Spare parts management is a persistent operational challenge, often constrained by long lead times, high inventory costs, and frequent supply chain disruptions. Digital spare parts (DSP), enabled by additive manufacturing and digital technologies, provide a promising alternative through localized production and demand-driven supply. Yet, successful DSP adoption requires more than technological readiness; it depends on a systemic understanding of the ecosystem of enablers that shape both economic and operational viability. This study develops and empirically validates a structural model of DSP ecosystem readiness in the United Arab Emirates, an emerging economy pursuing industrial diversification. Data were collected from 162 industry professionals across energy, manufacturing, healthcare, automotive, and aerospace sectors. Using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), the study examines the interdependencies among 13 critical ecosystem factors. Findings show that infrastructure readiness, cost management, vendor support, customization capability, and quality assurance significantly and positively influence DSP adoption. In contrast, material availability, scalability, and industry readiness demonstrate counterintuitive negative effects, while sustainability and risk management exhibit no significant direct impacts. The results highlight the complex interplay of economic and operational drivers, providing an empirically grounded framework for understanding DSP ecosystem readiness and offering analytical insights for researchers and industry practitioners.
Indonesia is vulnerable to natural disasters, which frequently disrupt ground transportation infrastructure and hinder humanitarian aid distribution. In such conditions, drone-based logistics offers a promising alternative for last-mile delivery to affected areas. This study proposes an integrated optimization framework for humanitarian drone logistics that combines a multi-objective Facility Location Problem (FLP) and a Vehicle Routing Problem (VRP). The FLP determines the optimal locations of drone stations to maximize service coverage while minimizing infrastructure and operational costs, and is formulated as a min-max weighted-sum objective with normalized components. The VRP, solved using a Simulated Annealing (SA) algorithm, optimizes pickup-and-delivery routes with multi-trip capability, subject to drone capacity, distance, and operational time constraints. The framework is evaluated using real disaster data from the 2022 Cianjur earthquake in Indonesia. Results show that a small number of strategically located drone stations achieve high service coverage at a reasonable daily cost, with weight coverage approaching completeness. Routing optimization substantially reduces the deployed fleet relative to the FLP upper bound, demonstrating the added value of the VRP stage as a cost-reduction mechanism. The findings demonstrate that integrated strategic and operational optimization significantly enhances the efficiency and feasibility of drone-based humanitarian logistics, and that objective weight tuning can serve as a practical policy lever for balancing coverage, cost, and equity in disaster relief operations.
Business Continuity Management (BCM) plays a critical role in an era of uncertainty and crisis events. Supply Chain Resilience (SCRES) enables organizations to respond rapidly to disruptions. This study aimed to (1) examine the relationship between BCM and SCRES, (2) compare the structural and contextual differences between manufacturing and service sectors in terms of crisis responses, and (3) validate the proposed BCM–SCRES model with real-world business operations in the manufacturing and service industries. A mixed-methods approach was applied in this study. For the quantitative study, data were gathered from 434 organizations across the manufacturing and service sectors. The data were then analyzed using Structural Equation Modeling (SEM). The proposed model was subsequently validated through in-depth interviews involving three business cases. The findings revealed that BCM had a direct and significant impact on SCRES. When comparing the role of BCM in SCRES between manufacturing and service industries, the Recovery Plan (RP) emerged as the most critical component, followed by Crisis Management (CM) in both sectors. For the manufacturing industry, the Business Continuity Plan (BCP) placed third in importance, whereas it held fourth place in the service industry. Conversely, Emergency Management (EM) had the lowest influence in the manufacturing sector but held greater significance in the service sector, where it ranked third. Qualitative validation confirms that these relationships are reflected in organizational practices. These findings not only enhance the long-term recovery capacity of these sectors but also strengthen the sustainable international competitiveness of Thailand’s manufacturing and service industries.
This study investigates the relationship between supplier power, affective trust, and supplier opportunism in buyer-supplier relationships. Drawing on transaction cost economics (TCE) and social exchange theory (SET), we propose that different types of supplier power influence the affective trust that purchasing agents place in their supplier agents, and that affective trust, in turn, negatively impacts both weak-form and strong-form supplier opportunism. We employ regression-based mediation analysis using the PROCESS module in SmartPLS 4 to test our hypotheses with survey data gathered from 548 purchasing agents in Vietnam. Our findings reveal that referent and legitimate powers positively influence affective trust, whereas coercive power has a negative effect. Surprisingly, reward power had no significant effect on affective trust. Furthermore, affective trust negatively impacts strong-form opportunism but positively impacts weak-form opportunism. These findings have important implications for academics and practitioners in managing buyer-supplier relationships.
This study investigates the economic viability of independent workers in platform-based last-mile grocery delivery systems, focusing specifically on Instacart as a representative service supply chain. While prior research has examined operational efficiency, platform-level matching, scheduling, and service performance, less attention has been given to worker-level net income viability within the operational design and cost structures set by platforms. To address this gap, we develop a hybrid simulation framework integrating discrete-event simulation (DES) and system dynamics (SD). The DES model captures order processing, shopping, and delivery operations, while the SD model represents the accumulation of earnings and costs over time. Drawing on Expected Utility Theory and Distributive Justice Theory, we model worker decision-making under uncertainty and evaluate income fairness relative to effort and cost exposure. Using empirically grounded parameters, we analyze how batch structure, store characteristics, fuel costs, and tipping behavior affect hourly net income. Results reveal substantial earnings variability and highlight trade-offs between throughput efficiency and income stability. This study contributes by introducing a human-centered perspective on last-mile delivery, developing a hybrid simulation approach for platform-based supply chains, and providing insights for platform design and compensation policies.
