cloud manufacturing: from concept to practice
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Cloud manufacturing: from concept topracticeLei Ren a b , Lin Zhang a b , Fei Tao a , Chun Zhao a , Xudong Chai c
& Xinpei Zhao da School of Automation Science and Electrical Engineering ,Beihang University , Beijing , 100191 , Chinab Engineering Research Center of Complex Product AdvancedManufacturing Systems , Ministry of Education , Beijing , 100191 ,Chinac Beijing Simulation Center , China Aerospace Science & IndustryCorp. , Beijing , 100854 , Chinad Beijing NDTech Co. Ltd. , Beijing , 100020 , ChinaPublished online: 01 Oct 2013.
To cite this article: Lei Ren , Lin Zhang , Fei Tao , Chun Zhao , Xudong Chai & Xinpei Zhao(2013): Cloud manufacturing: from concept to practice, Enterprise Information Systems, DOI:10.1080/17517575.2013.839055
To link to this article: http://dx.doi.org/10.1080/17517575.2013.839055
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Cloud manufacturing: from concept to practice
Lei Rena,b*, Lin Zhanga,b, Fei Taoa, Chun Zhaoa, Xudong Chaic and Xinpei Zhaod
aSchool of Automation Science and Electrical Engineering, Beihang University, Beijing 100191,China; bEngineering Research Center of Complex Product Advanced Manufacturing Systems,
Ministry of Education, Beijing 100191, China; cBeijing Simulation Center, China Aerospace Science& Industry Corp., Beijing 100854, China; dBeijing NDTech Co. Ltd., Beijing 100020, China
(Received 31 January 2013; accepted 26 August 2013)
The concept of cloud manufacturing is emerging as a new promising manufacturingparadigm, as well as a business model, which is reshaping the service-oriented, highlycollaborative, knowledge-intensive and eco-efficient manufacturing industry. However,the basic concepts about cloud manufacturing are still in discussion. Both academiaand industry will need to have a commonly accepted definition of cloud manufactur-ing, as well as further guidance and recommendations on how to develop and imple-ment cloud manufacturing. In this paper, we review some of the research work andclarify some fundamental terminologies in this field. Further, we developed a cloudmanufacturing systems which may serve as an application example. From a systematicand practical perspective, the key requirements of cloud manufacturing platforms areinvestigated, and then we propose a cloud manufacturing platform prototype,MfgCloud. Finally, a public cloud manufacturing system for small- and medium-sized enterprises (SME) is presented. This paper presents a new perspective forcloud manufacturing, as well as a cloud-to-ground solution. The integrated solutionproposed in this paper, including the terminology, MfgCloud, and applications, canpush forward this new paradigm from concept to practice.
Keywords: cloud manufacturing; cloud computing; service-oriented business model;cloud platform; architecture; MfgCloud; public cloud; enterprise information systems
1. Introduction
Today’s manufacturing involves all activities ranging from product design, production,fabrication, testing, maintenance and all other stages of a product life cycle (Li et al.2011). Collaboration, innovation, service and sustainability are increasingly playingcritical roles in competitiveness of manufacturing enterprises worldwide. Current perso-nalised and customer-oriented products, whether a small screw or a luxurious aircraft,cannot even be produced without collaboration among multiple professionals.Manufacturing is moving from production-oriented manufacturing to service-orientedmanufacturing now (Li et al. 2012). A variety of services through a product life cyclewill create an abundance of high value-added markets that promote efficient collaborationand amazing innovation. In addition, sustainable and green manufacturing (Ren andZhang 2010; Dornfeld 2012) has become an inevitable factor that manufacturers shouldconsider seriously. Therefore, a new highly collaborative, knowledge-intensive, service-oriented and eco-efficient manufacturing industry is expected to lead the way of the so-called ‘a third industrial revolution.’1
*Corresponding author. Email: [email protected]
Enterprise Information Systems, 2013http://dx.doi.org/10.1080/17517575.2013.839055
© 2013 Taylor & Francis
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Facing the new challenges, information technology has given powerful impetus tomanufacturing transformation. Manufacturing is going digital in almost all aspects.1
Especially in recent years, a number of remarkable information technologies, such ascloud computing (Armbrust et al. 2010) and Internet of Things (IoT) (Wolf 2009), aredeveloping rapidly, and influencing the way enterprises do business significantly. Theseideas have great impact on not only computing but also manufacturing. Cloud manufac-turing (Li et al. 2010, 2011, 2012; Ren 2012; Wu et al. 2012, 2013; Xu 2012; Zhang et al.2012), a new manufacturing paradigm as well as a business model, is taking its shape.Cloud manufacturing can be treated as an intelligent and collaborative manufacturingservice model. Distributed manufacturing resources (e.g. machine tools, 3D printers, CAx(computer-aided design/manufacturing/engineering/process planning) software, modelsrepository, databases, etc.) and manufacturing capabilities (e.g. design capability, fabrica-tion capability, assembling capability, simulation capability, testing capability, etc.) areinterconnected and form a shared pool in cloud manufacturing platform. Through cloudmanufacturing, customers can easily have access to services, such as design as a service(Wu et al. 2012), social networking as a service (Wu, Schaefer, and Rosen 2013),simulation as a service (Ren, Zhang, Zhang, Luo, 2011), production as a service,assembling as a service, test as a service, logistics as a service, etc. Moreover, accordingto the personalised requirements, cloud manufacturing platforms can provide intelligentservice composition solutions, which meet customised needs and support efficient colla-boration of services. In addition, a cloud manufacturing platform aggregates not only avariety of physical resources but also multi-domain experts and multidisciplinary knowl-edge. It can also help reach out the crowds (Von 2008) and stimulate collective innovation(Gruber 2008). In cloud manufacturing, customers, providers and platform managers allwin what they want by running manufacturing service businesses.
Despite the promising vision of cloud manufacturing that we described, great chal-lenges still exist. The basic concepts concerning what cloud manufacturing exactly meanshave not yet been definitely settled. Both academia and industry have an increasing needfor clear definitions to advance this new area. In addition, from a systematic and practicalperspective, research communities and enterprises are increasingly paying attention tocloud manufacturing platforms as references. To address these issues, this paper presentsthe basic definitions in Section 3, objectives and requirements of cloud manufacturingplatforms in Section 4, a cloud manufacturing platform prototype in Section 5 andapplication examples in Section 6.
2. Related work
The first definition of cloud manufacturing was proposed by Li et al. (2010), and then thisnew concept attracted much attention from both academic and industry communities. Chinahas launched a National High-Tech Research and Development Programme (Li et al. 2011),related to cloud manufacturing since 2009, and about 50 universities, institutes, corporationsand SMEs are involved in this project. The Europe seventh framework programme alsostarted a project named ManuCloud,2 promoting development of a service-oriented ITenvironment supporting manufacturing networks. The concept of cloud manufacturing isbuilt up on cloud computing (Foster et al. 2008; Buyya et al. 2009; Li et al. 2013), IoT(Wolf 2009; Gubbi et al. 2013), Cyber Physical Systems (Lee 2008), networked manufac-turing (Zhan et al. 2003), service-oriented manufacturing (Erl 2005; Jammes 2005; Hachani,Gzara, and Verjus 2013), virtual manufacturing (Mujber, Szecsi, and Jashmi 2004) andvirtual enterprise (Martinez et al. 2001; Gou et al. 2003; Wu and Su 2005; Ma, Wang, and
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Xu 2011). Some well-known definitions are as follows. Li et al. (2010, 2011, 2012) andZhang et al. (2012) defined cloud manufacturing as ‘a new service-oriented, high efficientand low consumptive, knowledge-based and intelligent networked agile manufacturingmodel and technology, allowing manufacturing resources and capabilities to be virtualizedand transformed into on-demand services available to users through a product life cycle.’Xu (2012) mirrored the definition of cloud computing and defined cloud manufacturing as‘a model for enabling ubiquitous, convenient, on-demand network access to a shared poolof configurable manufacturing resources that can be rapidly provisioned and released withminimal management effort or service provider interaction’. According to Wu et al. (2013),
Cloud-Based Design and Manufacturing (CBDM) refers to a service-oriented product devel-opment model where service consumers are able to configure products or services as well asreconfigure manufacturing systems through Infrastructure-as-a-Service, Platform-as-a-Service, Hardware-as-a-Service and Software-as-a-Service in response to rapidly changingcustomer needs.
Based on this definition, Wu, Schaefer, and Rosen (2013) further articulate two aspects ofCBDM: cloud-based design (CBD) and cloud-based manufacturing (CBM).
Based on the above definitions, they share some common terms, such as manu-facturing resource, capability and platform. They also share some familiar terms usedin cloud computing, such as virtualisation and cloud service with different meanings.The definitions of these basic terms play crucial roles in understanding cloud manu-facturing. Unfortunately, there still lacks a commonly accepted definition for theseterms.
Existing research articles in this area also present some system architectures for thedevelopment of cloud manufacturing platforms. Li et al. (2010, 2011, 2012) proposed a 5-layer system architecture, including resource layer, middleware layer, core function layer,platform portal layer and service application layer. Zhang et al. (2012) presented a 5-layerarchitecture consisting of resource layer, perception layer, service layer, middleware layerand application layer. Ren (2012) put forward a 3-layer system architecture (physicalresource, cloud manufacturing platform and service application layers), and discussed 5layers of the platform (resource perception, virtual resource, middleware, applicationsupporting and user interface layers). Xu (2012) proposed a 4-layer architecture, includingmanufacturing resource, virtual service, global service and application layers. Wu,Schaefer, and Rosen (2013) proposed a 5-layer reference model, including human–computer interaction, product configuration, service encapsulation and resource allocationlayers. Huang et al. (2012) presented a SME-oriented 12-layer platform architecture inwhich some layers are optional.
Although the proposed architecture solutions have slightly different system structures,almost all of them share some similar elements, such as resource virtualisation and cloudservice composition. However, more lower level details are needed. For example, we canfind the virtual resource pool in most of the existing systems; however, it is still not clearthat what specific functions the pool should have, as well as what the mechanisms ofvirtualisation and servitisation should be. From a system implementation perspective, therelationships between physical resource, virtual resource, capability and services need tobe clarified. But a few studies on such relationships have been conducted. In fact, mostcurrent architecture solutions are quite high level, and still lack details that can bereferential for application design and development.
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3. Fundamental definitions in cloud manufacturing
It is important to define the fundamental concepts used in cloud manufacturing as a basisof further discussions on future research and development.
Definition 1. A Manufacturing Resource is an entity that can support an activity or afunction involved in the life cycle of a product. There are two basic types manufacturingresource: hard resource and soft resource. A hard resource could be a manufacturingcell or IT hardware. A soft resource could be software, data, information, knowledge orother intellectual elements.
Definition 2. A Manufacturing Capability is professional competitiveness, e.g.manufacturing resources, intellectual advantages and credits, for undertaking a job in aproduct life cycle, which represents the competence, e.g. cost reduction and qualityimprovement that an organisation or an individual has to perform a specific task andachieve a particular target.
Definition 3. A Cloud Manufacturing Platform or Cloud Platform is an entity thatmanages a shared pool of manufacturing resources and capabilities over a network,offering integrated IT-based infrastructure and tools for both suppliers and demanders torelease and to utilise cloud services on demand, respectively.
Definition 4. A Cloud Manufacturing Service or Cloud Service is a function basedon a manufacturing capability that can achieve a goal in an activity of a product life cycle,such as design service, production service, testing service and management service. It isdescribed in a machine-processable format, and its information is under centralisedmanagement of a cloud platform. From a technical perspective, there are two basictypes of cloud services: OnCloud Service, which is in full control of a cloud platform,and OffCloud Service, which needs additional operations by an operator of a cloudplatform.
Definition 5. A Cloud User is an actor that participates in cloud manufacturing.There are three basic types: Cloud Provider, Cloud Consumer and Cloud Operator. ACloud Provider is an entity that provides manufacturing resources and capabilities ascloud services via a cloud platform. A Cloud Consumer or Cloud Customer is anentity that utilises cloud services via a cloud platform to fulfil demands. A CloudOperator or Cloud Broker is an entity that operates and manages a cloud platform(Wu et al. 2012).
Definition 6. Resource Virtualisation is a mapping process from a real manufacturingresource to a logical one.
Definition 7. Capability Servitisation is an encapsulation process from an abstractdescription of a manufacturing capability to a standard cloud service according to aspecification.
Definition 8. A Cloud Manufacturing System is a system consisting of cloud users,cloud platform, manufacturing resources and capabilities, supporting specific applicationsin a manufacturing domain.
4. Objectives and requirements of cloud manufacturing platforms
As an intermediary as well as a manager, cloud manufacturing platforms shouldprovide some common functionality that can support cloud providers and customersto accomplish their goals. The objectives of cloud manufacturing platforms are thefollowing:
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● Perception of manufacturing resources
A cloud manufacturing platform should allow a variety of manufacturing resources to beconnected together. Moreover, the platform should have the ability of identifying keyinformation of the connected resources. This will lay the foundation for all other functions.
● Virtualisation of manufacturing resources
A cloud manufacturing platform should be able to transform real manufacturing resourcesinto virtual resources (Ren, Zhang, Zhang, Tao, et al. 2011). Virtual resources refer to thelogical version of corresponding physical resource. Virtualisation enables decouplingresources from tightly coupled applications, hence supporting flexible construction ofcustomised applications.
● Management of virtual resource pool
The virtualised manufacturing resources will be pooled together to form a virtual resourcepool. This resource pool needs to be well managed, including storage of massive virtualresources, classification, on-demand matching, instantiation of virtual resources, etc. Theresource pool plays an essential role for flexible on-demand customisation.
● Servitisation of manufacturing capabilities
Servitisation can be defined as the transformation of manufacturing capabilities into cloudservices that meet a standard specification. Servitisation should consider the semantics(Zdravkovic et al. 2011; Paulraj, Swamynathan, and Madhaiyan 2012; Wu et al. 2012) ofcloud service specification so as to make an effective comprehensive description of themeaning of specific capabilities.
● Management of cloud services
Cloud services have a life cycle, along with the circle of the four-stage paradigm in cloudmanufacturing. It is necessary to support cloud service publishing, intelligent matchingand composition (Tao et al. 2013), runtime management and rating. Similar to the virtualresource pool, all kinds of cloud services aggregate to a service pool. This pool, or servicecatalogue, acts as the basis for operating cloud manufacturing systems.
● Management of knowledge and big data
The realisation of intelligence in cloud manufacturing depends on powerful support ofknowledge and data. Knowledge plays an essential role in enterprise interoperability andcollaboration (Jardim-Goncalves et al. 2013). Multidisciplinary knowledge cannot alwaysbe expressed in a structured language. Still a great deal of semi-structured and unstruc-tured knowledge needs to be incorporated into cloud platform. Furthermore, it is anessential requirement for cloud platform to extract the just-needed knowledge, even byreasoning, enabling intelligent actions. Besides massive knowledge, there will also pro-duce the so-called ‘big data’ (Beyer and Laney 2012) in cloud platform, as resources andbusinesses on cloud platform growing. The big data management should be seriouslyconsidered.
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● On-demand construction of virtual manufacturing systems
Facing the submitted requests from customer, a cloud platform is responsible for dis-covering service solutions. Once a solution is confirmed by the customer and serviceproviders, cloud platform need to configuring a virtual manufacturing system by assemblecollaborative services as well as integrating the relevant resources. Typically, this virtualsystem is established dynamically according to specific requirements. This is the respon-sibility of cloud platform.
● Execution management of virtual manufacturing systems
To ensure virtual manufacturing system run as expected, it is necessary for cloud plat-forms to monitor the runtime status and respond to changes. Based on manufacturing taskprocesses, cloud platforms need to coordinate different service providers among services,whether OnCloud or OffCloud services. In addition, cloud platforms should provide self-diagnosis and self-healing functions in support of self-adaption as exceptions happen at areal-time environment.
● Management of business and billing
From a business perspective, a cloud platform is playing a role of trading market, wherecloud providers sell services, and customers buy services. So, a cloud platform needs tocreate a business environment where sellers and buyers can handle business affairs such asnegotiation and contract. Especially, a cloud platform is also required to support the ‘pay-as-you-use’ usage-based payment scheme, based on capturing the actual consumptiondata.
● Evaluation and rating
Aiming at building a credit society in cloud manufacturing users, fair evaluation system isessential. A cloud platform needs to support not only subjective rating by people but alsoobjective rating by platform by using actual history data. This is also critical for customersto select trustworthy service providers.
● Management of social networks of cloud users
Three types of users, i.e., cloud providers, customers and operators, need to be coordi-nated through a cloud manufacturing platform. More importantly, these users areweaving a social network where materials, capital and information are flowing. Cloudplatform need manage well these valuable networks, which will promote deepercollaboration.
● Security and privacy
Like the argument in cloud computing (Subashini and Kavitha 2011), security and privacyis a critical problem, especially for competitive enterprises. Cloud platform should providea series of security policies that can be customised by end-users.
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● Interface customisation and ubiquitous services
There are various roles who participate a product life cycle, such as designers, engineers,production-line workers, testers, marketing staff and managers. Each role, each end-user,need an individualised user interface in support of his or her specific business (Ren 2012).Cloud platform had better support customisation of end-user interface and ubiquitousaccess to cloud services.
5. MfgCloud: a cloud manufacturing platform prototype
5.1 Architecture overview
Figure 1 illustrates the architecture of our cloud manufacturing system, which consists ofmanufacturing resource and capability, service application and MfgCloud. The cloudmanufacturing platform, MfgCloud, is composed of five layers, i.e. resource perceptionlayer, virtual pool layer, middleware layer, toolkit layer and user interface layer, as wellas two components playing a part in all layers, knowledge and data management andsecurity management.
5.2. Input and ouput
5.2.1. MfgCloud input: manufacturing resource and capability layer
If MfgCloud is considered as a black box, the input is manufacturing resource andcapability. Manufacturing resource refers to all relevant elements that play parts in aproduct life cycle. Here, it can be classified into two categories, hard resource and softresource. Further, hard resource can be divided into two types. One is manufacturing cell,involving machine tools, production facility, machine components, robots, etc. The otheris related to IT hardware infrastructure, such as network equipments, servers and storage.
MfgCloud:Cloud manufacturing platform
UI layer
Toolkit layer
Manufacturing resource andcapability
Service application for aproduct life cycle
Security managem
ent
Know
ledge and datam
anagement
Middleware layer
Virtual pool layer
Resource perception layer
Figure 1. Architecture of cloud manufacturing system.
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Soft resource mainly refers to CAx/customer relationship management/supply chainmanagement/enterprise resource planning (ERP)/product data management software,simulation tools, empirical models, multidisciplinary knowledge and database.
Manufacturing capability differs from manufacturing resource in that the latter means‘what I have’, while the former specifies ‘what I can achieve’. Indeed, manufacturingcapability not only includes necessary resources but also involves human knowledge,professional skill, adopted technology and management ability. To effectively describeconnotative meaning, semantic modelling approaches can be used. Luo et al. (2012)presented a method for manufacturing capability description, by using ontology language,e.g., web ontology language (OWL) (Martin et al. 2007), HTML ontology extension(SHOE) and DARPA agent markup language (DAML), to set up a conceptualmodel <resource, process, task, knowledge>.
5.2.2. MfgCloud output: service application layer
Regardless of internal structure of MfgCloud, the output brought to customers is service.The service applications mainly concern service provision in support of a product lifecycle. Typically, customers can acquire services as follows, Design-as-a-Service,Production-as-a-Service, Fabrication-as-a-Service, Simulation-as-a-Service, Test-as-a-Service, Maintenance-as-a-Service, Management-as-a-Service and Integration-as-a-Service. In addition, customers can get collaborative services by composing the servicesabove, thus support the applications that need across multiple stages of a product lifecycle.
5.3. Components
5.3.1. Resource perception layer
Figure 2 shows the perception mechanisms of manufacturing resource and capability. Thetechniques of IoT are utilised to perceive manufacturing cells, enabling intelligent identi-fication, locating, tracking, monitoring and management. The common adopted techni-ques include radio frequency identification (RFID) and sensor systems. These cantransform those passive machines into proactive agents. For example, pressure sensorsand temperature sensors can be used to perceive the real-time state of chemical devices,and once a dangerous signal appears, the remote controller in a cloud can throw an alarmin time. As known, RFID has been applied in tracing materials in logistics. Sensor datawill be collected and preprocessed, then delivered to a cloud platform via the Internet.
Internet
Sensor data collectionand preprocessing
IoTRFID Sensor
Hard resource:Manufacturing cells
Hard resource:IT hardware
Soft resource
Interfaces
Figure 2. Perception mechanisms of resource.
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The other type of hard resource, IT hardware, as well as soft resource, can beconnected to a cloud platform over the Internet by using traditional techniques. Hence,a cloud platform may keep watch on them through their exposed interfaces.
5.3.2. Virtual pool layer
There are two types of virtual pools in this layer: a virtual resource pool where the resources ofproviders are mapped to virtual ones through virtualisation, and cloud service pool where thecapabilities that providers have are formalised to standard services through servitisation.
● Virtual resource pool and resource virtualisation
Figure 3 illustrates the components of the virtual resource pool, as well as manufacturingresource virtualisation mechanisms.
The process of virtual encapsulation is responsible for the main process of virtualisa-tion of resources and capabilities. The IT hardware in hard resource and software resourcemay be encapsulated into IT virtual machine (VM), using the approach of computingsystem virtualisation (Anderson et al. 2005). A manufacturing cell in hard resource maybe described as a formal model with inputs and outputs according to its main function.Similarly, a manufacturing capability can be represented using a semantic model. In theencapsulating process, real-to-virtual mapping methods need be used to create logicalresources and capabilities flexibly. Generally, there are three kinds of mappings support-ing virtualisation, one-to-one, one-to-many and many-to-one (Ren et al. 2012). One-to-one mapping is no surprise. One-to-many mapping means that a single resource can be
Virtual resource pool
Virtual resource templates
Virtual encapsulation
Real-to-virtual mappings
One-onemapping
One-manymapping
Functionmodelling
Hard resource:Manufacturing cells
Hard resource:IT hardware
Soft resource
Computing systemvirtualization
Many-onemapping
Virtualmanufacturing cells
(Virtual factory, productionline, machine tool, test lab,
etc,)
IT virtual machines(VM templates of server,storage, software, etc.)
Figure 3. Virtualisation of resource.
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encapsulated into multiple virtual resources. Customers may feel like they are usingindependent resources, without knowing that they are sharing one. This usually applies toa resource that can provide multiple functions, e.g. multifunctional machine tools andmultifunctional software (CATIA, ANSYS, etc.). Many-to-one mapping represents a com-bination of multiple functions, and each function comes from a single resource. This is veryuseful to meet a customised resource demand by integrating fine-grained functions togetherto achieve one goal. Moreover, the complex combination details are transparent to an end-user. The end-user just considers the service is specially designed for him/her. For example,a group of machine tools may be combined and mapped to a virtual production line, andseveral functional interfaces from different CAE software, together with servers and storage,may be encapsulated to a specific engineering analysis tool. When defining a mapping,especially many-to-one, the relationships among the components also need modelling. Ren,Zhang, Zhang, Tao, et al. (2011) presented a modelling approach by using business logic,constraints and interaction rules to describe the real-to-virtual mapping.
After the process of virtualisation, the encapsulated results will be recorded in formaltemplates of virtual resource and capability. Then they will be stored into virtual resourcespool, which typically consists of two categories, virtual manufacturing cells and IT VMs.The pool of virtual manufacturing cells denotes virtual factories, production line, machinetool, etc. The pool of IT VMs contains VM templates of server, storage, software, etc.
● Cloud service pool and capability servitisation
Figure 4 shows the mechanism of servitisation of capability, enabling the transformationfrom those abstract concepts of capability to formal services understandable by cloudplatform.
As the basis for servitisation, it is essential to model manufacturing capability before acapability can be transformed to a service. In modelling, a provider often needs to answerseveral questions that a customer may be interested in. That is, what I can do, what resource
Cloud service pool
Cloud service templates
Servitisation of capability
Messagemodelling
Port modelling
Capability profiling
Jobdescription
What I cando?
Whatresource I
have?
What level Ican reach?
Why I canachieve?
Whatevaluation?
Virtualresource
QoSmetrics
Advantage Rating
Capability modelling
Protocolbinding
OnCloud servicepool
OffCloud servicepool
Figure 4. Servitisation of capability.
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I have, what level I can reach, why I can achieve and what evaluation by others. Therefore,several important aspects of the capability of a provider may be considered as follows: thejob that the provider can undertake, the resources in support of the job, quality metrics andthe advantages the provider have. Among the elements above, the resources of a capability,based on the virtual resource pool, associate themselves with the virtual resources. The QoSmetrics define a list of key performance indicators, e.g. machining accuracy, which reflectthe level of specific abilities. The advantage denotes human intellectual capital, professionalskills, management ability and other information that can offer proof. This claim can beviewed as a subjective evaluation by the provider itself, while the public ratings fromcustomers and a third party represents an objective assess.
The formal description of a capability, including the elements above, defines what thecapability is like. Then, the next problem that servitisation need solve is to tell customers,as well as cloud platform about how to use this capability. To achieve this, severalcomponents are needed to define a service model, messages, ports and protocols. Themessage modelling is to define the input the service can receive and the output the servicecan produce. The port modelling is responsible for defining the functional operation portused to achieve the job target. The protocol binding is to specify the communicationprotocol the service can accept. Hence, these three specifications, as well as the capabilityprofile and other necessary information related to business, e.g. price and transactionmode, together compose a cloud service template. As a result, a wide range of thetemplates accumulate and form the cloud service pool.
5.3.3. Middleware layer
Figure 5 illustrates main components of the middleware layer, including the middlewareof cloud service management, virtual resource management and virtual system
Virtual system management
Cloud service management
System constructionmanagement
System runtimemanagement
System metering &evaluation
Virtualresource
management
On-demand servicematching
Service and resourcemonitoring Utility metering Register
Search
Instantiation
Getstatus
Remotecontrol
Exceptionhandling
Eco-efficiencymetering
Automatic rating
Ratingmodel base
Service and resourcescheduling
Collaborative processmanagement
Adaptivereconstruction
Fault-tolerance andmigration
Virtual resourceconfiguration
Virtual resourceinstantiation
Service deployment
Publish Search Compose Bind ExecuteQos
assess Update Destroy
Figure 5. Main components of middleware layer.
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management. The middleware, virtual system management, consists of system construc-tion management, system runtime management and system metering and evaluation.
● Cloud service management middleware
As a bridge between the cloud service pool and the middleware layer, this middlewarelays a foundation for managing virtual manufacturing systems.
The frequently used interfaces for service management are provided in this middle-ware. The publish interface allows a cloud service template to be published in cloudplatform. The search interface is used to discover on-demand services by means ofintelligent matching on semantics. Intelligent matching algorithms are integrated in theinterface. For example, intuitionistic fuzzy set (Tao, Zhao, and Zhang 2010) and semanticsimilarity matching (Tao et al. 2009) can be used for resource service optimal-selection.The compose interface supports the composition of multiple services. The bind interface isused for create an instantiation of a service template and configure the needed resourcesfor it, and then the execute interface enables remote call for the service, run it and get theresponse. The QoS assess interface is to set the values of service quality metrics. Theupdate interface can update service status, and the service can be destroyed by the destroyinterface. These interfaces will be utilised by the middleware of virtual systemmanagement.
● Virtual resource management middleware
This middleware links the virtual resource pool to the middleware layer, contributing toresource management in virtual system.
Similar to the middleware of cloud service management, some basic operation inter-faces are offered here. The register interface is used to register virtual resources in cloudplatform. The search interface aims at finding suitable virtual resources that meet therequirement of a service, by using intelligent matching. The instantiation interface buildsa binding to the real resources according to the real-to-virtual mappings. The statusinformation of resources can be acquired by the get status interface, providing supportfor monitoring. The remote control interface allows the resources with owner’s authorisa-tion to be operated by cloud platform remotely. The exception occurred in a resource canbe detected by the exception handling interface that will be sent to the middleware ofvirtual system management for further decision. Both the middleware of cloud servicemanagement and virtual system management need the interfaces above to support acces-sing to the virtual resources and real ones.
● System construction management middleware
This middleware is used to customise a virtual manufacturing system by integratingneeded services and resources, responding to a request of a customer. There are mainlyfour components in this middleware, on-demand service matching, virtual resourceconfiguration, virtual resource instantiation and service deployment. The on-demandservice matching component is responsible for performing a semantic searching for thesuitable services or composed services, leveraging the functional interfaces of the mid-dleware of cloud service management and the knowledge from the middleware of knowl-edge and data management. The virtual resource configuration component is used tocustomise the virtual resources that are needed by the candidate services. The virtual
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resource instantiation component is used for identifying the real resources hiding behindthe virtual ones, and ensuring availability and usability. The service deployment compo-nent serves for assigning the job that a service undertakes to the real resources, preparingfor execution.
● System runtime management middleware
This middleware acts as the manager in the runtime of a customised virtual system. Withthe help of the middleware of cloud service management and knowledge management, thestatus of services, as well as the associated resources, is under observation of thismiddleware, which keeps track of the progress of the services and returns the keyparameters. Relying on the running information that is closely monitored, this middlewarecan carry out scheduling of the services and relevant resources by using some optimisa-tion models and algorithms. Collaboration management is one of the most importantfunctions of this middleware, ensuring the virtual system runs as a whole and as a realone. One thing needed to do is guaranteeing the valid time series of all running services inthe virtual system, which ensures the workflow for a job runs in order. Another is toprovide support for seamless integration among distributed services, enabling intelligentinteraction among different providers in a collaborative process. This middleware is alsoin charge of the flexibility management for a virtual system, in case of some changes thatoccur at runtime. Once a service provider breaks the collaboration contract, or a servicecannot fulfil its task, a substitute service would be found and take the place. Accordingly,the virtual system will be reconstructed with the minimum cost, adapting itself to thechanges. Besides the adaptive reconstruction, the fault–tolerance at runtime is implemen-ted by this middleware. In the event of a failure that occurs at a resource, this middlewarecould carry out a migration of the task running at that resource, together with the taskcontext, from the resource in trouble to another substitute that enables the service continueto run.
● System metering and evaluation middleware
This middleware mainly deals with the consumption measuring, eco-efficiency measuringand automatic rating. Depending on the data extracted from the runtime monitoringcomponent, the utility of service and resource, e.g., hours of use and number of uses,can be metered quantitatively. Meanwhile, if the providers mark the energy consumptionand emissions in the description of the services, or connect into cloud platform the sensorsused for perceiving the amount of energy and emissions, the whole quantity can becalculated by this middleware. This figure thus offers evidence for measuring eco-efficiency of the virtual system. Except the function of metering, this middleware realisesthe automatic evaluation of services, such as quality and credit, by taking advantage ofobjective data of service quality this time, as well as the history records. This automaticcalculation also relies on different rating models that are maintained in this middleware.
5.3.4. Toolkit layer
Figure 6 illustrates the toolkit layer, which consists of two types of tools, shared tools andspecial tools, facilitating utilisation of cloud platform functions for three kinds of users,provider, customer and operator, respectively.
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● Shared tools
The shared tools are open to all users of cloud platform. The cloud member registra-tion tool allows a user to create a member account as either a provider or a customer, andmaintain personal or company profile. The cloud social network tool provides a socialplatform which allows a cloud user to build and manage its cooperation relationships. Amanufacturing cloud may create a global business network where all the registeredmembers can take part in it. All the data concerning the relationships in the social networkis recorded in the cloud platform, and the data is protected by cloud platform so as toavoid leaking commercial secrets. The valuable social relationships can promote efficientmatching and collaboration by taking advantage of data mining technology, such asrelationship-based service composition (Tao et al. 2012). In addition, this tool allowseach cloud user to configure their privacy settings because some users do not want to beseen by others. Based on a social network, the manufacturing community tool allowsindividuals who have common interests to establish various manufacturing communitiesfor cooperation and knowledge sharing.
The intelligent matching engine provides a semantic search tool by which the recom-mended matching solutions for supply and demand are given, and then utilised bycustomers and providers. In manufacturing business collaboration, the business affairs,e.g. transaction agreement and consistent rules, can be managed with the support of thebusiness process management tool. To build a manufacturing society based upon credit,users can leverage the comprehensive rating tool to evaluate each other. The rating results,
Providerview
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& discovery
Onlinenegotiation
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Visual analysis& statistics
Businessstrategy
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Metadatamanagement
Customerrecommendation
& discovery
Figure 6. The shared and special tools in toolkit layer.
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combined with the automatic evaluation results made by cloud platform, may in turn havean influence on the whole manufacturing ecosystem. The payment platform offers anonline payment tool for cloud users who are allowed to select different methods ofpayment. To enhance security, as well as user flexibility, the security configuration toolcan supply a range of security strategies that can be customised by users.
● Special tools for providers
Besides the shared tools above, from the perspective of providers, there are still specialtools for them to do business. The resource management tool assists providers inorganising and managing the information of manufacturing resources they own, as wellas encapsulating real resources into virtual ones. The capability and service publishingtool enables providers to profile their capabilities, and package them with virtual resourcesand knowledge into cloud service model that can be published in cloud platform. Thecustomer recommendation and discovery tool can proactively mine the potential custo-mers who most likely need the services of the provider, and recommend them to theprovider. The provider also can use this tool to search the desired customers who match aspecific service supplied by the provider. Once a customised virtual system is built bycloud platform, each provider associated to the virtual system can manage the demandedservice by using the task dashboard tool, which enables management of runtime servicescontrolling and progress status updating by the provider.
● Special tools for customers
Similar to the special tools for providers, cloud platform also offers several tools, especiallyfor customers to facilitate their using of cloud services. The requirement management toolenables customers to raise their demands and specify the details, e.g. job description, price,time limits, quality and credit of providers. In addition, this tool supports describing ademand by means of structured templates, as well as semi-structured or unstructuredlanguages, e.g. design sketch, CAD files and multimedia. The provider recommendationand discovery tool, with the support of the intelligent matching engine, can seek the serviceprovider candidates that meet a demand well and present them to the customer for choosing.To support business negotiation between the customer and the providers, the onlinenegotiation tool can establish an online communication platform where business affairssuch as agreement details may be discussed. The project dashboard tool makes it possibleand convenient for customers to keep tracking on the service progress.
● Special tools for operators
As the manager of cloud platform, the operators are equipped with special tools aidingthem in platform maintenance. The metadata management tool is used to define andupdate a wide range of fundamental templates needed by various layers of cloud platform,such as the formal description for manufacturing resource, virtual resource, capability,cloud service, cloud user, demand, agreement, quality of service, utility and rating. Theplatform operating management tool allows the administrators to monitor the internaloperating status of cloud platform, and respond to emergent problems. The businessstrategy configuration tool enables operators to design business mechanisms that canpromote healthy development of cloud manufacturing ecosystem. For example, upgradingthe service providers with high comprehensive ratings and offering them more
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recommendations of potential valuable customers. The visual analysis and statistics toolacts as a decision support tool, which is used to help operators make multifacet analysis(Ren et al. 2010) of the massive data that accumulate in cloud platform, hence discovervaluable knowledge.
5.3.5. User interface (UI) layer
The user interface (UI) layer is depicted in Figure 7. This layer provides support for end-users to access cloud platform by using diverse terminal equipments, such as PC, padcomputer and smartphone, contributing to ubiquitous usability of cloud services. Tofacilitate different users’ using their special functions, three types of portals with thespecial tools are presented for cloud providers, customers and operators, respectively. Thecloud UI customisation tool allows cloud end-users to configure their individualisedinterfaces. An important component in this layer is intelligent UI engine, which enablesthat UIs have ability to perceive the intention and interest of a user, hence proactivelyrecommend to the user the content that may be useful. This capability of intelligentcontext-aware and recommendation is achieved by leveraging the databases of usercharacteristics, involving features of user role, personal preference and operation history.
5.3.6. Knowledge and data management
This component plays an essential role in empowering intelligent knowledge-intensivemanufacturing. Figure 8 shows its structure.
The first crucial function of this component is to provide knowledge needed by everylayer in the cloud platform, enabling intelligent processing in varying levels. All users canupload knowledge to the cloud platform. Four types of knowledge are gathered here,ontology and rule base, model base, method base and case base. The terms and conceptsfrom different areas, as well as the relations among concepts, restrictions and rules, aredefined in the ontology and rule base, which thus can support semantic computing incloud platform. The empirical models, e.g. hydraulic model, fluid model and mechanicalmodel, are collected in the model base. The validated methods and algorithms arerecorded in the method base. These two bases are used to enhance the ability of intelligentproactive actions in various processes of a product life cycle. The classic cases, or
Cloud provider portalCloud broker
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Intelligent UI engine
Features ofuser role
Personalpreferences
Ubiquitous terminal equipments adaption
Operationhistory
Context-aware and recommendation
Cloud customerportal
Figure 7. The UI layer.
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successful stories, are recorded in the case base, which enables reuse of successfulexperiences, especially for those businesses with similar demands.
In addition, this component is also responsible for managing the data generated fromall layers in cloud platform. The structured data, e.g. the interactive information in servicetransactions, are managed by traditional relational database management system(RDBMS). Typically this kind of data feature multiple read/write, frequent interaction,static structure, high integrity, non-linear extension and relatively small scale measured byGigaByte. The relational data can be mapped to data objects that handle operations onRDBMS. Figure 9 shows an example of a data objects model containing resources,
Knowledge management
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Data management
Data write Data read Data mining
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Ontology &rule base
Model base Method base Case base
Knowledge discovery
Inference engine
Knowledge update
Figure 8. The component of knowledge and data management.
Figure 9. An example of data objects model.
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capabilities and services. The semi-structured or unstructured data, e.g. the real-timevarying data from machine sensors, are administered by distributed file system (DFS)(White 2010). These data have the characteristics as following, one write/multiple read,batch processing, dynamic structure, low integrity, linear extension and large scale,measured by PetaByte. These massive data can be processed efficiently by adopting thetechniques in cloud computing, such as MapReduce and Hadoop framework (White 2010;Bao et al. 2012). The increasing amount of history data will be imported into a datawarehouse for further analysis.
By using this component, domain experts can customise knowledge descriptiontemplates used for knowledge editing. Users may also add metadata annotations to sourceknowledge materials to support semantic processing. Except the updates by users, newknowledge may also be generated by inference engine, as well as data mining in datawarehouse. The ontologies are often used for semantic similarity matching betweendifferent descriptions of domain concepts. The inference engine (e.g. Jena3) also usesthe ontologies and rules to support reasoning. The ontologies and rules are recorded inXML format, e.g., OWL (web ontology language). To improve efficiency, this componenttransforms the XML files to relational data that can be stored in RDBMS. For example, asimple rule fact A—>fact B can be represented by using a tuple <RuleID, ConditionID,ConclusionID, RuleConfidence, ConditionConfidence,… > where ConditionID is theidentity of fact A, ConclusionID is the identity of fact B and RuleConfidence is theconfidence of the rule, and ConditionConfidence is the confidence of fact A. AsFigure 8 shown, knowledge base and data base can exchange data through the interfaces.
6. Application: a public cloud manufacturing system for SMEs
SMEs, compared to conglomerates, typically do not have enough resources to support allactivities in a product life cycle. They usually have some particular competitive advan-tages, such as specific facilities, innovative design, improved quality, faster development,lower cost, better service, etc. Currently, producing a product often needs a dynamicfederation of SMEs in which each enterprise with special capabilities cooperates withothers. This virtual enterprise alliance is also required to have overall competitiveadvantages to quickly respond to changing markets. The problem is that SMEs need acentralised platform that can provide a broad range of choices, the most appropriatepartners, minimal management effort, efficient collaboration and solid credibility.
To establish public cloud manufacturing systems for networked SME, can help realisethe goals above. Generally, a public cloud platform is based on Internet and managed by athird-party operation centre that offers access via Internet. A public cloud platform cangather resources, capabilities and services from providers as well as demands fromcustomers. On that basis, intelligent matching between supply and demand can bringdesired professional partners that provide customised services. Besides, a public cloudplatform can dynamically build a customised virtual manufacturing system on demand,and provide powerful support for close fine-grained cooperation in a virtual enterprisealliance. In addition, the comprehensive evaluation and rating mechanism ensures thecredibility.
Public cloud manufacturing systems can be widely used in various manufacturingapplications with their own characteristics, such as collaborative innovative design in amanufacturing community, process-level crowdsourcing in supply chain networks andonline industrial cluster over Internet.
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For example, Figure 10 illustrates a public cloud manufacturing system for SME inequipment manufacturing industry in South China. This cloud system established a manu-facturing community where the equipment manufacturing SME across multiple cities candevelop and manage their social networks on the cloud platform. The service providers, i.e.product designers, process planners, processing plants and etc., publish their advantagedresources and competitive capabilities in the cloud platform, such as facilities, technologicallevel, intellectual properties, human resources, marketing resources and management level.Meanwhile, the customers publish their customised multidimensional demands, such asdesign drawings, process requirements, quality metrics, time of delivery and green standards.
The cloud platform allows two types of matching mode between supply and demandin the equipment manufacturing community: pushing and pulling. In pushing mode, thecloud platform proactively synthesises multidimensional semantic information from bothproviders and customers periodically, and performs intelligent multidimensional matchingto mine the potential targets for each other. The results will be pushed to those who mostlikely need them. So, what a customer needs to do is publishing demands and waiting forrecommended service providers. In pulling mode, generally it is a customer who launchesa business by carrying out a search in virtual pool or starting a bidding. Crowdsourcingmode is also used in the equipment manufacturing community, especially when customersdemand innovative designs beyond imagination or incredibly quick delivery of an orderthat seems a mission impossible.
Figure 11 shows some screenshots of the public cloud manufacturing system in theSouth China. Current applications are shown in Figure 11(a), such as bearings. An
Provider Customer
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Resource/Capability/Servicepublishing,
Demandpublishing,
Interaction,
Businesscollaboration
Interaction,
Businesscollaboration
Figure 10. A public cloud manufacturing system for SME.
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(a)
(b)
(c)
Figure 11. Some screenshots of the public cloud manufacturing system in South China. (a) Currentapplications. (b) Multidimensional semantic match for a composite service. (c) A social network ofthe manufacturing community.
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example of multidimensional semantic match for a composite service process is shown inFigure 11(b). Users can set the weight of each dimension of match features and get thesemantic match results, together with the matching rate of each dimension. Figure 11(c)shows a user’s social networks in the manufacturing community. Users can manage therelationships with his/her cooperative partners, and communicate with each other instantlyand conveniently. Some project opportunities with challenges are published in the com-munity for crowds bidding. There is also a variety of sharing professional knowledgebases, as well as innovative discussion forums designed for crowd innovation.
Compared to a private cloud system under centralised control, the public cloud systemmentioned above has many different focuses in the deployment process. From the perspec-tive of participated enterprises, there exist two application modes. Some small enterpriseswith weak IT infrastructure transformed them to complete IT tenants of the cloud platform,while the others were assisted to integrate their legacy IT systems, e.g. some expensive ERP,to the cloud platform. Considering security threats on Internet, many enterprises wereunwilling to connect the control interfaces of manufacturing facilities into Internet, andmost of them published OffCloud services concerning manufacturing cells finally. So, theoperations of the facilities are still in full control of their owners, while the running statuscan be monitored by the cloud platform to support virtual management. From the cloudplatform operator’s perspective, different business strategies were adopted in differentevolutionary stages of the cloud system. In the embryo stage, for example, the prior strategywas to have the leading enterprises connected as cloud customers into the cloud platform,which then could drive both the upstream enterprises in the supply chain and the down-stream distributors enter the cloud. When the majority leading enterprises and their relativebusiness networks ran on the cloud platform, the cloud system entered the growing stage. Inthis stage, the emphasis moved to developing more diverse potential enterprises as serviceproviders, which could introduce more competitive services to meet customers’ need. Withthe number of cloud members increasing, the cloud system tended to select the superior andeliminate the inferior, and the enterprises with high comprehensive ratings could be offeredmore superior opportunities. Currently, the public cloud system is evolving from thegrowing stage into the stage of fittest survival.
Now the practice of the public cloud system in South China has effectively promotedcollaboration among SMEs in the equipment manufacturing industry. The resource shar-ing and capability circulation across enterprises are enhanced in support of the cloudplatform. The cloud business model also transformed many enterprises from production-centric to service-centric, and created a large number of new specialised service compa-nies offering various manufacturing services. From an individual enterprise perspective,they can focus on their core resources and capabilities and utilise services that may befound in the cloud system with a lower cost and improved productivity. Thus, the cloudsystem creates more opportunities for SMEs to concentrate on core competences andinnovation capabilities so that an enterprise can respond to market requirements morequickly and cost effectively than before.
7. Conclusion
Recent remarkable advances in the information technology area, such as cloud computingand IoT, have opened up a new research area, cloud manufacturing. It has the potential totransform the way enterprises do business. Cloud manufacturing customers can haveaccess to on-demand services, such as engineering design, simulation, production, assem-bling, testing and management.
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To drive this new research area forward, both academia and industry are calling forcommonly accepted and clear definitions, as well as guidance on the design and devel-opment of cloud platforms. This paper proposes a systematic and complete solution toaddress these issues. We define a few fundamental terms and concepts in cloud manu-facturing. Moreover, we developed MfgCloud, a cloud manufacturing platform prototypethat can offer some of the basic functions that are required in cloud manufacturing.Finally, we present a public cloud manufacturing system for SMEs. We discuss theapplication examples in a SME cluster for equipment manufacturing in South Chinaarea, and show the effectiveness of our cloud manufacturing system.
As a result, we present a new point of view for the concept of cloud manufacturing.The complete solution proposed in this paper, including basic terminology, cloud platformdesign and typical applications, can push forward cloud manufacturing from concept topractice. The future work is to apply MfgCloud to more manufacturing applications,which will contribute to improving our prototype systems. In addition, we will investigatebusiness models for cloud manufacturing.
Funding
The research is supported by the National Science Foundation of China (NSFC) [projectnumber 61103096)] in China; the National High-Tech Research and Development Plan ofChina [grant number 2011AA040501]; the Fundamental Research Funds for the CentralUniversities in China.
Notes1. See http://www.economist.com/node/21552901/2. See http://www.manucloud-project.eu/3. See http://jena.apache.org/
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