Big Data and supply chain management: a review and bibliometric analysis

As Big Data has undergone a transition from being an emerging topic to a growing research area, it has become necessary to classify the different types of research and examine the general trends of this research area. This should allow the potential research areas that for future investigation to be identified. This paper reviews the literature on ‘Big Data and supply chain management (SCM)’, dating back to 2006 and provides a thorough insight into the field by using the techniques of bibliometric and network analyses. We evaluate 286 articles published in the past 10 years and identify the top contributing authors, countries and key research topics. Furthermore, we obtain and compare the most influential works based on citations and PageRank. Finally, we identify and propose six research clusters in which scholars could be encouraged to expand Big Data research in SCM. We contribute to the literature on Big Data by discussing the challenges of current research, but more importantly, by identifying and proposing these six research clusters and future research directions. Finally, we offer to managers different schools of thought to enable them to harness the benefits from using Big Data and analytics for SCM in their everyday work.

[1]  R. Färe,et al.  Productivity Developments in Swedish Hospitals: A Malmquist Output Index Approach , 1994 .

[2]  Yuan Yu,et al.  Dryad: distributed data-parallel programs from sequential building blocks , 2007, EuroSys '07.

[3]  MaryAnne M. Gobble,et al.  Big Data: The Next Big Thing in Innovation , 2013 .

[4]  David L. Olson,et al.  Business Analytics for Supply Chain: a Dynamic-Capabilities Framework , 2013, Int. J. Inf. Technol. Decis. Mak..

[5]  Rodrigo Fernandes de Mello,et al.  An Online Data Access Prediction and Optimization Approach for Distributed Systems , 2012, IEEE Transactions on Parallel and Distributed Systems.

[6]  Jason Thibeault,et al.  Recommend This!: Delivering Digital Experiences that People Want to Share , 2014 .

[7]  Huang Huang,et al.  Logic of cooperation:An evolutionary analysis of strong defection strategy , 2013 .

[8]  M. Terziovski Innovation practice and its performance implications in small and medium enterprises (SMEs) in the manufacturing sector: a resource‐based view , 2010 .

[9]  Qingcai Feng,et al.  We are integrating with the world--Journal of Environmental Sciences journey of twenty five years. , 2013, Journal of environmental sciences.

[10]  T. Schoenherr,et al.  Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential , 2015 .

[11]  A. Sharplin,et al.  The Relative Importance of Journals Used in Management Research: An Alternative Ranking , 1985 .

[12]  Carsten Bange,et al.  In-memory analytics – strategies forreal-time CRM , 2011 .

[13]  H. Teo,et al.  The effects of retail channel integration through the use of information technologies on firm performance , 2012 .

[14]  M. H. MacRoberts,et al.  Problems of citation analysis: A study of uncited and seldom-cited influences , 2010 .

[15]  Benjamin T. Hazen,et al.  Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications , 2014 .

[16]  C. DesRoches,et al.  A progress report on electronic health records in U.S. hospitals. , 2010, Health affairs.

[17]  C. Lynch Big data: How do your data grow? , 2008, Nature.

[18]  Ghi-Feng Yen,et al.  Reexamining supply chain integration and the supplier's performance relationships under uncertainty , 2014 .

[19]  J. Mervis U.S. science policy. Agencies rally to tackle big data. , 2012, Science.

[20]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[21]  O. Persson,et al.  How to use Bibexcel for various types of bibliometric analysis , 2009 .

[22]  Stuart E. Madnick,et al.  Data and Information Quality Research: Its Evolution and Future , 2014, Computing Handbook, 3rd ed..

[23]  Nada R. Sanders,et al.  The Emerging Role of the Third‐Party Logistics Provider (3PL) as an Orchestrator , 2011 .

[24]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[25]  E. Rogers Diffusion of Innovations , 1962 .

[26]  A. Ramos-Rodríguez,et al.  Changes in the intellectual structure of strategic management research: a bibliometric study of the Strategic Management Journal, 1980–2000 , 2004 .

[27]  Andreas Harth,et al.  Linked Data and Complex Event Processing for the Smart Energy Grid , 2010, LDSI@FIA.

[28]  Robert S. Erikson,et al.  Markets vs. polls as election predictors: An historical assessment , 2012 .

[29]  Joel H. Saltz,et al.  Impact of CPOE Order Sets on Lab Orders , 2003, AMIA.

[30]  Zongwei Luo,et al.  A bibliographic study on big data: concepts, trends and challenges , 2017, Bus. Process. Manag. J..

[31]  Christian Lovis,et al.  Comprehensive management of the access to a component-based healthcare information system , 2006, MIE.

[32]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[33]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[34]  Paul Goodwin,et al.  When simple alternatives to Bayes formula work well: Reducing the cognitive load when updating probability forecasts ☆ , 2015 .

[35]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[36]  Shahriar Akter,et al.  How ‘Big Data’ Can Make Big Impact: Findings from a Systematic Review and a Longitudinal Case Study , 2015 .

[37]  Ying Ding,et al.  Popular and/or prestigious? Measures of scholarly esteem , 2010, Inf. Process. Manag..

[38]  Angappa Gunasekaran,et al.  The impact of big data on world-class sustainable manufacturing , 2015, The International Journal of Advanced Manufacturing Technology.

[39]  Jianhua Hou,et al.  The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis , 2010 .

[40]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .

[41]  David S. Cochran,et al.  Big data analytics with applications , 2014 .

[42]  Ananth Raman,et al.  Special Issue of Production and Operations Management: Retail Operations , 2009 .

[43]  Christos Faloutsos,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[44]  J. Meredith,et al.  The evolution of the intellectual structure of operations management—1980–2006: A citation/co-citation analysis , 2009 .

[45]  Birger Hjørland,et al.  Citation analysis: A social and dynamic approach to knowledge organization , 2013, Inf. Process. Manag..

[46]  Ram Ganeshan,et al.  Special Issue of Production and Operations Management on “Big Data in Supply Chain Management” , 2015 .

[47]  E. Garfield Citation analysis as a tool in journal evaluation. , 1972, Science.

[48]  Helmut Küchenhoff,et al.  Limitations of Ensemble Bayesian Model Averaging for Forecasting Social Science Problems , 2014 .

[49]  Jeffrey Heer,et al.  Narrative Visualization: Telling Stories with Data , 2010, IEEE Transactions on Visualization and Computer Graphics.

[50]  Vladimir Batagelj,et al.  Pajek Program for Analysis and Visualization of Large Networks , 2007 .

[51]  Aa Alshehri Ay Ghazwani Ra Darwesh Sa Alzahrani Alotaibi,et al.  Big Data for the Enterprise , 2018 .

[52]  Joseph M. Hellerstein,et al.  MAD Skills: New Analysis Practices for Big Data , 2009, Proc. VLDB Endow..

[53]  S. Roberts,et al.  Self-experimentation as a source of new ideas: Ten examples about sleep, mood, health, and weight , 2004, Behavioral and Brain Sciences.

[54]  B. Flyvbjerg What you Should Know about Megaprojects and Why: An Overview , 2014, 1409.0003.

[55]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[56]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[57]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[58]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[59]  Richard P. Larrick,et al.  Intuitions About Combining Opinions: Misappreciation of the Averaging Principle , 2006, Manag. Sci..

[60]  Robert J. Vokurka,et al.  The relative importance of journals used in operations management research A citation analysis , 1996 .

[61]  Frank Moisiadis,et al.  Socio-environmental performance of transportation systems , 2015 .

[62]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[63]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[64]  Lianbiao Cui,et al.  Environmental performance evaluation with big data: theories and methods , 2016, Annals of Operations Research.

[65]  Dominic Barton,et al.  Making advanced analytics work for you. , 2012, Harvard business review.

[66]  Huan Liu,et al.  Topic taxonomy adaptation for group profiling , 2008, TKDD.

[67]  J. Manyika,et al.  Are you ready for the era of ‘big data’? , 2010 .

[68]  J. Scott Armstrong,et al.  Decomposition of time-series by level and change , 2015 .

[69]  Henry G. Small,et al.  Co-citation in the scientific literature: A new measure of the relationship between two documents , 1973, J. Am. Soc. Inf. Sci..

[70]  Seref Sagiroglu,et al.  Big data: A review , 2013, 2013 International Conference on Collaboration Technologies and Systems (CTS).

[71]  Anita Elberse Should You Invest in the Long Tail , 2008 .

[72]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[73]  D. Boyd,et al.  CRITICAL QUESTIONS FOR BIG DATA , 2012 .

[74]  Angappa Gunasekaran,et al.  Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature , 2016, Ind. Manag. Data Syst..

[75]  Kim Hua,et al.  Harvesting Big Data to Enhance Supply Chain Innovation Capabilities : An Analytic Infrastructure Based on Deduction Graph , 2016 .

[76]  Anna Sidorova,et al.  Uncovering the Intellectual Core of the Information Systems Discipline , 2008, MIS Q..

[77]  W. Erwin Diewert,et al.  Additive decompositions for Fisher, Törnqvist and geometric mean indexes , 2002 .

[78]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[79]  G. Hult,et al.  Bridging organization theory and supply chain management: The case of best value supply chains , 2007 .

[80]  D. Leong,et al.  A new revolution in enterprise storage architecture , 2009, IEEE Potentials.

[81]  B. Chae,et al.  Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research , 2015 .

[82]  G. Nolan,et al.  Computational solutions to large-scale data management and analysis , 2010, Nature Reviews Genetics.

[83]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[84]  Daniel E. O'Leary,et al.  Blog mining-review and extensions: "From each according to his opinion" , 2011, Decis. Support Syst..

[85]  Ram Ganeshan,et al.  Special Issue ofProduction and Operations Managementon “Big Data in Supply Chain Management” , 2015 .

[86]  Andreas Graefe,et al.  Improving Forecasts Using Equally Weighted Predictors , 2013 .

[87]  Loet Leydesdorff,et al.  Bibliometrics/Citation networks , 2015, ArXiv.

[88]  I. Yeoman Competing on analytics: The new science of winning , 2009 .

[89]  Katharine Armstrong,et al.  Big data: a revolution that will transform how we live, work, and think , 2014 .

[90]  Eric T. G. Wang,et al.  Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories , 2006, Decis. Support Syst..

[91]  S. Fawcett,et al.  Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management , 2013 .

[92]  J. Kleinberg Algorithmic Game Theory: Cascading Behavior in Networks: Algorithmic and Economic Issues , 2007 .

[93]  Joseph Sarkis,et al.  Green supply chain management: A review and bibliometric analysis , 2015 .

[94]  Peter Trkman,et al.  The impact of business analytics on supply chain performance , 2010, Decis. Support Syst..

[95]  Eric Gossett,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2015 .

[96]  T. S. Raghu,et al.  Information systems and technology sourcing strategies of e-Retailers for value chain enablement , 2013 .

[97]  Ari Paloviita,et al.  Stakeholder perceptions of alternative food entrepreneurs. , 2009 .

[98]  Peder Olesen Larsen,et al.  The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index , 2010, Scientometrics.

[99]  Francis X. Diebold,et al.  A Personal Perspective on the Origin(s) and Development of 'Big Data': The Phenomenon, the Term, and the Discipline, Second Version , 2012 .

[100]  E. S. Gardner EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[101]  S. Fawcett,et al.  Click Here for a Data Scientist: Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply Chain , 2013 .

[102]  David J. Ketchen,et al.  The effects of innovation–cost strategy, knowledge, and action in the supply chain on firm performance , 2009 .

[103]  David L. Olson,et al.  The impact of supply chain analytics on operational performance: a resource-based view , 2014 .

[104]  Elizabeth McGlynn,et al.  The Case for Keeping Quality on the Health Reform Agenda , 2008 .

[105]  Mary J. Culnan,et al.  The intellectual development of management information systems, 1972-1982: a co-citation analysis , 1986 .

[106]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[107]  Dursun Delen,et al.  Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud , 2013, Decis. Support Syst..

[108]  Fotios Petropoulos,et al.  An evaluation of simple versus complex selection rules for forecasting many time series , 2014 .

[109]  Stefan Stieglitz,et al.  Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior , 2013, J. Manag. Inf. Syst..

[110]  Thomas J. Steenburgh,et al.  Motivating Salespeople: What Really Works , 2012, Harvard business review.

[111]  Soumendra Mohanty,et al.  “Big Data” in the Enterprise , 2013 .

[112]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[113]  D. Kahneman,et al.  Before you make that big decision... , 2011, Harvard business review.

[114]  Sergei Maslov,et al.  Finding scientific gems with Google's PageRank algorithm , 2006, J. Informetrics.

[115]  Vicki R. Lane,et al.  A Stakeholder Approach to Organizational Identity , 2000 .

[116]  J. M. Whipple,et al.  Strategic Alliance Success Factors , 2000 .

[117]  Reuben E. Slone Leading a supply chain turnaround. , 2004, Harvard business review.

[118]  Jennifer Rowley,et al.  Conducting a literature review , 2004 .

[119]  Y. Narahari,et al.  A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks , 2011, IEEE Transactions on Automation Science and Engineering.

[120]  Jeff Tieman,et al.  Experimenting with quality. CMS-Premier initiative to reward best, punish worst. , 2003, Modern healthcare.

[121]  Yuqing Zhu,et al.  BigDataBench: A big data benchmark suite from internet services , 2014, 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA).

[122]  A. Pilkington,et al.  Is Production and Operations Management a Discipline?: A citation/co-citation Study , 1999 .

[123]  M. White,et al.  Digital workplaces , 2012 .

[124]  J. Brett,et al.  Managing multicultural teams. , 2006, Harvard business review.

[125]  George O. Strawn Scientific Research: How Many Paradigms?. , 2012 .

[126]  Daniel M. Batista,et al.  A Survey of Large Scale Data Management Approaches in Cloud Environments , 2011, IEEE Communications Surveys & Tutorials.

[127]  T. Davenport Competing on analytics. , 2006, Harvard business review.

[128]  Melnned M. Kantardzic Big Data Analytics , 2013, Lecture Notes in Computer Science.

[129]  Michael H. MacRoberts,et al.  Problems of citation analysis: A critical review , 1989, JASIS.

[130]  Adam Jacobs,et al.  The pathologies of big data , 2009, Commun. ACM.

[131]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[132]  Leonardo Neumeyer,et al.  S4: Distributed Stream Computing Platform , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[133]  M. Markus,et al.  Fluctuation theorem for a deterministic one-particle system. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[134]  Leysia Palen,et al.  Twitter adoption and use in mass convergence and emergency events , 2009 .

[135]  Feliciano B. Yu,et al.  Full Implementation of Computerized Physician Order Entry and Medication-Related Quality Outcomes: A Study of 3364 Hospitals , 2009, American journal of medical quality : the official journal of the American College of Medical Quality.

[136]  Shahriar Akter,et al.  Big Data Analytics for Supply Chain Management: A Literature Review and Research Agenda , 2015, EOMAS@CAiSE.

[137]  Cecil C. Bozarth,et al.  Stages of global sourcing strategy evolution: an exploratory study , 1998 .

[138]  James Caverlee,et al.  PageRank for ranking authors in co-citation networks , 2009, J. Assoc. Inf. Sci. Technol..

[139]  Chen Guo-qing,et al.  On the research frontiers of business management in the context of Big Data , 2013 .

[140]  D. Boyd Social Network Sites as Networked Publics: Affordances, Dynamics, and Implications , 2010 .

[141]  Alberto Soncini,et al.  The big kahuna , 2000 .

[142]  Mike Thelwall,et al.  Sentiment in Twitter events , 2011, J. Assoc. Inf. Sci. Technol..