《供应链系统设计与管理》课程教学资源（文献资料）The Framework of Information Processing Network for Supply Chain Innovation in Big Data Era

Chapter9TheFrameworkofInformationProcessing Network for Supply ChainInnovation in Big Data EraChian-Hsueng ChaoAbstract The challenges of the global marketplace and the growing complexityofbusiness philosophies and technologies mix, the enterprises are forced to utilizeknowledge, capabilities, and resources to be found within and outside theirinformation processing networks. The enterprises are demanding more than justaccess to data, they want processed and refined big data and information to helpthem to reach more responsive and effective tactical decisions. Under this para-digm shift, data and information-oriented productivity depends on the sharing ofknowledge and skills among workers, so that enterprise strategies can be driven bythe collectiveintelligence and competenceofthe groupto face business challengesand enable organizational learning and innovations. In the cloud computing andbig data era, management of enterprise knowledge to create business values andcompetitive advantages is especially important for supply chain practices. Thispaper focuses on the development of enterprise information processing networkand application framework that bind organizational strategies, business processes,data, information,technologies, and peopletogethertobetter utilizeknowledge inbusiness practices.The ultimate goal is thetransformation of an enterprise networkinto a knowledge network for supply chain organic innovations!Keywords Information processing network·Knowledge management.Supplychain management·Big data analyticsC.-H. Chao ()Department of Information Management, National University of Kaohsiung, 700Kaohsiung University Rd, Nanzih District, 81l, Kaohsiung, Taiwan, R. O. Ce-mail: cchao@nuk.edu.tw77L.Uden et al. (eds.), The 3rd International Workshop on Intelligent DataAnalysis and Management, Springer Proceedings in Complexity,DOI:10.1007/978-94-007-7293-9_9, Springer Science+Business Media Dordrecht 2013

Chapter 9 The Framework of Information Processing Network for Supply Chain Innovation in Big Data Era Chian-Hsueng Chao Abstract The challenges of the global marketplace and the growing complexity of business philosophies and technologies mix, the enterprises are forced to utilize knowledge, capabilities, and resources to be found within and outside their information processing networks. The enterprises are demanding more than just access to data, they want processed and refined big data and information to help them to reach more responsive and effective tactical decisions. Under this para￾digm shift, data and information-oriented productivity depends on the sharing of knowledge and skills among workers, so that enterprise strategies can be driven by the collective intelligence and competence of the group to face business challenges and enable organizational learning and innovations. In the cloud computing and big data era, management of enterprise knowledge to create business values and competitive advantages is especially important for supply chain practices. This paper focuses on the development of enterprise information processing network and application framework that bind organizational strategies, business processes, data, information, technologies, and people together to better utilize knowledge in business practices. The ultimate goal is the transformation of an enterprise network into a knowledge network for supply chain organic innovations! Keywords Information processing network Knowledge management Supply chain management Big data analytics C.-H. Chao (&) Department of Information Management, National University of Kaohsiung, 700, Kaohsiung University Rd, Nanzih District, 811, Kaohsiung, Taiwan, R. O. C e-mail: cchao@nuk.edu.tw L. Uden et al. (eds.), The 3rd International Workshop on Intelligent Data Analysis and Management, Springer Proceedings in Complexity, DOI: 10.1007/978-94-007-7293-9_9, Springer Science+Business Media Dordrecht 2013 77

78C.-H. Chao9.1IntroductionToday, the Supply Chain Management (SCM) is a boundary-spanning,channel-unifying, dynamic, and coevolving philosophy of inter-enterprise man-agement. The major contribution of today's supply chain model is to improve andenhancing collaboration between businesses and their trading partners [1]. Intoday's business practices, competing for supply chain requires the alignment ofcorporate strategies to what the organization knows, or developing knowledgemanagement (KM) capabilities to support a desired supply chain solution.Man-agement of organizational knowledge for creating business values and generatingcompetitive advantages is critical for organizational survival. A good knowledgemanagement should support people to access and learn from past and presentorganizational business practices/strategies and to apply the lessons learned whenmaking future decisions.Therefore,a successful knowledge-oriented business fororganizations should link supply chain management, relationship management,and knowledge management to function in an adaptive way to cope with everychanging business challenges.9.2TheBigDataImpactsBig data,as the nextfrontier for innovation, competition, and productivity [2] willhavetremendous impact on ourdaily life.Inbigdata era,theflowof data can beamong different devices and in different types. The data can be any type with anyforms, such as business data, social network messages, blog, forum, web page,multimedia, SMS, email, sensor data (e.g.NFC, GPS, RFID, M2M), and so forth.Because the data came froma variety of sources,thebig data is considered to beatthe scale of up to Zettabyte.Therefore, timely and cost-effective analytics overbig data is now a key ingredient for success in many businesses, scientific andengineering disciplines, and government endeavors [3]. The characteristics of bigdata analytics are variety, speed, and with big volume, and therefore the manip-ulation of data relies on intelligent approaches to deal with growing structured,semi-structured or unstructured big data. Currently, the cloud computing andparallel computing do much of the work in big data analytics.For the past few decades, enterprises have constantly reinvented themselvesthrough a series of business and technological innovations to fit into the globalspectrum of business.The increasing adaptivity and responsiveness of businesspractices has led to the role of big data analytics in business practices. Big dataanalytics relies heavily on the interpretation of data into useful knowledge forenterprise or supply chain to make more responsive and effective tactical decisions.Information, such as demographics, consumers'behaviors,and numerous otherbusiness statistics,and the associated processing power arecritical for the survivalof enterprises in business.With the global deployment of computers,mobiledevices,and interconnecting networks,participants canwork collaboratively,share

9.1 Introduction Today, the Supply Chain Management (SCM) is a boundary-spanning, channel-unifying, dynamic, and coevolving philosophy of inter-enterprise man￾agement. The major contribution of today’s supply chain model is to improve and enhancing collaboration between businesses and their trading partners [1]. In today’s business practices, competing for supply chain requires the alignment of corporate strategies to what the organization knows, or developing knowledge management (KM) capabilities to support a desired supply chain solution. Man￾agement of organizational knowledge for creating business values and generating competitive advantages is critical for organizational survival. A good knowledge management should support people to access and learn from past and present organizational business practices/strategies and to apply the lessons learned when making future decisions. Therefore, a successful knowledge-oriented business for organizations should link supply chain management, relationship management, and knowledge management to function in an adaptive way to cope with every changing business challenges. 9.2 The Big Data Impacts Big data, as the next frontier for innovation, competition, and productivity [2] will have tremendous impact on our daily life. In big data era, the flow of data can be among different devices and in different types. The data can be any type with any forms, such as business data, social network messages, blog, forum, web page, multimedia, SMS, email, sensor data (e.g. NFC, GPS, RFID, M2M), and so forth. Because the data came from a variety of sources, the big data is considered to be at the scale of up to Zettabyte. Therefore, timely and cost-effective analytics over big data is now a key ingredient for success in many businesses, scientific and engineering disciplines, and government endeavors [3]. The characteristics of big data analytics are variety, speed, and with big volume, and therefore the manip￾ulation of data relies on intelligent approaches to deal with growing structured, semi-structured or unstructured big data. Currently, the cloud computing and parallel computing do much of the work in big data analytics. For the past few decades, enterprises have constantly reinvented themselves through a series of business and technological innovations to fit into the global spectrum of business. The increasing adaptivity and responsiveness of business practices has led to the role of big data analytics in business practices. Big data analytics relies heavily on the interpretation of data into useful knowledge for enterprise or supply chain to make more responsive and effective tactical decisions. Information, such as demographics, consumers’ behaviors, and numerous other business statistics, and the associated processing power are critical for the survival of enterprises in business. With the global deployment of computers, mobile devices, and interconnecting networks, participants can work collaboratively, share 78 C.-H. Chao

799TheFrameworkofInformationProcessingNetworknetworkedresources,exchangeknowledge,andimprovecorporateorsupplychainperformance.Corporate and supply chain strategies canbe driven by the collectiveintelligence of groups to better meettoday's business challenges.9.3 The Value of Data and Information in Supply ChainMentioning about the values,Porter's value chain[4] concept is well adopted byorganizationstoprofiletheircompetitiveness andbusinessvalues.Thevaluechaindivides the organization into a set of generic functional areas, which can be furtherdividedinto a series ofvalueactivities.An enterprisewill beprofitableaslongasitcreatesmorevaluethanthecostof performing itsvalueactivities[5].Tomodela business system,the effortfor theseparation ofa complexpartfrom the whole inwhich we are interested is called an Abstraction. This is a very practical meth-odology for the modelling of a complex system,especially in the supply chainmodelling effort.Through abstractions, any complex business object in a systemcan be denoted as a black box that produces certain outputs regardless of itsinternal complexity.Andlater,whennecessary,thisabstractobjectcanbefurtheranalyzed and broken down into several sub-objects. Therefore we can modelsupply chain business process integration based on value chain as shown inFig.9.1.In Fig. 9.1, recall that Porter's value chain described an enterprise as a set ofgeneric functional areas (such as inbound logistics, operations, marketing,out-bound logistics, etc.).Porter also recognized linkages outside the enterprise, asnfHuman Resource ManTechnological DevelopmProSale&ServicDmthound Logis(Executive)LogistiMfiddleManaoeInformatioUpDownStreamStreamABPABPoILS&SOLFig. 9.1 Global network of value chain in abstract business process (after Gale and Eldred,1996modified)

networked resources, exchange knowledge, and improve corporate or supply chain performance. Corporate and supply chain strategies can be driven by the collective intelligence of groups to better meet today’s business challenges. 9.3 The Value of Data and Information in Supply Chain Mentioning about the values, Porter’s value chain [4] concept is well adopted by organizations to profile their competitiveness and business values. The value chain divides the organization into a set of generic functional areas, which can be further divided into a series of value activities. An enterprise will be profitable as long as it creates more value than the cost of performing its value activities [5]. To model a business system, the effort for the separation of a complex part from the whole in which we are interested is called an Abstraction. This is a very practical meth￾odology for the modelling of a complex system, especially in the supply chain modelling effort. Through abstractions, any complex business object in a system can be denoted as a black box that produces certain outputs regardless of its internal complexity. And later, when necessary, this abstract object can be further analyzed and broken down into several sub-objects. Therefore we can model supply chain business process integration based on value chain as shown in Fig. 9.1. In Fig. 9.1, recall that Porter’s value chain described an enterprise as a set of generic functional areas (such as inbound logistics, operations, marketing, out￾bound logistics, etc.). Porter also recognized linkages outside the enterprise, as Up Stream ABP Down Stream ABP Information Information IL O S & S OL Firm Infrastructure Marketing Outbound Logistics Inbound Logistics Sale & Service Operations Human Resource Management Technological Development Procurement (Executive) (Base Management) (Middle Management) Information Fig. 9.1 Global network of value chain in abstract business process (after Gale and Eldred, 1996–modified) 9 The Framework of Information Processing Network 79

80C.-H. Chaothey relate to the customer's perception of value. Therefore, it is also an openstructure, and the network can be developed in a fractal pattern just like theextended value chains.To this point, a Porter value chain is an abstraction of abusiness process, because an enterprise is a business process entity in a globalinformation-processing network.Therefore there is a different from the originalmodel proposed by Gale and Eldred [6], which focused on the process view ofabstraction instead of the global supply chain value management scheme.Again, the term “abstraction" is used here to describe the generalization of anybusiness process for the modelling purpose. Through this characterization, abusiness process can be generalized into what we call the Abstract BusinessProcess (ABP).The Abstract Business Process is just like a business process whichcan be decomposed into several sub-processes, which is, an ABP is made up oflower level ABPs.This interconnected value chain system can act as a supplychain or information processing network that encompasses the modern businessworld, and participating organizations can readily extend their technologies andknowledge to their partners. The extended enterprise aspect enables supply chainintegration and more effective outsourcing solutions for both internal and externalstakeholders [7]On the other hand, the rapid growth of the Internet brought about new businessphilosophies and fostered the growth of new strategic alliance,data, information,and business process integration across the borders ofenterprises.The informationprocessing view of an organization has been considered one of the most influentialcontributions to the contingency literature [8]. In this philosophy, informationprocessing network provides the channels for exchange and processing of infor-mation in a global system.The primary role of the information processing networkis to provide information exchange among its subsystem-the informationprocessingnodesasshowninFig.9.2In Fig. 9.2, the information processing network view of "virtual enterprise" canbe from different divisions, departments, or organizations.The information-pro-cessing nodes within the network areresponsiblefor sending,receiving,selecting,producing, and communicating (i.e. exchange data and information)with otherinformation processing nodes. An organization's value chain consists of allactivities performed to design, produce, market, deliver, and support its productand service. For the analysis of business data communication, the informationprocessing network connects its nodes, which in turn, are organized into businesscomponents.Thebusiness components of the organization include people,pro-cesses, events, machines,and information that interact and combineto produce theoutputs (e.g.information,product, service)of the organization.Recalled that a knowledge-enabled organization is a learning organization, onewhere all employees are using theirknowledge, skills, and learning to meet today'sbusiness challenges and to create new opportunities for the future. Therefore, thevalue is created whenever information flows through the information processnodes and the information processingnetwork.Inbusiness practices,collaborativeproblem solving,conversations, and teamwork generate a significant proportion of

they relate to the customer’s perception of value. Therefore, it is also an open structure, and the network can be developed in a fractal pattern just like the extended value chains. To this point, a Porter value chain is an abstraction of a business process, because an enterprise is a business process entity in a global information-processing network. Therefore there is a different from the original model proposed by Gale and Eldred [6], which focused on the process view of abstraction instead of the global supply chain value management scheme. Again, the term ‘‘abstraction’’ is used here to describe the generalization of any business process for the modelling purpose. Through this characterization, a business process can be generalized into what we call the Abstract Business Process (ABP). The Abstract Business Process is just like a business process which can be decomposed into several sub-processes, which is, an ABP is made up of lower level ABPs. This interconnected value chain system can act as a supply chain or information processing network that encompasses the modern business world, and participating organizations can readily extend their technologies and knowledge to their partners. The extended enterprise aspect enables supply chain integration and more effective outsourcing solutions for both internal and external stakeholders [7]. On the other hand, the rapid growth of the Internet brought about new business philosophies and fostered the growth of new strategic alliance, data, information, and business process integration across the borders of enterprises. The information processing view of an organization has been considered one of the most influential contributions to the contingency literature [8]. In this philosophy, information processing network provides the channels for exchange and processing of infor￾mation in a global system. The primary role of the information processing network is to provide information exchange among its subsystem—the information processing nodes as shown in Fig. 9.2. In Fig. 9.2, the information processing network view of ‘‘virtual enterprise’’ can be from different divisions, departments, or organizations. The information-pro￾cessing nodes within the network are responsible for sending, receiving, selecting, producing, and communicating (i.e. exchange data and information) with other information processing nodes. An organization’s value chain consists of all activities performed to design, produce, market, deliver, and support its product and service. For the analysis of business data communication, the information processing network connects its nodes, which in turn, are organized into business components. The business components of the organization include people, pro￾cesses, events, machines, and information that interact and combine to produce the outputs (e.g. information, product, service) of the organization. Recalled that a knowledge-enabled organization is a learning organization, one where all employees are using their knowledge, skills, and learning to meet today’s business challenges and to create new opportunities for the future. Therefore, the value is created whenever information flows through the information process nodes and the information processing network. In business practices, collaborative problem solving, conversations, and teamwork generate a significant proportion of 80 C.-H. Chao

819TheFrameworkof InformationProcessingNetworkInformationProcessingNodesInformationProcessingNodesReceiveSendProcessProcessChannelOperationOperationNetworkProcessProcessReceiveSendProcessProcess+1StateStateVirtualProcessIntegration(Inter-enterpriseProcessing)EnterpriseCEnterpriseAEnterpriseBCustomer ServiceProductionLogisticsProcessingProcessingProcessingFig.9.2Theinformationprocessingnodesforvalue creationtheknowledge assets that exist within a firm or entire supply chain.With networkconnectivity,the virtual enterprise can work collaboratively to share knowledgeand best practices that enable supply chain“"co-evolving."The beauty of a supply chain knowledge network is that the true value of theinformation surpasses the conventional boundaries that often restrict employeesthinking [9]. The information-processing nodes within each network in eitherorganization can work collaboratively to achieve strategic goals in the newlyjoined network.Therefore, values include all information that flows through anorganization and between an organization and its suppliers,its distributors,and itsexisting or potential customers.Indeed, data and information defines businessrelationships

the knowledge assets that exist within a firm or entire supply chain. With network connectivity, the virtual enterprise can work collaboratively to share knowledge and best practices that enable supply chain ‘‘co-evolving.’’ The beauty of a supply chain knowledge network is that the true value of the information surpasses the conventional boundaries that often restrict employees’ thinking [9]. The information-processing nodes within each network in either organization can work collaboratively to achieve strategic goals in the newly joined network. Therefore, values include all information that flows through an organization and between an organization and its suppliers, its distributors, and its existing or potential customers. Indeed, data and information defines business relationships. Enterprise A Enterprise B Enterprise C Operation Process Send Process Receive Process State Information Processing Nodes Send Process Receive Process Operation Process State Information Processing Nodes Channel Network Production Processing Logistics Processing Customer Service Processing Virtual Process Integration (Inter-enterprise Processing) Fig. 9.2 The information processing nodes for value creation 9 The Framework of Information Processing Network 81

82C.-H. Chao9.4Portal of Storm: Streams Computing,Data Analyticsand OrganicInnovationData analytics offers values! Enterprises want analytics to exploit their growingdata and computational power to get smart,get innovative that they never couldbefore [10]. Therefore, enterprises are becoming data and knowledge intensiveinstead of capital intensive. In business practices,data is the basic building blockofinformation,whereas information is the glue that unifies businesses partnershipsand ultimately of a knowledge-based business.Today,the streams computing and cloud computing are two major methodol-ogies todeal withbigdata analytics.The streams computingfocuses onprocessingbig and continuous "motion"data with less than a microsecond in a real-timebasis. Unlike traditional data analysis approach, the big data analytics can beprocessed and analyzed before being store in the database. Whereas, the cloudcomputing offers a variety of flexible computing schemes to deal with data in adistributedmanner.Senge [1i] in his book on system thinking described a learning organization as"an organization that is continually expanding its capacity to create its future."The information processing network and data analytics made organizationallearning more effective and will enable organization to behave like living organicstructure to adapt and evolve in a changing environment.Interdependency betweencore knowledge workers will increase as the success of the enterprise becomesmore dependent on how well they integrate theirknowledgeto produce innovativeproducts and services [12]. The innovation is never been close to reach for eachsupply chain members.9.5TheFrameworkforBigDataApplicationsWith the growing maturity of Internet technology, cloud computing,streamscomputing,and big data analytics, the KM system can become more active.Bigdata applications are needed for the entire supply chain knowledge networkbecause the effectiveness of an supply chain solution will depend largely on itsability todeliver an accurateand common view of customer demand data,as wellas any subsequent events, plans, or other business data. Knowledge managementsystem must be ableto capture,process,refine big data and information fromdifferent data sources that employees need. The technologies to facilitate thesehighly interactive communications are summarized in Fig.9.3.In this figure, there are business application and system domain, servers, andbig data analytics domain.The big data analytics domain consists of integratedknowledge application, composite repository and database.The business appli-cation and system domain provides greatest possible access for diverse computingdevices, such as PC,NB,Tablet PC,Smartphone, and PDA to use enterprise

9.4 Portal of Storm: Streams Computing, Data Analytics and Organic Innovation Data analytics offers values! Enterprises want analytics to exploit their growing data and computational power to get smart, get innovative that they never could before [10]. Therefore, enterprises are becoming data and knowledge intensive instead of capital intensive. In business practices, data is the basic building block of information, whereas information is the glue that unifies businesses partnerships and ultimately of a knowledge-based business. Today, the streams computing and cloud computing are two major methodol￾ogies to deal with big data analytics. The streams computing focuses on processing big and continuous ‘‘motion’’ data with less than a microsecond in a real-time basis. Unlike traditional data analysis approach, the big data analytics can be processed and analyzed before being store in the database. Whereas, the cloud computing offers a variety of flexible computing schemes to deal with data in a distributed manner. Senge [11] in his book on system thinking described a learning organization as ‘‘an organization that is continually expanding its capacity to create its future.’’ The information processing network and data analytics made organizational learning more effective and will enable organization to behave like living organic structure to adapt and evolve in a changing environment. Interdependency between core knowledge workers will increase as the success of the enterprise becomes more dependent on how well they integrate their knowledge to produce innovative products and services [12]. The innovation is never been close to reach for each supply chain members. 9.5 The Framework for Big Data Applications With the growing maturity of Internet technology, cloud computing, streams computing, and big data analytics, the KM system can become more active. Big data applications are needed for the entire supply chain knowledge network, because the effectiveness of an supply chain solution will depend largely on its ability to deliver an accurate and common view of customer demand data, as well as any subsequent events, plans, or other business data. Knowledge management system must be able to capture, process, refine big data and information from different data sources that employees need. The technologies to facilitate these highly interactive communications are summarized in Fig. 9.3. In this figure, there are business application and system domain, servers, and big data analytics domain. The big data analytics domain consists of integrated knowledge application, composite repository and database. The business appli￾cation and system domain provides greatest possible access for diverse computing devices, such as PC, NB, Tablet PC, Smartphone, and PDA to use enterprise 82 C.-H. Chao

839TheFrameworkofInformationProcessingNetworkBig Data Analytics DomainBusiness Application and System Domain(Access/Retrieval/Integration/Utilization)e-Commerce SystemIntegrated KnowledgeApplicationSupply Chain Management System (c.g. ERP)Office SystemSearching, Indexing Filtering, and QueryWireless Apps.DSS,ESS (Decision/Executive Support System)Front/BackOfficeSystemCloud Applications (laas, PaaS, SaaS)MIS (Management Information System)Others Apps. and Systems (e.g. Messaging)Data Mining (c.g, OLAP, Agent)Business Analytics (e.g. Facet, Time1series, Trend, Connection, Deviation)ServersOthers big data analytics methodologiesApplication Server+Web/FTP/News ServerTransaction Processing ServerComposite Repository/DatabaseDocument/WorkflowServerMetadataOthers(Internet/lntranet/Extranet)Data Warehouse/Data Mart(Knowledge and Innovation Incubator)Data in many type (e-g, business data Enterprise-wide IPN:socialnetwork,blog.forum,webData/lnformation/KnowledgeFlowspage, multimedia, SMS, email.Data/Information/KnowledgeSharingandExchangesensor data, GPS, RFID, etc.)Information MappingData sources(Knowledge Assets)Fig. 9.3 Schematic system framework for big data applicationsresources (applications).The role of servers can be a gateway or middleware thatmayreside in anywhere in the information processing network (public/private/hybrid cloud) that provide services.To help enterprises organize information residing in multiple locations and de-liver it to prospective users,directory service,indexing,and searching are requiredin integrated knowledge applications. In addition, the central or distributed datarepositorythatprovidesorcapturesthedata and informationforemployees anddecision-makingis veryimportantinthis applicationframework.In cloud appli-cation scheme, this would be the data center. The decision-making support system(DSS,ESS)isthedriverthatconsolidatesand directstheoverallresources of thesupply chain to the most mission-critical business activities. Therefore,the mostimportant part to differentiatefrom traditional framework of supply chain appli-cation is the big data analytics domain.The information can be managed only whenit is embodied as content, which represents a specific combination of informationand amanageabledata[13].Given theEnterpriseResourcePlanning(ERP)systemas an example, the ERP is good in managing processing data but fall short inproviding intelligent tactics and strategic suggestions for decision-makingsThebig data analytics domain enhances the capabilities ofmining,warehousingextracting,and analyzing of heterogeneous data.For theanalysis of data, there aregrowingnumbersofanalyticstools,algorithm,andartificialintelligencetoimprovea better extracting of heterogeneous data and turn into useful, valuable information

resources (applications). The role of servers can be a gateway or middleware that may reside in anywhere in the information processing network (public/private/ hybrid cloud) that provide services. To help enterprises organize information residing in multiple locations and de￾liver it to prospective users, directory service, indexing, and searching are required in integrated knowledge applications. In addition, the central or distributed data repository that provides or captures the data and information for employees and decision-making is very important in this application framework. In cloud appli￾cation scheme, this would be the data center. The decision-making support system (DSS, ESS) is the driver that consolidates and directs the overall resources of the supply chain to the most mission-critical business activities. Therefore, the most important part to differentiate from traditional framework of supply chain appli￾cation is the big data analytics domain. The information can be managed only when it is embodied as content, which represents a specific combination of information and a manageable data [13]. Given the Enterprise Resource Planning (ERP) system as an example, the ERP is good in managing processing data but fall short in providing intelligent tactics and strategic suggestions for decision-makings. The big data analytics domain enhances the capabilities of mining, warehousing, extracting, and analyzing of heterogeneous data. For the analysis of data, there are growing numbers of analytics tools, algorithm, and artificial intelligence to improve a better extracting of heterogeneous data and turn into useful, valuable information Integrated Knowledge Application Composite Repository/Database • Application Server • Web/FTP/News Server • Transaction Processing Server • Document/Workflow Server • Others (Internet/Intranet/Extranet) Servers • Metadata • Data Warehouse/Data Mart • Data in many type (e.g. business data, social network, blog, forum, web page, multimedia, SMS, email, sensor data, GPS, RFID, etc.) (Knowledge Assets) (Knowledge and Innovation Incubator) (Access/Retrieval/Integration/Utilization) • Searching, Indexing Filtering, and Query • DSS,ESS (Decision/Executive Support System) • MIS (Management Information System) • Data Mining (e.g. OLAP, Agent) • Business Analytics (e.g. Facet, Time￾series, Trend, Connection, Deviation) • Others big data analytics methodologies Enterprise-wide IPN: Data/Information/Knowledge Flows Data/Information/Knowledge Sharing and Exchange Information Mapping Data sources • e-Commerce System • Supply Chain Management System (e.g. ERP) • Office System • Wireless Apps. • Front/Back Office System • Cloud Applications (IaaS, PaaS, SaaS) • Others Apps. and Systems (e.g. Messaging) Business Application and System Domain Big Data Analytics Domain Fig. 9.3 Schematic system framework for big data applications 9 The Framework of Information Processing Network 83

84C.-H. Chaofor supply chain decision-making and corporate knowledge assimilation. Theproposed schematic structure for big data analytics combines automation,businessrules, artificial intelligence, workflow, analytical tools and advanced messaging-analysis technologies to allow e-businesses to deliver information and to respond tocustomerrequestsrapidlyand accurately[14].Thebusinessapplicationswillthencouple with data analytics applications to conduct daily business operations, such asERP,CRM, word-processing,spreadsheets, accounting,and soforth.9.6ConclusionWith the growing awareness of big data applications, enterprises have to recognizethis competitive advantage and shift their focuses on building a robustknowledge-based system with big data analytics and applications capability.In such a way theenterprises are capable of quickly consolidating critical competencies and physicalprocesses to gain competitive advantages easilyOn the other hand, the growing of strategic alliances and partnerships on aglobal scale that brought about the formation of inter-enterprise virtual organi-zations capableof leveraging the skills, resources,and innovativeknowledge thatre-side at different locations in a supply chain network.An organization's valuechain consists of all activities performed to design, produce, market, deliver, andsupport its product and service. The value chain view of information processingnetwork is introduced to enhance collaboration encourage innovation, boost pro-ductivity,achieve adaptivity, and increase the information system efficiency.Theinformation process nodes are the keys to transfer data, information into values.Bigdata,asthenextfrontierforinnovation,competition,andproductivity,inthe era of big data, enterprises usetheir information processingnetworks and strivetobecomeknowledge-enabled enterprises to ensure that all employees are able toutilize the knowledge and skills they need to meet their corporate goals. Theproposed application framework stresses on the big data analytics domain toenhance the capabilities of mining,warehousing,extracting,and analyzing ofheterogeneous data and turns into useful, valuable information for supply chaindecision-making and corporate knowledge assimilation. It also combines auto-mation, business rules,artificial intelligence,workflow,analytical tools andadvanced analyticstechnologies toallow enterprises to deliverinformation and torespondtocustomerrequestsrapidlyandaccuratelyThe proposed application framework,indeed, is integrated in terms of peoplefocused on processes and values that ultimately respond to customer demand, butits success requires data that can integrate and support every exchange of infor-mation across the entire supply chain. Enterprises will realize that content is asimportant as technological framework to the enterprise application architecture.Awell-designed and well-integrated knowledge-based SCM system with data ana-lytics capability will improve existing supply chain performance and provideenterprise agility in the change business environment

for supply chain decision-making and corporate knowledge assimilation. The proposed schematic structure for big data analytics combines automation, business rules, artificial intelligence, workflow, analytical tools and advanced messaging￾analysis technologies to allow e-businesses to deliver information and to respond to customer requests rapidly and accurately [14]. The business applications will then couple with data analytics applications to conduct daily business operations, such as ERP, CRM, word-processing, spreadsheets, accounting, and so forth. 9.6 Conclusion With the growing awareness of big data applications, enterprises have to recognize this competitive advantage and shift their focuses on building a robust knowledge￾based system with big data analytics and applications capability. In such a way the enterprises are capable of quickly consolidating critical competencies and physical processes to gain competitive advantages easily. On the other hand, the growing of strategic alliances and partnerships on a global scale that brought about the formation of inter-enterprise virtual organi￾zations capable of leveraging the skills, resources, and innovative knowledge that re-side at different locations in a supply chain network. An organization’s value chain consists of all activities performed to design, produce, market, deliver, and support its product and service. The value chain view of information processing network is introduced to enhance collaboration encourage innovation, boost pro￾ductivity, achieve adaptivity, and increase the information system efficiency. The information process nodes are the keys to transfer data, information into values. Big data, as the next frontier for innovation, competition, and productivity, in the era of big data, enterprises use their information processing networks and strive to become knowledge-enabled enterprises to ensure that all employees are able to utilize the knowledge and skills they need to meet their corporate goals. The proposed application framework stresses on the big data analytics domain to enhance the capabilities of mining, warehousing, extracting, and analyzing of heterogeneous data and turns into useful, valuable information for supply chain decision-making and corporate knowledge assimilation. It also combines auto￾mation, business rules, artificial intelligence, workflow, analytical tools and advanced analytics technologies to allow enterprises to deliver information and to respond to customer requests rapidly and accurately. The proposed application framework, indeed, is integrated in terms of people focused on processes and values that ultimately respond to customer demand, but its success requires data that can integrate and support every exchange of infor￾mation across the entire supply chain. Enterprises will realize that content is as important as technological framework to the enterprise application architecture. A well-designed and well-integrated knowledge-based SCM system with data ana￾lytics capability will improve existing supply chain performance and provide enterprise agility in the change business environment. 84 C.-H. Chao

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References 1. Computer Science Corporation (2000) ‘‘@ insights. A look at business transformation in today’s e-world’’. Computer Science Corporation (CSC), 4–7 2. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute (MGI), San Francisco, CA, pp 1–137 3. Herodotou H, Lim H, Luo G, Borisov N, Dong L, Cetin FB, Babu S (2011) Starfish: a self￾tuning system for big data analytics. In: Proceedings of the Fifth CIDR Conference 4. Porter M (1985) Competitive advantage: creating and sustaining superior performance. The Free Press, NY 5. Kuglin FA (1998) Customer-centered supply chain management: a link-by-link guide. AMACOM, a Division of American Management Association, New York 6. Gale T, Eldred J (1996) Getting results with the object-oriented enterprise model. SIGS Publications, Inc., New York 7. Curran TA, Ladd A, Keller G (2000) SAP R/3 business blueprint, understanding enterprise supply chain management. Prentice Hall, Inc, Sydney 8. Wang ET (2003) Effect of the fit between information processing requirements and capacity on organizational performance. Int J Inf Manage 23(3):239–247 9. Don Tapscott, (1999) Creating value in the new economy. Harvard Business Press, Cambridge, MA 10. LaValle Steve et al (2011) Big data, analytics and the path from insights to value. MIT Sloan Manag Rev 52(2):21–32 11. Senge PM (1990) The fifth discipline: the art and practice of the learning organization. Doubleday, Currency NY 12. Alen BJ (1999) Knowledge capitalism, business, work, and learning in the new economy. Oxford University Press, Oxford, pp. 56 13. Laugero G, Globe A (2002) Enterprise content services, connecting information and profitability. Addison Wesley, NY 14. Tiwana A (2000) The knowledge management toolkit, practical technique for building a knowledge management system. Prentice Hall, Upper Saddle River, NJ 9 The Framework of Information Processing Network 85