(pamuos)IJPDLMe insassSeeeeeandaeeBeeesse46,8eineroeae"saouoe000'-00001SE714o000'000<-000.00-000'000000-000.0000001000suoond00oeanonnsegueraanoaA.nsnpupar(ada)puepueeepo82=0iaseeonerasedeseenssnoscamgredaponnsopeaLI JOld品4roan000'09-000'001000'0900000000'01-0000suoer enn000'T-000'S00>sunoorosenp(odk)nsnpupaerooasenepuenodeueeaoaoanooeiopeaK.nsnpusissaoSGpresseanSnenuetSeeaoaseeSnpeueoareinssaneneaeesraoBo=0SS0色ooua na00000-000001000'09-000'001000T-000g000'T-000'S000'T-000'SvsnsuoonoonooisiueaeopadKnspuaded(aa)Table I.apoRespondents33S55
USA Industrial nations BRIC/MINTS Code Industry (type) Firm employee count Job title, supply chain classification Code Industry (type) Firm employee count Job title, supply chain classification Code Industry (type) Firm employee count Job title, supply chain classification U1 Paper products 100,000-50,000 Project integration manager, manufacturing G1 Automotive and aerospace 50,000-10,000 President and CEO, 3PL/ 4PL/ consulting C1 Kitchen and bath products 10,000-5,000 Chief information officer, manufacturing U2 Automotive 5,000-1,000 Purchasing and logistics manager, manufacturing G2 Automotive industry o100 Vice president, supplier/ distributer C2 Automotive 50,000- 10,000 IT supply chain manager, manufacturing U3 Automotive 100,000-50,000 System engineer, manufacturing G3 Information technology and system integration 5,000-1,000 Head of departure management, 3PL/4PL/ Consulting C3 Electronics and infrastructure 50,000- 10,000 Asset controller, manufacturing U4 3PL and publishing 5,000-1,000 Senior director of global business development, manufacturing G4 Sporting and athletic goods 100,000-50,000 Director of supply chain management, manufacturing I1 Technology engineering and infrastructure W300,000 American material manager, manufacturing U5 Healthcare 5,000-1,000 Purchasing director, retailing G5 Land transport, ocean and air freight, and contract logistics 100,000-50,000 Senior VP of IT, transportation I2 Automotive 50,000- 10,000 Deputy general manager, manufacturing (continued ) Table I. Respondents 714 IJPDLM 46,8 Downloaded by Huazhong University of Science and Technology At 22:37 29 November 2016 (PT)
Globaleddns o peeSeeaeerBearpreoessexplorationsoosoeaoepereeamnof Big Data三000T-000's000T-000's000T-000'S715-00000000aunn100aoeeaAnsnpa)pFZL33Sreeednsoioee KddnseuoeiouSuinsnoonad qorridtAS0a00001-00000suoneuenspl000'T-000S000'T-000'S001>une00ose(ok)nspupoope presaaoruoneuioyuooasaapoepedospue poodaspoos902soaooenSnoenueSeanemuoenorednosoe.o0SanosSugeary perangBb色品roa00001-000'00000'T-000S000000000vSnooA^Ansnp(aa)apoTable I.89n5B
USA Industrial nations BRIC/MINTS Code Industry (type) Firm employee count Job title, supply chain classification Code Industry (type) Firm employee count Job title, supply chain classification Code Industry (type) Firm employee count Job title, supply chain classification U6 Retail W150,000 Lead for corporate initiatives and transportation, retailing G6 Cosmeticscustomer packaged goods 50,000-10,000 Chief supply chain officer, manufacturing I3 Water and environment management 5,000-1,000 Head of field management, manufacturer U7 Dairy 50,000-10,000 Director supply chain, manufacturing K1 Research and development o100 Information service provider, 3PL/ 4PL/ consulting T1 Automotive 5,000-1,000 Group chairman and CEO, manufacturing U8 Energy 5,000-1,000 Strategic sourcing, manufacturing K2 Food and beverage 5,000-1,000 Manager, retailing T2 Automotive 5,000-1,000 Head of supply chain and operations, manufacturing and retailing U9 Retail W150,000 Director of transportation technologies, retailing K3 Transportation/ information technology 5,000-1,000 Senior fellow, 3PL/4PL/ consulting T3 Food and beverage 50,000- 10,000 Multichannel operations manager, retailing Table I. 715 Global exploration of Big Data Downloaded by Huazhong University of Science and Technology At 22:37 29 November 2016 (PT)
IJPDLMWhat are theFirst obstacleSecond obstacleThird obstacleFourth obstacleFifthobstacleobstacles towards46,8using Big Data incategory issuecategory issuecategory issuecategory issuecategory issueyour Supply Chainrelationships?Importance:,Importance:Importance:Importance:Importance:Rank:Rank:Rank:Rank.RankWhyisarobstacle?1//716How does thisobstacle impactyour relationshipperformance?()9100Who in therelationship isXimpactedthemosby this obstacle?Is there a way foryour supply chain toovercome thisobstacle?"Youhave"You have"You haveYou have"Youhave2discussed [firstdiscussed [seconddiscussed [thirddiscussed [fourthdiscussed [fifthLE:issue]. Whenissue].Whenissue]. Whenissuel.Whenissue]. Whenthinking about thethinking about thethinking about thethinking about thethinking about theTable II.future of yourfutureofyourfuture of yourfutureofyourfuture ofyourIVNative categorybusiness, are therebusiness, are therebusiness, are therebusiness, are therebusiness, are thereABflow exampleother things thatother things thatother things thatother things thatother things thatnconsideryuconsidervouconsidergyou consider?"you consider?Following Wallendorf and Belk (1989),we designed the data collection and analysis toLpueadhereto three dimensions of trustworthiness: integrity,confirmability,and triangulationaeaeoToassureintegrity-meaningguardingagainstfabrications-respondentswereassuredconfidentiality; we reference all respondents only by codes, and we identify firms only bysize,industry,and nationality.Confirmability means consistently applied techniques thatenablemultipleresearcherstoarriveseparatelyatsimilarconclusions.Weattainedthisdimension by employing three separate researchers to examine all interviews individuallyusing open coding,with specific attention tocommonalitiesanddifferenceswithinandbetweennationalities.Finally,data triangulationmeansmultipleresearchers'findings canreconciletoa commonmeaning.Weachieved triangulation throughmultiplemeetings of thecoding researchers to comparefindings.Afterwe identified themes,each coding researcherre-examined the data to determine if any ofthefindingsdid not correspond to comments inthe data.Additionally,a separate memberof the research team,who was not part of thecoding process,examined thefindings tofurther strengthen the analyses'confirmabilityResultsFollowing coding completion, the research team examined the results for concepts thatsurfaced from thedata relevant to the three research questions motivating this study.Thedescription of results begins with the definitions that emerged,followed bykeysuccess factors and enablers.Finally,we discuss applicabletheory as it relates tothevalue Big Data can bring to supply chains.Whatsupplychainmanagers call“BigData"[Big] Data contain a wide variety [of information] I see so many databases made withoutpurpose or definition. Everyone needs their own definition of Big Data in order to use BigData in a productive way (K3)
Following Wallendorf and Belk (1989), we designed the data collection and analysis to adhere to three dimensions of trustworthiness: integrity, confirmability, and triangulation. To assure integrity – meaning guarding against fabrications – respondents were assured confidentiality; we reference all respondents only by codes, and we identify firms only by size, industry, and nationality. Confirmability means consistently applied techniques that enable multiple researchers to arrive separately at similar conclusions. We attained this dimension by employing three separate researchers to examine all interviews individually using open coding, with specific attention to commonalities and differences within and between nationalities. Finally, data triangulation means multiple researchers’ findings can reconcile to a common meaning. We achieved triangulation through multiple meetings of the coding researchers to compare findings. After we identified themes, each coding researcher re-examined the data to determine if any of the findings did not correspond to comments in the data. Additionally, a separate member of the research team, who was not part of the coding process, examined the findings to further strengthen the analyses’ confirmability. Results Following coding completion, the research team examined the results for concepts that surfaced from the data relevant to the three research questions motivating this study. The description of results begins with the definitions that emerged, followed by key success factors and enablers. Finally, we discuss applicable theory as it relates to the value Big Data can bring to supply chains. What supply chain managers call “Big Data” [Big] Data contain a wide variety [of information]. I see so many databases made without purpose or definition. Everyone needs their own definition of Big Data in order to use Big Data in a productive way (K3). Table II. Native category flow example 716 IJPDLM 46,8 Downloaded by Huazhong University of Science and Technology At 22:37 29 November 2016 (PT)
Managers around the world careabout Big Data.Yet, we quicklylearned that there isGlobalno consensus among supply chain managersregarding Big Data's definition.Theexplorationrespondentsinthisstudydiscussedacontinuumofdata--fromunstructured"tweet"ofBigDatabased data to internal logistics and operations indicators - suggesting that thediscussion of Big Data maybe confounded by a lack of clearly differentiated andinterpreted terms. One respondent touched on Big Data's capacity to continually717examinethecompetitivelandscape:I am not familiar with that wording [Big Data] My understanding for what you are after isthat you want to understand how we are dealing with the oneversion of the truth, forexample, in terms of data information.What weare using in our group is Bl, which isBusiness Intelligence, and the different systems in our landscape, in our supply chainorganization are feeding the data information and should therefore result into one version ofthe truth (G4).Academia and consultancyprovide terms that defineBig Data's context,butthis maydiffertremendouslyfrompractitioners.This inconsistency mayberesponsible inpartforthecommunicationproblem(s)amongtop management,logisticsmanagers,datascientists,consultants,and softwaremanufacturerswhoare only nowattemptingtointegrateBigDataintosupplychainprocesses.Thefollowinganalysisprovidesanextracteddefinition ofBigData for supplychainmanagersand fleshesoutthemeaningof the four dimensions being discussed in popular literature. While managers maydescribe Big Data differently,more than one respondent discussed using Big Data toseize and configureresources to copewith changing trends:Ithink it's having access to and making sense of your industry data and taking advantage of it.as best you can, using it to drive decisions and to see trends. It's grasping at allthe information,putting it into some meaningful models, and using it to drive decision-making (U9).These types of responses that detail using Big Data to increase visibility ofdemand and improve responsiveness wereprevalentthroughoutthedata collectionprocess across all countries.Yet managers seemed to have differing understandingsof what Big Datais in formTo build aplatform for futureacademic scrutiny,wehavesynthesizedwhatwediscoveredamongthesesixcountriesandrespondentsCentral to the issue's complexity is that each respondent supply chain managerdefines Big Data according to his or her own needs from operational and strategicmindsets.Forinstance:To me the Big Data, more like the orders/details in supply chain, including like how manyshipments each shipment, the route for each shipment, the weight, the CBMs for eachshipment, things like that. So it's the very details of our daily operations in supply chain (Cl)This is a decidedly operational view of Big Data, focussed heavily on resource visibilityWhilethe corporation's internal data are large,growing,and thus certainlyBig,thecomment ishardly consistent with what SAS callsBig Data,namely,“theexponentialgrowth and availability of data, both structured and unstructured[3]" Other managerstake the concept to the 20,o00-foot view, perhaps beyond the SAS definition:Compiling all data in huge databases available across countries and business unit records (GI)This supply chain manager considers Big Data to betruly multi-level, multi-firm,andmulti-industry data across all locations and countries.This broad viewencompasseswhat most distressessupplychainmanagers-howdowemakethis data useful?andhowdowemakeviablewhatwecannot define strategicallyand operationally?McAfee
Managers around the world care about Big Data. Yet, we quickly learned that there is no consensus among supply chain managers regarding Big Data’s definition. The respondents in this study discussed a continuum of data – from unstructured “tweet”- based data to internal logistics and operations indicators – suggesting that the discussion of Big Data may be confounded by a lack of clearly differentiated and interpreted terms. One respondent touched on Big Data’s capacity to continually examine the competitive landscape: I am not familiar with that wording [Big Data]. My understanding for what you are after is that you want to understand how we are dealing with the one version of the truth, for example, in terms of data information. What we are using in our group is BI, which is Business Intelligence, and the different systems in our landscape, in our supply chain organization are feeding the data information and should therefore result into one version of the truth (G4). Academia and consultancy provide terms that define Big Data’s context, but this may differ tremendously from practitioners. This inconsistency may be responsible in part for the communication problem(s) among top management, logistics managers, data scientists, consultants, and software manufacturers who are only now attempting to integrate Big Data into supply chain processes. The following analysis provides an extracted definition of Big Data for supply chain managers and fleshes out the meaning of the four dimensions being discussed in popular literature. While managers may describe Big Data differently, more than one respondent discussed using Big Data to seize and configure resources to cope with changing trends: I think it’s having access to and making sense of your industry data and taking advantage of it, as best you can, using it to drive decisions and to see trends. It’s grasping at all the information, putting it into some meaningful models, and using it to drive decision-making (U9). These types of responses that detail using Big Data to increase visibility of demand and improve responsiveness were prevalent throughout the data collection process across all countries. Yet managers seemed to have differing understandings of what Big Data is in form. To build a platform for future academic scrutiny, we have synthesized what we discovered among these six countries and respondents. Central to the issue’s complexity is that each respondent supply chain manager defines Big Data according to his or her own needs from operational and strategic mindsets. For instance: To me the Big Data, more like the orders/details in supply chain, including like how many shipments each shipment, the route for each shipment, the weight, the CBMs for each shipment, things like that. So it’s the very details of our daily operations in supply chain (C1). This is a decidedly operational view of Big Data, focussed heavily on resource visibility. While the corporation’s internal data are large, growing, and thus certainly Big, the comment is hardly consistent with what SAS calls Big Data, namely, “the exponential growth and availability of data, both structured and unstructured[3].” Other managers take the concept to the 20,000-foot view, perhaps beyond the SAS definition: Compiling all data in huge databases available across countries and business unit records (G1). This supply chain manager considers Big Data to be truly multi-level, multi-firm, and multi-industry data across all locations and countries. This broad view encompasses what most distresses supply chain managers – how do we make this data useful? and how do we make viable what we cannot define strategically and operationally? McAfee 717 Global exploration of Big Data Downloaded by Huazhong University of Science and Technology At 22:37 29 November 2016 (PT)
JPDLMandBrynjolfsson (2012)pioneered thediscussion ofBigDatabydefining itaccordingto three specific aspects: the volume of information produced, the velocity at which it is46,8created,andthevarietyofformsittakes.SASexpandedthisdefinitiontoincludetheterms/dimensions ofcomplexityand variability.All of thesedimensions were evidentin our research but we found that Big Data could best be described through fourdimensions:volume,velocity,variety,and veracity.718Volumewas an issue ofcomprehensiveconcern.Respondents in all countries notedthe immeasurableamountof availabledataand theneedtomanage itto helptiemarketinformation todecision processes:aeaHow can you get sensible [...J information out of the Big Data volumes or enough data to beable to support [and], efficiently be able to analyze the strategies or improve the company (G3)Many of the respondents understand that thevolume of data being handled makessupplychainmanagement'sfutureverydifferentfromthepast.Despitedisagreementsabout the data's value,general agreement exists concerning the huge amount of dataavailable.How to handle the data's growth is a different concern.All respondents also noted the velocity of data growth as a defining characteristicthat makes the era of Big Data different from past data management projects.Considering themountinguseofRFIDtags,scannerdata,WMS,ERP,etc,thatprovidereal-time information, we can see a mountingrateofdata speed growth (Cakici et al,2011;Zhongetal,2015).Likemanyweinterviewed,respondentU2notedconcernsabouthis/her supply chain being able tokeeppacewithBig Data growth.Nearly everyrespondent noted the increased velocity of the data being produced. As discuss later,speed is a defining aspectof BigDataas supplychain managersconsidervelocitytobebothanobstacleandanopportunity.The huge variety of data sources is more cryptic, but is openly discussed by supplychain managers:[...Jthe combination of mainly bringing all different data areas together, and by having moreand more diverseportfolios or un-harmonized, Imean just that you bring things undermarket, likeapps,you bring things under market likeweb servicesor web solutions.Youbringthiskindofstuff,letmesaymoreandmoreintotheoperationsprocesses.Then,vouhave to takecare of theprep work that are connected so to say to“backroom or backbone,ofnotgettingreallyspreadout withthiskind of data.Andforsureitis alsothe increaseof thedata volume,while you haveall kinds of connections, which we havefrom outside into ourcompany,and fromtheinsideoutofourcompany.So,maybethatwhatweseeas[...|all theinteractions, which we haveto establish and to maintain and also then the collections betweenall our systems (G5).Data today comes in countlessformats from numerous locations.Most supplychainmanagers areaccustomedto structured data (accounting,operational numbers,etc.)This is largely qualitative data translated into numeric form for traditionaldatabases. Most of it is operational in context and can be within company or acrossintegrated partnerships. Unstructured data, or data that is not pre-defined forsoftware analysis,presents themorechallenging and less-used data source.Forms ofunstructured data include text documents, e-mail, audio, financial transactionstweets,andevenvideo.Supplychainmanagersarebeginningtolearnhowtotapthevast varieties of availabledata, such as addressing logistics-related customer servicecomplaints via Twitter (Bhattacharjya et al, 2016). One Turkish respondent (T2)brings the first three dimensions together in his/her definition of Big Data bycommenting that Big Data is“fast,""big,"and“diverse
and Brynjolfsson (2012) pioneered the discussion of Big Data by defining it according to three specific aspects: the volume of information produced, the velocity at which it is created, and the variety of forms it takes. SAS expanded this definition to include the terms/dimensions of complexity and variability. All of these dimensions were evident in our research but we found that Big Data could best be described through four dimensions: volume, velocity, variety, and veracity. Volume was an issue of comprehensive concern. Respondents in all countries noted the immeasurable amount of available data and the need to manage it to help tie market information to decision processes: How can you get sensible [.] information out of the Big Data volumes or enough data to be able to support [and], efficiently be able to analyze the strategies or improve the company (G3). Many of the respondents understand that the volume of data being handled makes supply chain management’s future very different from the past. Despite disagreements about the data’s value, general agreement exists concerning the huge amount of data available. How to handle the data’s growth is a different concern. All respondents also noted the velocity of data growth as a defining characteristic that makes the era of Big Data different from past data management projects. Considering the mounting use of RFID tags, scanner data, WMS, ERP, etc., that provide real-time information, we can see a mounting rate of data speed growth (Çakıcı et al., 2011; Zhong et al., 2015). Like many we interviewed, respondent U2 noted concerns about his/her supply chain being able to keep pace with Big Data growth. Nearly every respondent noted the increased velocity of the data being produced. As discuss later, speed is a defining aspect of Big Data as supply chain managers consider velocity to be both an obstacle and an opportunity. The huge variety of data sources is more cryptic, but is openly discussed by supply chain managers: [.] the combination of mainly bringing all different data areas together, and by having more and more diverse portfolios or un-harmonized, I mean just that you bring things under market, like apps, you bring things under market like web services or web solutions. You bring this kind of stuff, let me say more and more into the operations processes. Then, you have to take care of the prep work that are connected so to say to “backroom or backbone,” of not getting really spread out with this kind of data. And for sure it is also the increase of the data volume, while you have all kinds of connections, which we have from outside into our company, and from the inside out of our company. So, maybe that what we see as [.] all the interactions, which we have to establish and to maintain and also then the collections between all our systems (G5). Data today comes in countless formats from numerous locations. Most supply chain managers are accustomed to structured data (accounting, operational numbers, etc.). This is largely qualitative data translated into numeric form for traditional databases. Most of it is operational in context and can be within company or across integrated partnerships. Unstructured data, or data that is not pre-defined for software analysis, presents the more challenging and less-used data source. Forms of unstructured data include text documents, e-mail, audio, financial transactions, tweets, and even video. Supply chain managers are beginning to learn how to tap the vast varieties of available data, such as addressing logistics-related customer service complaints via Twitter (Bhattacharjya et al., 2016). One Turkish respondent (T2) brings the first three dimensions together in his/her definition of Big Data by commenting that Big Data is “fast,” “big,” and “diverse.” 718 IJPDLM 46,8 Downloaded by Huazhong University of Science and Technology At 22:37 29 November 2016 (PT)