Despite increasing managerial interest, many firms struggle to scale sustainable servitization initiatives beyond pilot projects, facing capability misalignments, measurement challenges and governance complexities. This paper proposes a maturity model to assess and support manufacturing firms' transition towards sustainable servitization. We structure and populate the model through a systematic literature review complemented by 19 interviews with managers, consultants and academics, and we subsequently test and deploy it in four companies. Our analysis shows that sustainable servitization is enabled by 11 capabilities, which we articulate into 62 items. These items populate 7 maturity dimensions and can be evaluated on five-level scales. The resulting model consolidates the fragmented sustainable servitization literature by identifying the capabilities that enable this transformation. In practice, it can be used to identify structured maturity profiles, support both internal analysis and cross-company benchmarking, guide targeted capability-development roadmaps and raise awareness of sustainable servitization opportunities, even in low-maturity contexts.
The rapid diffusion of emerging technologies is redefining how operations are designed and managed, shifting attention from techno-centric efficiency to socio-technical integration. This study investigates how artificial intelligence (AI), robotics, 3D printing, blockchain, immersive environments, and IoT-5G reshape operational processes and human-machine interactions. Drawing on Socio-Technical Systems (STS) theory and an Empirically Grounded Analytics approach, 10,559 operational practices were analysed through co-occurrence and correlation mapping. By comparing six technologies across eight socio-technical dimensions, results indicate that AI and robotics strongly relate to augmentation and data-driven decision-making, blockchain and IoT-5G enhance safety, traceability, and distributed control, and immersive environments foster engagement and experiential learning. These findings show that emerging technologies reshape operational architectures by redistributing decision authority, altering coordination mechanisms, and transforming the role of human participation. Moreover, thirteen empirically grounded propositions extend STS theory, offering a socio-technical framework for managing digital transformation in operations management.
Although traditional predictive methods support project planning, they often struggle to capture the high-dimensional interactions, nonlinearity, structural complexity, uncertainty and evolving nature of real-world projects. This limitation can reduce predictive accuracy, robustness and probabilistic reliability, thereby constraining managerial responsiveness. In response, this study is motivated by the limited number of field-validated empirical studies that apply advanced ensemble learning techniques, such as calibrated stacking, to model complex relationships among project variables and, in turn, predict project success. Accordingly, the objective of this study is to develop and rigorously evaluate a statistically validated ensemble learning framework for binary project success prediction under realistic, imbalanced project management conditions. The proposed framework integrates feature-selection enhancement, inferential statistical testing, multi-metric evaluation and embedded interpretability diagnostics. The results indicate that calibration-aware stacking with regularized meta-learning delivers robust predictive performance, offering more reliable analytics and stronger decision support for managers operating in complex project environments.
Grounded in middle-range theory (MRT), this study explores supply chain resilience (SCRes) during the 2021 Henan floods through 54 interviews with agri-food industry practitioners. The findings reveal SCRes to be a multi-level framework determined by individuals, organizations, supply chains, and broader environments collectively. The environmental level, comprising political, economic, social, technological, environmental, legal, and cultural (PESTELC) factors, functions as the resource layer, providing critical resources. The supply chain level is the direction layer, setting goals for preparation, response, recovery, and adaptation by the organizations and individuals involved. The organizational level is the transmission layer, disseminating resilience objectives both horizontally and vertically. Finally, the individual level is the implementation layer, operationalizing organizational and SCRes strategies. Organizational employees' personal attributes, such as openness, persistence, and extraversion, should be carefully considered when implementing SCRes strategies. China's hierarchical culture, chain leader system, and accountability mechanisms ensure coordination across all levels.
This paper examines the current state of research, key aspects, and implications of the relationships between Industry 4.0 technologies (I4.0T), lean management practices (LMPs), and sustainable operational performance (SOPR). Using a theory-driven lens, the study conducts a systematic literature review (SLR) of 50 peer-reviewed articles to explore synergies, tensions, and research gaps within this emerging domain. Drawing on Socio-Technical Systems (STS) and Complementarity Theory, the review identifies a core paradox: while digitalisation enhances lean performance through improved visibility, connectivity, and process integration, it also creates human-centric tensions related to autonomy, identity, learning demands, workforce adaptation, and organisational flexibility. The findings show that synergistic integration of LMPs and I4.0T can improve operational, social, and environmental outcomes, whereas misalignments between automation and human-centred practices generate paradoxical tensions. The paper proposes a future research agenda focused on paradox management, adaptability, and transitions towards Industry 5.0 systems.
Grounded in social exchange and social network theories, we study whether and how buyer firms' social sustainability draws on their suppliers' digital technology-oriented open innovation (DTOI). We focus on two dimensions of buyer social sustainability, namely societal and workforce sustainability, and examine the moderating role of buyers' director network structure. Using panel data on Chinese listed firms from 2014 to 2024, we find that both dimensions are positively associated with supplier DTOI. These relationships are stronger for buyers whose director networks feature higher degree centrality, betweenness centrality, and structural holes, but not closeness centrality. We further explore how these relationships vary across relational, ownership, and industry contexts. Our findings extend digital innovation research to supply chains and clarify when supplier spillovers reach buyers.
Generative AI (GenAI) is a promising enabler of predictive maintenance in capital-intensive manufacturing. However, there is limited empirical evidence on how it can be effectively embedded in production planning and control and on the performance gains it delivers. This paper reports an in-depth case study of ABC Tyres Limited (ABC), a tyre manufacturer that introduced a GenAI-enabled predictive maintenance solution for its curing presses. The solution combines a domain-adapted large language model (LLM) with the plant's process data lake, allowing engineers to query historical production cycles in natural language and generate diagnostic insights. Using before-and-after operational data, we assess the impact on key performance indicators. The intervention reduced the mean dry cycle time from 106 to 80 seconds, stabilized the curing process, increased daily curing capacity by 304 tyres, reduced steam consumption by 0.3 MT per day, and shortened the turnaround time for complex root cause analyses from around 10 days to roughly 2 hours.
Digitalisation and datafication are reshaping shopfloor management (SFM), yet many initiatives still digitise artefacts without improving decision-making or control. Building on data-oriented SFM, this paper develops an implementation-oriented reference framework that translates five aggregated dimensions - development path and implementation plan, process organisation, organisational structure, cross-functional operating model, and information system - into practice-oriented design impulses across 22 second-order themes. Using practice-based design science research, the framework adopts a socio-technical perspective that integrates technology, organisation, and human labour to make data decision- and action-effective in shopfloor routines. Empirical validation through eight expert interviews and an illustrative case study on organisational structure indicates high perceived usefulness, coherence, and completeness, and suggests improved integration of data into operational decision-making. The framework is proposed as an open, modular guideline for context-specific implementation paths, with future potential in AI-supported decision logic and low-threshold entry scenarios.
With the rise of the platform economy, firms increasingly adopt platform-based data sharing (PDS) and signal their initiatives through public announcements. As stock prices promptly reflect investor expectations, market reactions provide a direct assessment of PDS value. However, the specific impact of PDS on stock prices and its contingent factors remain underexplored. Drawing on Complex Adaptive Systems (CAS) theory, this study collects 150 PDS announcements released by Chinese A-share firms from 2014 to 2024. Using event study methodology, the results show that PDS announcements generate positive abnormal returns, especially for firms with higher proportions of independent directors, lower supply chain concentration, and operating in more competitive industries. Moreover, physically dominated platforms and platforms with higher technological sophistication elicit stronger market responses. This study contributes to the literature by uncovering the stock market value of PDS and extending the theoretical and empirical understanding of PDS in the platform economy.
The development of sustainable agricultural supply chains is critical for improving operational efficiency, coordination, and resilience under increasing demand uncertainty, climate variability, and resource constraints. These supply chains involve complex planning and control decisions across interconnected stages, including pre-production, production, post-harvest processing, and distribution. However, existing research on artificial intelligence (AI) and machine learning (ML) remains fragmented and lacks an integrated planning and control perspective. This study addresses this gap by developing a conceptual decision-support framework that positions ML as a decision-intelligence layer embedded within planning, coordination, and control processes. Using a structured literature synthesis, the study maps ML techniques to key operational decision contexts across the supply chain. The framework demonstrates how ML-enabled decision support can enhance yield planning, resource allocation, logistics coordination, quality control, and waste reduction, while linking these decisions to sustainability outcomes such as environmental efficiency, economic resilience, and transparency. The study also identifies key implementation challenges, including data availability, interoperability, infrastructure constraints, and organizational readiness. The study contributes a decision-centric framework that integrates ML with supply chain planning and control, providing actionable insights for improving performance and sustainability in agricultural supply chains.
This study explores the mechanisms through which reshoring initiatives contribute to the adoption of circular economy practices, and how contextual factors structurally enable or constrain these mechanisms. We analyse a longitudinal case study of a company producing locally assembled bicycles in the UK and a product-as-a-service model to understand how circular economy practices emerge from the interaction between firm-level resource orchestration and institutional conditions. Drawing on Resource Orchestration Theory and Institutional Theory, the study identifies three causal mechanisms through which reshoring facilitates circular economy practices: internal production control as an enabler of integration and experimentation; supply base reconfiguration towards innovative local SMEs as an enabler of co-development of circular components; and the creation of a local innovation ecosystem as an enabler of systemic value change. The activation of these mechanisms is contingent on institutional and contextual conditions, such as regulatory incentives and supply chain realities, suggesting that reshoring alone is insufficient to drive circularity.
