From Cloud Manufacturing to Cloud Remanufacturing

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From Cloud Manufacturing to Cloud Remanufacturing: a Cloud-based Approach for WEEE Xi Vincent Wang, Lihui Wang

Liang Gao

Department of Production Engineering School of Industrial Engineering and Management KTH Royal Institute of Technology Stockholm, Sweden e-mail: [email protected] [email protected]

The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan, China e-mail: [email protected]

Abstract — The modern manufacturing industry calls for a new generation of integration models that are more interoperable, intelligent, adaptable and distributed. Evolved from service-oriented architecture, web-based manufacturing and Cloud Computing, Cloud Manufacturing model is discussed world-widely which enables manufacturing enterprises to respond quickly and effectively to the changing global market. Especially for Waste Electrical and Electronic Equipments, it is a critical necessity to reuse, remanufacture, recycle and recover energy by re-shaping the lifecycle management patterns. In this paper, recent Cloud Manufacturing approaches are reviewed. Next, a novel serviceoriented remanufacturing platform is introduced based on Cloud Manufacturing paradigm. During case studies, a LCD television model is taken to evaluate the proposed Cloud Remanufacturing service mechanism and information management methodologies. Keywords - Cloud Manufacturing, Cloud Remanufacturing, WEEE, Remanufacturing

I. INTRODUCTION Cloud Manufacturing (CManufacturing) is a new manufacturing model that provides an interoperable and collaborative environment for distributed manufacturing industry. Based on NIST’s [1] Cloud Computing definition, Xu [2] defines CManufacturing as a model for enabling ubiquitous, convenient and on-demand network access to a shared pool of configurable manufacturing resources (e.g. manufacturing software tools, manufacturing equipment and manufacturing capabilities), which can be rapidly provisioned and released with minimal management effort or service provider interaction. It offers promising features such as resource pooling, on-demand service, rapid elasticity and so forth. In the paradigm of remanufacturing, the participants, such as consumers, collectors and recyclers, are widely distributed not only in commercial remanufacturing chain, but also among random customer locations. Thus, it is necessary to introduce a system to support the geographically dispersed remanufacturing business. In this paper, a Cloudbased remanufacturing system is proposed specifically for Waste Electrical and Electronic Equipment (WEEE) recovery. Recent CManufacturing and Remanufacturing approaches are reviewed, followed by the proposed system. Case study and discussions are presented towards the end.

II.

FROM CLOUD MANUFACTURING TO CLOUD REMANUFACTURING As mentioned above, CManufacturing is a new ServiceOriented Architecture (SOA) network that can be considered as an advanced process-centric manufacturing model. It provides a mechanism of multi-objective resource access that is particularly suitable for geographically separate and organisationally diverse business models. Integration and interoperability of manufacturing applications can be achieved at the service level. A. Cloud Manufacturing Approaches Li et al. [3] proposed a service-oriented networked manufacturing model as a CManufacturing model. In this research, a number of relevant technologies are discussed. Intelligent agent, product lifecycle management, resource modelling and evaluating technologies are identified potential support for Cloud architecture. However, there is no method that integrates various manufacturing processes in the Cloud. The model was further discussed by Zhang et al. [4] in which a dynamic Cloud Service (CService) centre in CManufacturing was defined as manufacturing Cloud. Web Ontology Language is used to describe the Cloud resources and support service management. Tao et al. [5] proposed a framework of CManufacturing and discussed some of the key advantages and challenges for future CManufacturing systems. It is predicted that a CManufacturing system can reduce the cost of resources, and increase their utilisation. The relationship between Cloud Computing and CManufacturing is discussed. A seven-layer model was proposed to support and provide a public manufacturing platform [6]. In 2010, a Cloud-based manufacturing research project was launched, sponsored by the European Commission [7]. The goal of this project (named ManuCloud) is to provide users with the ability to utilize the manufacturing capabilities of configurable and virtual production networks, supported by a set of applications. In the proposed system, customized production of technologically complex products is enabled by dynamically configuring a manufacturing supply chain [8, 9]. It is considered that the development of a front-end system with a Cloud-based manufacturing infrastructure is able to better support the specifications and on-demand manufacture of customized products. Based on the

conceptual architecture, two main types of users who interact with the front-end system are identified: the manufacturing service consumer (e.g. a product designer) and the manufacturing service provider (e.g. a lighting product manufacturer). Compared with a service consumer, more interaction is required between a service provider and the MCloud. Nevertheless, there is still lack of ability to support the activity provider. In this research work, Manufacturingas-a-Service was proposed to achieve configurable and customized manufacturing [10]. Manufacturing Description Service Language was developed to model and represent different types of product characteristics, for example shape, size, mechanical, electrical, etc. In another EU project (EU CAPP-4-SMEs), collaborative process planning is considered as a Cloud manufacturing service [11]. In this research, CManufacturing is utilized as an integrated solution to facilitate modular and configurable process planning service to increase robustness of existing processed. Moreover, function block method is utilized to monitor the availability and status of machines, simulates and optimizes operations, and performs remote machining in the distributed environment. In 2011, Houshmand and Valali [12] proposed a collaborative environment named LAYered MODular platform (LAYMOD) to realize CAx collaboration and product data integration. LAYMOD aims to overcome the shortcomings of the international standards (ISO10303), which is the cost and time of APs implementation. It is also able to eliminate duplication and repeated documentation of the same data entries in different APs. Very recently, LAYMOD was extended to XMLAYMOD by adopting an XML data structure and CComputing paradigm [13]. An XML Service Cloud and its database are inserted between the Interface Layer and Modular Interpretation Layer, unlike the original system. XML Service Cloud plays a central role in XMLAYMOD, which translates product data from an original format into XML, or vice versa. The new system benefits from the portability of the XML data structure. However, the advantages of Cloud are not fully utilised except accessing in remote data. Wang and Xu proposed an interoperable system based on CManufacturing concept [14-16]. In this system, manufacturing capabilities and related resources are integrated as service units that are modularized in the Manufacturing Cloud. Agent technology, virtual function block and international standards are deployed to provide a comprehensive service solution that fulfils the user’s original needs. B. Resource Management in CManufacturing To facilitate a CManufacturing environment, existing resources need to be scaled, modelled and integrated into an MCloud. Wu and Yang [17] defined CManufacturing as an integrated supportive environment for both sharing and integration of resources in an enterprise. A resource sharing method is also proposed to describe and scale the manufacturing resource in a Cloud. Manufacturing resources are divided into four layers: a manufacturing resources layer,

concrete web service layer, logical service layer and application layer [18]. Cheng et al. [19] studied a utility model and utility equilibrium of resource service transaction in CManufacturing. Decision-making methods have been developed to maximize the utility of a resource demander and resource provider. Yet it is difficult to represent the manufacturing resources without a standardized schema, not to mention that original user requests would be difficult to fulfil. Hu et al. [20] analysed the factors which influence classification of virtual resources in CManufacturing. Examples are introduced to validate the effect of these factors on task assignment. Luo et al. [21] discussed the CManufacturing system from the viewpoint of network, function and running. A multi-dimensional information model was proposed to describe manufacturing abilities [22]. This knowledge-based data model is helpful in providing the user with a manufacturing service via network. Flexibility of the Cloud has been classified into task flexibility, resource service flexibility, Quality of Service (QoS) flexibility, correlation flexibility and flow flexibility [23]. Selection of the composition of a resource service can be achieved through quantitative evaluation of flexibility. To control and manage the flexibility of the service composition in CManufacturing, Zhang et al. [24] proposed architecture that considers various major dynamic-change factors in the life-cycle of a resource service. A monitoring module is suggested to connect manufacturing resources and users, for flexible remote management. Multi-agent technology is reported to be an effective tool for solving problems through sharing knowledge in CManufacturing [25]. It has been pointed out that an agent-based mechanism provides flexible and effective sharing and utilisation of elastic resources. A Cloud-based structure has been proven capable of supporting the whole supply chain, building up business relations and, finally, better matching the demand-and-supply capacity data [26]. The challenge in resource integration comes after resource reorganisation. Fan et al. [27] proposed an integrated architecture of CManufacturing based on a federation mode. Federation integration rules are applied before resources are connected to the system. Thus, connecting or releasing a resource will not influence the operation of the whole Cloud environment. To maintain CManufacturing resources, an Optimal Allocation of Computing Resources (OACR) system is proposed [28]. In OACR, an improved Niche immune algorithm is introduced to solve the resource scheduling problem in grid systems or Cloud Computing systems associated with the Niche strategy. However, user interactions are fully involved during service execution, which may work against the spirit of Cloud. In a CManufacturing system, CUs hire the manufacturing service, instead of directly investing in devices and machines. CService should be well-defined in a project requirement, and CU interaction minimised, particularly after the requirement is submitted.

Li et al. [29] discussed the resource access issue in a CManufacturing environment. As an on-demand and multiobjective networked manufacturing model, a CManufacturing system should be able to provide multiscale and on-demand service capabilities through a variety of loose coupling service applications. In this research, a multitarget, backward-authority covering graph and an authority path-confirming algorithm was proposed. Manufacturing processes can be scheduled based on capacity constraints, with minimum calculation time required. Combined with pre-defined rules, e.g. limit of size, material, price and time, the CService can be optimized to fulfil the demand of user. An ontology-based method was proposed by Zhang and Zhong to profile a CManufacturing resource [30]. A peer-topeer registry network was suggested for building the CManufacturing database, though peer-to-peer infrastructure faces portability and synchronisation issues due to lack of standardized data structures and coordinating methods. C. WEEE Remanufacturing Rapidly increasing of production of Electrical and Electronic Equipment (EEE) has resulted in generation of enormous amount of WEEE. WEEE consists of a large number of components of various size and shape, some of which contain hazardous materials, while is resource-rich as they contain many valuable materials. So recycling WEEE can get benefit but cause environmental impact. In the EU, WEEE Directive [31] has been introduced in 2002, and the corresponding legislation in the EU member states was in place in 2005. There is also legislation similar to WEEE Directive in China named Administration Regulation for the Collection and Treatment of Waste Electric and Electronic Products. According to the WEEE Directive, a producer’s responsibility is extended to the postconsumer stage for their EEE, instead of terminating at selling and maintenance phase, which is defined as Extended Producer Responsibility (EPR) [32, 33]. Confronted by the global regulatory pressures and EPR, manufacturers are facing the need to foresee and plan their products’ End-Of-Life (EOL) treatment at the early stage of products such as disassembling and recycling process planning and assessment. There is a need to propose an intelligent system and systematic method to support operative producers. Shrivastava et al. [34] proposed a decision support system for evaluating obsolete electronic products for disassembly, material recovery and environmental impact. The system is designed to support recyclers but not manufacturers. The proposed method and system is based on the Bill Of Materials (BOM). Regarding the use of the product BOM, Das and Naik [35] point out the need of understanding product BOM in order to plan its further disassembly, while Lambert [36] used BOM in EOL decision modelling method since it provides information essential to disassembly planning. Based on BOM and physical connections of parts, González and Adenso-Díaz [37] proposed a decision model to determine an EOL strategy with maximum profit. The effect of BOM applying in disassembly and recycling of WEEE

can be improved by associating with the systematic method using Information and Communication Technology (ICT). To recap, there is still a need of a comprehensive platform at high level, to integrate all the current and future applications and systems to support WEEE remanufacturing. It is necessary to develop a smart system that is able to coordinate operative activities and participants. In the next section, a novel remanufacturing system is developed based on CManufacturing technology. III.

A CLOUD-BASED WEEE REMANUFACTURING S YSTEM As mentioned above, the remanufacturing industry calls for models that are more collaborative and intelligent. Hence, it is possible to introduce the CManufacturing concept into the WEEE paradigm to support the smart remanufacturing service and sustainable WEEE management. A smart Cloudbased WEEE Remanufacturing (CWR) system is developed to provision scalable and modularized Cloud Remanufacturing Service. In CWR system, the customers, suppliers, remanufacturers, experts and relevant personnel are coordinated in the remanufacturing Cloud (WEEE Cloud) via the network (Figure 1) based on SOA. It forms a shared pool of configurable remanufacturing services. Utilisation of Cloud resources could exist from random short-term contracts to strategic long-term cooperation.

Figure 1. Cloud-based WEEE Remanufacturing

From the manufacturer’s point of view, a business network is extended by the broad scope of the Cloud environment. In the traditional business approach, the exchange of materials and reused products heavily depends on the existing connections. The business opportunities are limited by the experience and ability of individual participants. In the Cloud Remanufacturing paradigm, on the other hand, the service arrangement is organized by the

service pool contributed by all the Cloud stakeholders. Manufacturing ability is strengthened by wide support from the whole Cloud at upper levels, which provides a rich collection of devices, equipment and resources. Additionally, service provider’s costly and rarely-used in-house facilities can be outsourced to the Cloud. From the viewpoint of customers, Cloud-based Remanufacturing provides a wide range of service and remanufacturing capabilities. Ondemand service can be easily achieved via the interaction between the user and WEEE cloud. Customized service can be evaluated, tested and realized with less effort and cost. A. Three-layer CWR System The CWR system is constructed based on three layers, i.e. User Layer, Cloud Coordinator Layer and WEEE Layer (Figure 2). The User Layer contains the domain of remanufacturing service participants who are connected to the system via network. It provides graphical user interface and interaction methods. The Cloud Coordinator Layer includes the central server and the supervision kernel of CRW. As the brain of CWR, it provides management mechanism and computing capability for the whole system. The Coordinator Layer is developed based on software agents, since agent technology helps to realize important properties, such as autonomy, responsiveness, redundancy, distributedness and openness [38]. The users’ queries and commands are interpreted and transferred into the system with the help of agents such as Purchaser Interface Agent, Recycler Interface Agent, Collector Interface Agent etc. Based on the service requests from users, the Broker Agent

analyses the demands with the remanufacturing capabilities described in the database. The request description is then compared with the capability description. The Broker Agent assists with the fulfilment of the user’s need and generates a complete service document containing the service/capability details. This forms a “Request-Find-Provide” procedure for the user. Before the service is sent back to the user, the capability and availability of resources can be verified by the agent. If negotiation is needed (e.g. waiting for coming availability or switching to another resource), the broker feeds the result back to the user and asks for an alternative choice. Supervision Agent plays as the service organizer and coordinator of the system. After the service document is approved by the user, the Supervision Agent organizes the allocated modules in the WEEE Cloud and merges them as a virtual service combination. Service modules are connected into the final package, which contains essential remanufacturing resources. The service is delivered to the user based on the process list defined in the service document. The Supervision Agent is responsible for launching/shutting down the service by controlling the event flow of service packages. After a service provider finishes a task on one module, the Supervision Agent detects the eventout trigger and delivers the module that is needed next. After being processed by the applications, the latest data is kept in the database with previous versions. Therefore data traceability and reliability are guaranteed.

Figure 2. Cloud-based WEEE Remanufacturing System

As the third part of Remanufacturing Cloud, WEEE Layer contains the modularised remanufacturing services including Purchase Module, Recycling Module and Collecting Module. It needs to be noted that one service module can be provisioned by one service provider or multiple remanufacturing participants. This means a remanufacturing service package meeting a customer’s need could be provided by a single service provider or a union of them. The shared pool of remanufacturing capabilities differs the Cloud-based solution from previous web-based remanufacturing systems. Firstly, a comprehensive Cloud solution is able to take care of resources from different stakeholders and provide an optimal solution based on a wide resource pool. The redundancy of resources also guaranties the availability of remanufacturing service. Furthermore, it enables time-distributed resources to be better utilised or outsourced. The elastic feature of the Cloud-based system helps a business utilise resources better. For example, a recycler may have an average low workload in a whole year except for a peak period after Christmas, with workloads as high as five times the average load. Additional resources are needed to cope with peak loads. With the help of Cloud-based resource pool, the workload can be balanced among multiple service providers. The user is able to access suitable capabilities on time in the Cloud. Eventually, identified modules are packaged as Cloud Services and deployed in the WEEE Layer. During conversion from a current remanufacturing status into Cloudbased remanufacturing, existing capabilities and resources can be integrated and utilised in the distributed environment. Thus an interoperable, service-oriented remanufacturing system can be realized. In the WEEE Layer, typical remanufacturing services are identified and maintained as reusable modules, such as:  Purchase Module provides the acquisition service of recycled materials and reused parts.





Recycling Module is responsible for the recycling service from service providers, such as supplier, OEM, repair station. The services include recycling, inventory, recycling line, warehouse, warehouse call-back and so forth. Collecting Modules maintains the services related to material collecting, including collection quoting service to order and submit collecting queries, and collector service to transfer the goods to expected locations.

B. Cloud-based Information Management Mechanism in CWR system Coped with remanufacturing services, it is also important to manage the information regarding products, materials and resources. A dynamic knowledge control mechanism is necessary to maintain the product specifications from the remanufacturing perspective. The data management mechanism is derived into three major modules i.e. Beginning-Of-Life (BOL) Module, Middle-Of-Life (MOL) module and EOL Module. In this research, BOL module maintains the knowledge at the stage of manufacture; MOL module keeps the product information that is generated among distributors, retailers and customers; EOL module manages the data related to recycler, remanufacturer, etc. The interaction between users and WEEE Cloud is processed with the help of CWR agents. The communication is firstly taken by the Interface Agents, and then Broker Agent is responsible to analyse different queries and find appropriate solutions. Eventually, approved service documents are executed and supervised by Supervision Agent to process the service package stage by stage. The data flow within the CWR information system is shown in Figure 3 with Use Cases (UCs).

Figure 3. Deployment Diagram with Use Cases

To further explain the information mechanism of CWR system, seven UCs are identified:  UC1: Provide Factory Information  UC2: Register New EEE  UC3: Update EEE Details  UC4: Provide Tracing Information  UC5: Register WEEE  UC6: Update WEEE Details  UC7: Provide Feedback Information Factory Information Service forms the static information of the product and parts, including Manufacturer, Description, Component, Process, Material, Location, Joining, etc. With UC1, the factory information is updated to the BOL module and stored in the Cloud database. At the MOL and EOL stage of products, dynamic information needs to be maintained during distribution and usage, which involves distributors, retailers, consumers, collectors, second-hand retailers, and recovery organizations. During the EEE usage, Users are able to register new EEE (UC2), update exiting WEEE details (UC3) and provide tracing information (UC4) with the help of MOL Module. After the WEEE is converted to WEEE, the CWR user is able to register WEEE cases based on existing EEE record (UC5), update WEEE details (UC6), and provide feedback information regarding the purchase, collect, and recycle service mentioned above. With the help of Cloud-based technology, it is possible to collect these data dynamically and achieve data interoperability and portability. The product information is maintained in the Cloud database, which keeps the dynamic data throughout the lifecycle of the product. Manufacturers, Users and recyclers are able to access the Cloud database via network. The database provides a standardized and collaborative environment to manipulate the knowledge. Furthermore, Electronic Product Code (EPC) [39] is utilized to document the identification scheme in CWR. The basic EPC format consists of 96 bits divided into four partitions: version (8 bits), manufacturer (28 bits), product type (24 bits) and serial number (36 bits). It offers a useful methodology to indentify and trace the product including its components and materials. Nowadays, the mobile devices are widely used in the automotive and engineering industry. To further strengthen the feasibility and mobility of EPC, Quick Response (QR) Code is combined with EPC in this research (Figure 4). QR code is a type of matrix barcode or two-dimensional barcode that is an optically machine-readable label that is attached to an item [40]. It records information related to the item and is able to encode the information based on four standardized modes or supported extensions, virtually any type of data. Compared with traditional barcodes, it provides fast readability and greater storage capacity, which is especially suitable for distributed environment like CWR. A QR code consists of black modules (square dots) arranged in a square grid on a white background, which can be read by an imaging device (such as a camera). The CWR users, including customers and service providers, are able to catch the code easily from mobile devices e.g. smart phones,

PDAs, scanners, etc. The code can be directly interpreted to the EPC product identification, which maps to more technical details stored in the Cloud database. In this way, CWR users are able to identify, create, update, search and locate WEEE products easily on site. When a service is created, for example product collecting, the detailed specifications and dynamic updates can be accessed by the collectors with the help of unique tracing code. Optimized service organization and route planning can be realized based on the standardized and comprehensive information mechanism. Consumers can be connected to the system via software applications without extra devices or investments. Thus it provides a smart information mechanism to support the remanufacturing chain for WEEE.

Figure 4. EPC and QR Code

IV. CASE S TUDY This research takes the liquid crystal display television (LCD TV) of Guangdong Changhong Electronics Company, Ltd. from China as a case study. Type LC24F4 LCD TV is selected as the example product. The product is typically assembled by three main parts: front cover assembly, back cover assembly and base assembly. The exploded view of the product can be found in Figure 5. The assembly structure and BOM specifications are initially maintained in the Cloud database.

Figure 5. Exploded view of LCD TV

It is assumed that an end consumer who needs to ship the product to the remanufacture and start the recycling service. With the help of QR code mentioned above, the user firstly specifies the profile of the product and submits the query to the Interface Agent. The Cloud Coordinator analyses the request and launches the recycling inventory service (Figure 6). The service unit takes the request, user data and product

identification as input before it submits a shipping request to the Broker Agent. The Broker Agent analyses the inputs with related information in the database, e.g. price, location, service carrier, time duration etc. Appropriate solutions are fed back to the user as service response before it is approved. With the user’s improvement and necessary payment, The Supervision Agent re-launches the line service unit who is responsible to communicate with the Broker Agent. A warehouse service is assigned including the shipping service from the user’s location to the warehouse destination. Broker Agent then delivers WEEE service response to both recycling module and Supervisions Agent. Then the service package is completed and all the related information is updated back in the Cloud database. The case study shows the Cloud-based remanufacturing service procedure that is supported by the Cloud Coordinator mechanism. It is possible to organize the current remanufacturing applications in terms of Cloud services and implement them in the CWR system.

platform that realizes a “Request-Find-Provide” service loop based on SOA. Systematic design and information management mechanism is introduced to streamline the operative activities throughout the lifecycle of product, especially the middle and end stage of product life. QR code is utilized with EPC to strengthen the readability and tractability of products. In the case study, a LCD TV model is utilized to evaluate the remanufacturing capability in the Cloud environment. In the future, semantic web service and standards can be developed to further support the Cloud Remanufacturing service. It is necessary to establish a standardized description methodology to profile the service data, which is compliant with product information, knowledge, processes and current international standards. Furthermore, cross-platform applications need to be developed to support bi-directional and efficient management with uniformed communication protocols. ACKNOWLEDGMENT This research is proposed as part of the Globally Recoverable and Eco-friendly E-equipment Network with distributed information service management (GREENet) project, Grant Agreement Number: PIRSES-GA-2010269122. It was supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme. REFERENCES [1]

Figure 6. Service-Oriented Use Case Sequence for UC3

V. CONCLUSIONS In the background of globalization and distribution, CManufacturing philosophy provides the opportunity to integrate current and future manufacturing applications at high level including WEEE remanufacturing. In practice, it is difficult for the product consumers to obtain full knowledge of remanufacturing industry, or for different participants to collaborate with each other in the distributed and heterogeneous environment on behalf of themselves. Cloud Remanufacturing is able to connect remanufacturing stakeholders via network and offers a collaborative environment. From the consumer’s point of view, the Cloud Remanufacturing system provisions a shared pool of essential recycling functionalities in terms of service. The scalable and elastic service package can be arranged to meet the customized needs from different users. In this research, a Cloud-based remanufacturing system is proposed to establish an intelligent and interoperable

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