This study examines how integrating total quality management (TQM) and sustainable supply chain management (SSCM) drives circular economy implementation (CEI) in manufacturing firms. Integrating the Malcolm Baldrige National Quality Award (MBNQA) framework and the natural resource-based view (NRBV), the study applies structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) on data from 340 manufacturing firms. Our findings indicated a positive association between TQM and CEI, with SSCM acting as a partial mediating mechanism. Importance–performance map analysis revealed that SSCM delivers higher operational performance, while TQM holds greater strategic importance. Moreover, fsQCA further identified multiple configurational pathways leading to CEI. Overall, the present study contributes to the literature by clarifying how internal quality management capabilities translate into sustainability outcomes through supply chain mechanisms, particularly in a developing-economy context. Importantly, these findings provide actionable insights for managers seeking to align quality and sustainability initiatives to advance circular economy transitions.
Building supply chain resilience has become a strategic imperative in recent years due to pandemics, globalization, geopolitical pressures, and growing risk exposure. This study explores the adoption of digital twin technology to strengthen supply chain resilience in the U.S. manufacturing sector during COVID-19 pandemic crisis. Drawing on a grounded theory approach and 21 expert interviews, we identify key factors driving digital twin adoption in supply chains. We define digital twin adoption stages to strengthen supply chain resilience and propose a theoretical framework based on dynamic capability theory. Digital twin adoption follows four stages: (1) establishing the business case, (2) piloting and defining models, (3) digitizing supply chain processes, and (4) developing predictive and prescriptive capabilities. Through these stages, digital twin adoption enables real-time sensing, facilitates dynamic scenario planning, and automates decision-making, allowing faster and more effective disruption responses. The findings reveal that digital twin integration reinforces critical resilience dimensions: agility, reconfiguration, robustness, and collaboration. This study advances the emerging body of knowledge on digital twin adoption for supply chain resilience through the lens of dynamic capability theory. We present a comprehensive framework detailing the adoption process, from its antecedents to outcomes and maturity levels. Given the scarcity of empirical studies and holistic frameworks linking digital twin technology to supply chain resilience, this research offers a novel approach for researchers and practitioners to develop more resilient supply chains.
Supply chain (SC) actors may respond quickly to disruption yet struggle to coordinate their actions when planning cycles, decision windows, and interpretations of urgency differ. This paper examines that problem by introducing Temporal Agility (TA), a meta-coordination capability concerned with aligning the timing of interdependent adaptive activities. Drawing primarily on the dynamic capabilities view, complemented by temporal cognition and process theory, we explain how differences in temporal interpretation shape organizational pacing and cross-firm coordination. We develop a three-level framework and a recursive process model of temporal orchestration to explain how individually responsive actors can achieve coordinated adaptation. TA extends existing supply chain agility (SCA) research by identifying the temporal conditions under which distributed adaptive responses reinforce or interfere with one another. The framework also provides a basis for managerial interventions that address differences in planning horizons, response windows, and decision rhythms during disruption.
Although startups drive innovation, economic growth, and new employment opportunities, they are inherently vulnerable to uncertainty. This research proposes an innovative and robust approach to forecasting the efficiency of startups' value chains while accounting for inherent uncertainty. The methodology employs robust network data envelopment analysis (RNDEA) within a three-stage structure. Using Weibull's weakest-link theory, the startup's value chain's overall efficiency is formulated to maximise the minimum (weakest) efficiency across its stages. A scenario-based approach is employed to develop the model's robust counterpart. A case study involving information and communication technology (ICT) startups from an emerging economy demonstrates the applicability of the proposed method by analysing three economic scenarios: (i) recession, (ii) stability, and (iii) growth. The findings reveal that the weakest stage of a startup’s value chain is expected to have at least 27
Building coalitions among autonomous entities with conflicting performance, beliefs, and capabilities is one of the most significant challenges in multi-agent systems. Optimal set partitioning of the players to maximise social welfare and distribute the cooperation payoff are two main research streams in this context. Nevertheless, achieving an integrated procedure for stable coalition formation remains a significant challenge in reaping the benefits of cooperation. Accordingly, this paper models a stable and optimal coalition structure using a solution concept in cooperative game theory. To do so, we use an exact mixed-integer model based on the least-core solution concept for a class of cooperative games known as the economic lot-sizing (ELS) game. We test the model on a complex problem in Iran’s rebar retail industry, an emerging economy, and validate it using Design of Experiments (DOE). The proposed model results indicate that the best condition for coalition formation among rebar retailers occurs when retailers’ demand is high across periods and joint ordering yields the lowest price from the closest supplier. Moreover, social welfare improvement is greater in this situation than in other scenarios.
