Background: Logistics process development increasingly relies on simulation and artificial intelligence (AI), yet existing approaches provide limited methodological guidance for integrating these technologies throughout the process development lifecycle and selecting economically justified digital maturity levels. Methods: Based on a structured literature review, this study develops an integrated methodological framework covering logistics process development from problem formulation and model design through validation to operational application. The framework assigns algorithm-independent AI functions to individual development stages and combines technical feasibility criteria with discounted net life-cycle cost (NLCC) evaluation to select among digital models, digital shadows and digital twins. Its applicability is illustrated through an industry-inspired proof-of-concept case study involving simulation-based resource optimisation and neural network predictions. Results: The framework establishes a systematic relationship between process development objectives, simulation modelling, AI-based decision support and digital maturity selection. The case study demonstrates how AI can approximate simulation-derived optimal operating parameters to support logistics process improvement. Under the assumed economic conditions, the digital model was the most favourable alternative. Conclusions: The proposed framework provides a structured basis for integrating simulation and AI throughout logistics process development while supporting technically feasible and economically justified decisions.
Background: Duty drawback refunds import duties to exporters, yet many eligible small and medium-sized enterprises (SMEs) do not claim it. We ask why, and what trying to claim it does to their supply chains. Methods: We analyzed 50 recorded technical-assistance sessions with 18 Peruvian exporting SMEs in a 2025 export-promotion programme, using codebook thematic analysis with coding by AI assistants, two rounds of blind double coding (Cohen’s κ = 0.66 with the initial codebook, 0.78 with the revised one) and adjudication. Results: For these firms, drawback is less a rate than a documentary chain of custody for a marginal imported input (labels, bags, boxes). Eligibility depends on how that input was bought; the proof runs through links the exporter does not control: informal producers and carriers, suppliers that will not size their sworn declaration, and brokers who leave the drawback flag empty. Intermediaries act as gatekeepers without mandate, and audit risk reaches firms second-hand. A rule-based six-position typology separates firms that cannot document their purchase from those that do not activate a documented chain, and identifies “phantom enrolment”. Only one organizational change was executed during the assistance; most were stated intentions. Conclusions: The findings point to the flag, the supplier declaration and the correction window.
Background: Supply chain resilience (SCRes) helps firms manage increasingly frequent and severe disruptions, yet its indicators remain fragmented across disruption phases and industrial contexts. Methods: This study presents a practitioner-based assessment of a multi-phase, contingency-based taxonomy of SCRes indicators. An umbrella review identified 41 indicators across pre-disruption, during-disruption, and post-disruption phases. Following conceptual refinement, six were excluded, leaving 35 indicators. Data were collected from 84 supply chain professionals in Morocco using a 10-point importance scale. Responses were analyzed using descriptive statistics, the proportion of ratings from 7 to 10, Cronbach’s alpha, and one-way ANOVA. Results: Cronbach’s alpha was 0.88, 0.91, and 0.88 across the three phases. Perceived importance differed significantly across sectors, F(3, 80) = 3.17, p = 0.029, η2 = 0.106. No significant difference emerged between junior and senior respondents, F(1, 82) = 0.60, p = 0.439, η2 = 0.007. Conclusions: The findings suggest sectoral differences in overall perceived importance but do not identify specific sector pairs or establish construct or predictive validity.
Background/Objectives: This paper reviews the role of additive manufacturing (AM) in maritime spare-part management, focusing on application areas, expected benefits, adoption barriers, and environmental implications. Methods: The review combines a structured Scopus search with backward and forward snowballing and includes peer-reviewed studies together with selected conference and grey-literature sources relevant to industrial applications. The selected literature was classified according to application domain, implementation challenges, and circular-economy relevance. Results: AM has been mainly investigated for prototyping, spare-part and end-use component production, repair and obsolescence management, and decentralized manufacturing through digital inventories, port-based hubs, or onboard production. The evidence indicates that AM can improve availability, reduce lead times and dependence on physical inventories, and support responsive maintenance, particularly for rare-demand, low-volume, obsolete, or difficult-to-source parts. Adoption remains constrained by certification, quality assurance, operational conditions, digital governance, economic feasibility, and skills. Environmental benefits may arise through waste valorization, recycled or locally available materials, and component life extension. Conclusions: AM emerges as a selective and complementary capability rather than a universal replacement for conventional supply chains. A literature-derived conceptual framework is proposed to synthesize the main dimensions relevant to maritime spare-part sourcing decisions.
Background: Electrifying last-mile delivery vehicles is central to cleaner urban logistics, yet behavioral evidence on commercial operators remains limited. This study examined the determinants of adoption intention among Thai delivery operators considering electric light commercial vehicles (eLCVs) or electric motorcycles (EMs) and whether these determinants are equivalent across groups. Methods: An extended Theory of Planned Behavior with perceived economic, social, and environmental benefits was tested on survey data from 729 operators using multigroup structural equation modeling with sequential invariance testing. Results: All six determinants positively and significantly affected intention in both groups. Attitude had the largest effect, followed by perceived social benefits, whereas perceived economic benefits were among the weakest. Configural, metric, scalar, and structural invariance were supported. Conclusions: The same determinants operate equivalently across both vehicle types, and adoption intention rests on evaluative and social judgments as well as cost savings. Policies and marketing can rely on common behavioral levers while tailoring delivery to each group’s operating context.
