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.
Digital technologies (DTs) are widely regarded as critical enablers of sustainable supply chain management. By using a matched panel of listed customer and supplier companies in China for the 2009-2024 period, this study analyzes the spill-over effect of DTs on social sustainability (SS) in supply chains and elucidates the mechanisms of the relationship. Results reveal the U-shaped spill-over effect of DTs on SS, characterised by the coexistence of crowding-out and empowering effects. Suppliers' innovation capability and financial constraints, and customer concentration, are the primary mechanisms through which the effect operates. Furthermore, the U-shaped spill-over effect is more pronounced in state-owned suppliers, mature suppliers, and suppliers in competitive industries and non-heavily polluting sectors. Overall, this study extends the literature on DTs within socially sustainable supply chains and provides important managerial insights to firms seeking to advance their SS practices during their digital transformation.
Manufacturing reshoring is often promoted as a strategic response to rising global uncertainty. However, empirical evidence on how reshoring affects firm performance over time, and whether realised outcomes differ from initial expectations, remains limited. This study addresses this gap by exploring reshoring performance outcomes through an Eclectic Paradigm (EP)-based four-dimension outcome analysis framework, covering performance impact, expectation alignment and reshoring phases. Based on 16 semi-structured interviews with Swedish manufacturing firms, the findings show that efficiency-related outcomes are most frequently observed, alongside capability-, market- and resource-related outcomes. Reshoring improves profitability, operational flexibility, controllability, product quality and environmental sustainability, but also creates challenges, including implementation delays, inventory build-ups and supplier disruptions. This study introduces a visual outcome model and provides practical guidance for managers evaluating relocation decisions and performance outcomes. It also advances future research on post-implementation reshoring evolution, and how reshoring outcomes emerge and changes over time.
Unlike traditional supply chains built on stable relationships and standardised processes, project-based supply chains (PBSCs) are temporary networks assembled around specific tasks and are prevalent in customised, temporary, and cross-organisational delivery settings. This study presents a conceptually oriented systematic literature review of 75 articles. The review clarifies how PBSCs have been conceptualised in prior research, identifies recurring challenges across different contexts, and synthesises the governance responses proposed in the literature. It shows that PBSCs commonly face fragmentation and complexity, lack of trust, insufficient knowledge translation, information deficiency, and dynamic boundaries. Correspondingly, existing studies propose contractual, relational, systemic, social, risk-oriented, and inter-project learning approaches to governance. Building on this synthesis, the article reconceptualises PBSCs as a hybrid organisational form shaped by persistent tensions between temporariness and uniqueness. It explains why coordination and governance problems recur across project contexts and outlines future research directions on temporal processes, sensemaking, and institutional differences.
As social sustainability risks increasingly threaten supply chain continuity, firms face growing pressure to manage social issues while maintaining operational resilience. Drawing on high reliability theory (HRT), this study introduces the concept of social supply chain mindfulness (SCMIN). The article examines how it enhances supply chain resilience through socially sustainable sourcing, under varying levels of digital technology advancement (DTA). We test a moderated mediation model using survey data from 297 UK manufacturing firms. The results show that social SCMIN enhances supply chain resilience through socially sustainable sourcing. Moreover, DTA amplifies the positive effect of socially sustainable sourcing on resilience. This study advances resilience and mindfulness research by showing that social sustainability is not only a matter of ethics or legitimacy, but a core reliability mechanism. By linking mindful attention, enacted practices and enabling infrastructure, we provide a process-based explanation of how firms translate attention to social risks into proactive sourcing practices that strengthen supply chain resilience.
Traditional sales and operations planning (S&OP) frameworks, designed for stable products and predictable demand, are not well-suited to engineer-to-order (ETO) manufacturing with its extreme customisation, project-based work, and complex interdependencies. ETO environments face two challenges: epistemic uncertainty (unknown final requirements) and equivocality (conflicting interpretations of available information). Using organisational information processing theory (OIPT) and planning quality, we explore how these challenges create unique information processing requirements (IPRs) that demand an adaptive S&OP process. In a multiple-case study of four ETO manufacturers, our analysis reveals sources of these challenges (9 uncertainty and 16 equivocality drivers) and persistent mismatches between IPRs and existing information processing capacity (IPC), especially related to order-size impact analysis, specification management, and resource allocation. These findings inform an IPC-building framework comprising 52 strategies that shift the focus from process standardisation to the development of adaptive S&OP capabilities. The framework emphasises organisational prerequisites (e.g. learning culture) and digital technologies.
Lean and Industry 4.0 (I4.0) mutually reinforce operational excellence. Recently, however, companies have been urged to move beyond efficiency towards Industry 5.0 (I5.0), centred on sustainability, resilience, and human-centricity. Since I5.0 builds on I4.0 technologies, and Lean-I4.0 synergies are well established, scholars have suggested that their integration could support the transition to I5.0. Yet research remains limited and fragmented, often addressing the three pillars separately and discussing Lean and I4.0 at a high level, without identifying concrete practice-technology pairings for implementation. This study addresses this gap through a systematic literature review and proposes a framework showing how Lean practices and I4.0 technologies contribute to the three I5.0 pillars. The findings reveal multiple pairings, while also showing that several Lean practices remain underexplored, some relevant I4.0 technologies lack Lean integration, and current links focus mainly on manufacturing and shop-floor management. A future research agenda is proposed.
Sustainable supply chain management has attracted growing scholarly attention. Despite the expanding focus on environmental sustainability, social sustainability-encompassing employee relations, product responsibility, diversity, and corporate social engagement-remains a critical yet underexplored challenge. Drawing on Resource Dependence Theory (RDT), this study examines how digital technologies adopted within supply chains generate interfirm spill-overs that shape focal firms' social sustainability performance. Using a sample of Chinese listed firms from 2007 to 2024, we find that greater digital technology innovation by both upstream suppliers and downstream customers is positively associated with the social sustainability performance of focal firms. Further analyses show that this relationship is moderated by supply chain structure and technological characteristics. Specifically, supply chain concentration and network centrality weaken this effect, whereas geographic proximity within the supply chain strengthens it. In addition, the effect is more pronounced when suppliers or customers possess stronger technological advantages, as well as among high-tech firms. Decomposing social sustainability into multiple dimensions, we find that supply chain digital technologies exert significant and broad-based effects across various aspects of social sustainability. Overall, our findings highlight the importance of supply chain digitalisation in shaping firms' social sustainability.
This study investigates a two-echelon fresh agricultural supply chain plagued by information asymmetry, where a supplier possesses private information regarding product freshness. The strategic distortion of this freshness information leads to escalated supply chain losses and diminished operational efficiency. Employing a game-theoretic model, we analyze how the integration of blockchain technology and contractual mechanisms can mitigate the adverse effects of such information asymmetry. Our analysis identifies the critical parameters that govern the thresholds for blockchain adoption and its requisite investment. Furthermore, a comparative evaluation reveals distinct optimal decision-making patterns for supply chain members under varying operational conditions. The principal findings are threefold: first, the supplier's misrepresentation of freshness information generates private benefits at the expense of both the retailer's profit and total supply chain welfare; second, the effectiveness of blockchain is parameter-dependent, indicating its non-universal applicability; and third, in low transaction-cost scenarios, contractual solutions can achieve informational accuracy comparable to blockchain, thereby challenging the presumed superiority of technological interventions. This research provides dual theoretical and managerial insights for mitigating informational opacity, offering evidence-based guidelines for optimizing blockchain deployment and designing contractual coordination frameworks. The findings establish pragmatic decision criteria for selecting appropriate countermeasures tailored to specific operational parameters.
eHospitals face increasing pressure to manage rising patient demand with limited resources, making efficient patient flow a critical operational challenge. While prior research has examined patient flow from various perspectives, little is known about its practical operationalization and how decision-making within production planning and control supports responsiveness. This study examines how hospitals allocate decision-making authority to manage patient flow by analysing what decisions are made, where, and by whom in daily operations. An international multiple-case study of five academic hospitals, based on site visits and interviews, shows that hospital operations require planning and control approaches distinct from manufacturing. Rather than strict hierarchical control, hospitals engage in continuous resource rebalancing and patient reprioritization across interdependent units. Decision-making is largely decentralized to enhance frontline responsiveness and patient safety, complemented by centralized coordination through command centres. We propose a framework extending traditional production planning and control by integrating local rebalancing with central reprioritization.
Manufacturing and supply chain operations increasingly face persistent volatility that makes traditional optimisation and resilience logics insufficient. This paper examines how artificial intelligence (AI) can enable antifragility, defined as the capability that improve through exposure to volatility. Drawing on Interpretive Structural Modelling supported by Delphi and Nominal Group Technique with insights from senior practitioners from advanced manufacturing firms,this study identifies thirteen AI-enabled functions for antifragiltiy. It further organises these functions into a hierarchical capability architecture. Foundational functions provide predictive sensing, causal diagnostics, and continuous learning loops. Mid-tier functions use these learning capabilities to orchestrate flexibility, inventory, logistics, and sourcing reconfiguration, while upper tiers turn turbulence into innovation, demand reframing, and strategic capacity shifts that culminate in adaptive scheduling and autonomous control. The study moves antifragility from metaphor to mechanism and positions AI as a structured capability system, offering a strategic roadmap for sequencing AI investments towards higher-order autonomy in production contexts.
Efficient operation on the factory floor has been hindered by data latency and communication delays, leading to production downtime and associated costs. This research investigates the use of an IoT-enabled Andon system integrated with a vendor-neutral Business Intelligence (BI) visualisation layer, within a genset manufacturing business. Through a sociotechnical intervention, this work examines how integration of real-time monitoring, data visualisation, and visual management influences operational performance. The single-case study method confirms the effect of implementing an IoT-enabled Andon: a 55.55% reduction in unscheduled downtime and a 4% increase in Overall Equipment Effectiveness. The effectiveness of the Andon and Power BI system was due to 'technology' and, importantly, its empowerment of operators to make responsive and aligned production decisions. The research provides empirical validation of an integrated Industry 4.0 intervention through a sociotechnical lens. It highlights the synergy between digital tools and human decision-making, offering strategic directions for digital transformation.
