ORIGINAL ARTICLE

Common engines of cloud manufacturing service platformfor SMEs

Tingxin Song & Huimin Liu & Chunmei Wei &Chenglei Zhang

Received: 18 December 2013 /Accepted: 11 April 2014 /Published online: 9 May 2014# Springer-Verlag London 2014

Abstract In order to improve the comprehensive competi-tiveness of small- and medium-sized enterprises (SMEs), acloud manufacturing service platform for SMEs was imple-mented with the idea and model of cloud manufacturing(CMfg) in this paper. Using the transaction mode of multipleenterprises, multiple orders, and multi-party collaboration, thearchitecture and workflows of this cloud manufacturing ser-vice platform were designed. Some common engines, such asintelligent matching engine for supply and demand, transac-tion coordinating logic engine, and credit evaluation engine,were researched, which implement the semantic intelligentsearching, order tracking, transaction task guiding, and col-laborative management in the whole process of CMfg. As acase, this platform has been applied in the automobile andmotorcycle parts manufacturing industry, which effectivelyintegrates manufacturing service resources in this fieldthrough online services and offline services (O2O), achievingthe effective management of service resources.

Keywords Cloudmanufacturing (CMfg) . Intelligentmatching engine . Transaction coordinating engine . Creditevaluation engine

1 Introduction

In recent decades, many experts and scholars, as well asenterprises themselves from all over the world, have beendevoted to exploring and studying how to solve the problem

of a lack of capital, technology, personnel, and equipment forsmall- and medium-sized enterprises (SMEs) so as to improvetheir market competitive power and open up new markets[1–3]. Meanwhile, with the continuous development of infor-mation technology and Internet of things [4], cloudmanufacturing arose at a timely moment [5, 6]. Cloudmanufacturing (CMfg) is defined as service-oriented, effi-cient, low consumption, and knowledge-based networks fornew manufacturing patterns and technology by Li [7]. Heverified the feasibility of the cloud manufacturing conceptby cloud simulation application of the prototype platformbased on cloud simulation—COSIM-CSP—and put forwardthe cloud manufacturing architecture [8]. Xu gave the defini-tions of manufacturing resource, manufacturing capability,and manufacturing service in the cloud background [9]. Then,he put forward an interoperable solution for cloudmanufactur-ing and developed an interoperable cloud-based manufactur-ing system to support multiple options for two types of usersand improved enterprise performance at the same time [10].Wu came up with a unique strategic vision for this field andproposed a four-layer CM framework consisting of amanufacturing resource layer, a virtual service layer, a globalservice layer, and an application layer [11, 12]. Even thoughthey have done a lot, it is still not enough to take cloudmanufacturing into practical use. With the support of theNational High-Tech. R&D program, this paper develops acloud manufacturing service platform for SMEs and emphat-ically studies the three main engines which will guide thecloud manufacturing processes smoothly into practice [13].

2 Design of cloud manufacturing service platform

2.1 Platform overview

Based on the cloud manufacturing idea [14–18], the cloudmanufacturing service platform for SMEs depends on the

T. Song (*) :H. Liu :C. WeiSchool of Mechanical Engineering, Hubei University of Technology,Wuhan 430068, Chinae-mail: songtx2006@163.com

C. ZhangSchool of Mechanical and Electronic Engineering, WuhanUniversity of Technology, Wuhan 430070, China

Int J Adv Manuf Technol (2014) 73:557–569DOI 10.1007/s00170-014-5863-y

Internet of things and information technology to promote thecombination and conversion of manufacturing and service. Itwill realize the integration of social resources in manufactur-ing processes and improve the resource utilization as well asreduce the enterprise resource consumption so as to provide anew direction for enterprises to steer towards service typefrom production type [19, 20]. What it mainly studies ischaracteristics including large-scale, long supply chain, com-plex organizational structures, and poor collaboration betweenenterprises. At the same time, enterprise groups can rely on thecloud manufacturing service platform to realize convenientaccess, knowledge aggregation and sharing, optimization con-figuration of service resources, and collaborative managementand transaction [21–24].

Its main idea is changing complex enterprise needs intoenterprise order services and process management services[25, 26]. Even though to different enterprise service resources,enterprise users will have access to search and match thosepublished service resources with a simple login so as to realizeseamless access and get service resources they ultimately need[27–30]. Taking the project need into consideration, a newmodel of service resource network for intelligent manufactur-ing is applied to achieve the cloud manufacturing operationmode with multiple enterprises [31–34], multiple orders, andmulti-party collaboration in this paper. However, it is impos-sible for those existing systems to support the multiple enter-prises and multiple orders, while enterprises will suffer lessrisk by using this mode [35]. In view of this, a kind of “222+2” cloud manufacturing service mode set which includes twokinds of service providing methods, two kinds of serviceresources types, two kinds of participation roles, and twokinds of business and profit models was proposed through acollaborative manner with many participants [36]. It makes iteasy for enterprises to search enterprise production needs in asystem and to control the transaction rights between enterpriseusers. In addition, processes for enterprise users to login andregister in multiple platform systems have been simplified,and it provides a concentrated and convenient way to searchand secure business transactions and rights management so asto meet the need of the cloud manufacturing service platformfor SMEs and the convenience for enterprise users to use theweb application system [37–39].

2.2 Platform architecture

Cloud manufacturing service platform for SMEs is dividedinto two systems: the front-end and the back-end. The front-end is a dynamic web page which is mainly used to searchservice resources and show demand or supply information,while the back-end is a datamanagement systemwhich guidesto final transaction tasks through intelligent matching enginefor supply and demand [40, 41], transaction coordinating logicengine, credit evaluation engine, and some adaptable tools. It

can mainly offer the user information management, serviceresource management, business process management, trans-action task management, credit evaluation management, andother functions.

It is the cloud server, cloud storage, and cloud database thatare the basis of support for the cloud manufacturing serviceplatform for SMEs in infrastructure, while the main develop-ment tools are Java Platform Enterprise Edition (J2EE tech-nology) and SSH2 (Spring2.0 + Structs2.0 + Hibernate3.0).

As the business layer, Spring2.0 mainly aims at reducingthe coupling degree between each layer, and it is a lightweightcontainer which can achieve the IOC pattern. Struts2.0, thecontrol layer, is used to separate the business logic layer fromthe presentation layer, but it does not involve any associationbetween the business layer and persistence layer. The Hiber-nate3.0 framework works as a data management tool toachieve object/relation mapping (ORM) and data persistenceprocessing. In addition, it depends on a loosely coupled man-ner to separate underlying database from front-end system,and it supports all major cloud databases. As the data layer,cloud database works for the management of data exchanging,user storage, service resource, industry knowledge, news an-nouncements, transaction orders, credit evaluation, and trans-action tasks.

From Fig. 1, we can clearly see the platform structure andtechnology architecture as well as their interrelationship.

2.3 Platform workflow

1. When accessing this platform for the first time, enterpriseusers should submit their information to register. At thesame time, their initial credit will be assessed based on theregistration information.

2. Identity verification: If registered successfully, users willbe able to enter the platform system by inputting accountdetails and a corresponding password. However, if thisfails, it will return to the registration page and users willneed to register once again.

3. The first thing for enterprise users to do is to releaseservice resource information (supply side) or demandinformation (demand side) after a successful login, thenthey can customize cloud manufacturing processes in-volved with themselves by the business process manage-ment tools.

4. The demand side searches service resources through theintelligent matching engine for supply and demand to findout those supply enterprises with matching service capa-bility, reasonable price, convenient traffic, and highercredit.

5. The demand side submits orders to the platform sys-tem and signs contracts with the supply side offline.Then, the platform system will accept these orders and

558 Int J Adv Manuf Technol (2014) 73:557–569

send them to the transaction coordination logic engineto execute.

6. The transaction coordination logic engine tracks andmanages orders and then guides both sides to com-plete the whole process including online payment,progress feedback, delivery, and acceptance as wellas transaction evaluation in accordance with businessrules. Enterprise users can log into the system toinquire the transaction progress and order status atany time or deal with those unfinished orders accordingto the prompt.

The platform workflow is shown in Fig. 2.

3 Common engines in the cloud manufacturing serviceplatform

According to the design requirements of the platform system,we focus on developing the intelligent matching engine forsupply and demand, transaction coordinating logic engine,and credit evaluation engine. These three engines reflect thecore idea of the cloud manufacturing service and directlydecide whether this cloud manufacturing service managementmode is suitable for small- and medium-sized enterprises todevelop new products in practice.

3.1 The intelligent matching engine for supply and demand

The intelligent matching engine for supply and demand willintegrate mass information of service resources, coordinateand optimize internal resources, and share and collaboratemanufacturing resources as well as manufacturing capacityso as to realize the intelligent service scheduling and serviceconfiguration management based on knowledge. The intelli-gent matching engine is mainly based on an efficient searchalgorithm to return function, data, and service related to inputconditions for users. With this intelligent matching engine,users will get results that match with their input conditions soas to obtain the best service support. This system mainlydepends on semantic search based on keywords to search forall kinds of service resources. It not only supports the tradi-tional keyword search but also sustains semantic search,through which we can access data with the same or similarsemantic, breaking the limitation of concept similarity.

Keywords are the words or phrases that can maximallygenerate the user-relevant information content. After login onthe homepage of this cloud manufacturing service platform,users will be able to search and query related manufacturingservice resources by inputting keywords. In order to improvethe hit rate and comprehensiveness of keyword searching, weneed to take a query expansion process, namely extending thequery search items according to synonyms, semantic

Cloud ManufacturingService Platform for

Small and Medium-sizedEnterprises

Service resource management

Cloud host

Control layer(Struts2.0)

View layer(JSP/Ajax)

Business logic layer(Spring2.0)

Persistencelayer

(Hibernate)

Cloud storage Cloud security

Intelligent matching enginefor supply and demand

Transaction coordinatinglogic engine

Credit evaluation engine

Main Common Engines

Main Function Modules

Business process management

Transaction task management

User information management

Credit evaluation management

Enterprise user

Fig. 1 Platform architecture

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implication words, semantic associative words, and semanticextension words. The basic idea of query expansion is that

considering the similarity between semantic concepts firstlyand then expanding the semantic based on similarity degree

Cloud ManufacturingService Platform for Small

and Medium-sizedEnterprises

Supply side(part B)Demand side(part A)

Enterprise userregistration

AuthenticationEnterprise user

registration

Publishingdemand resource

Intelligent matching engineof demand and supply

Publishing serviceresource

Orders managementPlacing an order

Transaction tasksmanagement

Accepting orders

Transactionfinished

Transactionevaluation

Transactionevaluation

Credit evaluation

true

false

Inputtingkeywords

Inputtingkeywords

Service resourcemanagement

Business processesmanagement

Customizingbusiness

processes

Searching for serviceresource

Initial creditevaluation

Servicefinished

false

Fig. 2 Platform workflowdiagram

560 Int J Adv Manuf Technol (2014) 73:557–569

according to the classification structure of service resources[42]. The computational process of semantic similarity is asfollows:

Assuming there are two words named as W1 and W2,if W1 has n concepts—S11, S12, L, S1n, and W2 has mconcepts—S21, S22, L, S2m, then the semantic similarityof W1 and W2 is the maximum value of each concept’ssimilarity, namely:

Sim W 1;W 2ð Þ ¼ maxi¼1…n; j¼1…m

Sim S1i; S2 j� � ð1Þ

where Sim(S1, S2) is the concept similarity, and the calculationmethod is as follows:

Sim S1; S2ð Þ ¼X

i¼1

4

βi ∏j¼1

i

Sim j S1; S2ð Þ ð2Þ

where S1 is the concept of the first word, and S2 is the conceptof the second word. βi(1≤i≤4) is an adjustable parameter, andβ1+β2+β3+β4=1, β1>β2>β3>β4, which reflects that theimpact decreases from Sim1 to Sim4 in order in the overallsimilarity.

In the above-mentioned intelligence semantic search algo-rithms, it is most important to establish a semantic dictionaryfull of synonyms, semantic implication words, and semanticextension words, which can be realized by setting up classifi-cation structure or domain ontology of service resources. Thesemantic dictionary will act as a bridge for keywords to matchwith the underlying data. Service resources are classified tostorage as industry, service type, and transaction type in thispaper, dividing into 20 sectors, six kinds of service types(designing service, processing service, logistics service,

procurement service, testing service, personnel service), andfour kinds of transaction types (commissioned processing,service outsourcing, leasing, bidding). According to this clas-sification, related semantic ontology has been established,forming a semantic lexicon. It will take a keyword semanticmatch from function, data, service quality, and executionwhen searching for services. As a result, those records thatmeet with search conditions will form a service recommenda-tion table for users to choose finally.

As shown in Fig. 3, a rough ontology was established forthe classification of manufacturing service resources. It isclear that CAD software is a semantic implication phrasewhich packages UG, AutoCAD, Pro/E, Inventor, and so on.When inputting CAD software to search, not only relatedinformation about computer-aided design is displayed but alsothose about Pro/E, and so on, will be presented. At thesame time, inverse three-dimensional equates to reversetechnology and reverse engineering, with these threeforming a synonym.

3.2 Transaction coordinating logic engine

In order to enable users to manage their orders conve-niently and realize communication and coordination oftransaction tasks in the cloud manufacturing service plat-form, a transaction coordinating logic engine has beendesigned [43]. This offers three main functions includingbusiness process management, transaction task guiding,and order tracking.

The business process management aims at helping enter-prises in need (demand side) customize and manage the

Owl:Thing

Manufacturingservice

resources

Humanresource

Logistics PurchasementDesigning Processing Testing

CADsoftware

DesignerCAE

software

Inverse three-dimensional

Reversetechnology

Reverseengineering

Hypeworks I-DEASUGInventor Catia Pro/EAutoCAD

equals

contains

Fig. 3 Service resource classification ontology

Int J Adv Manuf Technol (2014) 73:557–569 561

business processes in the cloud manufacturing service plat-form by themselves and add each signed order to a node in thebusiness process by service type, forming a vertical manage-ment mode of transaction tasks from top to bottom. Thetransaction task guiding takes every node in the businessprocesses as object. It then tracks the status of every order innodes and guides both sides to execute orders according to thebusiness processes, forming a horizontal management modeof transaction tasks from left to right. To different enterpriseusers, the longitudinal business process is different. A busi-ness process management tool has been designed, so users cancreate or modify business processes in the cloud manufactur-ing platform according to their own business needs. Similarly,the horizontal transaction process differs from each other if thetransaction type is different. In this paper, four kinds of trans-action types including commissioned processing, serviceoutsourcing, leasing, and bid are designed. Taking the situa-tion of multiple payments into consideration, now users canchoose the transaction type and the number of paymentsbefore signing orders by themselves, so the system can choosedifferent task guiding templates to guide transaction tasksautomatically.

With two management modes, the transaction coordinatinglogic engine can comfortably achieve the transaction taskmanagement in this platform, and its workflow is shown inFig. 4.

In the above transaction task management processes, weneed to track and maintain order status data in real-time so asto achieve cooperation between supply side and demand side.Only those unfinished orders will be managed by the transac-tion coordinating logic engine. During the transaction taskguiding process, operation permission of both sides willchange with the transferring of order status. When order statuschanges to the operation permission of demand side, thesystem will trigger a message to urge demand side to inputservice status information. While this information will rewritethe order status, at the same time, the operating permissionwill circulate to the supply side. The platform system realizescollaborative management of transaction tasks, through oper-ations to service status information, in turn from both sides. Itsdataflow is shown in Fig. 5.

3.3 Credit evaluation engine

The operational capacity of enterprise users is evaluated fromevaluationmanagement, transactionmanagement, credit spec-ulation management, and the way to divide levels, offering areference for both sides to select transaction partners. In viewof this, the initial credit rating is the fundamental arbiter ofcredit evaluation. The initial credit rating of enterprise userswill be calculated according to the information data providedby users and the system algorithm when users register into the

Tasknode(1)

Tasknode(2)

Tasknode(3)

Transaction task guiding (Horizontal management)

Contractsinged, waiting

for payment

Onlinepayment

Servicebegin

Serviceend

Contractsinged, waiting

for payment

Firstpayment

Servicebegin

Serviceend

Secondpayment

Contractsinged, waiting

for payment

Firstpayment

Servicebegin

Serviceend

Secondpayment

Delivery andacceptance

Thirdpayment

Serviceend

Serviceend

End

Tasknode(n)

Orderlist

Order list

Order list

Order list

Cloudmanufacturing

businessprocess

Business

pro ce ssm

an age me nt

(Vert ica l

man agem

ent )

Order status tracking (Different color means different status)

Blue:finished

task

Red:current

task

Gray:unfinished

task

Fig. 4 Transaction coordinating diagram

562 Int J Adv Manuf Technol (2014) 73:557–569

platform system. Each transaction evaluation result will thenbe accumulated to the business credit score.

Certification indicators of the initial credit evaluation in-clude quality, capital scale, collateral, solvency, environment,and conditions. “Quality” refers to payment records of enter-prise users, quality of management personnel, degree of man-agement practice, legal and tax operations, and product qual-ity. “Capital scale” means net assets, working capital, andsales revenue. “Collateral” is the pledged capital valuation.“Solvency” refers to the wealth status and operating condi-tions, while “Environment and Conditions” mean systemrisks, including politics, geography, environment, and so on.Taking all these certification indications into consideration, amultivariate discriminant model called “Arman” is used toachieve the authentication algorithm. The initial credit valuefollows the following expression:

Z ¼ 0:012X1þ 0:014X2þ 0:033X3þ 0:006X4þ 0:999X5;

ð3Þ

where X1 means the result of liquidity divided by total assets,X2 means the result of retained earnings divided by totalassets, X3 means the result of earnings before interest andtax divided by total assets, X4means the result of market valueof equity divided by total liabilities and their carrying values,and X5 means the result of sales divided by total assets.

The transaction comments from both sides will be collectedto a further cumulative credit score after the completion of a

transaction. Assuming there exists four evaluation indicatorsfor both the supply and demand side, then their correspondingweights are w1, w2, w3, and w4 (their sum is 1). If each indexscore values are s1, s2, s3, and s4, the credit score s userobtained from this transaction will follow the following ex-pression as the weighted average method, namely:

s ¼Xi¼4

i¼1

wisi ð4Þ

We will get a new overall credit rating score by weightaveraging this score and the existing credit rating score. Thecredit ratings are generally divided into five levels whichrespectively correspond to different score sections and creditrating of enterprise users depending upon their credit scores.

The algorithm flow chart of credit evaluation engine isshown below:

function processForm(form) {

var xf, hc1, hc2, hc3, hc4, hc5;

xf = hc1 = hc2 =hc3 =hc4 =hc5 = 0;

if (form.quality[0].checked == 1)hc1 = 1;

if (form.quality[1].checked == 1)hc1 = 2;

………

if (form.service[0].checked == 1)hc2 = 1;

if (form.service[1].checked == 1)hc2 = 2;

………

if (form.deliveryspeed[0].checked == 1)hc3 = 1;

if (form.deliveryspeed[1].checked == 1)hc3 = 2;

………

if (form.attitude[0].checked == 1)hc4 = 1;

Based on the combination of the Arman model calculationand the credit scoring mechanism in this paper, an interface forregistration and initial credit assessment and evaluation hasbeen developed. Enterprise users are required to fill in trueinformation and data when registering so as to be calculatedand audited. In order to prevent enterprise users offering falsebusiness information, they are required to submit an annualreport about business conditions during the last year and acredit report issued by a qualified third-party assessment unitfrom the same province, so management personnel of thisservice platform can readily audit enterprise qualification.

3.4 Relationship between these three engines

What the intelligent matching engine for supply and demanddoes is to offer efficient semantic searching based on a se-mantic dictionary and then matching-up both parties automat-ically according to the result of the semantic search and theusers’ history credit records. This will be a convenient process

registrationID <pk>userInfoID <fk>…….

resourceID <pk>userInfoID <fk>…….

transaction taskID<pk>orderID <fk>order status <fk>…….business processID <pk>

transaction taskID <fk>orderID<fk>…….

Registration

Service resource list

Transaction status list

orderID <pk>service resourceID <fk>userInfoID<fk>…….

Order list

Placing an order

userInfoID

Business process list

resourceIDuserInfoID

taskID

Enterpriseuser

registering Publishingserviceresource

Executingtransaction

tasksCustomizingbusinessprocesses

orderID

Fig. 5 Data flow diagram

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for users to hunt for suitable and reliable partners. After theintelligent matching, a transaction will duly start with theonline and offline contracts. Now, the transaction coordinatinglogic engine will track the state of each node and remind theuser what to do according to their business processes. Thisengine holds the transfer of transaction data, ensuring thesmooth and efficient conduct of cloud manufacturing. Lastly,users can evaluate their partners through the credit evaluationengine. Credit assessment and credit rating from the creditevaluation engine will directly affect the intelligent matchingof service resources published by both sides. Generally speak-ing, the credit evaluation data affects the intelligent matchingresult, while the matching result holds the service and opera-tion mode of transaction; the behavior during the transactionprocess will decide users’ credit rating. These three enginesrun through the whole process of cloud manufacturing,starting with service resource searching and ending in creditevaluation, involving order management, business manage-ment, and transaction management, and covering all aspectsof the traditional manufacturing, so they are sufficient tosupport the efficient operation of the cloud manufacturingservice platform for SMEs. The relationship between themmay be described in Fig. 6.

4 Development of prototype system

After classifying the mass service resources as industry,service types, and transaction types and establishingsemantic ontology, the prototype system of cloudmanufacturing service platform for SMEs—cloudManusystem—was developed.

4.1 Main interface

In order to display all kinds of service resources and supply–demand information dynamically, dynamic page design

techniques, such as Ajax, Flash and Struts2 Tag and Division,Cascading Style Sheets, and CSS style, are used to design thefront-end interface, and the final interface is shown in Fig. 7.

4.2 Semantic search results

Inverse three-dimensional equates to reverse technology andreverse engineering. In other words, the three are synonyms.Even though there exists great differences between them inconcept, they are the same in semantic terms, and we have setthese synonyms into the semantic dictionary. So, whensearching for “inverse three-dimensional,” service resourcesabout reverse technology also will form a list for users tochoose as Fig. 8.

4.3 Back-end interface

As a management system in cloud manufacturing, the back-end participates in managing data, including user informationand resource information, directly. Through the back-endsystem, we can take creating, requiring, updating, and deleting(CRUD) in the cloud database to monitor and manage theoperation of the whole system. The service resource publishinterface is shown in Fig. 9.

5 Case study

In order to verify the reliability and universality of the cloudmanufacturing platform and assess the performance and effectof these three engines, we took a verification application in theautomobile and motorcycle industry.

5.1 Background

Xingguo Lamps Co. Ltd in Chongqing mainly engages inresearching and developing as well as producing car and

Credit records and credit rating

Service

type and operatio

n

mode

Behavior from both sides duringtransaction

Intelligent matchingengine for supply

and demand

Transactioncoordinating logic

engine

Credit evaluationengine

Semantic of supplyand demandinformation

Fig. 6 Relationship betweenthree engines

564 Int J Adv Manuf Technol (2014) 73:557–569

motorcycle lamps, rearview mirrors, plastic parts, and generalmechanical components. In recent years, with the marketcompetition aggravating and customer demands increasingly

showing diversity and individuality, enterprise has to developmore and more new products to gain market competitiveness.However, because of the pool of technology, equipment,

Fig. 7 Main interface of cloudManu system

Fig. 8 Semantic search results

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personnel, and funds, it is difficult for this company to finishthe rapid development of some products by itself alone, sothey need to cooperate with other related companies or getsome other external resources. In this case, the production ofmotorcycle lamps is taken as an example. We took an inves-tigation on motorcycle lamp production processes includingoriginal reversing, retrofit designing, mold designing, moldmanufacturing, sample trailing, sample detection, and massproduction, and finding that these services are currentlyscattered in different places and most of these companiescan only offer parts of the required services for automobilelamp development. In addition, the development of motorcy-cle lamps from reverse engineering, research, and design tomanufacturing is a continuous and consistent process. So,only by the cloud manufacturing service platform and otherrelated network manufacturing measures can we complete thenew product development and manufacturing of motorcyclelamps in efficient and timely manner.

5.2 Business process

After visiting more than 70 corresponding enterprises inChongqing City, we found seven companies which are ableto offer inverse three-dimensional, retrofit designing, molddesigning, mold manufacturing, samples trailing, sample de-tection, and mass production service, respectively. They wereregistered into the cloudManu system, and we published therelevant resource information. Then, we conducted a confir-matory application by one by one execution to complete thewhole process of motorcycle lamp production in accordancewith the business process. Figure 10 shows the motorcyclelamp manufacturing business process of Xingguo and thosecompanies providing corresponding services.

5.3 Transaction process

Step 1 Register: Eight enterprises submit some basic infor-mation including enterprise name, contact informa-tion, and so on to register in the front-end.

Step 2 Login: As long as the user inputs the correspondingaccount details and password at the entrance of thefront-end, they will be able to enter into thecloudManu system.

Step 3 Information distribution: There exists an informationdistribution management module on the back-end,and all kinds of service resources need to be pub-lished from here. Therefore, all users are required tojump into the back-end to release their supply–de-mand information from the background login en-trance on the front-end. At the demand side, Xingguowill release out a series of information, such as whatkind of service they need and their demands forservice, then this demand information will displayon the demand information module on the front-end. Meanwhile, at the supply side, another sevenenterprises will publish out their supply informationincluding what service they can offer and their re-quirements for the demand side. At the same time,this service information will be seen on the supplyinformation module on the front-end.

Step 4 Ordering: Each supply enterprise can obtain demandinformation from Xingguo by entering related key-words into the service research tool or hunting fromthe supply information module. To Xingguo, theyalso can get supply information about all aspects inthis way. Both sides will investigate and understandeach candidate company carefully by viewing their

Fig. 9 Service resource publish interface

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qualification and credit evaluation and other basicinformation. They will sign an order online if enter-ing into cooperation intention. Many items, such asservice type, payment number, order number, trans-port method, and delivery time as well as some otherspecial requirements will be written in the contractaccording their actual conditions. In addition, theywill sign a formal contract offline. If needed, they willmodify or adjust contract contents during the orderfulfillment process after discussion. All enterprisesare allowed to publish more than one message andsign multiple orders at the same time.

Step 5 Transaction: To any contract, Xingguo is required topay a part of the funds to the supply side as depositbefore production. Thereafter, Xingguo will detectthose periodic service resources provided by supplysides as the contract contents. If Xingguo is satisfiedwith the semi-manufactured goods supply that enter-prises provide or those service resources meet theirrequirements, the next production will begin one byone to complete the whole transaction as the order.

Step 6 Evaluation: Each transaction will end up with thedelivery of complete product and the payment ofthe full amount. With the closing of transaction, both

1.Inverse three-dimensional

2.Retrofit design

3.Mold design

4.Moldmanufacturing

5.Sample trailing

6.Sample detection

7.Mass production

Qualified or not

true

Modificationreview

success

false

failure

Mold review

success

failure

Demand enterprise: Xingguo Lamps Co.LtdInformation: (1) Requirement specification

(2) Photos of original

Supply enterprise: Haiteke Promotion CenterInformation: (3) Cloud data diagram

Supply enterprise: Chongqing Institute ofmanufacturing engineeringInformation: (4) UG designing diagram

Supply enterprise: Zhitai Mold CompanyInformation: (5) Mold designing diagram

Supply enterprise: Xinyuan Mold CompanyInformation: (6) Diagrams of mold

Supply enterprise: Boji Machine CompanyInformation: (7) Diagrams of samples

Supply enterprise: Liuhe Testing CompanyInformation: (8) Testing report

Supply enterprise: Shizhi Machine CompanyInformation: (9) Diagrams of mass production

Fig. 10 Motorcycle lampmanufacturing business process

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sides are able to take an evaluation for their partnersby scoring or text description according to the qualityof products, service attitude, and other issues duringthe transaction process. These transaction evalua-tions will become part of the history record and affectthe credit rate directly.

5.4 Supplementary specification

It is impossible for Xingguo to cooperate with the other sevencompanies in the cloudManu system at the same time, espe-cially in the transaction process, because each product processrelies on the data, or parts from the last process, and we shouldfollow the motorcycle lamp manufacturing and processingprocess seriously. Of course, Xingguo needs to finish contractsigning with the seven supply enterprises and make contin-gency plans, preparations, as well as scheduling work beforethe first processing operation—inverse three-dimensional istaken into action actually. Only by this way can we enter thenext process immediately after the completion of the lastprocess so as to ensure the continuity and timeliness ofproduction.

5.5 Effect analysis

In the verification application, the whole platform ran smooth-ly with no error, integrating distributed manufacturing serviceresources well. In addition, the cost of the development andproduction of motorcycle lamps has halved, and the develop-ment cycle has shortened to a third of the traditional one. Withthe intelligent matching engine, Xingguo can find resourcesand reliable partner they need in less than 3 s. The transactioncoordinating logic engine helps them to customize or choosebusiness processes and tells them what they should do eachtime, leading the transaction task smoothly. The credit evalu-ation engine makes it easy for Xingguo and other companiesto evaluate their partners by grading or text comments.

6 Conclusions

Currently, the cloud manufacturing service platform for SMEsis only a manufacturing service platform mainly providingsupport for the management of transactions and tasks. It willevolve into a large virtual manufacturing enterprise with theimprovement of the intelligent matching engine for supplyand demand and the ability to manage service resources in thenext stage, and after enterprise users submit manufacturingservice demands, it will complete a series of complexmanufacturing business processes including enterprisematching and transaction management automatically. For thedemand side, the supply side is transparent and what they are

faced with is a cloud manufacturing service provider integrat-ed with resources from all the parties. There is no doubt thecloud manufacturing service platform will be acknowledgedby manufacturing industry due to its convenience, simplicity,and powerful functions. It will integrate mass manufacturingresources from small- and medium-sized enterprises and com-bine the advantages of each enterprise to ultimately improvethe competitiveness to survive for SMEs.

In the next stage, we will further improve the manufactur-ing service resource ontology library, focusing on studyingrelated engines and adaptation tools. We will devote time topromoting the cloud manufacturing service model so as toprovide better services for small- and medium-sizedenterprises.

Acknowledgement This work is partially supported by the ChineseNational Hi-Tech. R&D Program under grant 2011AA040504. Withinthis group-project community, several PIs, and Co-PIs give valuablesuggestions on this work. The authors would like to give special thanksto Professors Biqing Huang, Chao Yin, and Fei Tao.

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