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This article was downloaded by: [Dokuz Eylul University ]On: 06 November 2014, At: 02:37Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
International Journal of Computer IntegratedManufacturingPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tcim20
Towards a wisdom manufacturing visionXifan Yaoa, Hong Jina & Jie Zhanga
a School of Mechanical and Automotive Engineering, South China University of Technology,Guangzhou, Guangdong 510640, ChinaPublished online: 27 Oct 2014.
To cite this article: Xifan Yao, Hong Jin & Jie Zhang (2014): Towards a wisdom manufacturing vision, International Journal ofComputer Integrated Manufacturing, DOI: 10.1080/0951192X.2014.972462
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Towards a wisdom manufacturing vision
Xifan Yao*, Hong Jin and Jie Zhang
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong 510640, China
(Received 24 November 2013; accepted 24 September 2014)
Manufacturing enterprises are socio-technical systems, which necessitate overall integration of not only the technicalaspects from devices in shop-floor to enterprise resources planning vertically and from product order to shipmenthorizontally, but also the social aspects such as human interactions and consumers’ intentions. Moreover, there is a growingneed in the use of knowledge in enterprise contexts. To meet such needs, wisdom manufacturing (WM) is emerging withadvances in the Internet and manufacturing as well as intelligence. In this paper, the most recently developed manufacturingmodels such as smart manufacturing (SM)/smart factory (SF), cloud manufacturing (CM) and socialised enterprise(SE)/Enterprise 2.0 are analysed, and a WM vision is presented to aggregate SM, CM, SE and existing intelligentmanufacturing (IM) that are complementary to each other. Then pathways towards the WM vision are addressed inrelationship to knowledge, intelligence, creativity/innovation, learning and wisdom, especially from DIKW (data-informa-tion-knowledge-wisdom) and semiotic perspectives as well as from the web evolution. And wisdom and realisation towardsthe WM vision are investigated. Finally, a case study is used to illustrate the WM vision landscape followed by aconclusion. As a consequence, things, computers and humans, ubiquitous, artificial and collective intelligence, as well asexplicit and tacit knowledge, are integrated as a whole in the WM.
Keywords: web-based manufacturing; wisdom manufacturing; socio-technical systems; CIM; integration; pragmatics
1. Introduction
Since the concept of Computer integrated manufacturing(CIM) was proposed by Harrington (1973), tremendousefforts have been put into it. Enterprise integration (EI)has been a domain of research developed since 1990s asthe extension of CIM. The realisation of CIM requiresintegration technologies. The Internet is such an integra-tion means, and has become more and more important inCIM/EI over the past decades. Dramatic changes in theInternet are occurring and will continue to have a verystrong impact on CIM/EI. In Europe and other parts ofthe world, the Future Internet (FI) is becoming a strate-gic focus of research. The Framework Programme (FP)of the European Union states that Future networkedsociety will be supported by four pillars as depicted inFigure 1: (1) Internet by and for People (IbfP), (2)Internet of Contents and Knowledge (IoCK), (3)Internet of Things (IoT), and (4) Internet of Services(IoS) (Papadimitriou 2009). Undoubtedly, the FI willalso affect CIM/EI greatly. In fact, by drawing an ana-logy for future networked manufacturing, the so-calledwisdom manufacturing (WM) (Yao et al. 2014) wasproposed, as shown in Figure 2. This paper will furtherelaborate the WM from the DIKW (data-information-knowledge-wisdom) and the semiotic perspectives aswell as from the Web evolution, and address wisdomand realisation towards the WM.
Although CIM systems were emerging towards fullyautomated manufacturing systems which could be highlyflexible, reconfigurable, reusable, and interoperable aswell as autonomous and intelligent, however, such a pro-mise has not fulfilled, especially for integrating real-timemanufacturing intelligence and active management aboveand across the control systems of an entire productionoperation (Davis et al. 2012). The proposed WM visionin this study aims to illustrate how to reach this goal byusing available technologies, especially from the FI, andprovide a new framework for the future of CIM/EI.
2. Related manufacturing models recently developed
By means of computers and the Internet, CIM/EI tried tolink system functionalities such as design, analysis, plan-ning, purchasing, inventory control and distribution, withfactory floor functions such as materials handling androbotic control, providing direct control and monitoringof all the operations. However, the realisation of an enter-prise-wide integration within and across all the levels fromenterprise level (ERP level) to manufacturing level (devicelevel) is not adequately reached (Panetto and Molina2008). Although Manufacturing Execution System(MES) has emerged to bridge the gap between the shopfloor and the ERP systems that run in the back end, theyhave to be tailored to the individual group of devices and
*Corresponding author. Email: [email protected]
International Journal of Computer Integrated Manufacturing, 2014http://dx.doi.org/10.1080/0951192X.2014.972462
© 2014 Taylor & Francis
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protocols that exist on the shop floor (de Souza et al.2008). Even such an enterprise-wide integration exists,and business applications in the ERP level offer theirfunctionalities as services, but field devices communicat-ing with different protocols (e.g., Modbus, RS 232) anddifferent drivers will lead to tightly coupling integration(hard-wiring) with inefficiency and poor interoperability.As shown in Figure 3, different signals at the lowestdevice level are via message upwards to business services.As a consequence of such inadequate integration andinteroperability of the different enterprise levels, the estab-lishment of an enterprise-wide knowledge and learningcycle is hindered, and the control of enterprise value-creation processes is not enhanced (Grauer et al. 2010).
Advances in IoT along with Service-OrientedArchitecture (SOA), cloud computing and other technolo-gies enable the overall integration of manufacturing func-tions from design to product shipment and shop-floor toERP. Such related work is addressed as below.
2.1. Smart manufacturing/Smart factory
With the emergence of IoT, physical integration is inprogress in the CIM/EI, which results in the so-calledSmart factory (SF) (Zuehlke 2010), Smart manufacturing(SM) (SMLC 2011), or Ubiquitous enterprise (UE) (Kong,Jung, and Park 2009).
SF is defined as a factory that context-aware assistspeople and machines in the execution of their tasks, whichenables the real-time collection, distribution and access ofmanufacturing relevant information anytime and anywhere(Lucke, Constantinescu, and Westkämper 2008). SF isalso called as a factory-of-things (Zuehlke 2010) toemphasise the role of the IoT in enterprises, which uses
Future Networked Society
Future Network Infrastructure
Internet of Services
Internet of Things
Internet of Contents
and Know
ledge
Internet by and forpeople
Figure 1. FI overview.
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Figure 2. WM overview.
......
MES-Level
Device-Level
ERP-Level
from signals
via messages
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Figure 3. Hierarchical EI.
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ubiquitous/pervasive computing technologies and toolslike UE (Kong, Jung, and Park 2009).
The Smart Manufacturing Leadership Coalition(SMLC) defines ‘Smart manufacturing is the intensifiedapplication of advanced intelligence systems to enablerapid manufacturing of new products, dynamic responseto product demand, and real-time optimisation of manu-facturing production and supply chain networks’ (SMLC2011). However, the application of artificial intelligence(AI) in manufacturing is not new. In fact, such applica-tions had resulted in the so-called Intelligent manufactur-ing (IM) (Oztemel 2010) long before SM/SF/UEappeared. Historically, data mining methods and algo-rithms have been used in various manufacturing/servicedomains (Choudhary, Harding, and Tiwari 2009). In mostenterprises, rule-based system (RBS) or expert systemshave been used. Although knowledge-based, multi-agentor holonic systems have been existed in IM (Oztemel2010), they have not made significant inroads in manu-facturing plants in use today, and the lack of widelyaccepted standards and only involving part of the manu-facturing landscape resulted in a rigid patchwork of tech-nology islands with inefficiency and poor scalability(Jammes and Smit 2005). Furthermore, IM is facingsemantic interoperability (Jardim-Goncalves et al. 2011).
IoT bridges the gap between the cyber and physicalworlds, and the use of IoT in enterprises has led to theemergence of SM/SF, which strives to integrate productdesign all the way to the manufacturing floor for global
optimisation. In such a way, accurate context data can becaptured via smart sensors, and then can be used in real-timevisualisation of business processes, and vice versa, businessinformation can be downwards to device level. So decisionsmade on the virtual side can be reflected on the real envir-onment, and SM can optimise energy use, reduce carbonfootprints and promote environmental sustainability in addi-tion to cost and time savings (SMLC 2011).
As a consequence, SM/SF/UE makes CIM/EI shiftfrom the central hierarchical control as shown inFigure 3 to the decentralised item-level control as illu-strated in Figure 4. While SOA is becoming the de-factostandard to connect to enterprise applications, there alsohas been a move towards viewing device functionality as aservice (Haller, Karnouskos, and Schroth 2009). As SOAservices are loosely coupled and interoperable (Valipouret al. 2009), they collaborate with SM/SF to make CIM/EImove away from isolated stand-alone hardware and soft-ware solutions towards more cooperative ones (Spiesset al. 2009), from a rigid and centralised way to a flexible,decentralised and self-organised one, and change the waywe design, deploy and use services at all layers of aCIM/EI system including device, workshop, plant, orcompany level or even between collaborating organisa-tions. As such, the integration of back end applicationswith field devices is enabled, and the service-based factoryis empowered (de Souza et al. 2008). SOA and cloudcomputing, i.e., IoS in a broader sense, are further intro-duced to manufacturing as below.
ERP - Enterprise Resource Planning;
Servicefrom services
Enterprise Service Bus
Business process
to servicesERP/Enterprise applications
Service Service Service
WIP WIP
IoT
Tools,Materials,
parts
IoT - Internet of Things; WIP - Work In Process
Figure 4. Decentralised control for SM/SF.
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2.2. Cloud manufacturing: a typical IoS-orientedmanufacturing
CIM/EI is moving towards service-oriented integration(SOI) as SOA has been becoming the preferred integrationapproach (Guinard et al. 2010). Web Services are emer-ging as the de facto standard for SOA implementations(Mecheri and Souici-Meslati 2010). The Web Servicesarchitecture, as shown in Figure 5, defined SOA as anarchitecture modelled around the service requestor (con-sumer), the service provider and the service registry (Erl2005). SOA can be realised with the help of standardtechnologies such as WSDL (Web Service DefinitionLanguage), UDDI (Universal Description, Discovery, andIntegration), SOAP (Simple Object Access Protocol), andBPEL (Business Process Execution Language). All thosestandards use the eXtensible Markup Language (XML) asa means of standardising data formats and exchangingdata.
With the developments of SOA and other servicetechnologies, emerging is the concept of Internet ofServices (IoS) (Moreno-Vozmediano, Montero, andLlorente 2013; Schroth and Janner 2007). The IoSdescribes an infrastructure that uses the Internet as amedium for offering and selling services, where cloudcomputing plays an important role in enabling on-demandprovisioning of services.
Cloud manufacturing (CM) is a service-oriented net-worked manufacturing model (Li et al. 2010), which treatseverything as a service by borrowing the concept of cloudcomputing, and can be defined as ‘a model for enablingubiquitous, convenient, on-demand network access to ashared pool of configurable manufacturing resources thatcan be rapidly provisioned and released with minimalmanagement effort or service provider interaction’ (Xu2012). As all manufacturing resources in CM are encap-sulated into cloud services, e.g., Design as a Service,
Manufacturing as a Service. As such, users can accessall manufacturing resources on demand. The introductionof IoT and IoS in manufacturing results in a layeredarchitecture as shown in Figure 6: (1) Manufacturingresource layer: providing various physical manufacturingresources and capabilities of the shop floor, such as man-ufacturing equipment resources, computing resources,software resources, material resources, and so forth; (2)Virtual resource layer: responsible for the virtualisation ofmanufacturing resources and capabilities; (3) Servicelayer: defining service interfaces, and encapsulating theresources as CM services (including atomic services andcomposite services), and publishing services to registrycentres; (4) Business process layer: according to users’requirements, coordinating, e.g., using BPEL to orches-trate, the CM services into business processes; (5) Serviceintegration layer (service bus): providing the intermediary,routing and transport capabilities for services interactionand communication between service requesters and provi-ders; (6) Infrastructure service layer: providing servicesmonitoring and the management of security, quality ofservices, and so forth; (7) Cloud service managementlayer: providing the management of virtualisation andservitisation of cloud manufacturing resources, and offer-ing users with on-demand services; (8) Application layer:providing a portal for users constructing their application,and accessing CM services.
The left side of Figure 6 reflects the relationshipbetween consumers and providers similar as that inFigure 5, and layers 2–7 illustrate the integration of SOAand cloud computing (i.e., IoS) via IoT extending cloudcomputing resources to the physical-world manufacturingresources at the bottom layer.
With the introduction of IoT and IoS, CM faces notonly horizontal collaboration directly between devices, butalso vertical collaboration between devices, applicationsand people. This places new demands on CIM/EI systemssuch as event processing and Knowledge management(KM). The authors respond to this by making use of IoKand IbfP.
2.3. Socialised enterprise
The Internet is one of the most important informationexchange techniques in CIM/EI, especially in networkedmanufacturing. With the Internet evolution to Web 2.0(O’Reilly 2005), McAfee (2006) coined the termEnterprise 2.0, which was defined as ‘the use of emergentsocial software platforms within companies, or betweencompanies and their partners or customers’. It is shownthat Web 2.0 incorporates a social philosophy that iscomplementary to the technology-focused SOA philoso-phy (Schroth and Janner 2007). By utilising Web 2.0 toolssuch as Wikis, blogs, tagging and social book-marking,new and ingenious methods of social interaction across
Bind
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DDI
UDDI
SOA
P
Serviceregistry
Servicerequestor
Serviceprovider
SOAP - Simple object Access Protocol; UDDI - UniversalDescription, Discovery, and Integration; WSDL - Web ServiceDefinition Language; XML - eXtensible Markup Language
XML
Figure 5. Web Services architecture.
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geographic borders and industry silos are being created(IBM 2007).
In social networking or IbfP, humankind is offered anunprecedented level of interactivity and consumers can bepart of the creative flow of content and process, whichfosters the emergence of prosumers enhancing a betterinvolvement of customers in the product lifecycle(Papadimitriou 2009). This results in the so-called socia-lised enterprise (SE) that uses IbfP in enterprise context tohelp employees, customers and suppliers collaborate,share, and organise as Enterprise 2.0 (E 2.0) does.
With Web evolution to Web 3.0 (Agarwal 2009),semantic technologies, knowledge exchange and data gen-erated by machines/users are substantial, and it is manda-tory to develop intelligent methods for knowledgecollection processing and presentation to handle and ben-efit from the huge amount of information being availablenow or in future (Papadimitriou 2009). The same situa-tions will occur in WM, too.
3. Towards a WM vision
3.1. A WM vision
Each of the above manufacturing models is fragmented andlargely carried out in relative isolation or in parallel, andrepresents one aspect of enterprise demands. However, thosemodels are inter-related and complementary, so it is neces-sary to integrate those models as in a holistic and integratedmanner.
In future, the everyday life and work will be supportedby new convergent services of the FI that are availableubiquitously and can sense and react to the physical world(Papadimitriou 2009). Similar to such finding in EC FIprojects, CIM/EI will shift towards the future vision: theso-called ‘Wisdom manufacturing’ (WM) (Yao et al.2014).
The future of CIM/EI will be more heavily based onthe Internet. The advent of IoS, IoT, IoK and IbfP willtransform the CIM/EI into an all-encompassing network ofhumans, computers, knowledge, services and things. Avision for the WM is illustrated in Figure 7, where thelayers 1–7 are similar to those as shown in Figure 6, butfor layers 3 and 4 there are some differences due to event-driven SOA (EDSOA) adoption. A big difference appearsin the Application layer (layer 8) in that it integratesstakeholders (e.g., consumers/users, providers/ manufac-turers/vendor, and brokers) into the WM system via IbfP.In such a community with stakeholders as an integral partof the system, consumers/users can easily construct theirapplications and involved in the product/service life cycle.Compared with Figure 6, more than three layers, i.e.,layers 9–11, are added, that is, Complex event processing(CEP) layer (layer 9): which provides simple event pro-cessing and complex real-time event processing; Businessintelligence (BI) layer (layer 10) and Semantic Web layer(layer 11), which transform raw data into meaningfuland useful information, knowledge and/or wisdom forbusiness purposes, enable business-process management
S erv
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Manufacturingdevice resources Human resources
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Portal B2B (Business To Business)
Users
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Virtual resource layer
Service layer
Business process layer
Application layer
Bind
DiscoverPu
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Service integration layer (Service bus)
Infrastructure service layer
Cloud service m
anagement layer
Internet of Things
Internet of Services
Figure 6. Layered architecture of a cloud manufacturing with IoT extension to the physical world.
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(BPM) effectively, and monitor and manage businessactivities.
3.2. Wisdom concept
The definitions of wisdom are controversial. For example,Wikipedia (2013) defines ‘wisdom is a deep understandingand realisation of people, things, events or situations,resulting in the ability to apply perceptions, judgmentsand actions in keeping with this understanding’;Oxforddictionaries (2014) defines wisdom as ‘the qualityof having experience, knowledge and good judgement’.Rowley (2006) defines ‘wisdom as the capacity to put intoaction the most appropriate behaviour, taking into accountwhat is known (knowledge) and what does the most good(ethical and social considerations)’. Historically, wisdomwas ‘studied’ through different lenses: religious, scientific,practical, moral, experiential and societal, with emphasison different aspects of the same phenomena (Karelitz,Jarvin, and Sternberg 2010). After examining definitionsof wisdom published in peer-reviewed journals, Bangen,Meeks, and Jeste (2013) have pointed out that five sub-components of wisdom most commonly cited in orderfrom high to low are: (1) decision making/knowledge,(2) prosocial attitudes, (3) self-reflection, (4) acknowledg-ment of uncertainty and (5) emotional homeostasis.
As such, wisdom is often discussed linked with knowl-edge, which, in turn, is linked with information, which, inturn, is linked with data. The ‘Wisdom Hierarchy’(Rowley 2007), shown in Figure 8a, also known variously
as the ‘DIKW Pyramid’, the ‘Knowledge Hierarchy’, the‘Information Hierarchy’, and the ‘Knowledge Pyramid’,refers loosely to a class of models for representing pur-ported structural and/or functional relationships betweendata, information, knowledge, and wisdom, where infor-mation is defined in terms of data, knowledge in terms ofinformation, and wisdom in terms of knowledge. Ackoff(1989) argues that ‘Data are defined as symbols thatrepresent properties of objects, events and their environ-ment; Information is contained in descriptions, answers toquestions that begin with such words as who, what, whenand how many; Knowledge is know-how, and is whatmakes possible the transformation of information intoinstructions’, and ‘Wisdom adds value, which requiresthe mental function that we call judgement’. Throughreviewing the definitions of data, information, knowledgeand wisdom articulated in the selected 15 textbooks ininformation systems and knowledge management, Rowley(2007) states: ‘these definitions [of data] are largely interms of what data lacks’; ‘information is defined interms of data, and is seen to be organised or structureddata’; and ‘knowledge might be viewed as a mix ofinformation, understanding, capability, experience, skillsand values’. Although wisdom is linked with knowledge,the relation between them is complex. Knowledge isnecessary but not sufficient for wisdom. ‘To be wise onemust be knowledgeable, but being knowledgeable doesnot make one wise’ (Liew 2013).
Wisdom, on the other hand, is related to both intelli-gence and creativity (Sternberg 1985). Like definitions of
Service integration layer (Service bus)
Infrastructure service layer
Cloud service m
anagement layer
Semantic W
eb layer
Event processing layer
Business intelligence layer
Manufacturingdevice resources
Manufacturingdevice resources
Manufacturingresource layer
Service layer
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Applicationlayer
(Atomic service and Composite Service)
WikiRSSSNS
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EventEvent
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Internet by and for people
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Figure 7. A WM vision.
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wisdom, definitions of intelligence differ. Meystel andAlbus (2000) define ‘intelligence is to perceive the envir-onment in which the system is operating, to relate eventstaking place around the system, to make decisions aboutthose events, to perform problem solving and generate therespective actions and control them’. To such a need, Liew(2013) inserts one more level, intelligence, betweenknowledge and wisdom, extending the DIKW to DIKIW.What is more, Targowski (2010a) links intelligence andcreativity to define wisdom as ‘the intelligent ability tochoose an appropriate (right/meaningful) concept and dosomething with it in the right time, space, and group’, andas ‘the last stage of cognitive processes; applying evolvingintelligence-oriented tools and skills in the broad context;and driven by creativity, emotions, intentions, and motiva-tion’. Baltes and Kunzmann (2003) argue that wisdom isnot primarily a cognitive phenomenon as there coexistcognitive, emotional and motivational characteristics, anddefine wisdom as ‘expert knowledge and judgement aboutimportant, difficult and uncertain questions associatedwith the meaning and conduct of life’.
In fact, several or all subcomponents of wisdom should beholistically integrated in order to be wise because wisdom hasa complex, multidimensional characteristic with the wholebeing greater than the sum of its parts (Bangen, Meeks, andJeste 2013). For example, Sternberg (2001, 1998) views wis-dom as the synthesis or balance of intelligence (the thesis)with creativity (the antithesis); Birren and Fisher (1990) regardwisdom as ‘the integration of the affective, conative, andcognitive aspects of human abilities’. Subcomponentsrequired will depend on the context or culture, and wisdomcan be found on different levels such as individual, organisa-tion, and society (Goede 2011). In the global era, wisdombecome increasingly required at organisation level (Zeleny2006), or society level (Goede 2011). However, conventionaldiscussion of wisdom is confined within one’s life context(Karelitz, Jarvin, and Sternberg 2010), and resulting findingsoffer limited support for wisdom manufacturing, which isviewed as an integrated socio-technical system where both
social and technical aspects must be considered at the sametime. Fortunately, recent advances in organisational wisdom(De Meyer 2007), Wisdom Web of Things (Zhong et al.2013), decision informatics (Tien 2003) and open innovation(Chesbrough 2003), for example, provide further support forwisdom manufacturing.
Like knowledge and information, wisdom will becomea manageable resource for organisations (Zeleny 2006).De Meyer (2007) defines organisational wisdom as ‘thecumulative and integrated knowledge that can enable theorganisation to make the necessary and strategically cor-rect choices to enhance the productivity of the creativetransformation function, as well as the proper orientingand executing of that function, in the face of a high levelof uncertainty about the likely consequences of the deci-sions’. Different from De Meyer’s emphasis on innova-tion, Bierly III, Kessler, and Christensen (2000) putemphasis on organisational learning and term organisa-tional wisdom as ‘the judgment, selection and use ofspecific knowledge for a specific context’ in the DIKWframework, where wisdom is defined as the ability to bestuse knowledge for establishing and achieving desiredgoals and learning wisdom as the process of discerningjudgments and action based on knowledge; and the orga-nisational wisdom involves both the collection, transfer-ence and integration of individuals’ wisdom, and the useof institutional and social processes for storage. Tien(2003) has proposed so-called decision informatics fromthe systems engineering perspective, where data, informa-tion, knowledge and wisdom are used to make ope-rational, tactical, strategic and systemic decision,respectively. Adapted from Bierly III, Kessler, andChristensen (2000), Tien (2003) and Zeleny (2006),Table 1 illustrates the distinctions between data, informa-tion, knowledge, and wisdom with emphasis on learningprocess, decision making and purpose.
Creativity is a phenomenon where by something newand valuable is created, and in large part is a decision. Asthe successful implementation of novel and useful ideas
Com
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amm
abili
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CIM - Computer Integrated Manufacturing; EI - Enterprise Integration; IM - Intelligent Manufacturing; WM - Wisdom manufacturing
(a) Wisdom hierarchy (b) Basic semiotic levels (c) Manufacturing model evolution
Data
Information
Knowledge
Wisdom
MeaningApplicabilityTransferabilityValueHuman InputStructure
Syntactic
Semantic
Early CIM/EIDigital manufacutring
IM
WMPrag-matic
Figure 8. Manufacturing models corresponding to the DIKW and semiotic levels.
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(i.e., creativity) for the market or society, innovation iscritical to the development of sustainable competitiveadvantages for manufacturing enterprises. The introduc-tion of open innovation in wisdom manufacturing will bediscussed in Section 4. Therefore, innovation is focusedinstead of creativity for discussing wisdom in manufactur-ing context.
Data, information/ knowledge and wisdom in theDKIW (Figure 8a), respectively correspond to 3 semioticlevels: syntactic, semantic and pragmatic (Figure 8b),which originated from Morris’ work (Morris 1938).Historically, manufacturing models such digital manufac-turing and IM have been proposed. IM is knowledge-based manufacturing, and wisdom is located on knowl-edge and at the pinnacle of the wisdom hierarchy, so thefurther development of IM will result in the birth of WMin the perspective of evolution. While from the semioticviewpoint, IM is semantics-based manufacturing, so itsfurther evolution will result in pragmatics-based manufac-turing, i.e., the WM, as shown in Figure 8c.
Summarising existing research on the DIKW, Rowley(2007) confirms that there is a link between knowledgeand wisdom, and that meaning, applicability, transferabil-ity, value, human input and structure increase upwards inthe hierarchy, while computer programmability reverses,as shown in the left side of Figure 8a. This provides ususeful insights for designing a WM system, where humansare presumably required at wisdom level instead of com-puters due to the programmability issue.
Although exists an ongoing debate on the wisdomhierarchy, for example, Fricke (2009) arguing that infor-mation and knowledge are both weak knowledge, and thatwisdom is the possession and use of wide practical knowl-edge, this can be explained as both information andknowledge correspond to the semantic level (Figure 8)from the semiotic perspective. Anyway, the wisdom hier-archy or the DIKW provides a pathway for transformingdata into information, knowledge and wisdom, for anexample, which is illustrated in Figure 9 (Zhong et al.2013).
Currently, enterprises can adopt software tools forgathering, storing, organising and accessing explicitknowledge. Going beyond that requires that people makeextensive and flexible use of explicit enterprise knowl-edge, and relevant knowledge should be identified anddelivered to the right person at the right time in the rightmanner (FInES Research Roadmap Task Force 2010).Moreover, tacit knowledge should be exploited for use inmanufacturing systems of high intelligence, especially forWM, as explicit knowledge represents only the tip of theiceberg of the entire body of knowledge, a large part ofwhich is tacit. Historically, many AI techniques suchexpert systems, artificial neural networks and genetic algo-rithms are effectively used in manufacturing systems(Oztemel 2010), which has led to the emergence of IM.Ta
ble1.
Distin
ctions/relations
betweendata,inform
ation,
know
ledg
e,andwisdo
m.
Level
Definitio
nLearningprocess
Decisionmaking
Purpo
seEffect/O
utcome
Wisdom
Using
know
ledgeto
establishand
achievego
als
Discerningjudg
mentsandtaking
approp
riateactio
nSystemic
Kno
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hyExp
licability/Betterliv
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orsuccess
(wisdo
mbank
)Kno
wledg
eClear
understand
ingof
inform
ation
Analysisandsynthesis
Strategic
Kno
w-H
owEffectiv
eness/Und
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(kno
wledg
ebank
)Inform
ation
Meaning
ful,useful
data
Givingform
andfunctio
nality
Tactical
Kno
w-W
hat
Efficiency/Com
prehension
(informationbank
)Data
Raw
facts
Accum
ulatingtruths
Operatio
nal
Know-N
othing
Muddlingthrough/
Mem
orisation(data
bank
)
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But WM is different from IM in that the former includesapplications associated not only with explicit knowledge(AI), but also with human (natural) intelligence/tacitknowledge as well as with ubiquitous intelligence (UI).Besides, WM is linked with innovation (creativity), andmakes decision in an ethical way, as shown in Table 1.
Stamper (1996) extends Morris’ 3-level semioticmodel to that of 6 levels: physical, empirical, syntactical,semantic, pragmatic and social. The physical and empiri-cal levels concern the physical media and use of thephysical media for communication of symbols, while thesocial level concerns the shared understanding of themeaning of symbols, aiming at understanding of differentstakeholder viewpoints and an awareness of any biasesand other cultural and political issues involved (Shanksand Corbitt 1999). Despite the physical, empirical andsocial levels were the least developed in semiotic literature(Shanks and Corbitt 1999), and were almost not yet con-sidered in manufacturing system integration (Putnik andPutnik 2010), they are becoming increasingly importantfor CIM/EI, and SM and SE have emerged with the use ofIoT and IbfP in physical and social contexts of enterpriserespectively. To implement the WM vision from thesemiotic viewpoint, it is required that at least the prag-matic level be reached as shown in Figure 8, while thesocial level can enhance wisdom further and providesociability support that the other levels cannot.
In summary, wisdom is a multi-faceted and complexphenomenon. Nevertheless, it is widely accepted thatknowledge is a prerequisite for wisdom, judgment andaction are the core elements of wisdom (Bierly III,Kessler, and Christensen 2000), and more elements (sub-components) might be required in different contexts. Thesame is true for WM systems, but social and technicalaspects must be taken into consideration simultaneouslyfrom the systems engineering perspective, and advances inorganisational wisdom/learning, innovation, AI, CI, andUI facilitate the emergence of WM.
3.3. Wisdom in the web
The Internet is a foundation for CIM/EI. Since WorldWide Web (WWW, commonly known as the web) wasdeveloped between March 1989 and December 1990, ithas become the most widely used in the Internet. Now theInternet or the web has changed the way people worktogether in learning, doing research and business andmanaging healthcare. The web has evolved to Web 2.0and is underway to Web 3.0. Web 2.0 describes web sitesthat use technology beyond the static pages of earlier web(Web 1.0) sites, which allow users to interact and colla-borate with each other in a social media dialogue ascreators of user-generated content in a virtual community,in contrast to websites where people are limited to thepassive viewing of content (O’Reilly 2005). Agarwal(2009) argues that the most important features of emergingWeb 3.0 are the Semantic Web, personalisation, and intel-ligent search. It is estimated the web will enter the Web4.0 by 2020 (Farber 2007).
The web is a symbolic system that semiotics can beapplied to. On the web, syntax refers to tags (such asHTML or XML tags); semantics refers to what thosetags denote, and pragmatics refers to the context-sensitiveaspects of meaning (Singh 2002). So, there are threeversions of the web: Syntactic Web, Semantic Web, andPragmatic Web correlating to syntactics, semantics andpragmatics respectively, as shown in Figure 10.
With the web evolution, its sociability and the contentproduced by consumers increase. Note that although Web2.0 belongs to Syntactic Web, there is a social revolutionin the use of web technologies, which results in the so-called Enterprise 2.0, for example.
In the Semantic Web, information is given well-defined meaning, better enabling computers and peopleto work in cooperation (Berners-Lee, Hendler, andLassila May 2001). The Semantic Web includes techni-ques such as XML, for adding arbitrary structures todocuments; RDF (Resource Description Framework), toexpress meaning by simple statements about things havingproperties with values; and ontologies, to formallydescribe concepts and their relations. The current widelyused components of the Semantic Web framework are
Data collection
Data cleaningData
integrationData storage
Data management
Metadata construction
Data mining & characteristic
extraction
Case creation
Human information
processing & organization
Information extraction and storage
Information organization(granularity division, basic level set, starting point set, etc.)
Space/user/thing/mode
ling
Model integration
Knowledge retrieval
Common sense
knowledge expression
Knowledge extraction and expression
Human knowledge expression & storage
Autonomy oriented
computing
Granular computing
Complex network
Wisdom
Study of human
intelligence
Figure 9. DIKW conversion.
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RDF, RDF Schema (RDFS), and the Web OntologyLanguage (OWL). But the unifying logic, proof and trustlayers have not been fulfilled (Bratt 2006).
Both Web Services and Semantic Web enabling stan-dards are built on the foundation of URIs and XMLSchema (Cabral et al. 2004), as shown in Figure 11. WebServices provide a standard means such as SOAP, WSDL,UDDI and BPEL4WS (Business Process ExecutionLanguage for Web Services) for interoperable operations(Figure 11a). Due to WSDL descriptions at the syntacticlevel, it is not able to specify the meaning and semanticconstraints of data involved in Web Services (Dong,Hussain, and Chang 2013). This gives rise to the visionof Semantic Web Services (SWS), which apply semanticconcepts such as RDF and OWL to Web Services in orderto achieve automated invocation, composition, integrationand execution of Web Services (Figure 11b), and enablemachine interpretability of its capabilities as well as inte-gration with domain knowledge (Cabral et al. 2004).
Though adding meaning into Web Services can alle-viate the problem of semantic heterogeneities, there existpragmatic issues, so it is essential to address its pragmaticaspect, which leads to the emergence of the PragmaticWeb. According to De Moor (2005), ‘The Web’ consistsof a Syntactic, a Semantic, and a Pragmatic Web, wherethe Pragmatic consists of a set of pragmatic contexts ofsemantic resources, being built on the Syntactic Web andSemantic Web (Liu 2009), as shown in Figure 12. ThePragmatic Web primarily concerns with the factors such asintentions, communications, conversations, negotiationsand context, aiming to provide information consumerswith computational agents to transform existing informa-tion into relevant information with practical consequences(Liu 2009), and focusing on the adequate modelling,negotiation and controlling of the use of the meta dataand meaning representations of the Semantic Web in acollaborating community of users (Weigand and Paschke2012).
Web 1.0
Web 2.0
Com
pute
r In
put P
rogr
amm
abili
ty
Web 3.0
Web 4.0
Syntactic Web
Semantic Web
Pragmatic Web
Semantics of social connections
Con
tent
pro
duce
d by
com
sum
er
Mac
hine
und
erst
udin
g1990–2000
2000–2010
2010–2020
2020–MeaningApplicabilityValueHuman input
Figure 10. Evolution of the web.
XM
L
URIHTTP
XM
L-S U
DD
I
BP
EL
4W
S
XM
LX
ML
-S
SOAP
WSDL
URI
(b)(a)
BPLE4WS - Business Process Execution Language For Web Service; HTTP - Hypertext TransferProtocol; SOAP - Simple Object Acess protocol; UDDI - Universal Description, Discovery,andIntegration; WSDL - Web Service Definition Language; XML - eXtensible Markup Language;XML-S-XML Schema; OWL - Web Ontology Language; RDF - Resource Description Framework;RDFS - RDF Schema; URI -Uniform Resource Identifier
HTTP
RDF
RDFS
OWL
Figure 11. Enabling standards for (a) Web services and (b) Semantic Web.
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Now CIM/EI is moving to the integration based onWeb Services. But Web Services lack machine readablesemantics. This leads to study semantic interoperability(Mecheri and Souici-Meslati 2010) or SWS technology(Lastra and Delamer 2006; Dong, Hussain, and Chang2013). The emerging Web 3.0 is underway to theSemantic Web that will provide such interoperability.And the Pragmatic Web (Singh 2002) goes further, andtries to support pragmatic use.
Semiotics enables us to better understand the relationsof the Syntactical, Semantic, and Pragmatic Web.However, the (Pragmatic) Web only concerns 3 levelsout of the 6 semiotic levels. As such, the (Pragmatic)Web or information (DIKW in a broader sense) technol-ogy-centred view of CIM/EI is no longer sufficient. Atpresent, the Pragmatic Web is still at concept stage, andmodelling the context of pragmatic use is unsatisfactory,and the context is constantly and dynamically formed,deformed, configured and re-configured (Liu 2009), notto mention that many human (social) factors of context aretacit and non-formalisable.
Context awareness is required in WM. And the abilityof applications to take into account context is essential foradaptability and personalisation (Liu 2009), which is oneof the focuses of Web 3.0 (Agarwal 2009). Context iswhat related to the environment where the applicationsimplement and execute. So enterprise context is verycomplex as there are many dimensions of pragmatics tobe taken into account, such as purposes, communicativesituations, organisational norms, individual values, and soon (Moor 2005), usually including social and physicalaspects of context. The Pragmatic Web that focuses onthe social aspect of context is similar to the SocialSemantic Web that supports the pragmatics in social inter-actions on the Public Web (Weigand and Paschke 2012).
As the social and physical aspects are becomingincreasingly important for the interaction between the
cyber and real worlds, IbfP and IoT are adopted for socialcontext and physical context awareness in the WM,respectively.
3.4. Towards wisdom in the WM
Now move to address wisdom in the WM vision as shownin Figure 7. As stated above, knowledge plays a vital rolein wisdom. To some extent, the IoCK, or called theInternet of Knowledge (IoK) shortly, can be viewed asthe DIKW.
Manufacturing enterprises are socio-technical systems.That means both technological and social (human) factorsmust be considered in CIM/EI. As for a WM system, IoK,IoS, IbfP and IoT are converged as a whole, where com-puters, humans and things are integrated in a hyper worldconsisting of the social, cyber and physical worlds, asshown in Figure 13. As such, a so-called socio-cyber-physical system (SCPS) for the WM is formed bydoing so.
The IoS includes services-oriented technologies suchas Web Services, SOA and cloud computing. The CMmainly focuses on the integration and provision of CMServices. The IoK includes BI, CEP, and Semantic Web,which manage data, information, knowledge, events andbusiness rules for the WM. The CEP makes the combina-tion of SOA with Event-Driven Architecture (EDA) pos-sible. Although the Semantic Web as a whole is notfulfilled, its fulfilled part, e.g. OWL-S ontologies, can beused to make Web Services in the IoS become SemanticWeb Services (Lastra and Delamer 2006). However, theIoK only focuses explicit knowledge, and it combines theIoS to form the cyber world, which doesn’t consider thesocial and physical aspects in the real world. So it isextended to the social and physical worlds via the IbfPand IoT respectively, as shown in Figure 14.
The IbfP focuses on the social context, i.e., human-centred aspects of knowledge management (includingusers’ interaction and collaboration, and tacit knowledgemanagement), provides a community where people can goand seek help or collaboration, and facilitates human inter-action as well as interoperability. In such an online commu-nity, knowledge collaboration occurs in a variety of ways.As most of the body of knowledge is made up of tacitknowledge that exists in people’s minds, IbfP can helpfirms enhance and leverage internal capabilities such asknowledge work and collaboration, and seamlessly integratethe vast external knowledge available on the Internet.The SECI (Socialisation, Externalisation, Combination,Internalisation) model (Nonaka and Takeuchi 1995) can berealised via social software tools as shown in Figure 15:where tacit knowledge is socialised through observation,imitation, practice, and participation in online communities,implicit knowledge externalised through Blogs/Wikis/BBS,explicit knowledge combined through Rss/Tag/Mashup/
Syntactic Web HTML/Script languages
Semantic Web Ontology to define semantics
Pragmatic Web Agents with pragmatic knowledge
SocialComsumers
Figure 12. Conceptual model of the Pragmatic Web.
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Soci
o-te
chni
cal s
yste
m
Rea
l wor
ld
Cyb
er w
orld
Internet of Things
Internet by and for people
Internet ofServices
Tacit knowledgeSocial computingCollective intelligence
Explicit knowledgeCloud computingIntelligent computingArtificial intelligence
Ubiquitous/Pervasive computingUbiquitous intelligence
CM - Cloud Manufacturing; SE - socialized enterprise; IM - Intelligent Manufacturing; SM - Smart Manufacturing;WM - Wisdom manufacturing
Smart objects
Social world
Socio-cyber system
Cyber world
Cyber-physical system
Physical world
SM
CMIM
SE
Internet ofKnowledge
Figure 13. The socio-technical system of the WM vision.
Sem
antic
/BI/
CE
P
Socialization Externalization
Combination
commitments,contracts, beliefs,law, culture, ...
intentions,communications,conversation, , ...
meaning, validity,truth, ...
Structure, data,records, ...
Infomation
Data
Precision
High
Knowledge
information channel,noise, entropyefficiency, ...
Signals, hardware,traces, physicaltokens, ...
AI - Artifical Intelligence; CI - Collective Intelligence; IbfP - Internet by and for People; LoK - Intrenet of Knowledge;IoS - Internet of Services; IoT - Internet of Things; UI - Ubiquitous Intelligence
Social world:
Pragmatic:
Semantic:
Syntactic:
Empiric:
Physical world:
Internalization
Percepts
LowHigh
Intelligence
Services Discovery,Composition, and Provision
Actions
Low
loK
loT
loS
CIIbfP@
AI
UI
S E
CI
Figure 14. Towards wisdom for the WM.
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Folksonomies/Social Bookmarking, and explicit knowledge‘re-internalised’ into tacit knowledge through learning bydoing and collaborative learning.
The application of the IoT in manufacturing environ-ments results in the above stated SM as an ingredient ofthe WM. For dynamic integration, it is required to beaware of the state and conditions of shop-floor and theenvironment in time. The IoT provides the sensorial andactuating capabilities required to greatly enhance the inter-action between the physical and cyber worlds. Such IoT-enabled context-awareness (Perera et al. 2013) allows theWM to achieve increasing levels of real-world-awareness,as well as making enterprises’ environment more intelli-gent and sustainable.
As stated above, the IoS, IoK, IoT and IbfP are com-plementary to each other in the WM. In such a holisticway as shown in Figure 14 and Table 2, things, compu-ters/services, and humans are integrated, signalling fromthe IoT (things) to the IoK (DIKW), the IbfP (humans),the IoS (services), and then back to the IoT (things). Theuse of bottom-up and top-down communications betweenenterprise control level and device level results in the WMsystem with multiple control loops. At the bottom level,the IoT plays the role of perceptions, and reacts to the
physical world with UI or ambient intelligence. Physicalthings (or called u-things, smart objects) are able to actadaptively and automatically with some extent levels ofintelligence, and in combination with actuators, localdevice control is implemented. The IoK manages explicitknowledge and event processing, and enhances serviceswith Semantic Web ontologies, and the IoS provides basicconnectivity and on-demand CM services, along with theIoK playing the role of controlling business processes.The domain experts in the top level can make decisionon manufacturing and context information/knowledgefrom lower levels with tacit knowledge or collective intel-ligence (CI) for control of the enterprise value creationprocesses. As a consequence, the WM has the capabilityof ‘a deep understanding and realisation of people, things,events or situations, resulting in the ability to apply per-ceptions, judgments and actions in keeping with thisunderstanding’, as Wikipedia (2013) defines wisdom.
In other words, wisdom manufacturing (WM) is ahuman-computer-thing collaborative manufacturing modelthat intensively uses CI, AI and UI in manufacturingcontext that is deeply aware and identified by, forexample, the IoT and IbfP, and relevant knowledge isdelivered to the right user (whether a person, a machineor a thing) at the right time in the right manner, resultingin enabling ubiquitous, convenient, transparent, on-demand access to a shared collection of configurabledistributed manufacturing resources (services), rapid man-ufacturing of products, and real-time optimisation of man-ufacturing processes and supply chain network, withsound and competitive reactions to the external turbu-lences proactively in keeping with such context aware-ness. As such, knowledge is used best to establish andachieve the goal, i.e. providing products/services to meetmarket demand, with systematic decision at the wisdomlevel as shown in Table 1, from the systems engineeringperspective.
Note that the Table 2 only lists the major focuseddomains with check marks for the IM (IoK), SM (IoT),CM (IoS) and SE (IbfP), as each of those models isrelevant to multidisciplinary domains. IM focuses on theuse of AI in manufacturing with patternbased (intelligent)computing in cyberspace, SM/SF the use of UI withubiquitous (pervasive) computing in physical space, CM
NetworksCommunities
Blog/Wikis/BBS
Learning by doingMulti-user simulations
Trial and error
RSS/Tag/MashupSocial Bookmarking
Folksonomies
Tacit knowledge
Socialization
Internalization
Explicit Knowledge
Tac
it k
now
ledg
e
Combination
Externalization
ExplicitK
nowledge
Figure 15. SECI model implemented with social softwaretools.
Table 2. Complementary comparison of IM, SM, CM and SE with the resulted WM.
AI(EK) CI(TK) UI Humans Things Services Cyber Physical Social
IM (IoK) √ √SM (IoT) √ √ √CM (IoS) √ √SE (IbfP) √ √ √WM √ √ √ √ √ √ √ √ √
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providing services and the framework with cloud comput-ing in cyberspace, and SE the use of CI with socialcomputing in social space. The WM resulted from theemergence of IM, SM, CM and SE, supports the integra-tion of CI, AI and UI that conforms to the Principle ofIncreasing Precision with Decreasing Intelligence (Saridis1989) with highest intelligence at the top level and lowestat the bottom, as shown in the right side of Figure 14.
From the semiotic viewpoint, as shown in the left sideof Figure 14, the IoT covers the physical and empiriclevels, including hardware, signals, patterns, variety,noise, channel capacity, codes, etc. The IoK and IoScover syntactic and semantic levels, including data, in-formation, knowledge, services, etc. The IbfP coverspragmatic and social levels, including intentions, com-munications, negotiations beliefs, expectations, com-mitments, contracts, law, culture, decision-making, etc.Wisdom is embodied in the use of knowledge in the socialand physical worlds.
4. Towards the realisation of the WM vision
Wisdom aims to increase effectiveness and add value(Ackoff 1989). How value is co-created in the WM?Similarly to that value network analysis for thePragmatic Web (Weigand and Arachchige 2010), theWM realises the co-created value as shown in Figure 16,which connects the social, cyber, physical and economicsystems.
Traditionally users retrieve Web Services using key-words to represent their requirements. As there is always a
lot of information that users cannot explicitly express, it isdifficult for providers to understand consumers’ real inten-tion to some extent (Liu 2009). However, IbfP, orsocial networking, has been credited with the ability toexpand social contacts, accelerate business processes, theimprovement of customer relations, cost-effective recruit-ment of high-calibre staff, and the improvement of morale,motivation and job satisfaction among staff (van Zyl2009). Nowadays, web applications in manufacturinghave evolved towards collaborative ones in such a waythat the social dimension is becoming more important thanthe technological one. Therefore, products/services arecustomised in a way that all technologies are directed byconsumers’ preferences and expectations. Although con-cepts such as socialised enterprises and Enterprise 2.0have emerged by the use of social networking in enterprisecontexts, they are usually limited to the field of immaterialproduction such as open source software and freely avail-able content on the Internet. WM extends the philosophyof such concepts to the real word and produce physicalgoods by further using IoT and other technologies asdiscussed above.
In real world of business, people communicate,research, compare, think, consult, negotiate, decide andultimately commit to the next steps. So in WM, by usingclose interaction via IbfP and the buildup of shared under-standing and trust among stakeholders, consumers canmake orders for products/services with commitments. Inmake-to-order manufacturing, necessary process activities(e.g., with ERP/MES, etc.) are planned at enterprise con-trol level on receipt of orders, business processes (work-flows) created in the cyber/virtual world, and then step bystep refined down to the device level and fulfilled in thephysical system. Meanwhile, the execution of the ordersand manufacturing processes are monitored, and the statesin physical system are fed back to the cyber and socialsystems to track if the agreements made under commit-ments are fulfilled or not. At last, value is created inthe WM.
The emergence of the knowledge economy, intenseglobal competition and considerable technologicaladvance has seen innovation become increasingly centralto competitiveness (Lawson and Samson 2001). Atthe same time, enterprises are being forced to react tothe growing demands of customisation/personalisation(Kumar 2007). As such, enterprises need to speed upinnovation to improve their market and business opportu-nities by collaborating and co-creating with customers/users, which results in the concepts such as co-creation,co-production/design, and prosumption (Humphreys andGrayson 2008). WM meets those needs by collaboratingwith participants/ stakeholders through the value chainfrom ideas to marketing, as shown in Figure 17.Therefore, stakeholders can involve in activities such asresearch, design, manufacturing, assembly and marketing.
Social systemcommitments web
services
Cyber system
services businessprocess
Physical system
events device services
Economic system
value Valuenetworks
countedon
fulfilledby
create
implement
coordinate
realize
Figure 16. Value realisation in the WM.
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As a consequence, the concept of open innovation(Chesbrough 2003) is incorporated in the WM.Conventional enterprises focus on the use of internalknowledge such as employees’, invest in internal researchand design (R&D), and use intellectual property protectionmechanisms to appropriate returns from those activitiesand investments. Open innovation can enable enterprisesto use external knowledge such as consumers’ and suppli-ers’ as well, allows knowledge flow across enterpriseboundaries, and shifts conventional manufacturing froma closed to an open system. Although open innovationprovides a way of integrating stakeholders during theR&D period, it focuses at the level of a single enterpriseand usually does not take account of the role of theenterprise’s external institutional and geographic context.Nowadays collaborative innovation networks are increas-ingly regarded as a competitive advantage (Gloor 2006),and a global and civilisational context is especiallyrequired with the support of business intelligence, globalintelligence and sustainability intelligence (Targowski2010b). As shown in Figure 18, WM takes those intoconsideration and generalises the concept of open innova-tion into more open, collaborative, networked one, andprovides platforms with an ecosystem of not only employ-ees, partners, suppliers, and customers but also otherindividuals, teams, institutions (research laboratories, uni-versities, government agencies, etc.) to co-develop pro-ducts with the consideration of sustainability, policy andgovernance in a global context. In the meanwhile, partici-pants are affected by each other’s experience, yieldinglearning effects for further innovation. So WM can
harness the CI of crowds (the wisdom of crowds) andcollaborative learning, and democratise manufacturing asanyone can be a potential participator.
As discussed above, WM merges CM, IM, SM andSE with integration of AI, CI and UI. CM, IM, SM andSE are complementary aspect models of the WM. Forexample, as shown in Figure 19, CM (IoS) provides on-demand services and a framework for WM integrationby using Enterprise Service Bus (ESB), and gets sup-ports from the other three partners to complement itsdeficiencies in syntactic interoperability and request-reply mechanism, as well as lack of KM and real-timeEDA supports, where IM (IoK) provides semantic WebServices interoperability complementary to syntacticinteroperability in CM, publish-subscribe instead ofrequest-reply, as well as KM and decision-making sup-ports; SE (IbfP) assists in achieving social integration byadding CI and social awareness (SA); and SM (IoT)helps in achieving physical integration by adding UIand physical awareness (PA).
WM has a sufficient level of intelligence to performvarious activities, and is designed to support humans, notto replace them. In this way, as an integral part (notexcluding interfaces accessible) and the centre of thewhole WM system, people actively take part in all phasesof enterprise operations from R&D through the operationto the maintenance and repair services, but mainly focuson their unique qualities of creative thinking, balancingoptions and wisely use their unlimited supplies of tacitknowledge involved in the product life cycle. The WMdiffers from the unmanned factory devoid of humans.
Ideas
Research
Design
ManufacturingAssembly
Marketing
Products/services
Customers
employees
Open innovation
Outsourcing
community-based design
Customized/personalized products
Insourcing
PartnerRetailer
Stakeholders
Competence Development
Figure 17. Integrating stakeholders through the value chain in WM.
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Rules, contexts and CEP play an important role inpragmatic use (Weigand and Paschke 2012) or decision-centric business process management (Paschke 2011). To
meet such needs, it is required to integrate SOA and EDAfor event-driven business process by, for example, extend-ing WSDL and BPEL (Juric 2010), translating BPMN
Global and Civilizational environment
(Open collaborative innovation/Global innovation)
Networking
Crowd(Public)
Policies and Governance
Innovation in wisdom manufacturing
Suppliers/Customers
Company(Employees)
Innovationin conventional manufacturing
Innovation in open manufacturing(Open innovation)
Figure 18. Innovation in wisdom manufacturing.
...
IoT
Infomation
Data
Knowledge
Wisdom
IbfP
IoSIoK
Web services
Business process
Manufacturing resources and contexts
SEC
MWM
SM
IM
CI/SAAI/C
EPSemantic
UI/PA
ESB
S E
CI
@
AI - Artificial Intelligence; CEP - Complex event processing; CI - Collective Intelligence; CM - Cloud Manufacturing; ESB - Enterprise Service Bus; IbfP - Internet by and for People; IM - Intelligent Manufacturing; IoK - Internet of Knowledge; IoS -Internet of Services; IoT - Internet of Things; PA - Physical Awareness; SA - Social Awareness; SE - Socialized Enterprise; SM - Smart Manufacturing; UI - Ubiquitous Intelligence; WM -Wisdom Manufacturing
Internalization
Socialization
Combination
Externalization
Figure 19. Towards the realisation of the WM vision.
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(Business Process Modeling Notation) (Ouyang et al.2006) or EPCs (Event-Driven Process Chains) (Wielandet al. 2009) to BPEL, or using ECA (Event-Condition-Action) rules (Amaral et al. 2011). The authors selectedApache Synapse (2013b) for ESB, Apache ODE (2013a)for BPEL engine, Esper (2013) for CEP engine, andDrools (JBoss 2013) for ECA engine, to implementevent-driven SOA for cloud manufacturing(EDSOA4CM) (Yao et al. 2013). Based on the previouswork (Yao et al. 2012, 2013, 2014), an integrated frame-work based on ESB for the WM vision is given as shownin Figure 20. As such, all enterprise resources (e.g., ERP,and MES systems, manufacturing process, machines,humans, things, etc.) at different levels are integrated viathe ESB.
Complex events are aggregations of basic events orcomplex events using a specific set of event constructorssuch as disjunction, conjunction, and sequence (Amaral,
Hessel, and Correa 2011), as shown in Figure 21 (Yaoet al. 2013), where basic RFID (Radio FrequencyIdentification) events are processed with Fosstrak RFIDmiddleware (Fosstrak 2013), complex events with Esperengine, and ECA with Drools engine. An event-drivenbusiness process for a workpiece machined on threemachines (M1 to M3) is shown in Figure 22 (Yao et al.2013), which illustrates that CEP cooperates with ECA tofulfil the process.
In summary, advances in organisational wisdom, manu-facturing technologies (such as CM, IM, SM and SE),creativity/innovation (such as open innovation, co-creation,and co-design), and intelligence (such as on-line knowledgesharing/learning, AI, CI, and UI) provide a path to realisethe WM vision. As such, the WM realisation can be view asthe synthesis of those elements, which exemplifies the com-bination of ‘organisational wisdom’ and ‘wisdom as thesynthesis of intelligence and creativity’ (Sternberg 2001)
ESB (Enterprise Service Bus)
BusinessProcess
ERP/MES
IoS
Rulesengine
IbfP
EventsData
CEPengine
EventsData
IoT
IoK
Data mining
CEP - Complex event processing; IbfP - Internet by and for People; IoK - Internet of Knowledge; IoS - Internet of Services; IoT - Internet of Things
Figure 20. An integrated framework for the WM vision.
BasicEvent
ComplexEvent
ECARule
RFIDEvent
Non-RFIDEvent
BasicConstructor
Action
LogicalConstructor
TemporalConstructor
triggercomposedBy
ekatyBdesopmoc
EsperFosstrak etc. Drools
subClassOf
Figure 21. CEP with ECA.
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for manufacturing settings with a balance between eco-nomic, environmental and social interest as future en-terprises should be economically vital, environmentallyaccountable and socially responsible (Targowski 2010b).
5. Case study
As discussed above,WM is featured by the best use of knowl-edge, blended intelligence, socialisation, customisation/perso-nalisation, co-production/co-creation, organisational learning,
democracy of manufacturing, environmental stewardship, ser-vice dimension, collaborative or open innovation in globalcontext, etc. Companies like Quirky turns people’s ideas intoproducts through interaction between online global commu-nities and their expert product design staff, which involvesmany of those features of wisdom manufacturing. In thissense, Quirky exemplifies wisdom manufacturing in a certainextent.
Headquartered in New York City, Quirky provides anopen platform for innovation, collaborative design and
Evente1
MachineM1
Evente2
MachineM2
Evente3
MachineM3
e1
e11m1
e2
e21
e3
e31m3
m2
Business process
Drools
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ON e1DO M1
MachineM1
MachineM2
MachineM3
ON e2DO M2
ON e3DO M3
CEP ECA rules
Oher events
triggertake
triggertake
triggertake
Figure 22. An illustration for event-driven business process.
Employees
CommunityEvaluation Design
Outsourcing
Manufacturing Marketing
Retailer
WebsitePrototypes
Public
Voting
Receivingmarket data
Idea submission
Quirky
Figure 23. Quirky business model.
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manufacturing, as shown in Figure 23. With the help of itsonline community, Quirky comes up with two new con-sumer products a week. Anyone can submit an idea toQuirky, and community members vote for ideas they like,and offer comments. Design will follow if an idea passesthe community evaluation. More voting happens for theproduct name/colour/price, tagline, feature set, and otherbranding. Quirky’s engineers make the winning designmanufacturable and work with factories to make it(Anderson 2012). The product is sold directly throughthe Quirky website as well as through retail chains ifdemand grows. Inventors who submit ideas that are thencreated and influencers who contribute to the developmentof products share in royalties based on product sales(TechHubz 2014).
As an emerging manufacturing paradigm, WM focusesnot only on the technical aspects, but also on economic,social and environmental issues, which make it differentfrom that conventional Internet-based manufacturingmainly focused on the technical aspects, characterised bytightly coupling integration with predefined sequenceswhich were hard to reconfigure to make customised pro-ducts, thus having limits in integration and collaborationas the Internet (Web 1.0) initially focused on the commandand control of information itself. Although the Quirkybusiness model reveals outstanding WM features, Quirkyplaces emphasis on R&D and outsources product manu-facturing to outside factories that have not sufficientlyintroduced IoT in their manufacturing settings. As such,the value chain is not integrated fully as a whole. To fullyrealise the WM vision in practice is challenging, and thereis still significant work that remains to be done from asystems engineering perspective.
6. Conclusion
From the above investigation, wisdom manufacturing(WM) can be seen as the synthesis of organisational wis-dom, collaborative learning, innovation/creativity, AI, CI,and UI in such a way that the whole is greater than thesum of its parts from the systems engineering perspective,with the best use of knowledge to establish and achieveefficient and effective production of products/services(especially customised/personalised products/services), orto create new markets for innovative products, whichpresents a revolutionary paradigm shift in manufacturing,e.g. a shift from IM in the DIKW perspective. As a socio-cyber-physical system, the WM vision is required to rea-lise integration from the physical to cyber and to socialsystem, resulting in the holistic integration of things,computers and humans, explicit and tacit knowledge, aswell as UI, AI and CI. As such, WM can harness humanskill, interaction, knowledge, ingenuity and intelligencemore efficiently and effectively than existing manufactur-ing models.
To address the WM, this study focuses on the emer-gence of SE, IM, SM and CM, the pathways towards theWM vision as well as approaches towards wisdom andrealisation of the WM, especially on how IM, SM, CMand SE being combined together to function as a whole,and to complement their strengths and mitigate their weak-nesses with available relevant technologies. The imple-mentation of the WM will heavily rely on the furtherdevelopment and fusion of SE, IM, SM and CM. Theresearch into the WM is at an initial stage, and significantamounts of efforts are required in order to make its visiona reality. More effort is needed to put on the realisation ofthe WM vision.
AcknowledgementThe authors would like to thank the editor and anonymousreviewers for their constructive comments and suggestions.
FundingThe project was supported by the National Natural ScienceFoundation of China [grant number 51175187], [grant number51375168]; the National High-Tech. R&D Program of China [grantnumber 2007AA04Z111]; the Science & Technology Foundation ofGuangdong Province [grant number 2012B030900034]; and theScience & Technology Foundation of Dongguan City [grant number2012108102010].
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