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Purpose: This article examines the relevant literature to show how integration between Collaborative Manufacturing and Production Planning can be used and benefitted in the manufacturing business. This study seeks to provide a complete picture of the present condition, research trend, and application of Collaborative Manufacturing by taking into account some production planning indicators to aid many firms implementing greater technical collaboration amongst them.Design/methodology/approach: This analysis located all currently available studies on collaborative manufacturing and production planning through a thorough examination of the literature. During the review, a series of measures were taken. The study began by determining the study's objectives, selecting acceptable keywords, and reducing the selected papers based on a variety of criteria. The final paper that met the review's criteria was subjected to a more thorough examination. A review of literature has been conducted which include 62 primary studies from Academic databases: Scopus and Sciencedirect with main focus on collaborative manufacturing and production planning.Findings: The study's three primary conclusions are as follows. An overview of the variables influencing collaborative manufacturing and production planning is presented first. The second point is the identification of a number of ideas, techniques, and mathematical modeling in collaborative manufacturing and production planning. Third, Collaborative Manufacturing and Production Planning's future research directions are given.Research limitations/implications: Future research may also take into account numerous goals, the fusion of optimization and metaheuristic techniques, and other tools to enhance the performance of the model's goal. The paper's objectives are to present a succinct review of the current situation and to pave the way for further field research.Practical implications: Collaborative manufacturing should not just concentrate on strategic aspects while managing their networks. Rather, consideration should be given to more technical factors like production planning. Future academics will use the future research direction provided in this study as a guide for how to approach joint production planning.Originality/value: Additionally, this study provides various areas for future research and serves as a guide for research to advance studies in collaborative manufacturing and production planning.

Robots are perfect substitutes for skilled workforce on some repeatable, general, and strategically important tasks, but this substitution is not always feasible. Despite the evolution of robotics, some industries have been traditionally robot-reluctant because their processes involve large or specific parts and non-serialized products; thus, standard robotic solutions are not cost-effective. This work presents a novel approach for advanced manufacturing applied to the aerospace industry, combining the power and the repeatability of the robots with the flexibility of humans. The proposed approach is based on immersive and symbiotic collaboration between human workers and robots, presenting a safe, dynamic, and cost-effective solution for this traditionally manual and robot-reluctant industry. The proposed system architecture includes control, safety, and interface components for the new collaborative manufacturing process. It has been validated in a real-life case study that provides a solution for the manufacturing of aircraft ribs. The results show that humans and robots can share the working area simultaneously without physical separation safely, providing beneficial symbiotic collaboration and reducing times, risks, and costs significantly compared with manual operations.

This paper outlines an Internet of Things (IoT)-based collaborative framework which provides a foundation for cyber physical interactions and collaborations for advanced manufacturing domains. A general framework for collaborative manufacturing is proposed followed by a discussion of such an IoT-based framework for the domain of micro devices assembly. The design of this collaborative framework is discussed in the context of cloud computing as well as the emerging Next Internet which is the focus of recent initiatives in the USA, EU, and other countries. The data/information exchange between the various software and physical components is modeled using the engineering Enterprise Modeling Language (eEML), which provides a structured foundation for designing and developing this IoT-based collaborative framework. The key cyber physical components and modules are described followed by a discussion of the implementation of this framework.

Smart manufacturing systems combine sensor systems and manufacturing processes, and they have been widely adopted in the industry to solve real production problems, help manufacturing enterprises achieve rapid decision-making, and improve manufacturing value. However, manufacturing enterprises still face huge challenges with the coexistence of continuously changing dynamic demands, collaborative scheduling of dynamic resources, precise matching of manufacturing resources, and multiple resource constraints. To address this challenge, this research combines digital twin (DT) technology to propose a smart site-selection system with dynamic resource-accurate matching characteristics based on the attributes and associations of both resource sides, supply and demand sides, and site-selection sides, which can integrate and optimize resources according to the requirements of manufacturing tasks. In addition, by establishing the discovery mechanism of bottleneck processes and resource allocation methods, generating configuration priorities, and thus reducing the solution space for resource allocation, the precise allocation of limited resources is achieved more quickly and easily, and the scheduling chaos in the parallel scheduling of multiple resources is solved and the multi-objective robust optimization model is solved by combining smart optimization algorithms. Combined with the example analysis, the results show that the smart site-selection system and multi-resource cyclic allocation mechanism proposed in this paper can collaboratively match a large amount of dynamic resources, and the utilization rate of idle manufacturing resources can be increased by 60%. This research effectively realizes the optimal allocation of multiple manufacturing resources in a resource-constrained environment and helps manufacturing enterprises create more manufacturing value.

The purpose was to investigate the impact of the Industry 5.0 paradigm on the supply chain research field. Our study contributes to the conceptualization of supply chain 5.0, a term that has been receiving increased attention as supply chains adapt to the fifth industrial revolution. We conducted a systematic literature network analysis (SLNA) to examine the research landscape of Industry 5.0 supply chains. We used VOSViewer software and Bibliometrix R-package for multiple bibliometric analyses using 682 documents published between 2016 and 2022. We present a comprehensive framework of supply chain 5.0, including its key concepts, technologies, and trends. Additionally, this research offers a future research agenda to inspire and support further development in this field. We utilized three academic databases for bibliometric analyses: Dimension, Scopus and Lens. Additional databases could provide a wider research landscape and better field representation. We demonstrate how Industry 5.0 enables supply chain evaluation and optimization to assist companies in navigating disruptions without compromising competitiveness and profitability and provide a unique contribution to the field of supply chain 5.0 by exploring promising research areas and guiding the transition to this new paradigm for practitioners and scholars.

Purpose Timing constraints affect the manufacturing of traditional large-scale components through the material extrusion technique. Thus, researchers are exploring using many independent and collaborative heads that may work on the same part simultaneously while still producing an appealing final product. The purpose of this paper is to propose a simple and repeatable approach for toolpath planning for gantry-based n independent extrusion heads with effective collision avoidance management. Design/methodology/approach This research presents an original toolpath planner based on existing slicing software and the traditional structure of G-code files. While the computationally demanding component subdivision task is assigned to computer-aided design and slicing software to build a standard G-code, the proposed algorithm scans the conventional toolpath data file, quickly isolates the instructions of a single extruder and inserts brief pauses between the instructions if the non-priority extruder conflicts with the priority one. Findings The methodology is validated on two real-life industrial large-scale components using architectures with two and four extruders. The case studies demonstrate the method's effectiveness, reducing printing time considerably without affecting the part quality. A static priority strategy is implemented, where one extruder gets priority over the other using a cascade process. The results of this paper demonstrate that different priority strategies reflect on the printing efficiency by a factor equal to the number of extrusion heads. Originality/value To the best of the authors’ knowledge, this is the first study to produce an original methodology to efficiently plan the extrusion heads' trajectories for a collaborative material extrusion architecture.

Industry 5.0 revolution is prioritizing human-centricity and adapting technologies to augment shopfloor workers’ cognitiveergonomics. To provide a user-friendly, efficient virtual planning tool, virtual reality (VR) is adopted by the industry toprovide a virtual work environment for layout planning, design reviews, and training use cases. However, the user interface(UI) of VR programs is not yet standardized for universal design, and thus causes issues such as difficult scalable technologyadoption due to high mental workload. Creating intuitive and accessible interfaces is a key challenge in VR. As the com-plexity of VR platforms grows, it is vital that users can effectively explore and engage with them. Improved UI design mayimprove the whole user experience, making VR more accessible, scalable, and attractive to a larger audience. Navigation ina VR environment can impose a significant mental workload on users, affecting their cognitive capacities, including layoutperception, navigation, attention for collaboration, and response/completion time. This study aims to identify and assessthe UI design features of a virtual work environment for manufacturing regarding mental workload, spatial navigation, andperformance-based evaluation for human centricity. Three design features of typical mini maps examples, which are extractedfrom the literature and the gamification industry, are portability, tangibility, and dimensionality. By identifying the associationbetween design features and user navigation experience, we may observe patterns for broader VR user interface standardiza-tion that address human factors. This study employed a qualitative approach to assess five different prototypes of interactivemap designs, categorized into three design features, involving both students and industry practitioners, and resulting in 114valid data collection sessions in the prototype-based experiment. Reducing mental workloads in VR interfaces can increaseefficiency and user satisfaction in Industry 5.0 through intentional use of specific design features—portability proved to havethe most significant impact, consistently reducing mental, physical and temporal demands, and frustration, while simultane-ously improving performance, layout perception, navigation, and collaborative efficacy. The study provides effective designfeature identification, highlighting the importance of user-centric approaches in VR development for cognitive ergonomics.

Fueled by advances in cloud technologies and industrial platforms, networked collaborative manufacturing platforms (NCMPs) are reshaping how products are priced and customized. As decision rights increasingly shape value creation within these platforms, platform leadership—whether driven by the manufacturer or the designer—emerges as a critical determinant of product strategy. However, the effects of different leadership structures on pricing and customization remain unclear. To address this issue, we develop game models comparing manufacturer-led and designer-led platforms. Our analysis reveals that under manufacturer-led platforms, dual-product strategies remain viable across a wider range of customization conditions, ensuring pricing stability and broader demand coverage. In contrast, designer-led platforms are more sensitive to the commission rate—excessive commissions tend to crowd out standard product offerings and distort pricing incentives. Moreover, platform control does not always guarantee superior profit: while designers consistently outperform manufacturers under manufacturer-led platforms, profit dominance in designer-led settings shifts with commission rates. Notably, by jointly optimizing product strategy and pricing mechanisms, firms can achieve more balanced value distribution and sustain collaboration. These findings offer a strategic framework for manufacturers and designers to align platform governance with product architecture, contributing new insights into collaborative pricing, platform leadership, and dual-product innovation in industrial platform ecosystems.

In today’s dynamic manufacturing environments, the adoption of virtual reality (VR)-based simulation technologies to help in product and process design activities is becoming more widespread. With the onset of the next IT-oriented revolution involving global cyber manufacturing practices, the recent emergence of Internet of things (IoT)-related technologies holds significant promise in ushering an era of seamless information exchange which will provide a robust foundation for the next generation of smart manufacturing frameworks dependent on cyber physical system (CPS)-based principles, approaches, and technologies. In this paper, we present a broad framework for IoT-based collaborations involving the adoption of VR-based analysis environments networked with other cyber physical components. The process context for this VR-centered approach is electronics assembly, which involves the assembly of printed circuit boards. In such manufacturing contexts, it is essential to have a seamless flow of data/information among the various cyber physical components to ensure an agile collaborative strategy which can accommodate changing customer needs. VR-based simulation environments play a key role in this framework which supports multiple users collaborating using haptic interfaces and next-generation network technologies. The simulation outcomes and production data from physical shop floors can be compared and analyzed using this IoT framework and approach.

Recent rapid developments in information and network technology have profoundly influenced manufacturing research and its application. However, the product’s functionality and complexity of the manufacturing environments are intensifying, and organizations need to sustain the advantage of huge competitiveness in the markets. Hence, collaborative manufacturing, along with computer-based distributed management, is essential to enable effective decisions and to increase the market. A comprehensive literature review of recent and state-of-the-art papers is vital to draw a framework and to shed light on the future research avenues. In this review paper, the use of technology and management by means of collaborative and cloud manufacturing process and big data in networked manufacturing system have been discussed. A systematic review of research papers is done to draw conclusion and moreover, future research opportunities for collaborative manufacturing system were highlighted and discussed so that manufacturing enterprises can take maximum benefit.