Toward open manufacturing. A cross-enterprises knowledge and services exchange framework based on blockchain and edge computing
| Date | 05 February 2018 |
| DOI | https://doi.org/10.1108/IMDS-04-2017-0142 |
| Pages | 303-320 |
| Published date | 05 February 2018 |
| Author | Zhi Li,W.M. Wang,Guo Liu,Layne Liu,Jiadong He,G.Q. Huang |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
Toward open manufacturing
A cross-enterprises knowledge and
services exchange framework based on
blockchain and edge computing
Zhi Li, W.M. Wang, Guo Liu, Layne Liu and Jiadong He
Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing
Systems, School of Electromechanical Engineering,
Guangdong University of Technology, Guangzhou, China, and
G.Q. Huang
HKU-ZIRI Laboratory for Physical Internet,
Department of Industrial and Manufacturing Systems Engineering,
The University of Hong Kong, Hong Kong
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 a nd controlled way
to share knowledge and s ervices, thereby sup ports the company to de velop scalable and fle xible
business at a lower cost, and ultimately improve s the overall quality, efficiency, and effec tiveness of
manufacturing servic es.
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 Knowledge management, Blockchain, Edge computing, Manufacturing knowledge,
Open manufacturing
Paper type Research paper
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 Industrial Management & Data
Systems
Vol. 118 No. 1, 2018
pp. 303-320
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-04-2017-0142
Received 10 April 2017
Revised 21 August 2017
Accepted 27 September 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
This work was supported by the National Natural Science Foundation of China (51405089), and the
Science and TechnologyPlanning Project of Guangdong Province (2015B010131008, 2015B090921007).
303
Cross-
enterprises
knowledge
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,
whileSMaimsatconnectingcustomersandmanufacturers 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
concerninimplementingopenandsharingplatform(Jinget al.,2014).Therefore,itis
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 servic es exchange framewor k for sharing among manu facturers.
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
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