Afourth dimension resulted specificallyfrom the“native"discussion,whichwetermGlobalveracity. SAS's non-specific term “variability"is used to discuss fluctuations in dataexplorationvolume, and that dimension is beginning to appear in popular literature.Our job inof BigDatasupply chainmanagement istohandlechanges in volumeand usage,so itis only slightlysurprising that none ofour respondents voiced concern with this issue/dimension.Yetvariability (perhaps inconsistency)concerns supply chainmanagers.In particular,719preservingbothstructuredandunstructureddata'svaluewasaconcern.Otherconcernsthat surfaced included the possibility of partners or SBUs altering data, what to shareand what to safeguard, and the data's long-term value:aeaWe sort of like translate Big Data as having access to as much information as possible to beableto thentranslatethat into somethingthat'suseable.The issuewithBigData ofcourse,itbecomes unwieldy when it becomes so finite that you actually don't know how to use it andhow to translate it, or even if it is usable (U4).The SAS use of the term“complexity"denotes the perceived importance of supplychain relationships. Specifically,..J it's necessary to connect and correlaterelationships,hierarchies and multiple data linkages oryour data can quickly spiraloutofcontrol[4]"We see this inthecontextofevery interaction inthesupply chain,soitisnotspecifictoBigData.Thisdiscussionalsoappliestothetermforthefourthdimension - veracity, which deals with the legitimacy or correctness of data, and isdetailed below.This term was recently adopted by other researchers in SCML(see Barratt et al.,2014):Big Data is data that you don't know what to do with it. In this day and age, we get so muchdata and complexity,it becomes so complex, and the key isto be ableto know what to do withitFor example,you usecomplexdata in manyplacesin thesupply chain,CRMorProcurement, everywhere. Each and every department within the business, they don't havepieces or sets of systems to make use of this data.Thekey,and I'm sure in many otherbusinesses, is to know how much of this data you need really, and there's just a slew" ofinformation, Some of the times, some of it may just be useless, and the key, like I said, is toknow what to do with it and how much of it to use, and also how much to invest in technologyand to turn all of this into information (T1)Fromaglobal perspective,and consistentwithextantdefinitions of BigData,wesuggest that Big Data in supply chain management should be characterized asstructured and unstructured relationship-based information unique to businessbecause of its volume, velocity,variety,and veracity.Table III summarizes thesefourdimensions as employed in supply chain relationships.ManageriallyderivedkeysuccessfactorsWe identified several key issues and topics that were most heavily noted in theinterviews.Wehavesummarizedthesein TableIVwhichindicatescountsandpercentages pertaining to the number of respondents who discussed a particularissue within each country category. We provide further support with respondentsquotations in TableV.SC systems integrationRespondents from all six countries commented on Big Data systems integration.Theyviewed communicatingwithsuppliers onthe samesystemasawaytoreducewasteTouching on Big Data's ability to improve visibility by connecting supply chain
A fourth dimension resulted specifically from the “native” discussion, which we term veracity. SAS’s non-specific term “variability” is used to discuss fluctuations in data volume, and that dimension is beginning to appear in popular literature. Our job in supply chain management is to handle changes in volume and usage, so it is only slightly surprising that none of our respondents voiced concern with this issue/dimension. Yet variability (perhaps inconsistency) concerns supply chain managers. In particular, preserving both structured and unstructured data’s value was a concern. Other concerns that surfaced included the possibility of partners or SBUs altering data, what to share and what to safeguard, and the data’s long-term value: We sort of like translate Big Data as having access to as much information as possible to be able to then translate that into something that’s useable. The issue with Big Data of course, it becomes unwieldy when it becomes so finite that you actually don't know how to use it and how to translate it, or even if it is usable (U4). The SAS use of the term “complexity” denotes the perceived importance of supply chain relationships. Specifically, “[.] it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control[4].” We see this in the context of every interaction in the supply chain, so it is not specific to Big Data. This discussion also applies to the term for the fourth dimension – veracity, which deals with the legitimacy or correctness of data, and is detailed below. This term was recently adopted by other researchers in SCML (see Barratt et al., 2014): Big Data is data that you don't know what to do with it. In this day and age, we get so much data and complexity, it becomes so complex, and the key is to be able to know what to do with it. For example, you use complex data in many places in the supply chain, CRM or Procurement, everywhere. Each and every department within the business, they don’t have pieces or sets of systems to make use of this data. The key, and I'm sure in many other businesses, is to know how much of this data you need really, and there’s just a “slew” of information. Some of the times, some of it may just be useless, and the key, like I said, is to know what to do with it and how much of it to use, and also how much to invest in technology and to turn all of this into information (T1). From a global perspective, and consistent with extant definitions of Big Data, we suggest that Big Data in supply chain management should be characterized as structured and unstructured relationship-based information unique to business because of its volume, velocity, variety, and veracity. Table III summarizes these four dimensions as employed in supply chain relationships. Managerially derived key success factors We identified several key issues and topics that were most heavily noted in the interviews. We have summarized these in Table IV, which indicates counts and percentages pertaining to the number of respondents who discussed a particular issue within each country category. We provide further support with respondents’ quotations in Table V. SC systems integration Respondents from all six countries commented on Big Data systems integration. They viewed communicating with suppliers on the same system as a way to reduce waste. Touching on Big Data’s ability to improve visibility by connecting supply chain 719 Global exploration of Big Data Downloaded by Huazhong University of Science and Technology At 22:37 29 November 2016 (PT)
JPDLMBig Data - structured and unstructured relationship-based information that is unique to business46,8because of itsvolume,velocity,variety,andveracityBig Data dimensionExplanationVolumeSensors embedded into everyday objects will soonThe total amount of data availableresult in billionsofconstantlyupdated data feedsrelaying environmental,location,cost,andother720informationVelocityMachine to machine processes exchange dataThe increasing speed of data creationbetween devices at an increasing rated)91VarietyStructured (numeric) operational databasesTable III.The multiple types of data that are createdUnstructured text documents, e-mail, video, audio,Supply chainand financial transactions2Veracitymanager derivedThe adjustment, conversion, communication,dimension ofThe changes in data occurring during andmanipulation, and safeguarding of dataBig Datafollowing collection that influence its usefulnessUSA% ofBRIC/MINTSRawIndustrialized nation(n= 9)totaln=27partners (n = 9)partners (n=9)Most heavily noted issues (>40%)9631867SC systems integrationImproved forecasting/decision77672234174263makingHuman capital1659884321452Risk and security governance1348Storage6311244Operational efficiency61141Partner transparencyTable IV.Othernotable issues(>25%)Big data key23233398742233One version of the truthsuccess factors30as noted byClimbing the learning curve26respondentsCustomerorientationmemberswithmarketneeds.Somenoted thatusingintegratedinformationcanprovideimprovements all along the supply chain:I think that every party in the supply chain may benefit from the rewards created by usingBig Data. It is good to use the integrated information among business partners (U2).However,thebenefits of systems integrationamong supplychainpartnerscomewithcosts.Firms looking tointegrateinformation confrontinherentexpenses associatedwith acquisition and implementation_of required resources among supply chainpartners.Two examples might be buying equipment and training personnel.Admittedly,specific equipment costs (e.g.purchase and training) can vary greatlydepending on firm and industryfactors.Respondents discussed platforms such asSAP,andthechallengesthatoneUSfirmfacesareexplainedhere:It is expensive for companies to afford a technology team that has capability to apply BigData. Ail the integrated computerized systems such as SAP are expensive. It costs millions
members with market needs. Some noted that using integrated information can provide improvements all along the supply chain: I think that every party in the supply chain may benefit from the rewards created by using Big Data. It is good to use the integrated information among business partners (U2). However, the benefits of systems integration among supply chain partners come with costs. Firms looking to integrate information confront inherent expenses associated with acquisition and implementation of required resources among supply chain partners. Two examples might be buying equipment and training personnel. Admittedly, specific equipment costs (e.g. purchase and training) can vary greatly depending on firm and industry factors. Respondents discussed platforms such as SAP, and the challenges that one US firm faces are explained here: It is expensive for companies to afford a technology team that has capability to apply Big Data. All the integrated computerized systems such as SAP are expensive. It costs millions Big Data – structured and unstructured relationship-based information that is unique to business because of its volume, velocity, variety, and veracity Big Data dimension Explanation Volume The total amount of data available Sensors embedded into everyday objects will soon result in billions of constantly updated data feeds relaying environmental, location, cost, and other information Velocity The increasing speed of data creation Machine to machine processes exchange data between devices at an increasing rate Variety The multiple types of data that are created Structured (numeric) operational databases. Unstructured text documents, e-mail, video, audio, and financial transactions Veracity The changes in data occurring during and following collection that influence its usefulness The adjustment, conversion, communication, manipulation, and safeguarding of data Table III. Supply chain manager derived dimension of Big Data USA (n ¼ 9) BRIC/MINTS partners (n ¼ 9) Industrialized nation partners (n ¼ 9) Raw total % of n ¼ 27 Most heavily noted issues (W40%) SC systems integration 9 6 3 18 67 Improved forecasting/decision making 7 6 4 17 63 Human capital 7 7 2 16 59 Risk and security governance 8 2 4 14 52 Storage 8 2 3 13 48 Operational efficiency 6 3 3 12 44 Partner transparency 6 4 1 11 41 Other notable issues (W25%) One version of the truth 3 2 4 9 33 Climbing the learning curve 3 3 2 8 30 Customer orientation 3 2 2 7 26 Table IV. Big data key success factors as noted by respondents 720 IJPDLM 46,8 Downloaded by Huazhong University of Science and Technology At 22:37 29 November 2016 (PT)