Aug 2026· Systems· Vol 14, pp. 926· 0 citations· 70 references
TL;DR
A Data-Driven Operations Synchronization Stack is proposed that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture in high-throughput manufacturing contexts.
Abstract
Smart Factory programs increasingly connect shop-floor, quality, asset and planning data, but integrated data infrastructures do not necessarily align operational decisions. This paper reviews how smart manufacturing literature explains the transition from industrial data integration to closed-loop operations synchronization and value capture in high-throughput manufacturing contexts. Using the Systematic Search Flow method, 1949 records were screened and reduced to a final portfolio of 73 studies. The papers were coded by thematic cluster, dominant technology, research method, primary theme, value-stream coverage and operations-synchronization relevance. The coding shows that roadmaps, interoperability architectures, analytics applications and digital-twin models dominate the portfolio. Explicit operations-synchronization mechanisms are addressed in 16 of the 73 studies, mainly through planning-execution coupling and digital-twin-based decision support. Coverage across value streams is uneven, with stronger evidence for Strategy, Make and Plan than for Quality and Assets. Based on this evidence map, the paper proposes a Data-Driven Operations Synchronization Stack that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture.
This paper examines how contemporary project-control practice is being transformed by the integration of Earned Value Management, Business Intelligence, predictive analytics, and real-time performance visualization. Its purpose is to clarify how these interrelated capabilities strengthen cost, schedule, risk, resource, and governance visibility in complex project environments. The study adopts a conceptual review approach, drawing on scholarly and professional literature on project controls, performance measurement, analytics, data architecture, cybersecurity, forecasting, and digital transformation. Through this approach, the paper synthesises existing knowledge to explain the technical, organisational, and strategic conditions required for project-control systems to become more predictive, transparent, and decision-oriented.
The review finds that Earned Value Management remains a foundational framework for measuring cost and schedule performance, but its value increases substantially when integrated with Business Intelligence platforms and visual analytics. Business Intelligence enables the consolidation of dispersed project data, while real-time dashboards translate complex indicators into accessible intelligence for managers, executives, contractors, and other stakeholders. The study further finds that predictive analytics and early-warning systems can improve forecasting accuracy, reveal emerging delivery risks, and support corrective action before deviations become irreversible. However, these benefits depend on reliable data architecture, strong governance, cybersecurity assurance, employee readiness, and a culture that supports evidence-based decision-making.
The paper concludes that modern project controls should evolve from retrospective reporting into strategic performance governance. It recommends that organisations standardise control metrics, invest in interoperable digital systems, strengthen data governance, develop analytical competencies, and embed human-centred automation into formal decision processes to improve delivery certainty and organisational accountability across diverse, data-intensive project-based organisations and institutional delivery contexts.
Ogochukwu T Izuchukwu, Dominic Feboh, Abeebat Ajirotutu· International Journal of Mul...· 0 citations
The automation of production activities in off-site manufacturing plants has demonstrated measurable gains in cycle time, labor efficiency, and dimensional precision. Yet despite these well-documented benefits, adoption among manufacturers remains notably low, driven in part by an insufficient understanding of what automation implementation operationally entails beyond the automated station itself. Existing decision-support frameworks evaluate automation at the station level through performance comparisons, neglecting the system-wide knock-on effects that may propagate through interconnected production activities when a single station is automated. These effects, including upstream shifts in data preparation and procurement demands, downstream bottleneck migrations, workforce restructuring, and spatial reconfiguration, represent real operational costs that remain invisible in current assessment approaches. To address this gap, this paper proposes the Automation Impact Analysis (AIA) framework, a modular, system-oriented analytical structure adapted from the Change Impact Analysis methodology established in the manufacturing industry. The AIA traces how a defined automation scenario propagates through the production system by following the material flow, informational, technical, spatial, and organizational dependencies that link activities to one another, surfacing the consequential activities and responsive actions that the system must accommodate. These propagated effects are then translated into system-level performance shifts and consolidated into a total cost-benefit evaluation that brings implementation costs and operational gains into the same analytical frame. The framework provides manufacturers with a structured, auditable pathway from automation intent to informed investment decision, grounded in the realistic complexity of their production systems rather than idealized station-level projections.
The rapid digital transformation of industrial ecosystems is turning manufacturing environments into interconnected Cyber-Physical Production Networks (CPPN), where coordination across multiple actors is driven by continuous data exchange. In this context, Service-Oriented Architectures provide an effective paradigm for exposing modular, reusable, and interoperable services that encapsulate data access, processing, and sharing capabilities across organizational boundaries. Despite their central role, identifying and structuring such services remains challenging in multi-actor CPPN due to data sovereignty constraints, heterogeneous systems, and distributed processes. This paper presents an interactive visual tool for data service identification in CPPN, which implements and extends the P2S (Process-to-Services) methodology to explicitly account for data dependencies and organizational boundaries of CPPN. Starting from Business Process Model and Notation (BPMN) process descriptions, the tool combines hierarchical clustering with interactive visual representations to generate and explore multiple service aggregations, enabling designers to reason about tradeoffs between cohesion, coupling, and data governance constraints. By supporting the interactive exploration of alternative solutions, the tool instantiates a visual knowledge management paradigm that bridges implicit process knowledge embedded in BPMN models and explicit architectural decisions, enabling human-in-the-loop decision-making with reduced cognitive complexity.
D. Bianchini, Massimiliano Garda, M. Melchiori· Annual International Compute...· 0 citations
The combination of customer relationship management (CRM) and enterprise resource planning (ERP) has shifted from a back-office interoperability issue to a strategic requirement for real-time revenue operations. Revenue execution now depends upon the continuous integration of lead qualification, quoting, order capture, contract administration, invoicing, collections, renewals and service-led expansion. Revenue leakage, forecast distortion, and poor operational accountability are caused by fragmented data models, inconsistent transaction ownership, and slow synchronization within this environment. This review discusses integration frameworks that integrate CRM and ERP in near real time with particular focus on architectural patterns, semantic alignment, process orchestration, governance controls and performance implications. The literature suggests that the quality of integration is not dependent on the raw connectivity but rather designed event flows, canonical business objects, exception management and stewardship structures. The review also finds that the scholarly base remains fragmented as there is little direct peer-reviewed research concerning the revenue operations with an evidence base scattered within the enterprise systems, process management, data governance and digital platform research. The field requires stronger conceptual consolidation, comparative empirical studies, and better measurement of revenue-operational outcomes across integration designs.
Ansha Bundela· World Journal of Advanced En...· 0 citations
Manufacturing-as-a-Service (MaaS) facilitates on-demand access to distributed manufacturing resources and services provided by third parties through platform-based ecosystems. It enables a distributed platform service-oriented production, but despite advances in AI-enabled planning, intelligent matchmaking, optimisation-driven orchestration and cyber-physical integration, the literature remains fragmented. Most studies report isolated decision layers (process planning, supplier selection, scheduling, pricing or trust) without proposing lifecycle-integrated architectures. A PRISMA 2020-compliant systematic review was conducted on 62 MaaS studies published between 2013 and 2026 to examine how intelligent manufacturing platforms are implemented and which decision functions they support. This paper analyses the literature identifying: (i) an archetype classification of MaaS solutions, from conceptual frameworks to trust infrastructures; (ii) a granularity-based input–output taxonomy that maps information units to intelligent decisions; and (iii) a limitation analysis highlighting barriers in data readiness, interoperability, governance and real-time orchestration under uncertainty. Findings show persistent silos across engineering intelligence, marketplace design, operational optimisation and governance mechanisms. Building on this evidence, the Company-as-a-Service concept is proposed as an extension that integrates MaaS modules into lifecycle-consistent organisational capabilities, coordinated through a “Digital Thread and Governance Kernel”. This paper designs principles for scalable, governance-aware, AI-enabled manufacturing ecosystems. Implications are discussed for platform developers and manufacturers seeking resilient, compliant deployment across SMEs, multi-site networks, and heterogeneous equipment fleets worldwide.
Ilaria Pietrangeli, G. Mazzuto, Filippo Emanuele Cirapica et al.· Journal of Intelligent Manuf...· 0 citations
This study develops an evidence-informed framework for the strategic integration of AI through a PRISMA-guided systematic literature review and design science artifact construction and offers a rigorous and practical blueprint for scalable and trustworthy AI-enabled operations.
