A Review of Blockchained Product Quality Management Towards Smart Manufacturing

Abstract

Trustworthy product quality data forms the foundation of digital and distributed manufacturing, yet current centralized product quality management (PQM) systems remain vulnerable to data manipulation, traceability breaks, single points of failure, and related adverse effects. To clarify how blockchain can address these weaknesses, this paper presents a systematic review of blockchained product quality management (BPQM). Firstly, the paper groups the architectures and models related to BPQM and proposes an ISA 95-aligned reference framework that secures a real-time quality data exchange. Secondly, seven key BPQM enablers are analyzed, including (1) visual intelligence-based quality inspection, (2) cyber–physical twinning and parallel control of manufacturing systems, (3) blockchained agent modeling and secure data sharing, (4) multi-level blockchain mapping, (5) smart contract-based decentralized system configuration and operation, (6) artificial intelligence-based decentralized BPQM applications, and (7) traceability of process coordination and control. Thirdly, through analysis of social barriers and technological challenges, four research directions are identified, namely, (1) optimal granularity of data in system configuration; (2) smart contracts for self-organizing intelligence; (3) balancing system security, cost, and performance; and (4) interoperability and integration with legacy systems. It is expected that this paper lays a solid foundation for the practical use of blockchain in PQM engineering.

1. Introduction

In the era of smart manufacturing, multi-level cyber–physical interconnections lay the groundwork for a digital, transparent, and networked approach to PQM [1,2]. However, existing PQM systems are vulnerable to data tampering and system attacks [3,4]. A prime example of this is the scandal at Kobe Steel in November 2017, where Japan’s third-largest steel producer confessed to falsifying the performance data of its aluminum and copper products. These materials are critical across various sectors, ranging from national defense to the automotive industry. Safeguarding the integrity of product lifecycle data within the manufacturing process is not only a critical issue but also a pivotal strategy for enhancing user transparency and bolstering brand credibility [5,6].

Conventional manufacturing system management relies on centralized platforms and uniform management frameworks [7,8]. As these systems scale up, their structural weaknesses become increasingly evident [9]. Coordination between upstream and downstream entities is often weak, and tracing product information through disparate, inconsistent data silos is a persistent challenge [10,11]. The root cause of a quality failure, whether in design, materials, or a specific manufacturing process, becomes difficult to pinpoint. Furthermore, reliance on centralized cloud infrastructure means a critical failure can trigger large-scale operational disruptions [12]. The heterogeneity of equipment and customized service requirements also complicates peer-to-peer interaction and interoperability [13]. While organizational countermeasures are often applied, they cannot fundamentally alter the centralized control logic that is the source of these issues [14].

Blockchain technology, with its core attributes of decentralization, immutability, and transparency, presents a promising paradigm for overcoming these challenges [15]. By providing a decentralized data structure and consensus-based coordination via smart contracts, blockchain can establish a trusted environment for PQM in smart manufacturing [16,17]. Smart contracts can automate and enforce complex PQM tasks, ensuring robust coordination among distributed nodes and preventing the propagation of failures from a single point [18]. The vast data collected within a BPQM system can fuel artificial intelligence applications, enabling continuous learning and intelligent decision-making to enhance production management [19,20,21].

While the potential of blockchain in manufacturing and supply chain management has been recognized, most existing reviews remain broad in scope. Some concentrate on overall supply chain traceability [10], whereas others survey blockchain uses across the larger Industry 4.0 ecosystem [5]. Although informative, these studies do not yet provide a focused and systematic synthesis of the architectural frameworks, key technological enablers, and domain-specific challenges involved in integrating blockchain into PQM. Accordingly, there is still a clear need for a dedicated review that maps the current BPQM landscape and supports both academic inquiry and industrial deployment.

To address this gap, this paper conducts a systematic literature review of BPQM studies, synthesizing existing architectures, frameworks, and models. It then provides a comprehensive review of the key technological enablers, identifies the primary social and technological challenges, and outlines promising future research directions to guide further development in this field.

By fulfilling these objectives, this paper aims to lay a solid foundation for the theory and practice of BPQM engineering. The remainder of this paper is organized as follows. Section 2 details the systematic review methodology employed. Section 3 reviews existing architectures, frameworks, and models of BPQM. Section 4 provides a comprehensive review of seven key technological enablers. Section 5 discusses the social and technological challenges. Section 6 identifies four promising future research directions. Finally, Section 7 concludes the paper.

2. Research Method

This chapter outlines the research methodology for our systematic literature review of BPQM in smart manufacturing. We followed a structured procedure to locate, screen, and synthesize relevant studies, ensuring the process remained rigorous, transparent, and reproducible. Clear search strategies, inclusion and exclusion rules, and documentation steps were specified to limit bias and enable an objective assessment of the existing literature.

A critical step in conducting this systematic review was the formulation of a comprehensive search string capable of retrieving a wide range of relevant literature. To ensure coverage and relevance, expert input was sought to identify core research dimensions, specifically focusing on blockchain technology, PQM, and smart manufacturing. Based on these core dimensions, a list of potential synonyms and conceptually related terms was developed. These terms were then logically structured and connected using Boolean operators (AND, OR) to form a robust search query suitable for implementation across multiple scholarly databases. Meanwhile, this query was applied to four major academic databases: Scopus, IEEE Xplore, Web of Science, and ACM Digital Library. These sources were selected for their extensive coverage and disciplinary relevance in engineering, computer science, and manufacturing systems. The literature search was conducted across article titles, abstracts, and keywords, with no initial time restriction and with the end date set to 28 July 2025.

All retrieved records were first imported into NoteExpress for centralized management and screened following the PRISMA guidelines. Figure 1 presents the search string and the flow of study selection, while

Table 1. Detailed inclusion and exclusion criteria.

Category	Criteria	Clarification
Inclusion	Relevant content	The study applies blockchain to PQM tasks—for example, traceability, inspection data integrity, defect tracking, compliance verification, or quality decision support—in smart manufacturing contexts.
	Review articles	Rules for related content do not apply to review articles. Therefore, a review is included if it addresses the BPQM research constructs defined in this study.
Exclusion	Duplicate record	Same study retrieved from more than one database; only the most complete version is kept.
	Language mismatch	Title and abstract in English but the main text in another language.
	Irrelated content	Paper does not analyze blockchain performance or quality metrics (e.g., focuses on physics, pure mathematics, materials science, or uses blockchain only as an illustrative case without metric analysis).
	Short length	Short items (≤4 pages)—often editorials, letters, or extended abstracts—are discarded because they rarely report complete methods or results.
	Low quality	Studies that are not empirical, simulation-based, or analytical, or that lack enough detail to let others reproduce or verify the findings, are excluded.

lists the inclusion and exclusion criteria, addressing blockchain applications in PQM, page count, language, and methodological rigor.

During the initial phase, 254 studies outside the review scope—namely those in physics, pure mathematics, materials physics, and chemistry—were removed from an original pool of 1324 records, leaving 1070 records for further assessment. In the next phase, after reviewing titles and abstracts, we excluded 949 records on content and quality grounds. A detailed breakdown of the reasons for exclusion at this stage is provided in Figure 1. This process left 121 articles for a full-text evaluation.

In the third phase, the authors performed a full-text evaluation of these 121 articles. To secure a rigorous and reproducible selection process, we applied researcher cross-checking and a standardized data extraction form to evaluate every candidate study. Four qualitative criteria guided inclusion: (1) clarity in defining the study scope and key terms; (2) transparency of data collection and analysis procedures; (3) logical consistency between evidence and conclusions; and (4) relevance to theory or practice of BPQM. Successive consensus discussions removed 35 records that were off-topic or insufficiently documented, leaving 86 core studies.

Furthermore, after the core database search and screening yielded 86 eligible studies, we performed a supplementary search to reduce retrieval bias and ensure thematic saturation, following PRISMA recommendations for citation searching and manual searching. First, we conducted backward and forward citation tracking in our four primary databases and Google Scholar for each of the 86 papers (i.e., screening their reference lists and all records that cited them). Second, we hand-searched the table of contents of three leading journals (IEEE Transactions on Industrial Informatics, Journal of Manufacturing Systems, and Computers & Industrial Engineering) and the proceedings of two flagship conferences (IEEE IEM, and ACM/IEEE ICCPS). All records discovered in these two steps were de-duplicated against the core set and screened with the same inclusion/exclusion criteria. A total of 26 papers met every criterion and were therefore added to the review, bringing the final corpus to 112 studies. These additional papers close gaps in the initial set by addressing key enabling topics, such as product data management, vision-based quality inspection, and smart contract optimization, which are essential for a comprehensive understanding of BPQM in smart manufacturing contexts.

3. Frameworks, Architectures, and Models Related to BPQM

3.1. Frameworks and Architectures Related to BPQM

Existing research about BPQM architectures varies in scope and depth of integration. Leontaris et al. [22] proposed a blockchain–AI fusion for zero defect manufacturing, focusing primarily on in situ quality inspection via lightweight deep residual networks and Ethereum-based accountability. While innovative, their architecture targets component-level defect detection rather than holistic PQM system integration. Similarly, Du et al. [23] introduced an Oracle-aided industrial IoT blockchain system to enhance smart contract interactivity with off-chain data, yet it lacks the explicit alignment with manufacturing operational hierarchies needed for comprehensive PQM. D’Emilia et al. [24] emphasized data reliability for Industry 4.0 but offered no structured architectural blueprint for PQM’s implementation. Durán et al. [25] streamlined blockchain onboarding for industrial applications but did not address multi-level production coordination. Dehkordi et al. [26] designed a hybrid IIoT architecture optimizing latency and security through edge computing, blockchain, and software-defined networking integration, though it remains decoupled from established manufacturing standards. Suhail et al. [27] implemented a distributed ledger solution for electronics’ supply chain provenance using an IoT application (IOTA) architecture. While effective for data’s traceability, their framework did not extend to the operational control capabilities required for comprehensive PQM. Taken together, these architectures do incorporate blockchain and have delivered gains in PQM, but there is still scope to broaden their functional reach, enhance coordination between manufacturing layers, align more closely with established standards, and confirm scalability across complete production flows.

Building on the Integration of Enterprise and Control Systems framework, which follows internationally recognized standards by the International Society of Automation (ISA), this paper proposes a reference architecture that incorporates blockchain’s five-layer architecture into the ISA 95 five-tier model.

As shown in Figure 2, at the production perception and control level (Level 1), raw manufacturing data is captured from sensors and serves as input for blockchain storage. At the production monitoring and control level (Level 2), raw data from the Data Acquisition and Monitoring Control System is broadcast and verified through the blockchain network layer. At the manufacturing operations management level (Level 3), a smart contract-based execution script automates and autonomously manages workflows. At the business planning and logistics level (Level 4), decentralized applications handle error tracing, fault location, and PQM in the distributed manufacturing context.

The proposed reference architecture focuses on configuring the blockchain network to securely share real-time quality data throughout the manufacturing process, based on hardware configuration and current conditions, while revealing the mechanism of data’s traceability. By adopting a service computing paradigm with blockchain at its core, this architecture enhances PQM, improving both security and intelligence in a distributed manufacturing context.

3.2. Models and Systems of Inter-Enterprise BPQM

Inter-enterprise BPQM typically involves the production planning management subsystem, program control management subsystem, production planning monitoring subsystem, equipment management subsystem, and quality management of the system. The inter-enterprise-level system integrates production equipment, cells, lines, workshops, and factories to coordinate and optimize the overall process. Several key challenges need to be addressed during implementation. First, the blockchained system implementation should minimize the impact of operations on existing production processes and the outputs of enterprises. Secondly, the production operation process before and after system implementation should remain as consistent as possible, specifically to reduce the adaptation costs of the production process post-implementation. Finally, after the implementation of the system, the blockchained system and the production control system should maintain a certain degree of independence from each other. Since the operation of a new system is often unstable, the challenge lies in reducing the cost of resolving faults during the initial operation of the blockchain-based quality management system.

BPQM will improve the ability to cooperate among enterprises with a diversity of customers to supply [28]. As shown in Figure 3, during system implementation, a database system independent of the blockchain system should be designed. To coordinate and control overall quality at the inter-enterprise level, implementation will begin with the production planning management subsystem. The BPQM system can coordinate order data, material data, and various aspects of production capacity. Based on reliable and high-quality data guaranteed by blockchain, production planning knowledge can be applied to guide overall planning and adjust the real-time production status. Following this, the program control management subsystem and the equipment management subsystem can be considered for system implementation. In this system, PQM fully utilizes the tamper-proofing ability of blockchain and secure multi-party computation. Reliable verification and execution of control instructions enhance the security of production operations and improve the efficiency of PQM within distributed manufacturing. Additionally, the system implementation incorporates the production planning monitoring subsystem and the quality management subsystem. The system uses blockchain to record production data and automatically store it. This prevents serious consequences from single-point data loss due to misoperation and protects against malicious data attacks from both internal and external sources. These measures ensure the safety and reliability of production plan monitoring data during system implementation, ultimately improving the security and reliability of production plan monitoring.

3.3. Models and Systems of Intra-Enterprise BPQM

The intra-enterprise-level system implementation coordinates the production capacity and PQM of all participants/enterprises, formulates feasible and efficient production plans and controls, and monitors their implementation status. Similar to inter-enterprise-level implementations, it also accounts for subsystems such as production plan management, production program control, production plan monitoring management, production capacity management, and PQM.

System implementation at the intra-enterprise level is more highly coordinated and integrated than at the inter-enterprise level, focusing on the coordination of production control between enterprises. As shown in Figure 4, to coordinate and optimize the overall production process at the intra-enterprise level, the system implementation should begin with the production planning system. A BPQM system can coordinate order data, material data, and part of capacity across the supply chain, optimizing both efficiency and cost structure [29]. Knowledge within the enterprise can be learned based on reliable, high-quality data guaranteed by the blockchain, and the learned knowledge can be further used to predict and adjust real-time production instructions. Afterward, program control management and equipment management can be considered in the system implementation.

3.4. Metrics of Adopting Blockchain in PQM

Three main metrics of the proposed reference architecture can be identified as follows (see

Table 2. Metrics of the proposed reference architecture.

Metrics (M)		Benefits
M1	Bi-level PQM system with dual logics	Dynamic control of CPS Efficient information flow Improved decision-making
M2	Manufacturing resource-based polymorphic self-organization model with blockchain	Data integrity Rapid error tracking Flexible resource organization
M3	Context-aware data mining for manufacturing process	Knowledge discovery Intelligent decision-making Self-increasing intelligence

).

Firstly, the proposed reference architecture introduces a bi-level PQM system (i.e., upper-level inter-enterprise and lower-level local intra-enterprise), along with resource organization logic and system operation logic. It also reveals the dynamic control principle of the cyber–physical system (CPS) in the manufacturing process. By presenting the feedback control between perception and optimization decisions, the architecture demonstrates the mechanisms of manufacturing information’s creation, aggregation, association, transfer, and sharing, as well as the new decision principle of the manufacturing execution process.

Secondly, this reference architecture proposes a manufacturing resource-based polymorphic self-organization model that combines blockchain-sharing mechanisms. Through empirical research on the characteristics of network interconnection and the self-organizing clustering of manufacturing resources, the architecture reveals the interaction logic and mechanism between manufacturing requirements and capabilities, which takes manufacturing resource-based polymorphic distributed self-organization as structural features. Blockchain is then introduced, and the smart contract is developed to support the construction of distributed multi-morphological manufacturing resource self-organization, enabling data anti-tampering and rapid error-tracking functions in the PQM process.

Thirdly, this reference architecture proposes context-aware data mining for the manufacturing process. It identifies knowledge cognition, learning, and prediction models based on cognitive computing. By uncovering new knowledge hidden in multi-dimensional, multi-granularity data, it establishes a mapping model between the current system status and historical data. This enables intelligent decision-making based on data analysis for changes in physical processes and forms self-increasing intelligence.

Blockchain technology offers significant advantages in PQM by enhancing transparency, traceability, and security across the supply chain. With immutable records of every transaction and process, blockchain ensures that all stakeholders—from suppliers to end consumers—have access to accurate, real-time data regarding a product’s origin, manufacturing conditions, and quality checks. This transparency is especially valuable for quality assurance, as it reduces the risk of fraud, counterfeiting, and unauthorized modifications, while also enabling more efficient audits and compliance with regulatory standards. Additionally, smart contracts can automate quality control processes, ensuring that only products meeting specific criteria advance to the next stage of the supply chain.

Blockchain’s decentralized structure mitigates single points of failure, providing a more robust and resilient system for managing product quality, especially for industries with stringent compliance requirements. The ability to verify product integrity throughout the entire lifecycle—from sourcing of raw materials to final delivery—strengthens trust among stakeholders and enhances overall quality management practices. Furthermore, the immutable nature of blockchain serves as a powerful anti-tampering mechanism, ensuring that once data is recorded, it cannot be altered without detection. This safeguards the integrity of quality records and prevents unauthorized modifications that could compromise product standards. Additionally, blockchain can drive cost efficiency by streamlining operations, reducing the need for intermediaries, and minimizing errors and delays. As shown in

Table 3. Key features for adopting blockchain in PQM.

Key Features	Notes	References
Traceability	Provides end-to-end tracking of the product journey, ensuring transparency in production and logistics.	[30,31]
Data integrity	Provides immutable records that protect quality-related data from tampering, ensuring accurate product information throughout the lifecycle.	[32]
Real-time	Allows for continuous monitoring of product conditions during production and distribution, crucial for maintaining product quality.	[33]
Smart contracts	Automates quality checks and ensures compliance with predefined standards, removing the need for manual quality control steps.	[34]
Decentralization	Enhances the security and resilience of PQM by distributing data across a decentralized network, reducing reliance on a single point of failure.	[35]
Anti-tampering	Prevents product counterfeiting by ensuring an auditable chain of custody, verifying products’ origin and quality at every stage.	[36]
Cost efficiency	Reduces the costs associated with manual quality checks and paperwork by automating quality management processes with blockchain.	[37]

, many scholars are actively researching and developing advancements to further harness blockchain’s potential for PQM.

4. Key Enablers of BPQM

Key enablers are summarized and reviewed to guide the implementation and operation of a blockchained quality management system.

4.1. Visual Intelligence-Based Quality Inspection

Visual Intelligence (VI) in PQM extends beyond basic defect detection and inspection by transforming the way data is utilized and decision-making processes are structured. VI employs advanced computer vision algorithms and deep learning models to not only identify surface-level defects but also to analyze complex patterns and correlations within the production environment. For example, Zhao et al. [38] proposed a dynamic inference network (DI-Net) to enhance fabric defects’ detection in smart manufacturing, improving real-time detection efficiency and accuracy in PQM. Liang et al. [39] introduced a causal deep learning method (CDLM) for vision-based quality inspection to handle visual interference in complex products. Similarly, Liu et al. [40] developed a period-sensitive LSTM (PLSTM) network for predicting laser welding’s quality, focusing on dynamic keyhole features. This profound analysis enables the prediction of potential quality deviations based on historical data and real-time inputs, leading to a more proactive and adaptive quality management system. By creating a feedback loop that continuously refines its detection capabilities, VI enables dynamic adjustments in production, which can help maintain consistent quality standards and reduce reliance on fixed inspection protocols.

From a PQM perspective, VI also serves as a bridge between digital and physical manufacturing realms, enhancing the visibility and control manufacturers have over their processes [41]. VI technology can monitor multiple dimensions of production simultaneously, including material properties, equipment performance, and environmental factors. By integrating these diverse data streams, VI contributes to the formation of a multi-layered data ecosystem that offers a granular view of the entire production cycle. This level of integration supports more effective root cause analysis when deviations occur, as all relevant data is cross-referenced in real time. Consequently, VI can help manufacturers achieve a more resilient and adaptable PQM framework, reducing the likelihood of quality-related disruptions.

When combined with blockchain technology in a BPQM system, the impact of VI is amplified. Blockchain’s decentralized and tamper-proof architecture offers a secure way to log and store the detailed data collected by VI systems, ensuring that each data point, from initial material inspection to final product verification, is immutably recorded. This integration adds a layer of trust and transparency that is critical for maintaining the integrity of the quality management process, especially in complex supply chains where data’s consistency and traceability are paramount. VI’s role in this ecosystem is to continuously feed real-time, validated information into the blockchain, creating a secure and verifiable chain of quality data. This collaboration enhances the accountability of the manufacturing process, as every quality-related action and decision becomes traceable, promoting a more robust and trustworthy BPQM system.

4.2. Cyber–Physical Twinning and Parallel Control of Manufacturing Systems

Cyber–physical twinning forms the foundation for configuring and operating cyber–physical systems in PQM. As shown in Figure 5, the cyber–physical system for PQM integrates physical space and cyberspace across multiple levels (e.g., equipment, workshop, enterprise). The effective and secure use of cyber–physical twinning data enhances the value of data utilization and compensates for deficiencies in intelligent manufacturing within both physical and cyber spaces.

Studies on this topic suggest that blockchain-based frameworks play a key role in securing digital twin data in smart manufacturing by ensuring integrity, privacy, and traceability [42]. Furthermore, integrating digital twins with blockchain enhances the interaction between physical and cyber spaces, improving data accuracy and trust in collaborative manufacturing processes [43]. The digital twins in cyberspace include multiple digital entities such as Manufacturing Execution Systems (MESs), Enterprise Resource Planning (ERP), blockchained quality management systems, production monitoring systems, and other forms. For instance, the next generation of MESs is expected to integrate digital twins in production lines, with the development of simulation software to test new features before implementation [44]. Digital twin-based intelligent manufacturing systems, using a layered framework where MESs and ERP handle planning and scheduling, can reduce complexity and uncertainty in industrial processes [45]. Blockchained quality management systems offer a secure, traceable record of quality data by utilizing decentralized solutions, including web applications, decentralized storage, smart contracts, and blockchain technology [46]. Additionally, digital twin feedback mechanisms in cyber–physical systems enhance real-time monitoring, improving coordination and enabling rapid failure recovery, which ultimately contributes to greater production stability [47]. These digital entities are real-time images of physical manufacturing systems, used for system control, planning, optimization, and monitoring functions. The blockchain is the new digital entity, and its data retrieval and synchronization mechanisms are different from the traditional E-R database-based entity.

Synchronization of multiple digital twins is an effective enabler to achieve the fusion of physical space and cyberspace. Building on this, Leng et al. [48] proposed a digital twin-based remote semi-physical commissioning (DT-RSPC) method to address challenges in the traditional physical commissioning of manufacturing systems. By using semi-physical simulation, the DT-RSPC method supports the distributed integration of the whole smart manufacturing process. Human et al. [49] proposed a design framework that integrates digital twins with service-oriented approaches, enabling the effective aggregation and utilization of data from complex physical systems. Driven by a new generation of information technology and manufacturing technology, physical systems can be modeled through the integration of various types of data. In the running process of physical objects, relevant knowledge can be accumulated continuously to realize the high fidelity of physical systems and the optimization of physical systems based on models [50]. BPQM should focus on key technical issues, such as the consistency of communication in the blockchain system, real-time communication of heterogeneous systems, synchronization of asynchronous sampling periods of simulation model soft controllers, semantic translation of instructions, and mutual check of multi-channel feedback information, to support the parallel control of the PQM system.

4.3. Blockchained Agent Modeling and Secure Data Sharing

In the operation process of a smart manufacturing system, all departments of upstream and downstream enterprises need to constantly retrieve the production operation data in real time to make timely reactive decisions and to intelligently formulate production operation plans at the next moment. While information sharing can enhance supply chain efficiency, issues like delayed feedback, data distortion, and data security in multi-stage supply chains remain challenges, hindering optimal value addition [11]. Moreover, as upstream and downstream enterprises gradually expand, the need for secure real-time data and retrieval capabilities becomes increasingly evident [10]. Current big data analysis methods can assist in improving product quality in smart manufacturing systems, which places more emphasis on analyzing data at the equipment level of the manufacturing site, but decision-making and response strategies throughout the supply chain are equally critical [51]. For example, the supplier decides how to cooperate with the next batch of raw materials and when to deliver them, and each production department decides when to overhaul their equipment and start the production of the next batch of products. Downstream enterprises decide when to promote sales and cut down inventory. To complete these functions, real-time secure data sharing is required. Many enterprises or enterprise groups today use centralized databases and systems, which may offer benefits like easy control and standardized procedures [52]. When using traditional centralized databases to complete such functions, participants need to obey a specific workflow (which is established to make the process more secure against malicious attacks) and to submit information to the upper-level managers first and then apply for retrieval and inquiry when necessary. In this conventional mode, it is difficult to ensure information retrieval efficiency. For example, Xiang et al. [53] proposed three approaches for manufacturing data integration and sharing (MDIS) to boost security and streamline data management. However, physical centralization increases vulnerability to breaches, while logical centralization, though keeping data at its source, may expose metadata and require coordinated cooperation, potentially hindering secure access. Similarly, Li et al. [54] introduced an industrial dataspace for machining workshop (IDMW) framework, using a schema-centered, data-distributed approach to improve flexibility and scalability while centralizing knowledge management. Although this method enhances security and efficiency, it can lead to data’s duplication and increased management complexity. Efficiently integrating and querying this distributed data also requires careful coordination. Meanwhile, how to achieve a balance of authority and obligations between the information provider and the caller also brings challenges to the security management of the database.

Blockchain is highly transparent, decentralized, trustless, and collectively maintained. Blockchain can achieve peer-to-peer data sharing and collaboration based on decentralized credit in distributed systems where nodes do not need to trust each other, and it provides solutions to the problems of low efficiency and insecure storage in traditional data sharing. Zhang et al. [55] introduced a blockchain-based trust mechanism for Internet-of-Things (IoT)-driven smart manufacturing, reducing trust costs through secure, transparent data transactions, which improve product quality, material tracking, and transaction efficiency. Shi et al. [56] reviewed blockchain applications across various platforms, noting its potential to improve aspects like production, sales, tracking, and transparency, which can contribute to better product quality and management. To achieve real-time secure data sharing in the whole process of smart manufacturing, the integration of blockchain, IoT, and business workflow should be achieved to support the organic interaction between networking and flexible production operation. In a related application, blockchain and IoT’s integration has been explored. Liu et al. [57] developed a blockchain-based smart tracking and tracing platform integrating blockchain and IoT for drug traceability, using IoT devices for real-time monitoring. However, challenges include off-chain data storage risks, limited scalability, and potential latency issues, which may hinder performance when handling larger data volumes [58]. A typical mechanism of secure blockchained data sharing in the whole process can be envisioned as shown in Figure 6. The large-scale original plaintext data can be hashed and uploaded into the blockchain. This kind of hash-based data compression method can achieve data encryption, ensure data security, and avoid data leakage. In addition, it can reduce the storage space required for data uploaded and reduce the cost of BPQM operations.

4.4. Multi-Level Blockchain Mapping

The entire process of PQM may be divided into several different strip blocks by business. Observing from the granularity of the workshop, for example, the information about product A may need to be shared among the relevant production equipment and production units of product A, and a production circle related to product A may be formed. As a case in point, Westerkamp et al. [59] introduced a system where each batch of manufactured goods is represented as non-fungible tokens on a blockchain. This ensures transparency and traceability by recording not only the final product but also the components used in its production. By utilizing smart contracts and token “recipes”, the transformation of input goods into final products is securely documented, enhancing the traceability and quality management of goods across multiple tiers. Similarly, information on product B may need to be shared among the relevant production equipment and production units of product B, and another production circle related to product B may be formed. However, product A may be a component of product B, so the two circles A and B also share relevant information. In such scenarios, traceability becomes essential for managing interconnected products and assemblies, particularly in complex production environments. Kuhn et al. [60] proposed a blockchain-based traceability system, which addressed the challenges of tracking multi-hierarchical assembly structures where individual components, such as product A, are utilized in multiple assemblies, like product B. By utilizing a decentralized ledger, their system ensures consistent visibility of component relationships throughout all production stages. In a related approach, Liu et al. [61] developed a mechanism that dynamically analyzes and adjusts manufacturing tasks based on product quality information across different production units. The system ensures that the quality of components like product A can be traced and optimized as part of larger assemblies, such as product B, enhancing overall production efficiency. In this way, either A and B are placed in a centralized system, or A and B circles must somehow achieve data coordination. From a larger-granularity perspective, there are also such data sharing and interoperability issues between two workshops and even between two companies with supply relationships. Addressing this, Belchior et al. [62] emphasized the importance of blockchain interoperability for seamless communication across different organizations. Their research highlighted that cross-chain communication protocols (CCCP) and blockchain interoperability frameworks can enable secure data sharing between companies in a supply chain, even when using different blockchain platforms. This allows for the integration of decentralized applications and systems between companies, ensuring that relevant information flows consistently across all involved parties. Therefore, it is necessary to design a collaborative and scalable method of data mapping between different blockchains to improve information sharing in the industry.

Multi-level blockchains and multi-channel mapping can solve the above information-sharing problem. A node—whether a single device or multiple devices collectively accessing and submitting information—can join a channel. The shared ledger information is confined to that channel. This approach improves privacy by isolating information within the channel and ensures that each node only retains relevant data, enhancing storage efficiency and network communication and ultimately improving the blockchain’s performance. Additionally, in certain cases, terminals may need to access data across different chains. For example, terminals producing B products may need detailed information about the composition of a raw material (the A product), and at this time, cross-chain inquiry may be required. Multi-level blockchain mapping supports cross-chain queries and access control.

Data sharing for equipment’s operation and production tracking poses challenges to the multiple blockchain mapping structure. A mapping structure of data elements between equipment operation monitoring data and production real-time tracking data can be established. A typical cross-chain architecture is shown in Figure 7, where the anchor node acts as a cross-chain node. The upper layer is the anchor chain, which is mainly used for cross-chain routing. The anchor nodes join both the main chain and their respective service chains. The lower layer is multiple business chains, specifically storing related production events. For example, the A chain mainly stores related records of production cell A, and the terminals in the chain can perform cross-chain operations through the anchor node (production cell A).

The mapping of blockchains can be formulated as a key–value pair. For instance, for devices’ operation and production tracking, practitioners may store only the hash value of the key in the mapping table, rather than the actual data. When retrieving values, practitioners need to use this hash value to access detailed information. Various heterogeneous data models can be uniformly described via a data dictionary logical structure that meets data integration requirements. An image dictionary meta-model can be designed to independently and uniformly describe the mapping structure of production operations and tracking data.

4.5. Smart Contract-Based Decentralized System Configuration and Operation

With the successful application of advanced production modes such as flexible production and smart manufacturing in industrial manufacturing, traditional high-volume and specialized production lines have gradually shifted to flexible production lines with multiple varieties and small batches. Moreover, in flexible manufacturing networks (FMNs), the increasing diversity of product types introduces complex interactions between machine reliability and product quality [63]. To address these challenges, Leng et al. [64] proposed a blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) system to enhance resilience and adaptability in flexible manufacturing by using blockchain and smart contracts for peer-to-peer task coordination and real-time adaptability in response to disruptions, making it particularly suitable for flexible production environments. Flexible manufacturing in a production line (or a production cell) can produce a variety of similar products of different models and proportions with minimal adjustments. This adaptability is further enhanced by advancements in reconfigurable manufacturing systems (RMSs). As Morgan et al. [7] covered in their review, an RMS utilizes distributed and decentralized machine control and intelligence; these systems ensure seamless transitions between product variants, minimizing downtime and maximizing efficiency, especially for production environments demanding highly customized products with a high quality in low volumes. In response to growing demand for personalized products, enterprises have adopted flexible smart manufacturing systems that adapt to dynamic needs. This involves optimizing production layouts and equipment to handle customized orders efficiently, reducing costs and improving space utilization [65]. However, the current PQM system has two key limitations in the self-organizing of the dynamic mixed-flow production process in the distributed context. Firstly, self-organizing relies on historical manufacturing parameters and demand data as the support of learning and optimization. The quality of data is directly related to whether the results of self-organizing are reliable and effective and may even affect the control and modeling of the system [66]. Secondly, during the instruction-issuing process in dynamic mixed-flow self-organizing, the devices in the manufacturing process suffer from security risks. Disruptions such as unplanned breakdowns or stochastic processing could compromise systems’ stability, making these systems more vulnerable to attacks. [67]. When the production equipment receives the attack, it may directly lead to the failure of the manufacturing’s execution. Furthermore, cyber–physical attacks not only disrupt operations by altering equipments’ functionality but also tamper with product data, resulting in production breakdowns and compromised quality control [4,68]. Therefore, how to improve security in PQM in the distributed manufacturing context becomes an urgent problem to be solved.

The product process is strongly correlated with many factors. For example, in terms of the coating process of an EV cell panel, films’ thickness depends on the coating time and various equipment factors, such as temperature, chemical usage, and pressure. Typically, equipment parameter analyses and adjustments are based on historical and real-time production data. However, adjusting quality control parameters presents several challenges: (1) All historical data needs to be centrally saved for analysis. (2) Analyzing all distributed data for each adjustment is difficult due to the sheer volume, slowing the adjustment process. (3) The privacy of individual devices may be compromised during correlation analysis, and the results may be subject to tampering.

Dynamic mixed flow, as shown in Figure 8, refers to the MES dynamically coordinating the production equipment and links according to the current production requirements to meet the current production needs during the manufacturing process. On the one hand, the blockchain system can provide high-quality data support for the self-organizing of the dynamic mixed-flow production process, making the learning and operation optimization process more reliable, because it is easily recoverable after a single point failure occurs, and its data are not easy to tamper with. On the other hand, blockchains are distributed systems, and their system nodes can act as smart and secure agents by running smart contracts. The use of smart contracts on a blockchain further enhances the reliability of dynamic mixed flow’s execution.

Given the computational challenges of intelligent implementation and the rapid changes in resource states, centralized control systems are slow to respond to production disturbances and cannot adapt quickly. Therefore, a blockchained decentralized computing and decision method is critical to provide theoretical support for systems’ configuration and operation in a mass personalized production process. As noted by Yetis et al. [69], blockchain can enhance mass customization frameworks by optimizing production management and ensuring data transparency, traceability, and security. In decentralized systems, this enables faster adaptation to changing customer needs and production conditions, which is crucial for personalized manufacturing. Additionally, Liu et al. [70] emphasized that blockchain-based decentralized systems significantly improve collaboration by creating a transparent, traceable, and decentralized consensus among stakeholders, facilitating real-time decision-making and data management in dynamic production environments and ensuring that mass customization processes remain agile and adaptable. Thus, a blockchained manufacturing system configuration and operation paradigm can be designed based on analyzing the interaction logic and mechanism between devices in the self-organizing operation of the dynamic mixed-flow production process. To optimize the processes, either the machine learning methods or intelligent optimization methods can be combined with the smart contract to establish autonomous crowd intelligence so as to obtain the dynamic balance of self-organizing system operations that support local initiative.

4.6. Artificial Intelligence-Based Decentralized BPQM Applications

The development of PQM has gone through three phases: digitalization, networking, and intellectualization. The digitalization phase realizes the interconnection and management of all links of the manufacturing process. The networking phase improves the efficiency of resource allocation by realizing data coordination among upstream and downstream enterprises in the industry chain. Currently, blockchain technology further strengthens this coordination by providing transparent, traceable, and secure data sharing across the supply chain [71]. The intellectualization phase is realized through the accumulation of industrial big data [51]. Machine learning automates the extraction of manufacturing knowledge, revolutionizing the generation, application, and inheritance of this knowledge. For example, Wang et al. [72] employed spatial–temporal multi-task graph learning to predict production quality in computer numerical control (CNC) machining systems, where machine learning models capture spatial and temporal dependencies to optimize quality predictions in dynamic environments. Similarly, Gauder et al. [73] developed an adaptive quality control loop for micro-production using machine learning and inline metrology, improving precision and process control through continuous feedback. As a result, the innovation and service capabilities of the system were significantly improved.

However, as the cornerstone of PQM, industrial big data faces significant security threats. Data represents manufacturing instructions and coordinated commands. Data pollution or tampering will severely disrupt production processes and erode consumer confidence in products’ quality. The reliability of data in the supply chain is crucial for maintaining trust and ensuring operational efficiency [74]. Equally, data serves as the raw material for mining manufacturing knowledge, and unreliable, low-quality industrial data can result in poor manufacturing insights, negatively impacting decisions and leading to losses in industrial manufacturing. Meanwhile, unreliable data not only compromises knowledge mining but also exposes the system to risks such as data’s interception and manipulation [75]. There is no universal security solution for PQM.

A blockchain security scheme can be designed to architect a PQM system, providing a unified description framework for data’s traceability and process optimization in a manufacturing system by integrating data, networks, and intelligent systems. As shown in Figure 9, based on the CPS framework, the scheme is supposed to incorporate the blockchain as the security guarantee elements of the system, forming a Blockchained Cyber–Physical System (BCPS), in which the CPS undertakes the coordination functions and production tasks, while blockchain ensures system security.

To achieve industrial artificial intelligence for blockchained quality management in a distributed context, a potential solution is the combination of federated learning and the blockchain. Implementing AI within blockchain can accelerate the process of product design, collaboration, and manufacturing [76]. Additionally, combining blockchain with federated learning enhances data privacy and decentralization, showing promise for improving quality management [77]. A typical federated learning workflow for blockchained quality management can be illustrated in Figure 10. The basic process can be designed as follows: each blockchain node includes two parts of federated learning; one part is called a global model for a holistically fitting parameter prediction model, and the other part is called a local agent, which records real-time parameters of equipment and the processes of products. Model modifications from any node will be synchronized with other nodes using the blockchain. At the same time, each node runs a local agent, which learns from local data to update the global model locally and adjust the parameters of the equipment locally. In this way, on the one hand, large-scale learning and repetitive learning are avoided. On the other hand, because the global model is synchronized through blockchain, nearly all relevant data is analyzed, and the learning performance is usually slightly lower than full-scale machine learning. In the learning process of the local agent, a verifiable computation can be introduced to record the intermediate learning process proofs on the blockchain step by step to prevent individual nodes from forging computation results. This step-by-step recording also facilitates subsequent audits of errors. The whole training and prediction process can be implemented via a smart contract. Finally, the global model is incrementally improved based on the latest equipment and product data, providing decision support for the accurate adjustment of subsequent equipment parameters. In addition, the intermediate process of federated learning is securely recorded in the blockchain using a verifiable computational approach to prevent a single terminal from maliciously changing the learning outcome.

4.7. Traceability of Process Coordination and Control

Data analytics are the foundation of PQM in Industry 4.0. By recording extensive online manufacturing process data of parts and equipment, in-depth data mining can be carried out to support PQM and process optimization. Firstly, PQM is a data-dependent system. From the data mining and learning of manufacturing knowledge to the production planning in ERP and to the production process optimization in MESs, and then to the control and management of production quality, all of them are supported based on real, reliable, and high-quality data [36]. Secondly, PQM records contain detailed production processes and require suppliers to provide data as one of the proofs of production quality, which helps build mutual trust relationships more securely among production participants. At the same time, objective and real-time data can more accurately represent PQM and ensure consistency in product quality [78].

However, data’s acquisition, processing, storage, analysis, and service in production processes face numerous security challenges. For instance, the traditional PQM network suffers from many inefficiencies. After a system error event occurs in the network, it is often difficult and time-consuming to trace and locate the cause of the error. Blockchain technology has been shown to enhance traceability and reduce inefficiencies in supply chains [79]. Rufino Júnior et al. [80] emphasized that the integration of blockchain and data analytics significantly enhances the traceability and reliability of data across supply chains, ensuring higher transparency and reducing the risks of data manipulation. Similarly, Horst et al. [81] demonstrated that the use of blockchain labels in the supply chain strengthens consumer confidence in products’ quality by providing real-time access to verified information, making it easier to trace and prevent system errors from causing production delays or quality issues. Therefore, as shown in Figure 11, it is urgent to achieve data traceability in production processes’ perception, computation, communication, decision-making, and feedback control, which can be achieved by deploying a multi-level blockchain corresponding to the multi-level manufacturing system (e.g., workpiece–equipment–cell–workshop–factory–enterprise–supply chain). Blockchain can record state hash values in a distributed production process to reduce process conflicts caused by information asymmetry, such as the mismatch between purchasing data and logistics data [82], the mismatch between inventory data and sales data [83], and the verification between core enterprise data and upstream/downstream Small and Medium Enterprise (SME) data [84].

Many physical and chemical dimension data are extracted to generate an independent and unique identification (ID) number for each blank of the workpiece. Various ID numbers are taken as input, and a highly confidential hash algorithm can be selected for each finished product. Data extraction can be accomplished automatically through Radio Frequency IDentification (RFID), sensors, and other IoT technologies. For example, Lu et al. [85] focused on secure and efficient data storage and sharing for sensors with limited local storage and computing power, and they proposed a blockchain-based secure data storage protocol for sensors in Industrial Internet of Things (IIoT) environments to improve manufacturing efficiency and product quality. In a similar vein, Paul et al. [86] discussed how RFID-integrated blockchain technology enables traceability and transparency in circular supply chain management, improving data accuracy and security through distributed ledgers. Meanwhile, it can also be completed by embedding the finished product with a unique coding Near-Field Communication (NFC) chip or by measuring the unique physical and chemical properties of the products.

5. Challenges

As BPQM systems are still in the developmental stage, there will be a prolonged period of coexistence and integration challenges with traditional quality management systems. As shown in Figure 12, there are numerous social barriers and technology challenges involved in properly selecting, designing, deploying, using, and updating blockchain solutions within PQM systems to fully unlock their potential.

5.1. Social Barriers

Implementing a PQM system is a complex engineering process with no standard reference path [87]. In manufacturing enterprises, its implementation involves a hybrid process of exploration and continuous optimization. During this process, PQM systems can be divided into several levels of manufacturing systems, such as equipment–cell–plant–factory–enterprise–supply chain, from the scope of the application coverage. Modern manufacturing requires holistic quality management approaches that account for process–product interactions throughout the entire process chain [88]. PQM affects the dispatching and coordinating of production, which indirectly affects the system’s stability. With advancements in information technology, increasing production quality requirements also demand improvements in system stability [89]. How to smoothly integrate the blockchain system with the quality management system requires a dedicated design. Otherwise, if there is a failure, it will lead to huge losses. For example, in automotive supply chains, a well-designed blockchain integration is essential for the smooth operation of quality management systems. Lack of visibility and traceability can lead to risks such as counterfeit products or incorrect certifications, resulting in operational failures, financial losses, and even threats to human safety [90]. Therefore, thorough prototyping and simulation operations are necessary. The deployment in the actual production environment should also adopt the mode of parallel operation of the new blockchained system and the original management system. If the prototype system’s performance is insufficient, it should be optimized by further testing overall functionality and security, simulating system failures, and assessing their impact. System simulations will test the functionality, performance range, and fault tolerance of the system. Based on this strategy, the system can be improved and optimized to operate normally, efficiently, and safely in a production environment.

Currently, the industrial Internet and blockchain are rapidly developing, and various reference architectures and standards are gradually being established. However, there is still a significant gap in developing security standards and regulations for blockchain-enabled industrial Internet systems, which poses a barrier to the large-scale implementation of product quality management systems driven by blockchain in the industry. For one thing, due to the lack of relevant standards and reference architectures, manufacturers planning to adopt blockchain-driven product quality management systems lack reliable guidelines for system planning, design, implementation, testing, and evaluation. This gap increases the likelihood of implementation failure. For another, manufacturers already using blockchain-driven product quality management systems face challenges in effectively analyzing, evaluating, and improving their current operations due to the absence of relevant standards and reference architectures. As a result, this absence will further hinder the promotion of PQM and industrial upgrades. Developing reference architectures, standards, and specifications will provide crucial guidance for the planning, design, and configuration of various application systems.

Three aspects of standardization work can be conducted, namely, (A) industrial Internet-based PQM reference architectures and standards; (B) industrial blockchain reference architecture and standards; and (C) reference architecture and standards for BPQM system security. Among them, Class A can provide the overall understanding and analytical basis for the industrial Internet and PQM. Class B can provide the reference basis for the design of an industrial blockchain system. Class C can provide the comprehensive content of a secure BPQM system implementation.

5.2. Technology Challenges

The PQM system has a large amount of data flow. It may also contain maliciously tampered-with data that needs to be detected and excluded [91]. The blockchain used needs to meet performance requirements, especially the instantaneous data processing capability. Ensuring high-speed data processing is essential to maintain real-time monitoring and decision-making efficiency within the system [92]. Additionally, the function of the blockchain system needs to be integrated with the PQM system. The smart contract and decentralized application are required to support the general programming language to accommodate integrated management system software development. Moreover, the multitude of smart contract languages and blockchain platforms complicates and confuses the ecosystem, lacking a universal or standardized language, and platform selection results in vendor lock-in, presenting numerous challenges when transferring smart contracts between platforms [93]. Lastly, the blockchain system needs to be scalable and adaptable to the expansion of manufacturing equipment, cells, workshops, and even factories. Designing a blockchain system that meets the specific needs of PQM is a key challenge to address in prototype development.

The manufacture of the final product requires the cooperation of multiple manufacturers in a supply chain. Many manufacturers use product lifecycle management systems to manage the data transaction of components and the modification of related data, use the production management system to track the status and modification of the relevant parameters of the production equipment, and use supply chain management systems to accomplish data sharing between upstream and downstream enterprises [14,94]. Therefore, multiple data systems are often introduced throughout the production process, leading to potential data redundancy and inconsistency between systems. Network transmission errors and malicious tampering could further complicate data exchanges, reducing efficiency. These issues may also affect product traceability and data sharing between systems [95,96]. Consequently, there is an urgent need for efficient mapping across multiple data systems to maintain consistency and eliminate discrepancies caused by data exchange and synchronization between different systems.

6. Research Directions

Building upon the social and technological challenges identified in the previous section, this section outlines four key future research directions. These directions are not proposed in isolation; they are strategic responses to the fundamental issues hindering the advancement of BPQM. The technical challenges of managing a high data throughput and inconsistency across systems motivate research into optimal data granularity. The need to manage system complexity and enable continuous optimization points toward the development of smart contracts for self-organizing intelligence. The inherent trade-offs between security, cost, and system performance demand a holistic approach to balancing these competing objectives. Finally, the significant practical barriers of implementation necessitate a focus on interoperability and integration with legacy systems. Addressing these areas, as shown in Figure 13, will be essential for unlocking the full potential of BPQM for improving production quality, efficiency, and resilience in a rapidly evolving industrial landscape.

6.1. Optimal Granularity of Data in System Configuration

It is expected to explore the optimal granularity design of production process tracking throughout a distributed manufacturing system, as well as methods for locating and tracing system error events. Granularity refers to the level of refinement or integration of data stored in a blockchain network, corresponding to the system level (e.g., equipment level, manufacturing cell level) where the blockchain agent is hosted [97]. Dividing data by levels allows for a clearer analysis of data characteristics, providing stable, high-quality data to support system applications and predictive analysis [98,99]. Finer granularity results in greater refinement but requires more data storage, while coarser granularity reduces refinement and data storage needs. The choice of granularity for production process tracking (e.g., process, equipment, manufacturing cell, production line, workshop) significantly impacts the volume of data stored on the blockchain, as well as system implementation costs and data processing speed. Meanwhile, optimizing data storage helps resolve the complex interaction problems between data storage and blockchain data processing [100], and it reduces both storage overheads and data retrieval latency [101]. Therefore, granular design and optimization are critical in developing an effective BPQM system. Balancing the data storage capacity of the BPQM network with production management requirements and optimizing tracking granularity can improve system efficiency and reduce storage space demands.

To advance this direction, future work should move beyond conceptual discussions to establish actionable frameworks for determining optimal data granularity. A primary research challenge lies in developing quantitative models that can dynamically adjust data granularity based on specific production contexts and quality objectives. Such models can be approached using methodologies like multi-objective optimization, which balances competing factors such as storage costs and traceability precision, or reinforcement learning, where an agent learns an optimal policy from system feedback [102]. A logical research roadmap would progress from foundational modeling and discrete-event simulation to the validation of these mechanisms through prototypes in controlled pilot studies, ultimately providing a validated solution for industrial applications.

6.2. Smart Contracts for Self-Organizing Intelligence

A blockchain for a production process generates a large amount of industrial data, interaction behavior, and decision data in its operation [5]. These heterogeneous and personalized data conceal valuable insights into machines’ operation, production management, and decision-making processes. Advances in blockchain technology facilitate the efficient and secure management of heterogeneous data [103]. To fully mine and utilize this latent knowledge, production process control can be integrated with quality analysis to improve daily quality management [104]. Thus, using heterogeneous historical data/information to discover new knowledge hidden from multi-dimensional and multi-granularity data is a promising research direction. Establishing a mapping model between current data and historical data is critical for intelligent decision-making based on the analysis of process data for physical process changes, which continuously enhances systems’ intelligence and fosters knowledge learning, updating, and sharing. In smart manufacturing, heterogeneous IIoT environments typically require multi-party collaborative information processing, and these data often contain sensitive information, necessitating security protection [105]. A promising research direction is the incorporation of secure multi-party computing techniques into a BPQM system, allowing each node to share only encrypted data on the ledger, while enabling in-depth data mining without exposing private information. The advantages are twofold. According to the requirements of the production enterprise, a blockchain for privacy protection can relieve the risk of leaking production or product information data for each production part or enterprise. Equally, it can also provide data processing and analysis methods based on encrypted data.

Meanwhile, the PQM system needs to be able to perceive disturbances in real time, respond swiftly to disruptions in the production process, and continuously optimize itself according to emerging trends. To achieve this, an efficient self-organizing intelligence is essential, as holistic optimal control in manufacturing is particularly challenging when frequent disturbances and quality issues arise in complex systems. A typical feature of self-organizing intelligence is distributed consensus to realize group decision-making [50]. The smart contract tree model enables fine-grained task decomposition and self-organizing coordination, enhancing the system’s overall self-organizing intelligence [106]. Based on the analysis of quality issues based on “demand–capability” matching, a smart contract of manufacturing resource allocation can be established to provide an online operation environment and group decision support for product quality management in a distributed manufacturing context. This self-organizing mechanism also mitigates declines in manufacturing efficiency and reduces production interruptions caused by information asymmetry in large-scale, personalized mixed-flow manufacturing.

To operationalize this vision, research must focus on creating robust mechanisms for decentralized coordination and intelligence. A key area of inquiry is how to design hierarchical smart contract architectures that enable autonomous, multi-agent process control in response to disruptions. This challenge can be tackled through agent-based modeling to simulate emergent behaviors or by developing novel cryptographic protocols that integrate privacy-preserving techniques like federated learning directly into smart contract logic [107]. A viable research roadmap would begin with the design of a theoretical framework, proceed to development of a core algorithm for secure data analysis and resource allocation, and culminate in deployment of a pilot within a specific supply chain to demonstrate real-world value.

6.3. Balancing System Security, Cost, and Performance

BPQM is expected to achieve a reasonable balance in the dimensions of security, implementation cost, and system performance under the premise of satisfying the requirements of functional completeness and scalability.

Security is a crucial consideration when establishing a blockchain network for PQM. Ensuring blockchain security involves system engineering across three key areas, including process security, data security, and infrastructure security [15], which will affect the feasibility and reliability of PQM applications. Process security refers to providing secure and reliable services for blockchain applications, ensuring the secure operation of blockchain systems. Data security refers to supporting and guaranteeing the security of data in the process of storage and synchronization in a blockchain network, covering data access management, integrity, and confidentiality. Infrastructure security, or physical layer security, refers to providing basic support and guarantees for the secure operation of the blockchain network from the aspects of infrastructure, hardware conditions, and external environment, including failure and damage handling. Ensuring the security of blockchain networks for PQM requires systematic design thinking.

The cost of implementing a blockchain network for PQM can be divided into development costs and maintenance costs. Development costs refer to the hardware and software costs during the initial setup, including establishing distributed data nodes, compiling smart contracts, and reconstructing the consensus mechanism and encryption algorithms to fit the specific use case. Maintenance costs refer to the maintenance and operation costs that need to be paid for daily use after the blockchain network is implemented. Blockchain data’s circulation involves large-scale encryption and verification, which are computationally intensive and require significant computing resources. As the blockchain network expands, the storage space requirements for each node also increase, raising costs [108]. Faced with the large cost of the blockchain, how to control the cost of implementation is a very practical and important issue.

From the performance aspect, BPQM is designed to enhance the efficiency of production and management by effectively linking equipment operations with production practices. It also enables the rapid and efficient tracking and resolution of system error events, thereby minimizing potential losses. The BPQM network should be scalable; that is, one can dynamically adjust the scale of the blockchain network according to the production management requirements, and ensure this with the continuous development of PQM. The BPQM network can be adaptive to meet production and operation needs.

BPQM implementation needs to maintain a balance between safeguarding system security, controlling implementation costs, optimizing system performance, and comprehensively considering risks, costs, and benefits [109]. Concurrently, future work should therefore focus on developing holistic frameworks, using methodologies like multi-criteria decision analysis, to create practical design guidelines for balancing these competing objectives.

6.4. Interoperability and Integration with Legacy Systems

Manufacturing enterprises often depend on various legacy systems to manage production processes, coordinate supply chains, and ensure quality control [110]. Integrating BPQM systems with these established infrastructures represents a promising direction. Achieving seamless interoperability requires the development of standardized communication protocols and unified data formats to bridge the blockchain network with traditional enterprise systems. Additionally, robust methodologies for migrating data from legacy systems to blockchain platforms should ensure data’s integrity and consistency during the transition.

Focusing on seamless integration is essential for the widespread adoption of BPQM systems, as it minimizes disruptions to operations and leverages existing investments in legacy technologies. Research should prioritize the development of middleware solutions that act as intermediaries between blockchains and traditional systems, simplifying the integration process. Middleware technology can significantly enhance systems’ interoperability and transparency, enabling the smooth integration of blockchain services with existing systems [111]. Establishing comprehensive interoperability standards is vital to ensure consistent and reliable communication across diverse platforms. Furthermore, developing flexible and adaptable integration frameworks that can accommodate the heterogeneity and complexity of legacy systems within manufacturing environments is crucial.

Moreover, exploring real-time data synchronization methods and ensuring backward compatibility with legacy systems can further enhance integration’s efficacy. By focusing on these areas, researchers can develop practical solutions that enable manufacturing enterprises to seamlessly integrate BPQM systems into their existing technological ecosystems. This integration not only enhances operational efficiency but also improves overall PQM, ensuring manufacturing processes remain robust and competitive in an increasingly digital landscape.

To bridge the gap between innovative BPQM solutions and existing industrial infrastructure, a dedicated research effort regarding integration technologies is paramount. A primary challenge is to design optimal middleware architectures for seamless, bidirectional data synchronization between blockchains and legacy systems like MESs or ERP [112]. This requires investigating robust and verifiable data migration strategies to onboard historical data securely. A practical research path would involve the prototype-driven development of such middleware, evaluated using performance metrics like API response time and data throughput. This can be complemented by the design of standardized APIs for common BPQM functions. A logical roadmap would start with an analysis of existing standards like OPC-UA, progress to the development of an open-source middleware prototype, and culminate in a full case study to document the results and encourage wider adoption.

7. Concluding Remarks

This review offers important guidance in both academic and application terms for the integration of the IoT, blockchain, emerging computing techniques, and big data into PQM. Based on systematically selected literature, our analysis first reviews and synthesizes existing architectures and models in the field, followed by a comprehensive overview of seven key technological enablers that are critical for implementation, such as Visual Intelligence-based quality inspection and multi-level blockchain mapping. Building on this, this paper identifies significant social and technological challenges that hinder BPQM’s adoption and proposes four promising future research directions to guide further development.

While this review has followed a rigorous methodology, certain limitations should be acknowledged, including the constraints of the selected databases and the exclusion of “gray literature” like industry reports. Despite these limitations, this review makes a significant contribution by structuring a fragmented body of knowledge. It is hoped that this review provides a solid foundation for both academics and practitioners and guides the future development of BPQM engineering.