The remainder of this paper is organized as follows. “Literature review” reviews the related literature and identifies research gaps. “Methodology” details the design methodology and architecture of BSFCS. “System implementation” describes the implementation process and experimental setup. “Testing” presents testing cases and performance results. “Discussion and limitations” discusses significant findings and key challenges. “Conclusion” concludes the study and suggests future research directions.
Small and medium-sized enterprises (SMEs) face unique challenges in their evolution toward Industry 4.0. Rauch et al. identified functional requirements for SMEs using axiomatic design, providing insights into key manufacturing system goals. The major challenges and corresponding needs are outlined below:
To maintain their unique agility and flexibility, SMEs must establish interconnected networks for quick collaboration. Incorporating human roles in process improvement, democratized decision-making, and intelligent systems integration are essential steps. BCT has emerged as a potential enabler of solutions to these challenges, offering decentralized and secure frameworks to enhance system adaptability and scalability.
BCT has emerged as a transformative force in addressing the limitations of traditional manufacturing systems. Industry 4.0 emphasizes the integration of digital technologies to enhance operational efficiency, transparency, and sustainability. However, centralized architectures often fail to provide flexibility, real-time adaptability, and secure data sharing, all requirements if this integration is to take place. BCT does offer them through its decentralized, immutable, and programmable architecture.
Key benefits of blockchain in Industry 4.0 include:
However, challenges remain in deploying blockchain effectively in manufacturing. Issues such as transaction latency, scalability, and energy consumption must be addressed to ensure its widespread adoption. Emerging solutions, including sharding, off-chain processing, and optimized consensus mechanisms, are actively being developed to mitigate its limitations. Furthermore, the integration of blockchain with existing manufacturing systems requires careful planning and phased deployment to minimize disruptions.
BCT enables the integration of digital threads, connecting the entire lifecycle of a product — from design and production to end-of-life management, ensuring seamless information flow across all stages and applications of a product’s lifecycle. BCT enhances the implementation of this framework with its inherent properties of transparency, immutability, and interoperability. These features provide a trustworthy backbone for digital thread architectures, enabling intelligent manufacturing and creating new opportunities for process optimization and industrial innovation.
Recent studies have explored a role for blockchain unifying digital and physical systems in smart manufacturing. For instance, Lee et al. and Barenji et al. presented CPS-based architectures where blockchain enhances system collaboration and autonomous interactions. To support manufacturing customization and secure access, Perez et al. and Zhai et al. developed blockchain-integrated data architectures and access control mechanisms. These frameworks ensured traceability and customized production workflows. For industrial data scalability, Lim and Nam and Ding et al. demonstrated blockchain-driven MES implementations combining big data integration and smart contract logic to enable efficient production control. Customer-centric approaches have also emerged. Ding et al. and Perez et al. emphasized blockchain’s role in aligning product design and production plans with real-time customer requirements. Complementing these efforts, Lee and Su introduced a knowledge-centric framework for semantic interoperability, enriching the features blockchain provides as a digital thread backbone.
The Industry 4.0 Reference Architecture Model (RAMI 4.0) provides a structured framework for analyzing blockchain applications across three dimensions: Life Cycle Value Stream, Hierarchy Levels, and System Integration Layers. The results are listed in Table 1. Key findings of this report are that initially, blockchain was used to address transparency in supply chains, but its use has since expanded to production planning, control, and lifecycle security. Applications have progressed from enterprise-level systems to factory and shop floor levels, with an increasing focus on real-time data synchronization and process adaptability. Furthermore, blockchain supports innovative models for asset management and process optimization through distributed ledgers and smart contracts, enabling transformative business practices.
In the area of Life Cycle Value Stream developments, the primary focus of early blockchain architectures on supply chain transparency was for the benefit of stakeholders during product delivery. The subsequent expansion of BCT’s role into production planning and control enabled advanced data sharing and lifecycle security. However, attention to lifecycles on the shop floor remains limited, particularly in the area of iterative upgrades and real-time adaptability.
In Hierarchy Level developments, blockchain applications have evolved from enterprise-level systems to factory floors, workshops, and individual workstations, reflecting the growing trend toward real-time collaboration and transparent management across multiple levels. These advancements facilitate seamless communication between enterprise systems and shop floor operations, bridging the gap between high-level strategic planning and on-site execution. Despite this progress, addressing the unique challenges of shop floor-level operations, such as real-time data synchronization and dynamic process adjustments, remains a key area where further research is required.
System Integration Layer developments have highlighted blockchain’s ability to enable reliable information exchange and secure functions, fostering the development of innovative business models. Research has demonstrated how blockchain can support asset management modules and optimize production systems through distributed ledgers and smart contracts. However, integrating blockchain with physical assets on shop floors continues to be a critical task for advancing these innovations.
Supplementing this RAMI 4.0-based analysis, we evaluate representative blockchain frameworks based on three features or functions essential to shop floor control: Dynamic workflow updates, ECO support, and/or real-time adaptation. Dynamic workflow updates as an issue remain underdeveloped in most studies. These studies suggest some potential for process flexibility but lack mechanisms for or evidence of achieving seamless runtime modifications. Xiao et al. focuses on lifecycle traceability without addressing workflow-level changes. ECO support is mentioned conceptually in some cases but concrete implementations or validated use cases are lacking, highlighting the difficulty of in-process modification. In contrast, real-time adaptation is an issue more explicitly addressed. Some work emphasizes real-time data integration and system responsiveness, while other offers only partial or localized capabilities. Overall, current blockchain-based systems show these early efforts at flexibility fall short in supporting live workflow updates and ECO integration. Our approach fills this gap by enabling consensus-based, contract-driven workflow adaptation with minimal disruption to ongoing operations.
Recent advances in smart contract engineering further highlight the importance of modularity and reuse. Górski introduced the AdapT framework which supports processing multiple transaction types via abstract contract components and configurable verification logic. Park et al. proposed a Smart Contract Broker to enhance contract discoverability and reuse through metadata tagging and agent-based mediation. Meanwhile, Ibba et al. developed MindTheDApp a structural analysis toolchain for Ethereum DApps that captures complex inter-contract dependencies. These approaches collectively reflect a growing focus on smart contract adaptability, traceability, and maintainability.
While blockchain demonstrates strong potential for enabling digital threads, practical limitations persist, particularly in adapting to dynamic shop floor requirements. Scalability remains a concern as high-frequency transactions challenge current frameworks. Real-time ability to handle large incremental changes in manufacturing processes requires further empirical validation. These insights underscore the need for continued research and system refinement to fully leverage blockchain’s potential with digital threads for Industry 4.0.
BCT has significant potential for enhancing shop floor capabilities through improved data transparency, security, and automation. The automatic execution of smart contracts can be regarded as the introduction of an innovative distributed expert system that automates logistics and production management tasks, minimizing reliance on manual intervention and reducing operational error. Immutable records of production step-by-step enable comprehensive traceability, fostering trust among stakeholders and allowing real-time monitoring of production processes. Furthermore, the integration of robust encryption mechanisms ensures secure data communication across machines and operators, in particular, preventing internal cybersecurity threats on shop floors.
Despite these benefits, shop floors, the core site of real-time manufacturing operations, present unique challenges that conventional blockchain frameworks struggle to address. Key requirements include high transaction volumes, low latency, and data synchronization across distributed systems. First, high transaction volumes generate significant blockchain synchronization congestion, leading to increased latency as node count and payload size grow. Second, while exact throughput and latency requirements may vary with production scale, the need for processing transactions within seconds is a baseline expectation in real-time industrial control and has been treated as a design premise throughout this study. Traditional consensus mechanisms, while secure, often fail to deliver the rapid transaction validation required for real-time operations. Similarly, ensuring rapid data synchronization across nodes remains a formidable challenge due to stringent requirements for availability and consistency in manufacturing workflows. Ensuring consistent transaction throughput takes place in the range of thousands of transactions per second (TPS) is critical for production control. These constraints highlight the well-documented trade-offs between system performance and security, which remain a critical focus in distributed system research.
Emerging solutions such as sharding and partial replica storage offer promising avenues to address these scalability challenges. Sharding splits the blockchain into smaller, manageable parts, allowing parallel processing of transactions and reducing network congestion. Partial replica storage, on the other hand, minimizes storage requirements by enabling nodes to maintain only subsets of the blockchain, improving responsiveness without compromising critical data access. While these solutions have demonstrated potential in theoretical models and controlled simulations, their direct application to shop floor scenarios requires further empirical testing to validate claims to their feasibility and effectiveness in real-time operations.
These challenges and potential solutions are broadly understood within the blockchain research community, underscoring empirical testing’s relevance not only to manufacturing but to distributed systems in general. Acknowledging the shared nature of these challenges, a focus shift toward tailoring these technologies to the specific requirements of shop floor environments is one that the distributed systems research community can support. Future research should prioritize pilot implementations of these technologies in manufacturing settings, where their performance can be rigorously evaluated under practical conditions. This approach will provide valuable insights into their operational viability and scalability, paving the way for their broader adoption in Industry 4.0.
Hyperledger fabric (HLF), a widely adopted enterprise-grade blockchain framework, offers high transaction speed, modular architecture, and robust scalability, making it particularly advantageous for shop floor operations and SME applications. Also, HLF is widely recognized for its ability to meet the demands of complex industrial applications through enhanced security, privacy, and performance optimization. HLF has a well-developed ecosystem, including reference architecture performance testing performance tuning and security assessment making it particularly advantageous for SME applications. Therefore, it was chosen to implement the BSFCS in this study.
Important features of HLF that are relevant to this study include:
These features collectively enable HLF to address critical challenges posed by shop floor operations, such as real-time control, secure data management, and scalable transaction processing. If it can effectively leverage HLF’s modular and scalable architecture, it is hypothesized that the BSFCS will meet the unique demands of shop floor environments, delivering real-time production control, secure data exchange, and operational efficiency essential for Industry 4.0.
Despite its transformative potential, blockchain technology faces persistent barriers to adoption. Technical complexity, resource constraints, and managerial skepticism often deter enterprises from exploring its benefits. The lack of real-world demonstrations of its merit, in particular, stands out as a significant hurdle, preventing decision-makers from appreciating its practical value.
Demonstration projects would play a critical role in showcasing blockchain’s potential to address operational challenges such as scalability, real-time control, and secure data management. For SMEs contemplating Industry 4.0 development, such cases could provide clear evidence of how blockchain can improve efficiency and adaptability while integrating seamlessly into existing systems. They would also offer measurable outcomes, fostering confidence among stakeholders and mitigating uncertainty about the use of the technology’s feasibility.
This study highlights the urgency of implementing industry-specific pilot projects to validate blockchain’s utility. By focusing on tangible, real-world applications, these initiatives can bridge the gap between theoretical promise and operational reality, facilitating the integration of blockchain technology into the manufacturing industry.
Despite the growing interest in blockchain technology (BCT) for Industry 4.0, current research largely neglects the specific challenges of SMEs’ shop floor operations. Current solutions predominantly address supply chains and enterprise systems but fall short in providing dynamic adaptability and phased deployment tailored to SMEs. First, existing studies lack detailed explanations or mechanisms to ensure the flexibility needed for continuous adaptation and may fail to deliver agile, real-time responses essential for shop floor operations. Second, there is an absence of incremental deployment approaches, leaving SMEs without cost-effective, phased strategies for digital transformation. Third, practical challenges specific to shop floors, such as high transaction frequency, low latency, and real-time data synchronization, remain inadequately addressed. Lastly, limited empirical validation of blockchain’s feasibility in realistic manufacturing shop floor environments heightens skepticism among SMEs, hindering broader adoption.
This study aims to address these gaps with a blockchain-based solution specifically designed for SMEs transitioning to Industry 4.0. The proposed instruction is not intended as a universal solution but as a tailored response to the operational needs of SMEs, particularly in shop floor contexts. It focuses on bridging critical gaps by enhancing dynamic adaptability and workflow management. The incremental, phased deployment approach ensures alignment with SMEs’ constrained resources and unique operational requirements. Importantly, the study emphasizes practical validation within realistic operational environments, showcasing how blockchain technology can meet specific industrial demands without disrupting existing workflows.
By combining agile methodologies with blockchain technology, this study seeks to show the way to the incremental digitization and automation of shop floor processes. The goal is not only operational flexibility but also the introduction of a verifiable and adaptable framework that can evolve alongside SME requirements. This approach ensures a gradual yet impactful process of transformation, facilitating a seamless integration of Industry 4.0 systems that respects the practical limitations and expectations of SMEs.
