Toward open manufacturing: A cross-enterprises knowledge and services exchange framework based on blockchain and edge computing

Zhi Li (Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China)
W.M. Wang (Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China)
Guo Liu (Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China)
Layne Liu (Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China)
Jiadong He (Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing Systems, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China)
G.Q. Huang (HKU-ZIRI Laboratory for Physical Internet, Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong)

Industrial Management & Data Systems

ISSN: 0263-5577

Publication date: 5 February 2018

Abstract

Purpose

The purpose of this paper is to propose a cross-enterprises framework to achieve a higher level of sharing of knowledge and services in manufacturing ecosystems.

Design/methodology/approach

The authors describe the development of the emerging open manufacturing and discuss the model of knowledge creation processes of manufacturers. The authors present a decentralized framework based on blockchain and edge computing technologies, which consists of a customer layer, an enterprise layer, an application layer, an intelligence layer, a data layer, and an infrastructure layer. And a case study is provided to illustrate the effectiveness of the framework.

Findings

The authors discuss that the manufacturing ecosystem is changing from integrated and centralized systems to shared and distributed systems. The proposed framework incorporates the recent development in blockchain and edge computing that can meet the secure and distributed requirements for the sharing of knowledge and services in manufacturing ecosystems.

Practical implications

The proposed framework provides a more secure and controlled way to share knowledge and services, thereby supports the company to develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency, and effectiveness of manufacturing services.

Originality/value

The proposed framework incorporates the recent development in edge computing technologies to achieve a flexible and distributed network. With the blockchain technology, it provides standards and protocols for implementing the framework and ensures the security issues. Not only information can be shared, but the framework also supports in the exchange of knowledge and services so that the parties can contribute their parts.

Keywords

Citation

Li, Z., Wang, W., Liu, G., Liu, L., He, J. and Huang, G. (2018), "Toward open manufacturing", Industrial Management & Data Systems, Vol. 118 No. 1, pp. 303-320. https://doi.org/10.1108/IMDS-04-2017-0142

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction

In today’s highly competitive and knowledge-based economy, successful manufacturers require to manage massive amount of knowledge and services. Such knowledge and services are diversified, growing in an accelerating velocity, and highly complex. Manufacturers are becoming more and more difficult to acquire and organize them alone. It is necessary for manufacturers to work together to sustain their competitiveness and create innovative exploitation and exploration in the market (Wang et al., 2016; Ai and Wu, 2016). Recently, with the advancement of internet technologies, the increased connectivity and the sophisticated data gathering and analytics capabilities enabled by the Internet of Things (IoT), manufacturers start to exchange their information with the others through different online platforms, so that they can leverage each other’s strengths and focus on their core competence (Sniderman et al., 2016). By integrating the collected data and information along the manufacturing chain, enterprises can analyze the information to support their production and business operations.

Previous studies have proposed cloud manufacturing (CM) and social manufacturing (SM) to address these challenges. Li et al. (2010) coined the term “cloud manufacturing” in 2010 and they defined CM to be a service-oriented, knowledge-based smart manufacturing system with high efficiency and low energy consumption. CM is a manufacturing paradigm that use network, cloud computing, IoT, service computing and manufacturing enabling technologies that transforms manufacturing resources (hardware and software) and capabilities into the cloud as cloud services and providing some sort of service control and management capabilities to manage manufacturing resources, processes, operations, and transactions (Tao et al., 2011; Zhang et al., 2014; He and Xu, 2015). On the other hand, SM is emerged in mass individualization paradigm due to the growing trend of socialization (Jiang et al., 2016). The Economist (2012) magazine first purposed the term “social manufacturing” as a “third industry revolution”. It is defined as a novel manufacturing mode, in which the consumers are involved fully into the production process by the internet (Shang et al., 2013). Consumers can control and manage distributed socialized manufacturing resources and activities through online platforms to facilitate personalized, real-time, and socialized production (Mohajeri et al., 2014; Leng and Jiang, 2016). In other words, CM focuses on providing an integrated cloud framework for converting manufacturing resources into on-demand networked manufacturing services, while SM aims at connecting customers and manufacturers to co-create personalized products and individualized services through online social platforms. Both of the approaches have great merits for connecting manufacturers and consumers. However, most of the existing platforms have several challenges needed to face. First, they are designed based on centralized framework (Wu et al., 2015). The information is owned by a small group of parties. As mentioned by Fu et al. (2017), trust and relationship commitment are important between companies. They have significantly positive effects on information sharing. Due to trade secrets, regional policies and many other different issues, the centralized framework is difficult to develop trust among manufacturers. The sharing of knowledge and services remains superficial and limited. Second, there is a shortage of knowledge to plan, execute, and maintain new systems (Zhang et al., 2014). The number of workers trained in handling analytics of the Big Data that is generated from IoT is gradually increasing, but is still far below the demand (Woo, 2013). Third, it is lacking of standards and interoperability for full adoption of integrated systems. Many applications are proprietary and can present integration challenges (Mariani et al., 2015). When the number of participants of the platform increases, questions regarding to ownership, access, and control arise (Roman et al., 2013). Finally, security is always a concern in implementing open and sharing platform (Jing et al., 2014). Therefore, it is necessary to develop a distributed, sharing, standardized, and secured framework to achieve a higher level of sharing among manufacturers.

In order to deal with these challenges, this paper aims to propose a cross-enterprise knowledge and services exchange framework for sharing among manufacturers. By comparing with CM and SM, this paper focuses on providing a decentralized framework for sharing knowledge and services among the parties within the manufacturing ecosystem, and suggesting blockchain and edge computing as the supporting technology for realizing the proposed framework. The framework incorporates the recent development in edge computing technologies to achieve a flexible and distributed network to prevent the problems of centralized framework that all information and knowledge is owned by a small number of parties. Not only information can be shared, but the framework also supports in the exchange of knowledge and services so that the parties can contribute their parts. With the blockchain technology, it provides standards and protocols for implementing the framework and ensures the security and identity issues based on its advanced data cryptographic algorithms. Both technologies are believed to have profound effects on the nature of companies, which can change how they are managed, how they create value, and how they perform (Tapscott and Tapscott, 2017). They are recently applied in different industries, such as digital currency (Nakamoto, 2009), software development (Xu et al., 2016), private securities (Lamarque, 2016), insurance (Underwood, 2016), medical (Dastjerdi et al., 2016; Shi et al., 2016), etc. However, few studies can be found in the manufacturing industry.

This paper contributes to the manufacturing industry in the following three aspects. First, it presents an open manufacturing concept for future manufacturing ecosystem based on the connection among customers and enterprises within the entire manufacturing ecosystem. Second, it provides a knowledge model for describing the contents and processes of knowledge and services exchange of manufacturers. Third, it proposes a framework and its mechanism to realize the open manufacturing based on blockchain and edge computing.

The paper is organized as follows. Section 2 provides a review on blockchain and edge computing. Section 3 presents the concept of open manufacturing. Section 4 describes a knowledge modeling for explaining the knowledge processes involved in a manufacturing company. A cross-enterprise knowledge and services exchange framework is proposed in Section 5. The architecture of the proposed framework and the supporting technologies are also presented in Section 5. In Section 6, a case study is presented to illustrate the effectiveness of the proposed framework. Implication of the study is discussed in Section 7. Finally, conclusions and possible further research are summarized in Section 8.

2. Literature review

2.1 Blockchain

Blockchain is essentially a distributed database system that records transactional data or other information (Swan, 2015). It is a data structure that combines data records, called blocks, in a chain. The concept of blockchain is proposed by Satoshi Nakamoto in 2008 (Nakamoto, 2009). It is a fundamental technology that supports the implementation of the digital currency bitcoin (Nakamoto, 2009). Blockchain has also been proposed in different industries. For instance, Tian (2016) has proposed an agri-food supply chain traceability system based on blockchain technology. The system used RFID to track agricultural products in the supply chain and used blockchain to ensure that data are reliable. It can prevent the fraud, corruption, tampering, and falsifying of information. Blockchain has the advantages of secure, irreversible, distributed, transparent, and accurate (Iansiti and Lakhani, 2017). The technology realizes the mining and trading of bitcoin by constructing the data structure and encrypting the transmission of transaction information (Crosby et al., 2016). Once data are added to the blockchain, it cannot be changed. Data encryption is used to ensure that updating or deleting existing transactions is prohibitively expensive, making the blockchain to be tamper-proof (Weber et al., 2016). The data block in the system is maintained by the nodes with the maintenance function. There are no centralized management agencies, the rights and obligations of any node are equal. It is suitable for the storage of data that requires identification and verification. It enables participants to establish a decentralized consensus on the sequence of events and the current status of the transaction (Drescher, 2017). It has the advantages of decentralization so that no central authority is needed, and yet trust still maintained (Weber et al., 2016). It enables the exchange of data autonomously and securely in the untrusted environment (Tian, 2016). Once the information is verified and added to the blockchain, it will be permanently stored. The openness and unchangeability of blockchain ensure the high transparency, stability, and reliability (Badzar, 2016). It offers liquidity, more accurate record-keeping, and transparency of ownership (Yermack, 2017).

As reviewed by YliHuumo et al. (2016), more than 80 percent of the academic papers on blockchain technology are related to bitcoin system, and less than 20 percent deals with other blockchain applications involve smart contracts and licensing. The recent innovations of blockchain include smart contract, proof-of-stake systems, and blockchain scaling (Gupta, 2017). Smart contract can be regarded as digital contract, which is a set of parties’ promises expressed in digital form. It builds small computer programs directly into blockchain. It allows more complex data structure to be represented, rather than only cash-like tokens of the bitcoin. The implementation of cryptographic protocols improves the tradeoffs between transparency, verifiability, privacy, and enforceability in smart contracts (Kosba et al., 2016). Conventional generation blockchains are proof of work systems, in which the group with the largest total computing power makes the decisions. These groups operate vast data centers to provide the security. The new proof-of-stake systems replace proof of work systems by ensuring the security level with less energy consumption. Therefore, in the long term, proof-of-stake systems are more energy-efficient and cost-competitive (King and Nadal, 2012). Conventional blockchains require every computer in the blockchain network to process every transaction. A scaled blockchain accelerates the process, without sacrificing security, by determining the optimal number of computers to validate each transaction and divide the work efficiently (Croman et al., 2016).

2.2 Edge computing

Edge computing is an open platform that integrated network, computing, storage and applications for providing edge intelligence services (Shi and Dustdar, 2016). It refers to distributing the data, applications, and services to the edge of a network that is close to the data source. Sometimes, it can be treated as a network edge layer or network buffer layer between the terminal and the central data center (Goh et al., 2006). Some works or data that do not need to be processed or stored by the central nodes can be directly processed at the edge. The distributed network can greatly reduce the pressure of the cloud, improve the efficiency, and transmission speed (Rayes and Salam, 2017). The origin idea of edge computing can be traced back to the end of 1990s, when Akamai introduced a distribution network to accelerate the network performance. Users can prefetch and cache web content at an edge node close to the user (Satyanarayanan, 2017; Davis et al., 2004). As mentioned by Lin et al. (2007), edge computing the advantages of highly distributed network and low latency computing which ensure the stability and speed of data storage and transmission. According to the usage and performance of the edge clouds, the computing power and storage space can be adjusted on-demand to minimize the usage of resources, and improve the utilization of resources (Varghese et al., 2016). It can reduce the pressure on network traffic and decrease the response time to improve user experience (Khalid et al., 2016; Salman et al., 2015). Due to its advantages, edge computing has been applied in different applications, such as smart homes, smart factories, medical care, government, logistics, insurance, traffic monitoring, etc (Dastjerdi et al., 2016; Shi et al., 2016).

Although edge computing has many benefits, there are also challenges inherent in edge computing. Roman et al. (2018) performed a holistic analysis on the security threats and challenges of edge computing. The specific challenges include identity and authentication, protocol and network security, privacy, etc. We find that the data encryption mechanism of blockchain provides a possible solution for meeting the security threats and challenges. One of the advantages of using blockchain is that it enables the exchange of data autonomously and securely in the untrusted environment (Tian, 2016). Moreover, the realization of blockchain requires the distributed network that can be provided by edge computing. Therefore, both technologies are adopted in the proposed framework.

3. Open manufacturing

The evolution of the manufacturing ecosystems can be classified into several stages based on different perspectives. In the view of industrial revolutions or based on the breakthrough of industrial technologies, manufacturing ecosystems can be divided into Industry 1.0, 2.0, 3.0, and 4.0 (Lasi et al., 2014). In this paper, we provide a new perspective for the classification of stages of evolution of the manufacturing ecosystems. As shown in Figure 1, we classify the evolution of the ecosystems into three stages based on the characteristics of interactions among customers and enterprises. The three stages include standalone manufacturing, networked manufacturing, and open manufacturing.

In standalone manufacturing, manufacturers worked alone as a centralized unit to take care of their customers (Agostinho et al., 2016). Manufacturing was an industrial production process by transforming raw materials into finished products to be sold in the market. It is a one-direction communication between manufacturers and customers. Manufacturers designed and produced expert-based products to their customers. The feedback from customers to manufacturers was relatively rare.

In networked manufacturing, the Big Data and social networks of customers are utilized due to the advanced development of information technology, IoT, and Big Data technology (Agostinho et al., 2016). Manufacturers can obtain the information of their customers through internet-enabled devices, social network sites, and different proprietary systems. There is interactive communication between customers and manufacturers. Manufacturers invite customers to involve in the design process to co-create innovative products and services. The resulted product and services are hence more customer oriented. Moreover, this is also a golden age of integrated manufacturing system. Different systems and knowledge are integrated in a centralized platform within an enterprise for effective management and control (He and Da Xu, 2014). Manufacturing is an integrated concept at all levels from marketing to design to production to sales to entire business operation. However, unlike the social networks of customers, enterprises are connected based on contracts. Most of them are only connected within the same supply chain. The cross-enterprises interactions are limited. It is particularly true in the manufacturing domain, in which limited open systems can be found and there is lacking of formal representation of knowledge (Hoang et al., 2014).

It is argued that the future manufacturing ecosystem should be highly connected. As mentioned by Tseng (2014), the sharing of knowledge and services among the enterprises could help to increase the standard and quality of the entire industry. In Tao et al.’s (2015) study, it was mentioned that the collaboration across different enterprises can enhance the core competence of the enterprises, as well as saving time and cost. The decentralized manufacturing network also helps to achieve both social sustainability and environmental sustainability (Chen et al., 2014). Therefore, we believe that the future manufacturing ecosystem should be extended from the networked manufacturing that based on centralized crowdsourcing to open manufacturing that based on distributed knowledge and services exchange. As shown in Figure 2, the manufacturing system will also be shifted from integrated manufacturing system to open manufacturing system. Not only customers should be connected, but also enterprises should also be connected. Not only information should be shared, but also knowledge and services of enterprises on how to handle the information should be shared. Therefore, we define open manufacturing as a new stage of manufacturing ecosystem, which use open, distributed, and decentralized manufacturing network to support the sharing and exchange of manufacturing knowledge and services.

4. Modeling of knowledge processes of manufacturers

In order to achieve effective sharing and exchange of knowledge and services in open manufacturing, we need to understand the model of processes of knowledge creation, learning and application of manufacturers. Nonaka’s socialization, externalization, combination, and internalization (SECI) model is one of the most famous models of knowledge creation (Nonaka and Takeuchi, 1995). The SECI model describes the dynamic nature of knowledge processes based on a spiral of knowledge creation (Nonaka et al., 2000). It has been applied in many different industries and applications (Seidler-de Alwis and Hartmann, 2008). However, arguments have also been raised that the notion of explicit knowledge is self-contradictory and the SECI model is heavily rely on tacit knowledge (Wright, 2005).

The SECI model is a generic model which fits for all different kinds of organizations. In this paper, we propose a more specific model for manufacturers, which is named 4A1C model. As shown in Figure 3, the 4As are acquisition, analysis, adaption, and application; and the 1C is the core knowledge of a manufacturer. Rather than classifying knowledge into tacit knowledge and explicit knowledge, we focus on knowledge sources, knowledge contents, knowledge analysis, and knowledge applications. Each manufacturer has its own core knowledge and competence. It makes use of its core knowledge to carry out its knowledge processes.

The acquisition process is a process of collection of data, information, and knowledge from the external and internal knowledge sources. External sources include market news, customers, external workers, technology news, etc. Internal sources include internal workers, organizational databases, different kinds of relationships (e.g. customers, suppliers, governments, etc.), and so on. The collected data, information, and knowledge are then analyzed through different kinds of analytical tools and methods. The knowledge contents are hence extracted and converted into an explicit format, and socialized and internalized among the workers and communities of the organization, so that they can be managed in an effective and efficient manner. The extracted knowledge is also required to adapt to fit for the needs of the updated situations of the markets and the manufacturers. The adapted knowledge should be concrete and flexible enough for easing the retrieval and reuse of the knowledge. The adapted knowledge can then be applied in the application process through different kinds of applications.

Similar to the SECI model, the 4A1C model is also spiral for achieving continuous learning. Moreover, in each process, there is a small learning-by-doing cycle to enrich the manufacturer’s core knowledge and hence enhance the capability of each process. Based on this model, we identify the knowledge sources, contents, analysis, and applications that can be extracted and exchanged. And the exchange process can be occurred in the processes of acquisition, analysis, adaption, and application.

5. Knowledge and services exchange framework

5.1 Conceptual framework

In order to achieve knowledge and services exchange among manufacturers in the future manufacturing ecosystem and to realize the modeling of knowledge exchange, a conceptual framework based on blockchain and edge computing is proposed in this paper. As shown in Figure 4 and Table I, the proposed framework consists of six layers which are a customer layer, an enterprise layer, an application layer, an intelligence layer, a data layer, and an infrastructure layer:

  1. The customer layer is one of the most important input sources of the framework. It consists of social networks of customers, big data from IoT (such as sensory data collected from different products or services), big data from various internet applications (such as search engines, homepages, portals, customer databases, online survey, websites, etc.), and customer data from many other offline resources (such as market survey, customer interview, focus group, etc.). The data and information provided from the customer layer will be collected and analyzed by the enterprise layer and the application layer.

  2. The enterprise layer consists of different kinds of stakeholders within the manufacturing ecosystems. It includes manufacturers, suppliers, distributors, marketing companies, investors, logistics companies, data centers, data analysts, etc. The companies and parties make use of and analyze the data and information collected from the customer layer, work through the application layer, and hence to generate and provide different kinds of knowledge, resources, and services for the ecosystem.

  3. The application layer is composed by a series of systems, services, and software products provided by the companies in the enterprise layer. They include customer relationship management system (CRMS), supply chain management systems (SCMS), logistics management systems (LMS), document or data management systems (DMS), decision support system (DSS), manufacturing execution systems (MES), software for enterprise resource planning (ERP), computer-aided design, computer-aided manufacturing and computer-aided process planning (CAD/CAM/CAPP), and many other proprietary software and systems. The application layer works closely with the intelligence layer and data layer.

  4. The intelligence layer provides the processing power for the application layer to carry out analytical and reasoning processes. By making use of computer power, different artificial intelligence (AI) tools, statistical methods, and computational technologies are provided for conducting different cognitive functions that simulating human minds in an efficient and effective manner. The functions include learning, planning, inference, searching, optimization, mathematical calculations, natural language processing, and many other different kinds of problem solving.

  5. For the data layer, it provides as the memory for the application layer. It consists of the data and information collected at the customer layer and the application layer, and the analyzed and processed results from the enterprise layer and the application layer. It is composed by databases and knowledge repositories for storing the knowledge of products, customers, manufacturing processes, etc.

  6. The infrastructure layer supports the establishment of the entire framework by providing the hardware and software infrastructure for the ecosystem. It is mainly composed of two major technologies which are blockchain and edge computing. Blockchain provides methods and tools, which include application program interfaces, protocols and software development kit to support the exchange of data and knowledge in a transparent, safe, and decentralized way; while edge computing supports network, computing and storage of such knowledge and services in a distributed network. The detailed flows of knowledge and services exchange are discussed in the following sections.

5.2 Characteristics of the proposed framework

5.2.1 Knowledge exchange

The knowledge exchange process between a single company and the ecosystem is shown in Figure 5. The process mainly composes of five parts which are local knowledge, knowledge blockchain, distributed knowledge, knowledge application, and transaction blockchain. A company owns its core local knowledge which may include its knowledge on market and technology trends, its knowledge and network on workers, experts, customers, supplier chain and the society, and its organizational knowledge, such as supportive infrastructure, structured processes, and databases of the organization. This knowledge can be acquired through knowledge audit and represented in an explicit format (Liebowitz et al., 2000). The purpose of knowledge acquisition and representation not only assist the sharing and reuse of the knowledge within the company but also facilitate the exchange in the ecosystem. A typical knowledge representation is composed of metadata and content. The metadata consists of an abstract description, highlights, indexing keywords, taxonomy (classification), and owner(s) of the recorded knowledge. The content can be composed of detailed description, figures, audios, videos, mappings, networks, formulae, stories, locations, expert locators, etc.

In order to effectively share and exchange the local knowledge within the ecosystem, it is necessary to convert the local format into some common knowledge representations. The W3C Web Ontology Language (OWL) is one of the most commonly used knowledge representations developed based on the concept of ontology (McGuinness and Van Harmelen, 2004). It provides rich and complex knowledge about things and their relationships. The local knowledge can be converted into OWL or other common knowledge representations. The formatted knowledge will then be stored based on blockchain technology.

In this paper, based on data encryption of blockchain, the formatted knowledge as shown in Figure 5 will be used to create a new knowledge block in the knowledge blockchain. The new knowledge block will be broadcasted to the nodes of the distributed network for approval and validation. Each node can be provided by different enterprises or parties within the ecosystem. So that there is no single institution is required as a third party to control and store the knowledge. When the knowledge block is approved, the new block will be added to the chain of each distributed node.

In the knowledge application, when a user would like to search for a particular knowledge, a search query will be formulated. It will then look up the knowledge chain of the distributed knowledge to find the most related knowledge. A matching will be performed based on the query and the information provided in the metadata of each knowledge block. After the user reviews the search results, she/he can retrieve a particular knowledge block. The knowledge will be adapted to fit for the situation of the user. Moreover, a request of retrieval will be made and hence a new transaction block will be created, broadcasted, validated, and finally added to the transaction blockchain for record. The entire knowledge exchange process can also be done automatically by implementing an automatic search and retrieval process through the application layer. And different payment or credit scheme can be applied as incentives for facilitating the process of exchange.

5.2.2 Services exchange

The blockchain offers fundamental technologies for knowledge exchange, while the edge computing provides fundamental facilities that support the network, storage, and computing power for the knowledge blockchain and services exchange. As shown in Figure 6, different parties within the ecosystem are connected through distributed edge clouds. In each edge cloud, it consists of a knowledge blockchain for storing the knowledge, and it also provides different services owned by different parties for the ecosystem to exchange. Similar to knowledge exchange, an enterprise can share its internet application services through the network. It can register different services that it created to the edge clouds. When there is another enterprise or party that requires a particular service, a request will be made. A matching will be performed to search for the most related services. When the enterprise chooses to adopt a particular service, authority will be granted to that enterprise. A transaction will also be added to the transaction blockchain, which have been mentioned in the above section, for tracking and tracing. In order to reduce the amount of data that flows back and forth between knowledge blocks and services, a smart assignment service should be available in each edge cloud for routing and allocating the knowledge blockchains and services. Based on the location of the processing data, the application services might be cloned to an edge cloud which is closer to the source of data. Or the required knowledge blocks will be added to the edge cloud that is close to the service.

6. Case study

In this section, a case study is conducted to illustrate the effectiveness of the proposed framework. This case study is based on the Greatoo Intelligent Equipment Incorporated (the enterprise). The enterprise is China’s first enterprise which develops and manufactures tire mold. With an annual output of more than 6,000 molds, each mold composed of hundreds of components to meet the different needs. In particular, a tire mold consists of major components, such as pattern circle, upper moldboard, lower moldboard, adjustable mold washers, and middle mold sets. Each component needs to rely on different manufacturing processes and knowledge. The enterprise faces two main challenges: Due to the large demand and short delivery time, the enterprise does not have enough capacity to fully meet the demand. Due to the complexity of the mold processing, the enterprise lacks knowledge of the manufacture of all high quality components.

In order to solve these challenges, the enterprise needs to accelerate the entire manufacturing process, and leverage the competitive advantage of the manufacturing network. Therefore, the proposed framework is proposed to be implemented in the enterprise, enabling the enterprise to work with partners to produce the components in an open, efficient, and secured manner. According to the framework presented in Section 4, an enterprise knowledge sharing (EKS) prototype system has been built and trial implemented in the tire mold production to share and exchange manufacturing services and knowledge in the manufacturing network.

6.1 Manufacturing service exchange

As shown in Figure 7, when the enterprise obtains a purchase order, according to the customer request, it creates a production plan through the MES. Tasks may include component design, raw material procurement, part production, component assembly, logistics, and so on. As shown in Figure 8(a), manufacturing companies can register on the platform. The enterprise can assign different tasks to its partners through EKS based on their manufacturing capabilities and strength. The current phase of the system can establish two partnerships, namely, outsourcing and collaboration. Outsourcing means that an enterprise contracts out of a specific task to other party. Collaboration means that the enterprise cooperates with the other party to perform a specific task.

The partnerships are formulated as smart contracts including sets of promises form the parties, expressed in digital form. Based on the blockchain and edge computing technologies, smart contracts are built and stored on blockchain. Blockchain provides the observability, verifiability, privacy, and enforceability. Edge computing provides an infrastructure that supports distributed network, storage and computing capabilities in the manufacturing environment. As shown in Figure 9, transactions are continuously verified by the blockchain network. Digital blocks are created and stored in the network and a chain is formulated. Each block must refer to the preceding block to be valid. This structure permanently stores the exchanged value, and prevents anyone from changing the ledger. The smart contract initiated by the enterprise is sent to a partner for confirmation or rejection. The transaction and the status of the partner are then updated on the blockchain. Finally, a signed connection between the enterprise and the partner is established for further collaboration to perform the requested task.

6.2 Manufacturing knowledge exchange

On the other hand, the enterprise can complete the task by itself. However, in order to effectively complete the task, the enterprise may require external manufacturing knowledge. Similar to the exchange of manufacturing services, the enterprise initiates a knowledge request on EKS. As shown in Figure 8(b), taking into account the shortage of the experienced workers, the knowledge of manufacturing process of each component can be obtained from the relevant company in the manufacturing network. According to the required knowledge, a smart contract is built and published to the blockchain.

The data model of the main part of the required knowledge is defined by using the Go programming language, as shown in Figure 10. It is a custom data structure for storing knowledge about mold projects. The primary model is called MoldProject, which in turn has primitive and complex data types, namely, task and process info. The knowledge is represented based on key value stores of the custom data model. A key value store takes in a byte array as the value that can be used to store a serialized JavaScript Object Notation structure. Each block in the blockchain contains a hash of the values and it is also linked to other blocks to formulate a series of blocks. When a company has the required knowledge, it can accept the request. The transaction is then stored on the blockchain to protect the interests of the original knowledge owner. A signed connection is established between the enterprise and the partner to further collaborate to share the required knowledge.

7. Discussion and implication

7.1 Research implication

This paper presents a cross-enterprises knowledge and services exchange framework for the exchange of manufacturing knowledge and services among manufacturers. An open manufacturing concept is proposed for future manufacturing ecosystem based on the connection between customers and enterprises. Moreover, we present a knowledge model that describes the content and processes of manufacture knowledge and services exchange. In addition, compared with the existing research, this paper presents a decentralized framework, and proposes blockchain and edge computing to realize the proposed framework. There is almost no similar study in the manufacturing industry. These two technologies have a profound impact on the nature of companies to change how they collaborate and how to create value.

7.2 Practical implication

The open manufacturing network can speed up the production cycle. On the one hand, service sharing can make effective use of the slack manufacturing resources to increase the utilization of the facilities and manpower of the manufacturers. On the other hand, knowledge sharing among companies can help them focus on their core competencies. In addition, blockchain enables a secure and standardized approach to achieve a higher level of sharing among manufacturers. Furthermore, edge computing can implement a flexible and distributed network in the manufacturing environment.

The proposed framework provides on-demand services for the whole life cycle of manufacturing. The possible services include marketing services, design services, testing services, manufacturing services, logistics services, management services, maintenance services, etc. The services are embedded with collective knowledge and resources shared among the enterprises connected. Companies do not need to develop all around services by their own. It is particularly important for small or startup companies. They can focus on their core business and use the others’ knowledge and services based on their needs. The framework supports companies for fast development of scalable and flexible businesses at a lower cost. Knowledge and services can be shared in a more secured, rewarded, and controlled manner. Companies are not only selling their products and services, but also selling their knowledge. Moreover, the framework promotes diversification, but at the same time, a more depth integration of manufacturing chain. Knowledge and services are effectively reused. Reinventing the wheel can be prevented at the industry level. New products and services can be developed with the help of previous work. And hence, the overall quality, efficiency, and effectiveness of the manufacturing services can be improved.

8. Conclusion

The manufacturing ecosystem is changing from integrated and centralized system to shared and distributed system. Not only traditional manufacturers can enter the system, but also many other different parties, such as consumers, software house, network providers, data analysts, etc., are involving into the system.

The paper aims to face this emerging phenomenon. We propose a cross-enterprise knowledge and services exchange framework by embedding the recent technologies in blockchain and edge computing. The proposed framework supports an open, decentralized, and yet secured network. Different parties can work on its own core business, and exchange its slack resources on its knowledge and services. Different parties can co-work to provide new products and services by effectively reusing their each other’s previous works and knowledge.

The future work is to apply the framework to manufacturing applications. We will also investigate the business models for open manufacturing. With the successful development of the framework, the overall quality, efficiency, and effectiveness of the entire manufacturing chain can be improved.

Figures

Evolution of manufacturing ecosystem

Figure 1

Evolution of manufacturing ecosystem

Comparison between integrated manufacturing system and open manufacturing system

Figure 2

Comparison between integrated manufacturing system and open manufacturing system

A spiral model of knowledge processes of manufacturers

Figure 3

A spiral model of knowledge processes of manufacturers

Architecture of knowledge and services exchange framework

Figure 4

Architecture of knowledge and services exchange framework

Knowledge exchange based on blockchain technology

Figure 5

Knowledge exchange based on blockchain technology

Services exchange of manufacturers based on edge computing

Figure 6

Services exchange of manufacturers based on edge computing

System workflow of the knowledge and service exchange

Figure 7

System workflow of the knowledge and service exchange

An application of manufacturing service and knowledge exchange

Figure 8

An application of manufacturing service and knowledge exchange

The procedure of blockchain-based knowledge and service exchange

Figure 9

The procedure of blockchain-based knowledge and service exchange

The data model of knowledge exchange

Figure 10

The data model of knowledge exchange

Six layers of the knowledge and services exchange framework

Layer Purpose Main components
Customer layer To collect data from customers Social network sites, IoT, internet applications, portals, mobile apps, customer surveys, etc.
Enterprise layer To collect data from different enterprises
To allow the enterprises to share and access the knowledge and resources provided by framework
Manufacturers, suppliers, distributors, marketing companies, investors, logistics companies, data centers, data analysts, etc.
Application layer To provide applications for enterprises
To allow enterprises to share their applications
Enterprise systems such as CRMS, SCMS, LMS, DMS, DSS, MES, ERP, CAD/CAM/CAPP, etc.
Intelligence layer To carry out analytical and reasoning processes Different artificial intelligence (AI) tools, statistical methods and computational technologies, such as machine learning, planning, inference, searching, optimization, data mining, natural language processing, etc.
Data layer To store data and information collected, shared and generated from the different layers Different databases and knowledge repositories
Infrastructure layer To provide the infrastructure for supporting the different layers Blockchain and edge computing

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (51405089), and the Science and Technology Planning Project of Guangdong Province (2015B010131008, 2015B090921007).

Corresponding author

W.M. Wang can be contacted at: wang_wai_ming@hotmail.com