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.
This study investigates how Industry 4.0 (I4.0) technologies enhance supply chain collaboration (SCC) and circular economy (CE) performance in Türkiye’s automotive industry. It addresses a gap in the literature. Specifically, prior research has not adequately examined whether different collaborative mechanisms within SCC produce distinct effects on separate dimensions of CE performance through a mediated structural framework. This issue remains underexplored, especially in SME-dominated auto supply chains in emerging economies. Drawing on data from 134 firms, Partial Least Squares Structural Equation Modeling reveals that I4.0 technologies bolster SCC through improved visibility, interoperability, and integration. In turn, SCC drives key CE outcomes, including eco-design, resource efficiency, waste management, and recycling/recovery, while also mediating the direct positive effects of I4.0 on CE performance. This study contributes a novel theoretical framework grounded in resource-based view (RBV), stakeholder theory (ST), and information processing theory (IPT), with each theory assigned a distinct explanatory role, to explain how I4.0 technological resources are translated into collaborative capabilities and directed toward CE outcomes in complex supply chains. The central finding of this study is that supply chain collaboration, rather than direct technological adoption, serves as the dominant pathway through which I4.0 capabilities generate CE outcomes, accounting for approximately 55
The exponential growth of Artificial Intelligence (AI) technologies offers significant transformative opportunities for modern supply chains to address disruptions, but its adoption remains difficult, particularly in developing countries. The present study investigates the patterns influencing AI adoption for supply chain management using the Behavioral Reasoning Theory (BRT). It examines how values, reasons for and against, attitudes, and intentions influence organizational decision-making regarding AI adoption in SCM. The presented research collected data from 392 respondents across various manufacturing sectors in India. The study uses Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess a conceptual model underpinning the research and to test eight direct and four mediation hypotheses. The results indicate that ‘reasons for’ AI adoption, including economic, social, and environmental benefits, significantly impact attitudes and intentions. Besides this, ‘reasons against’ do not significantly affect intention, indicating that perceived benefits outweigh concerns. In addition, values significantly shape attitudes and intentions, directly or indirectly through mediating effects. The results highlight the crucial role of contextual and psychological factors in the adoption of AI for supply chain management (SCM). The paper contributes to the rapid adoption of AI by extending the application of BRT in technology-driven SCM. From a practical standpoint, the paper provides managers and policymakers with crucial insights into perspectives that may facilitate or hinder the effective adoption of digital transformation in existing supply chains.
Large-scale disasters and catastrophic events pose a high risk of shortages in humanitarian funding. Recently, a novel form of index-based catastrophe insurance has been proposed to offer an advanced risk-sharing mechanism by providing financial liquidity for relief organizations during the disaster response stage. However, although it bypasses the complicated process of loss estimation, delays in insurance payouts may still occur, which greatly reduce the effectiveness of risk mitigation. To address this problem, we design an incentive contract as a supplementary agreement to index-based catastrophe insurance between a local authority and an insurance company, and use deprivation cost to measure the effectiveness of risk mitigation in disaster relief. By constructing a Stackelberg game model, we derive the optimal incentive coefficient for the local authority and the optimal claim settlement time for the insurance company, thereby achieving a win–win situation. To further evaluate the feasibility and optimality of this contract, we compare it with a reserve pooling strategy. The applicability of the proposed contract is validated using empirical evidence from Shenzhen, China. The findings indicate that the incentive mechanism operates effectively in practice and significantly enhances risk mitigation performance. Moreover, its advantages become increasingly pronounced under severe disaster scenarios and over an extended planning horizon.
This paper addresses the limited theory-driven understanding of how Industry 4.0 (I4.0) technologies can be effectively adopted to enhance responsiveness in operations and supply chain management (OSCM), particularly in terms of flexibility and agility. Drawing on a systematic literature review structured through the context–intervention–mechanism–outcome (CIMO) logic, this paper develops a conceptual framework that integrates the diffusion of innovation (DOI) and dynamic capabilities (DC) theories. This framework conceptualises digital transformation as a cumulative learning process and introduces engagement as a supporting capability that links sensing, seizing, and transforming. A 14-step roadmap translates the framework into actionable steps for practice, and both the framework and roadmap were validated through expert focus groups to ensure applicability. This paper contributes to the theory by clarifying how DOI and DC jointly explain digital transformation in OSCM by providing structured guidance for orchestrating technology adoption and enhancing responsiveness.
Traceability is often implemented as a regulatory requirement in supply chains. Organizations have started to recognize its potential to improve supply chain resilience, transparency, and operational performance. Despite this growing importance, many firms struggle to move beyond fragmented, compliance-driven approaches to traceability. This study examines how organizations operationalize traceability as a strategic capability within complex supply chains. Using a multiple case study design, the research analyzes traceability initiatives across three sectors: food production, healthcare manufacturing, and industrial construction based on semi-structured interviews with practitioners and documentation from case companies. Cross-case analysis reveals three foundational pillars that enable effective traceability: technology integration, supplier engagement, and process redesign. The findings further show that the strategic impact of traceability depends on complementary enabling mechanisms, including governance structures, performance measurement, and change management practices. Together, these six dimensions form an integrated capability framework that explains how traceability initiatives evolve from compliance tools into drivers of operational visibility and responsiveness. The study contributes to operations and supply chain research by bridging practitioner experience with theory and offers actionable guidance for organizations seeking to embed traceability into their operating models rather than treating it as an isolated compliance activity. By positioning traceability as a strategic capability, the study offers managerial implications for building adaptive, efficient, and resilient supply chains in a dynamic global environment.
This study examines how relational mechanisms among key airport stakeholders, specifically airport operators, airlines, and ground handling firms, shape airport and firm performance. Drawing on stakeholder theory, airports are conceptualized as multi-actor service platforms in which performance emerges from interdependent coordination rather than isolated organizational activities. Prior research has primarily emphasized operational, structural, and technological determinants, with limited attention to the relational micro-foundations of coordination. In particular, the roles of inter-organizational voice and transparency remain underexplored in complex airport settings. Addressing this gap, the study investigates inter-organizational promotive and prohibitive voice behaviors as complementary coordination mechanisms through which inter-organizational actors propose improvements and raise concerns, and examines how transparency conditions their effectiveness. Using qualitative data from in-depth interviews with managerial representatives across the three inter-organizational actor group, the findings show that both forms of voice enhance airport- and firm-level performance by enabling collaborative problem-solving, continuous improvement, and early risk detection. Transparency further strengthens these effects by improving the clarity and usability of shared information, thereby increasing coordination effectiveness. By distinguishing between inter-organizational promotive and prohibitive voice and examining their interaction with transparency, this study advances stakeholder theory and operations management research by identifying relational micro-foundations of coordination in multi-actor systems. The findings show that performance variation in complex service platforms is better explained by how actors communicate and respond to one another than by structural arrangements alone. The study also offers actionable insights for managers seeking to improve coordination and performance through the design of voice and transparency mechanisms.
Businesses often struggle to monitor supply chain performance effectively due to overlapping, overly complex indicators, which can lead to confusion and the neglect of essential metrics. To address this complexity, supply chain leaders must create a streamlined yet integrated set of key performance indicators that enable efficient tracking with rigor, relevance, and ease of monitoring. Drawing on insights from industry experts, the authors’ industry experience, and professional discussions, this practice note outlines a plan to integrate performance metrics in a specific domain. Its practical approach makes it a helpful resource for mid- to large-scale enterprises operating in planning, logistics, warehousing, and retail, specifically those that need to manage multi-node, high-volume, or cross-border supply chains. This positions our practice note as a potential standard for consumer-driven industries, including automotive, e-commerce, pharma, and others. Our model offers an innovative approach to integrative supply chain monitoring, positioning it as a potential standard for managing the complex process and enabling historical benchmarking.
Global supply chain (SC)s face intensifying pressure from non-stationary disruptions on both the supply and demand sides — a vulnerability that the COVID-19 pandemic exposed with particular severity in ocean shipping, where transit time instability undermined pre-existing resilience assumptions. This study advances supply chain orchestration (SCO) as a business model theory by adopting a theory elaboration approach, focusing specifically on how SCO’s demand-supply synchronization logic operates within the context of SC resilience. Drawing on perceptual survey data from 50 mid-senior industrial executives and 63,850 secondary transactional records from a multinational firm operating in the ocean shipping ecosystem, we empirically assess the elements of SCO as a business model through cluster analysis on the demand side and logistic regression predictive modeling on the supply side. Our findings reveal three distinct resilience demand segments among industrial customers and demonstrate that stakeholder complementarity and synergy among structurally disconnected ocean shipping actors — ports, carriers, and terminals — constitute an activity system capable of generating measurable resilience value. These results provide significant empirical support for SCO as a viable business model framework. What our two analyses reveal, taken together, is that resilience value in ocean shipping is present but structurally uncaptured — distributed across fragmented actors who lack a coordinating mechanism to convert latent customer demand into realized performance advantage. Theoretically, this study elaborates SCO’s core construct of architectural market knowledge within the resilience theme, contributing to both business model theory and the SC resilience literature by demonstrating how ecosystem-level coordination, rather than firm-level operational capability, drives resilience value in fragmented global SC contexts.
AI is fundamentally transforming all areas of our lives, including research and education. It opens new opportunities and unfolds a multitude of critical discussions. Among others, the discussion about future trends in research and education is lively and diverse, ranging from optimistic sentiments about “AI takes it all” to pessimistic tones about dehumanization and the degradation of thinking capabilities. We do not know to what extent AI will come to dominate in the future. However, thinking about research and education in an AI-augmented world might be useful regardless of how much AI will be in our lives in the future. In this note, I try to take a balanced position and provide my opinion on how research and education in general, and operations management in particular, could evolve in the AI era, considering both chances and risks. As one of the outcomes of this viewpoint note, a framework is proposed, featuring three main elements that education and research should be composed of, i.e., human knowledge base (i.e., what we know without asking AI), AI-augmented thinking and learning, and human-AI collaboration in solving complex problems. I also discuss how journal editorial and review practices, universities and their (operations management) course curricula, and research funding assessments could evolve over time, concluding that the academic profession would change, and not necessarily for the worse.
Enhancing supply chain resilience (SCR) is a key process in improving modern industrial systems. Although supply chain leadership firms play an important role in supply chains, they have not sufficiently used dual innovation in enhancing SCR. This study examines the influence of dual innovation in supply chain leadership firms on SCR. A theoretical framework is first developed to elucidate this relationship. The hypotheses proposed are subsequently tested empirically using data from Chinese A-share listed companies covering the years 2014–2023. Results reveal that substantive innovation (SUI) by leadership firms enhances SCR. However, strategic innovation (STI) does not appear to have an impact. Mechanism analysis reveals that the resilience-enhancing effect of SUI operates through knowledge spillovers and production efficiency spillovers. This impact is enhanced by effective market competition and proactive government intervention. Heterogeneous analysis indicates that SUI leads to a larger enhancement of SCR for non-state-owned, downstream, and labor-intensive firms. Implications are presented for supply chain leadership firms seeking differentiated innovation strategies to enhance SCR and boost the formation of modern industrial systems.
In the arena of green warehousing, lighting plays a crucial role in the energy demand and carbon emissions, and simulation can help identify effective energy efficiency measures. However, the successful implementation of these measures depends on various factors, including operational constraints, human behaviour, and site-specific characteristics. This paper reports the results of a collaborative project between Politecnico di Milano and Mondo Convenienza, a major Italian furniture retailer, aiming at identifying a customized roadmap for achieving the Net-Zero condition by highlighting the most suitable energy efficiency interventions. The simulation results indicated that the integration of daylight and motion-based controls with the existing LED lighting bulbs would yield substantial benefits. Considering these results, the company has decided to proceed with the installation of smart lighting sensors in the storage area. Nonetheless, during the implementation phase the company faced calibration challenges, particularly in determining the sensor detection width required to activate the LED lighting in the storage aisles. To address this challenge, a participatory calibration process was initiated by the company to incorporate the human factor in the design process, involving forklift operators to determine an operationally acceptable detection width. The implemented intervention was well accepted by operators and resulted in a total energy reduction of approximately 15
As supply chains are increasingly disrupted by climate change, a growing consensus has emerged regarding the need for an adaptive balance between production operations and the ecological environment. Drawing on an organizational capability perspective, this study conceptualizes climate change adaptation (CCA) capability as a two-dimensional construct comprising awareness and action capabilities, and examines its relationships with supply chain resilience (SCR) and supply chain performance (SCP). Using survey data from agricultural supply chains in China, the results show that CCA capability has a positive direct effect on SCP and an indirect effect through enhanced SCR. Notably, these effects differ between agricultural and non-agricultural firms. The study contributes to the operations management literature by situating CCA within a capability-oriented perspective in supply chain research and providing a clearer construct definition of CCA capability. The findings also offer practical guidance for CCA strategies focused on organizational capability building.
Motivated by the need to understand how leadership and technology jointly drive green and digital transformation in complex, volatile environments, this study examines how transformational leadership fosters “green digital supply chain transformation” and resilience by promoting the adoption of disruptive technologies and aligning green and digital initiatives. Drawing on the “stimulus-organism-response framework”, “resource orchestration perspective”, and “transformational leadership theory”, quantitative data were collected from 324 Saudi Arabian professionals across diverse industrial sectors. Robust statistical analyses, including “partial least squares structural equation modelling”, “combined importance-performance map analysis”, and “necessity condition analysis”, reveal that transformational leadership significantly enhances the adoption of green disruptive technologies and fosters green digital congruence, both of which are critical mediators in achieving resilient and sustainable supply chains. The results highlight a multi-stage pathway that transformational leaders adopt to orchestrate resources and capabilities, thereby improving supply chain resilience. Practical implications suggest that firms should prioritize leadership development, strategic alignment of green and digital goals, and targeted investment in disruptive technologies to build adaptive, sustainable supply chains. However, the study’s cross-sectional design and reliance on self-reported data may limit causal inference and introduce response bias. Nonetheless, this research advances the theoretical understanding of integrated leadership and technology strategies for supply chain sustainability, providing actionable guidance for managers operating in evolving digital environments.
Coffee shops are strongly influenced by their location, which determines customer accessibility and pedestrian traffic potential, thereby impacting sales performance. The irrevocability of location decisions further amplifies their strategic importance. Coffee shops operate in highly competitive environments oriented toward profit and sales maximization. However, existing studies have predominantly conducted static trade area analyses centered on macroeconomic physical variables such as distance, population density, and store size. Additionally, entrepreneurs often rely on intuition and experience rather than scientific analysis for site selection. We analyzed various trade area characteristics, including competitive factors, potential demand, and infrastructure, using machine learning models to predict coffee shop locations and business sustainability. By identifying key variables that significantly influence post-opening business continuity, we highlight the need to reconsider several traditional assumptions related to site selection. Furthermore, we suggest that sufficient data combined with machine learning techniques can generate meaningful insights into business phenomena.
This paper aims to help managers in the decision-making process of improving sustainability by implementing joint lean and resilient supply chain strategies. Lean and resilient strategies have a positive impact on sustainability performance and, moreover, there exists a positive relationship between practices pertaining to both strategies in the sense that some of the practices facilitate the implementation of other practices. A decision-making model that improves the company’s sustainability level while maximizing the future impact of the present supply chain strategies’ implementation is developed. Under budgetary constraints, it becomes especially important to take advantage of the existing synergies between supply chain practices’ implementation to decrease the total implementation cost. A case study in the aerospace sector is carried out in order to illustrate the benefits of the proposed model with respect to traditional models found in the literature. The results imply that, under budgetary stress, a sequential implementation of supply chain practices is recommended, and the proposed mathematical model performs more efficiently than traditional models.
In a dynamic business environment, firms face numerous uncertainties that significantly impact operational effectiveness. Recently, artificial intelligence (AI) has become a powerful toolkit for identifying and managing known and unknown uncertainties. This study examines how AI technology enhances operational performance amid uncertainty, a key concern for firms seeking to gain a competitive edge through advanced technology. Grounded in dynamic capabilities theory (DCT), we developed a construct-based model that includes mediating, moderating, and direct relationships. An empirical analysis was conducted on a sample of 811 leading logistics firms, focusing on the implementation of AI in their operations. We employed partial least squares structural equation modeling (PLS-SEM) to test the proposed hypotheses among latent variables and constructs. The results demonstrate that both known and unknown uncertainties positively influence operational performance. Furthermore, AI provides stability and improvement in logistics operations, thereby enhancing competitive positioning. AI has significantly advanced the management of supply chain uncertainties, reducing operational errors and mitigating risks. The findings suggest that policymakers should consider adopting AI technologies in logistics operations to effectively address and navigate uncertainties.
This research develops a managerial tool to monitor and evaluate the sustainability performance of Digital Agricultural Technological Solutions (DATS). This tool is an assessment framework designed to evaluate both the benefits (monetary and non-monetary) and costs of DATS’s implementation. The assessment framework is developed with the study of 30 cases in Europe, working with different crops and breeds, with and without DATS. The framework is developed in three phases. First, it combines top-down and bottom-up approaches to identify the most relevant assessment categories, subcategories, and key performance indicators from a sustainability perspective, according to the type of technology adopted and the process in which the DATS are implemented. Next, the assessment framework design involves determining the calculation methods, considering both monetary and non-monetary evaluations. Finally, the framework is applied to each case to evaluate the costs and benefits, as well as the appropriateness and feasibility of the performance calculations. The contributions of the study to literature and practice are threefold, i) developing an assessment framework with a feasible and affordable subset of indicators linking theory and reality, ii) combining a dual lens assessment in monetary and non-monetary terms, and, iii) develop a dynamic living assessment tool that facilitates processing information about DATS adoption in a multi-season perspective. In the future, researchers could further refine, replicate and expand the use of the assessment framework in other geographies or for specific DATS or agricultural sectors.
Existing research mainly focuses on the perspective of digital platform developers and examines the advantages of platforms. However, there is still insufficient analysis of why participating enterprises enhance their supply chain resilience (SCR) after engaging in platform boundary-spanning search (PBS) activities through digital platforms. This study aims to investigate, from a resource orchestration perspective, how platform boundary-spanning search, heterogeneous resource acquisition (HRA), and alliance portfolio diversity (APD) are interrelated and jointly influence supply chain resilience. This study employs SPSS 26.0, SmartPLS 4.0, and AMOS 28 to test the proposed model using a covariance-based structural equation modeling (CB-SEM) approach. Through stratified sampling, data were collected from 234 senior supply-chain managers. Common method bias was preliminarily assessed via multicollinearity tests, and content validity was established using item-level and scale-level indices. Structural relationships and mediating effects were analyzed with AMOS 28 and SPSS 26.0. The results indicate that platform boundary-spanning search via digital platforms enhances enterprises’ supply chain resilience. Both heterogeneous resource acquisition and alliance portfolio diversity serve as mediating variables in this relationship. Furthermore, a chained mediation effect exists between these two factors. By emphasizing the role of resource orchestration, this study fills the research gap between platform boundary-spanning search and supply chain resilience. It identifies the crucial roles of heterogeneous resource acquisition and alliance portfolio diversity in improving the effectiveness of supply chain resilience. Moreover, the findings provide theoretical guidance for enterprises seeking to enhance their resilience through boundary-spanning search activities on digital platforms.
The wind energy sector continuously improves manufacturing processes to meet the growing demand for renewable energy. Recognizing the complexity and extended duration of the wind turbine blade production process, alongside the need to uphold high-quality standards, this work aims to increase the efficiency of the production process of wind turbine blades by improving its cycle time production. To attain this objective, bottlenecks and critical subprocesses were identified, lead time and process cycle time were reduced, and production throughput was increased by minimizing non-value-added activities. To achieve these objectives, researchers employed action research and implemented a set of lean manufacturing tools. After implementing the suggested tools and improvements, the company experienced positive impacts including cost and waste reduction, better workplace organization, enhanced safety and final product quality, and improved efficiency and productivity. This study is relevant to firms seeking to enhance operational efficiency and quality by implementing lean practices. Thus, advancements in materials, automation, quality control, and digital technologies are driving improvements in the production of wind turbine blades. This work examines an alternative method for enhancing the production process of wind turbine blades by employing various lean manufacturing tools. These tools are relatively easy to implement, do not necessitate significant investments, and align well with the realities faced by many companies.
