024. Modeled hoteling CO2 decreased from 124,350 to 114,406 tons, while modeled transit CO2 increased from 1.027 to 1.076 million tons; transit CO2 per voyage decreased from 309.1 to 283.7 tons. Conclusions: Traffic frequency alone does not determine modeled emissions; integrated AIS analysis can support differentiated port- and corridor-level transportation planning and emissions mitigation.
Background: The demand for natural gas and advancements in LNG technologies have made it easier and more cost-effective to transport large quantities of natural gas over long distances by cooling it and then delivering it to customers according to an annual delivery plan (ADP). This plan is crucial for the LNG supply chain to minimize operational costs and meet contractual obligations; however, there are random factors that deviate from the ADP’s initial plan, such as production rate interruptions, weather conditions, and a reduction in the number of berths. Methods: Available optimization methods for ADPs in the literature were reviewed using a systematic literature review (SLR) and validated with the PRISMA 2020 Statement checklist and flow diagram, which showed that many existing studies use simplified deterministic models to optimize ADPs within reasonable time and effort. This research suggests establishing a simulation-based optimization (sim-heuristics) framework to develop ADPs with an objective function that minimizes expected total costs while capturing real-life uncertainties. This approach will provide tactical insights to decision-makers and is adaptable for various operational policy evaluations. Conclusions: This simulation-based optimization approach could offer the LNG industry a powerful analytical engine for robust, real-world decision-making, bridging the gap between theoretical planning and operational complexity.
Background: Global maritime supply chains increasingly face overlapping disruptions, yet existing studies often analyse individual crises rather than comparing how stress regimes evolve across system states, channel-specific conditions, and recovery duration. Methods: This study develops a diagnostic Stress–Activation–Recovery (SAR) framework to analyse container-shipping freight-market resilience during the January 2007–June 2026 period. Layer 1 classifies monthly system states using the Global Supply Chain Pressure Index. Layer 2 identifies supply-related, demand-related, logistics/freight-market-related, concurrent, and diffuse system state conditions using six standardized monthly indicators, while crude oil price parameters are recorded separately as energy-state indicators. Layer 3 measures freight-market recovery duration using HARPEX time-to-baseline. Results: Stress regimes unfold as evolving sequences rather than isolated events. The 2008–2009 financial crisis combines system contraction with concurrent supply–demand–logistics/freight-market conditions, while the COVID-19/post-pandemic period shifts from demand- and supply-related abnormal conditions through diffuse system state to logistics/freight-market dominance condition. Recovery duration varies substantially, from 3 months after the early COVID-19 freight collapse to 42 months after the financial crisis. The 2024–2026 high-freight deviation remains right-censored. Conclusions: The SAR framework provides a transparent diagnostic tool for comparing stress regimes and restorative freight-market recovery.
Background: Agile humanitarian logistics (HL) has emerged as an imperative approach for improving preparedness, response, and recovery in flood disasters. However, achieving agility in HL depends on identifying and implementing critical strategies that enhance logistics performance. Using the 2024 Bangladesh flash flood as an empirical case, this study provides an early investigation into the critical success factors (CSFs) required to strengthen agile HL performance. Methods: First, a PRISMA-guided review was conducted to systematically identify the most relevant CSFs and their associated dimensions from prior studies. Next, the Content Validity Index (CVI) method was applied to evaluate and refine the CSFs through expert consensus, ensuring strong content validity. Results: The results indicate that 12 CSFs, organized across five dimensions—collaboration, managerial, operational, strategic, and technological—collectively contribute to improving HL performance and supporting long-term agility. Conclusions: This study offers a structured set of validated factors that can guide policymakers and humanitarian organizations in prioritizing interventions, strengthening coordination, improving decision-making and resource utilization during flood emergencies. These insights extend agile HL knowledge by linking validated CSFs to performance enhancement in a real-world, high-impact disaster context.
Background: Managing agrologistics supply chains under infrastructure scarcity requires integrative, spatially explicit decision-support tools. This study develops a macro-level digital twin of the multimodal agricultural supply chain in Kazakhstan’s Almaty region to optimize freight allocation and guide strategic investment planning. Methods: Our methodology integrates Earth observation data (ESA WorldCover 10 m) with a large-scale multimodal road–rail graph network (1.39 million nodes) to identify 135 crop production clusters. Using linear programming in MATLAB, we optimize the regional distribution of 322.2 thousand tons of seasonal maize, wheat, and soybeans while localizing new storage silos using Green Field Analysis. Results: The baseline simulation reveals a critical storage capacity deficit, yielding a Capacity Coverage Ratio of only 23.8%. However, implementing optimal multimodal rail-road routing mathematically reduces the Logistics Cost Index from 8,642,195 to 4,716,175 units, achieving overall cost savings of 45.4%. Conclusions: The proposed digital twin and its performance metrics provide a scientifically grounded, data-driven toolkit for public–private partnerships, ensuring robust infrastructure investment localization and facilitating the transition toward the Agriculture 4.0 paradigm.
Background: Manufacturing productivity increasingly depends on reliable interorganizational flows, yet supply chain disruptions can interrupt materials, information, finance, and efficient use of productive inputs. Although supply chain resilience is widely treated as a continuity capability, its relationship with firm-level total factor productivity remains insufficiently established. Methods: This study uses 22,509 firm-year observations for Chinese A-share listed manufacturing firms from 2009 to 2024. An entropy-weighted resilience index is constructed from adaptability, resistance, recovery capacity, human capital, institutional support. Firm-level revenue productivity is estimated using the Olley Pakes method, and the analysis employs fixed effects regressions, robustness tests, a two-step selection correction test, mechanism regressions, heterogeneity analysis, and dimension-specific tests. Results: Supply chain resilience is positively associated with firm-level total factor productivity, and this association remains robust to alternative productivity and resilience measures, sample restrictions, industry-by-year fixed effects, and selection correction. Resilience is also associated with lower financing constraints and investment inefficiency. The association is stronger for firms with higher managerial incentives, high-technology industries, and competitive markets, while recovery capacity is negatively associated with contemporaneous productivity. Conclusions: Supply chain resilience supports efficient resource utilization, but its productivity value depends on capability composition, timing, and efficient resilience investment rather than maximizing resilience resources.
Background: The downstream oil and gas processing industry faces substantial environmental, regulatory, safety, and operational risks. Advanced technologies can reduce these risks and improve productivity, compliance, sustainability, and worker safety. However, despite adopting AI, analytics, machine learning, and IoT, Indian firms continue to lag behind their global counterparts, and Industry 5.0 adoption in this sector remains underexplored. Methods: This study examines Industry 5.0 adoption barriers using data from an Indian public-sector oil and gas organisation. Guided by the Resource-Based View and supply chain integration perspective, it applies a mixed-methods design combining a literature review, focus group discussion, Delphi analysis, and Fuzzy ISM–MICMAC. Forty-two barriers were identified and reduced to eight critical barriers. Results: Low technological maturity and lack of value-chain integration emerged as the principal driving barriers, influencing implementation failure, organisational technological readiness, and management commitment, which subsequently affect data quality. Geopolitics emerged as an autonomous barrier with both positive and adverse effects. Conclusions: This study develops a sector-specific framework explaining the hierarchical relationships among technological, organisational, sociotechnical, and value-chain barriers. It extends Industry 5.0 research in hazardous, human–technology-dependent operations and offers practical guidance for a human-centric, sustainable, and resilient transformation.
Background: Population growth and rising healthcare demand increasingly strain public healthcare in emerging economies. Further, poor supply visibility, frequent stock-outs, and medicine expiry weaken healthcare supply chain performance (SCP) and patient outcomes. In response, the Internet of Things (IoT) offers promising ways to improve the monitoring, distribution, and management of medical supplies. However, empirical evidence on how technological, organisational, and environmental factors influence IoT adoption and its effect on public healthcare SCP remains limited. This study examined factors influencing IoT adoption and its effect on supply chain performance in public healthcare facilities. Methods: Data were collected from 102 respondents drawn from 90 public healthcare facilities. Results: Factors associated with technological factors have the most significant influence on the adoption of IoT in public healthcare SCs. The lack of significance of organisational and environmental factors may be attributed to the early stage of IoT adoption in public healthcare SCs. Conclusions: This study contributes to the theoretical understanding of IoT adoption by highlighting the dominant role of technological factors over organisational and environmental considerations in resource-constrained public healthcare settings. From a practical perspective, the findings encourage policymakers and healthcare managers to prioritise investments in relevant ICT infrastructure to accelerate IoT adoption.
Background: Global food supply chains have become increasingly complex, sourcing ingredients from multiple countries and intermediaries, creating opportunities for fraud, adulteration, and mislabeling that may compromise consumer safety and market confidence. Digital traceability technologies have been suggested as potential countermeasures, but there is little concrete evidence of their impact in practice. This research presents an assessment of the maturity and effectiveness of these technologies, identifies implementation barriers and security/privacy concerns, and maps research gaps/future directions. Methods: A scoping review of 64 studies from 2023 to 2026 with data extracted from the Scopus and IEEE databases was carried out according to the PRISMA-ScR guidelines and a structured pre-specified data extraction framework. Results: The field is empirically immature, with none of the reviewed solutions offering a provably correct, adversarially tested solution to the oracle problem. There is a lack of alignment between on-chain immutability and GDPR right to erasure and an unequal burden of implementation costs imposed on smallholder producers. Conclusions: A gradual implementation of traceability regulations, along with cost-of-ownership models and harmonized certification measures that do not disadvantage smaller producers are proposed. Finally, field trials, adversarial testing and reporting results in a standardized format, capturing detection performance and implementation costs, are essential.
Background: Blockchain adoption in supply chain management has attracted growing academic and practitioner attention; yet the impact varies significantly by implementation context, and the knowledge of the conditions driving this variation remain limited. Methods: This study conducts a systematic review of 112 papers to systematically synthesize how blockchain integration affects supply chain management practices (SCMPs), including upstream practices (e.g., supplier partnerships), downstream practices (e.g., customer relationships), and practices spanning both sides of the supply chain (e.g., information sharing and information quality). Results: The review finds that the benefit of blockchain adoption depends on a firm’s supply chain position, cost structures, and market conditions. Two theoretical perspectives are used to interpret these findings: the resource-based view, which viewed blockchain as a capability for integrating processes and transactions across organizations, and the practice-based view, which viewed blockchain as an imitable activity requiring context-specific deployment conditions. Fourteen propositions are developed to guide future research. Conclusions: This review provides practitioner guidance for evaluating when and how blockchain is likely to generate values across different SCMPs. Also, it provides researchers with a theory-grounding agenda for testing the proposed propositions empirically.
Background: The allocation of non-divisible inbound deliveries across multiple warehouses requires the simultaneous consideration of capacity constraints, category-specific restrictions, workload balance, and long-term allocation consistency. Methods: This study proposes a hierarchical two-level allocation framework combining strategic category-rotation policies, normalized marginal scoring, and signed historical feedback. The algorithm is executed once per day to generate warehouse assignments for the following operational day. Historical correction is based on a rolling window covering the preceding 30 daily planning periods. Results: The framework was evaluated using daily simulation instances ranging from 100 to 1500 pallets, with an average of approximately 130 lots per pallet. Across all evaluated instances, the complete allocation procedure was completed in less than 5 s on the specified test system. The results indicate balanced warehouse utilization, progressive reductions in category–location imbalance, stable historical correction, and preservation of hard operational constraints. Conclusions: The framework provides an interpretable and computationally efficient approach for next-day inbound allocation. By combining explicit feasibility filtering, strategic policy signals, and a 30-day historical correction mechanism, it supports both short-term operational decisions and longer-term allocation balance.
Background: Reducing production time while making efficient use of resources is a key challenge in modern manufacturing, particularly in environments with high variability in specific components for the automotive industry. Methods: This paper presents a hybrid approach to production scheduling that combines linear programming (LP) principles with heuristic decision-making. A structured literature review is conducted to compare exact methods, heuristics, and metaheuristics in terms of their applicability and limitations. Based on this analysis, a hybrid scheduling method is proposed, where LP defines the objective function and constraints, while heuristic rules enable efficient assignment of operations to workstations under capacity limitations. The approach is validated through a case study involving over 900 product variants in an automotive part production system characterized by interchangeable workstations. The proposed heuristic algorithm was tested in terms of real company daily scheduling performance and compared with former scheduling performance. Results: The results show that the proposed approach achieves better solution quality with significantly lower computational effort, while also improving time utilization and production efficiency. Conclusions: The hybrid LP-heuristic approach provides a computationally efficient and practical tool for real-time production scheduling in high-variability manufacturing environments, effectively balancing solution quality and sub-minute execution speed under strict capacity constraints.
Background: Maritime container shipping carries over 80% of global trade, yet compliance verification creates a confidentiality–verifiability conflict: carriers treat telemetry as commercially sensitive, while regulators, insurers, and port authorities require verifiable proof that cargo remained within specification. The EU Ecodesign for Sustainable Products Regulation (ESPR) mandates Digital Product Passports (DPPs), but no standardised DPP architecture exists for the multi-stakeholder maritime domain. Methods: We present Ocean DPP, a blockchain-anchored platform combining GS1 EPCIS 2.0, oneM2M, IOTA, and Groth16 zero-knowledge proofs (ZKPs), letting stakeholders verify compliance predicates without revealing raw sensor values; Merkle-tree batching reduces anchoring costs. We evaluate it in 16 experiments on a single-host testbed using synthetic workloads and a local IOTA network. Results: The platform achieved 95th-percentile latency of 48 ms without ZKP and 500 ms with proof generation, throughput of 7 events/s per host, 304 ms mean proof generation and 9.8 ms verification, 100% EPCIS 2.0 compliance, and zero permanent message loss across four failure-injection scenarios; horizontal scaling reduced the median latency by 37%. Conclusions: To the best of our knowledge, Ocean DPP is the first implemented, quantitatively evaluated platform integrating EPCIS 2.0, oneM2M, IOTA, and Groth16 ZKPs for privacy-preserving maritime DPPs; broader multi-host and public-network validation remains for future work.
Background: Manufacturing supply chains must increasingly coordinate cost, environmental impact, and service continuity under demand uncertainty and limited capacity. Methods: This study develops a stochastic mixed-integer linear programming framework for carbon-aware production–distribution planning in an automotive supply chain. The model jointly optimizes production quantities, inventory levels, shipments, truck usage, and lost sales over a multi-period horizon. Demand uncertainty is represented through scenarios, while production- and transportation-related emissions are monetized using an internal carbon price. Lost-sales penalties capture service degradation when demand cannot be fulfilled by the focal plant, and a rolling-horizon analysis evaluates planning responsiveness as demand information is updated. The framework is applied to an industrially inspired, capacity-constrained automotive case with multiple products, production lines, destinations, and demand scenarios. Computational experiments assess carbon pricing, lost-sales penalties, demand volatility, deterministic versus stochastic planning, and rolling-horizon replanning. Results: Results show that carbon pricing mainly acts as an economic valuation mechanism under the studied fixed-structure configuration, whereas lost-sales penalties strongly influence service performance. Demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand rather than improved efficiency. Conclusions: The study provides a decision-support framework for evaluating cost–carbon–service trade-offs under stochastic demand while acknowledging single-plant and fixed-routing limitations.
Background: The growth of e-commerce has intensified last-mile delivery challenges, including failed deliveries, delivery-time uncertainty, and pressure on urban logistics. Smart parcel lockers (SPLs) offer a technology-enabled out-of-home delivery solution, yet limited evidence explains consumer adoption in Saudi Arabia. This study examines which factors motivate Saudi consumers to adopt SPLs, how trust shapes adoption intention, and whether perceived risk affects intention, using an extended UTAUT2 framework. Methods: Data were collected through an online self-administered questionnaire from 415 residents in Saudi Arabia, and the model was analyzed using partial least squares structural equation modelling (PLS-SEM). Results: Performance expectancy was the strongest determinant of behavioral intention. Effort expectancy, social influence, trust, facilitating conditions, and hedonic motivation also had significant positive effects, whereas price value and perceived risk did not directly influence intention. Conclusions: The study contributes to SPL adoption literature by validating an extended UTAUT2 model in an underexamined Saudi context and highlighting the role of trust in technology-enabled LMD acceptance.
Background: Agricultural logistics in Kazakhstan is critical for export-oriented supply chains, but its resilience is limited by infrastructure constraints, fluctuating export demand, and insufficient coordination between market and logistics processes. Methods: This study develops a conceptual multi-level model of the agricultural logistics system and a hybrid simulation model combining system dynamics and discrete-event simulation to analyze intermodal transportation under demand and capacity constraints. The model integrates demand formation, storage, transport, and export operations, as well as feedback mechanisms between fulfilled demand, repeat orders, and logistics performance. The model is implemented in AnyLogic 8.9. Results: The conceptual model structures the interaction of key participants, logistics facilities, and infrastructure levels within Kazakhstan’s agricultural logistics system. Simulation experiments reproduce cyclic logistics behavior and show that reduced logistics capacity increases the demand gap and system pressure, while stronger market signals intensify demand and infrastructure load. The results confirm that resilience depends on the balance between demand activation, logistics capacity, and replenishment policy. Conclusions: The proposed approach provides a tool for analyzing the resilience of agricultural intermodal logistics in Kazakhstan and supports scenario-based evaluation of infrastructure and market factors. The novelty lies in combining a conceptual multi-level logistics model with hybrid simulation of demand and logistics flows.
