Evaluation of Big Data and Innovation Interaction in Increase Supply Chain Competencies
pp. 88-102 | Published Online: December 2017 | DOI: 10.22521/unibulletin.2017.62.7
Zumrut Ecevit Sati
In business today, it means a great deal to uncover meaningful relationships, patterns and trends from the huge stacks of data that are often now available. The explosion in data diversity and volume coming from enterprise content and application data, data from social media, sensor data and also data including streams from third parties is significantly changing the ways and methods of interaction for both companies and their customers. This pressure is felt considerably more in the management of innovation through trying to develop the capability to integrate the supply chain to match the correct methods with the right information. This situation has directed companies into using “big data” in managing both their structured and unstructured data. Big data, which is information, held on a vast scale, can reveal significant potential in its transparency and convenience. To bring about a balanced approach to the use of internal and external information, supporting improved capabilities to better predict future competence, and provide that all important “big picture” through business analytics can improve the vision of businesses through the provision of more in-depth information about how to best access their customers. Improved communication and information links between partners of the supply chain may create major sources of information by bringing together both internal and external resources for customers, partners, stakeholders and suppliers in managing innovation. In this study, it is aimed to provide an extensive literature review on the interaction of innovation and big data in order to increase supply chain competencies and to study the problem, obstacles and driving forces for such interactions, and to consider projections for the future through the application of technology-based methods.
Keywords: supply chain competence, innovation management, big dataReferences
Accenture (2013). High Performance in IT: Defined by Digital. Accenture. Retrieved from http://www.accenture.com/Microsites/high-performance-it/Pages/home.aspx on February 10, 2015.
Anholt, S. (2004). Global Markaların Yerel Çuvallamaları (Çev. Gonca Canan) (2. Baskı), İstanbul: Media Cat.
APICS (2012). Big Data Insights and Innovations, Executive Summary.
Arlbjørn, J.S., deHaas, H. & Munksgaard, K.B. (2011). Exploring supply chain innovation, Logistics Research, 3, 318.
Attaran, M., & Attaran, S. (2004). The Rebirth Of Re-Engineering, XEngineering. Business Process Management Journal, 10(4), 416-432.
Bakhshi, H., & McVittie, E. (2009). Creative Supply-Chain Linkages and Innovation: Do The Creative Industries Stimulate Business Innovation In The Wider Economy?, Innovation: Management, Policy and Practice, 11(2), 169-189.
Bstieler, L. (2006). Trust Formation in Collaborative New Product Development. Journal of Product Innovation Management, 23(1), 56-72.
CFAR, & Branwen, G. (2012). Was Nate Silver the Most Accurate 2012 Election Pundit? Centre for Applied Rationality. Retrieved from http://rationality.org/resources/updates/2012/was-nate-silver-the-most-accurate-2012-election-pundit.CFAR.
Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD skills: new analysis practices for big data. In H. V. Jagadish, S. Abiteboul, T. Milo, J. Patel, & P. Rigaux (Eds.), Proceedings of the VLDB Endowment, Volume 2 (pp. 1481-1492). Very Large Data Base Endowment Inc. (VLDB Endowment).
Cooper, M., Lambert, C., Douglas, M., & Pagh, J. D. (1997). Supply Chain Management: More Than a New Name for Logistics. The International Journal of Logistics Management, 8(1), 1-14.
Çağlıyan, V. (2009). Yenilikçilik, Tedarikçi Katılımı Ve İşletme Performansı Üzerine Değer Zinciri Yönetimi Temelli Bir Yaklaşım: Otomotiv Sektöründe Görgül Bir Araştırma (Unpublished Doctoral Dissertation). Selçuk Üniversitesi Sosyal Bilimler Enstitüsü, 2009, Konya.
Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Cambridge, MA: Harvard Business Press.
Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters, Communications of ACM, 51(1), 107-113.
Erciş, A., & Can, P. (2013). Tedarik Zinciri Yönetiminin İnovasyon Stratejilerine Etkisi Üzerine Bir Araştırma. Karabük Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(2), 95-122.
Flint, D. J., Larsson, E., & Gammelgaard, B. (2008). Exploring Processes For Customer Value Insights, Supply Chain Learning And Innovation. Journal of Business Logistics, 29(1), 257-281.
GE Global Innovation Barometer. (2013). Global Research Finding & Insights, January 2013. GE & Strategy One. Retrieved from https://www.ge.com/sites/default/files/Innovation_Overview.pdf.
Grill, A. (2013, April 06). Using Big Data To Fight Crime And Predict What Products Consumers Might Purchase In The Future. London Calling. Retrieved from https://londoncalling.co/2013/04/using-big-data-to-fight-crime-and-predict-what-products-consumers-might-purchase-in-the-future/.
Goodwin, G. (2013). Takeaways from the MIT/Accenture Big Data in Manufacturing Conference. MIT/Accenture Big Data in Manufacturing Conference Cambridge, USA.
Google. (n.d.). Google Flu Trends and Google Dengue Trends. Google. Retrieved from https://www.google.org/flutrends/about/.
Huddar, M. G., & Ramannavar, M. M. (2013). A survey on big data analytical tools. International Journal of Latest Trends in Engineering Technology, Special Issue, IDEAS 2013, 85-91.
IBM (2013). What is big data? Bringing big data to the enterprise. Retrieved from https://www-01.ibm.com/software/in/data/bigdata/.
IBM (2017) Marketing Cloud, Retrieved from https://public.dhe.ibm.com/common/ssi/ecm/wr/en/wrl12345usen/watson-customer-engagement-watson-marketing-wr-other-papers-and-reports-wrl12345usen-20170719.pdf
IDC (2016). Worldwide Big Data Technology and Services Forecast 2016-2020. Retrieved from https://www.idc.com/getdoc.jsp?containerId=US40803116
Issa, N. (2013). Supply Chain: Improving Performance in Pricing, Planning and Sourcing. Opera Solutions. Retrieved from https://azslide.com/supply-chain-improving-performance-in-pricing-planning-and-sourcing_5989ef981723dd559fea593e.html.
Kemp, S. (2017), The global state of the internet in April 2017. Retrieved from https://thenextweb.com/contributors/2017/04/11/current-global-state-internet/
Kim, S. W. (2009). An Investigation On The Direct And Indirect Effect of Supply Chain Integration On Firm Performance. International Journal of Production Economics, 119(2), 328-346.
Kiron, D. (2013, January 28). Organizational Alignment is Key to Big Data Success. MIT Sloan Management Review. Retrieved from https://sloanreview.mit.edu/ article/organizational-alignment-is-key-to-big-data-success/.
Koçoğlu, İ. (2010). Tedarik Zinciri Yönetiminde Yenilik ve Bilgi Paylaşımının Önemi. Gebze İleri teknoloji Enstitüsü, Sosyal Bilimler Enstitüsü, Strateji Anabilim Dalı, Gebze.
Krabbe, M. (2007). Leverage supply chain innovation. Industrial Engineer, 39(12), 26-30.
Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity and Variety. Meta Group. META group Inc., 2001. Retrieved from http://blogs.gartner.com/doug-laney/files/ 2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.
Langley, J. C. J. (2014). 2014 Third-Party Logistics Study: The State of Logistics Outsourcing. Capgemini Consulting. Retrieved from https://www.capgemini.com/wp-content/uploads/2017/07/3pl_study_report_web_version.pdf.
LaValle, S., Hopkins, M. S., Lesser, E., Shockley, R., & Kruschwitz, N. (2010, October 24). Analytics: the new path to value. MIT Sloan Management Review. Retrieved from https://sloanreview.mit.edu/projects/analytics-the-new-path-to-value/.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. San Francisco: McKinsey Global Institute.
Manyika, J., Chui, M., Groves, P., Farrell, D., Kuiken, S. V., & Doshi, E. A. (2013). Open data: unlocking innovation and performance with liquid information. San Francisco: McKinsey Global Institute.
Mervis, J. (2012). Agencies rally to tackle big data. Science, 336(6077), 22.
Milliken, A. L. (2015). Transforming Big Data into Supply Chain Analytics, Journal of Business Forecasting, 33(4), 23-27.
Mishra, S., Modi, S. B., & Animesh, A. (2013). The relationship between information technology capability, inventory efficiency, and shareholder wealth: a firm-level empirical analysis. Journal of Operations Management, 31(1), 298-312.
Mohanty, S., Jagadeesh, M., & Srivatsa, H. (2013). Big Data Imperatives: Enterprise Big Data Warehouse, BI Implementations and Analytics. New York: A press.
NASA (2015). “NASA‘s Climate Modeling Center (NCSS) supercomputer cluster handles 32 petabytes of information” Retrieved from http://www.nccs.nasa.gov/ .
Oh, L., Teo, H., & Sambamurthy, V. (2012). The effects of retail channel integration through the use of information Technologies on firm performance. Journal of Operations Management, 30(1), 368-381.
Ohlhorst, F. J. (2012). Big Data Analytics: Turning Big Data into Big Money. Hoboken: John Wiley & Sons.
Oracle. (2011). Oracle Big Data Appliance. Oracle. Retrieved from https://oracleant.files.wordpress.com/2012/11/bigdataappliance-datasheet-1453665.pdf.
Petersen, K. J., Handfield, R. B., & Ragatz, G. L. (2005). Supplier Integration Into New Product Development: Coordinating Product, Process. Journal of Operations Management, 23(3-4), 371-388.
Saas (2012). Big Data Meets Big Data Analytics Three Key Technologies for Extracting Real-Time Business Value from the Big Data That Threatens to Overwhelm Traditional Computing Architectures, White Paper, Retrieved from https://www.sas.com/content/dam/SAS/en_us/doc/whitepaper1/big-data-meets-big-data-analytics-105777.pdf
Sammarra, A., & Biggiero, L. (2008). Heterogeneity and Specificity of Inter-Firm Knowledge Flows in Innovation Networks. Journal of Management Studies, 45(4), 800-829.
Schultz, J. (2017, October 10). How Much Data is Created on the Internet Each Day? Retrieved from, https://blog.microfocus.com/how-much-data-is-created-on-the-internet-each-day/
Smith, K. (2017, December 5). Marketing: 47 Facebook Statistics for 2016. Retrieved from https://www.brandwatch.com/blog/47-facebook-statistics-2016/
Soosay, C., Hyland, P.W., & Ferrer, M. (2008). Supply Chain Collaboration: Capabilities for Continuous Innovation. Supply Chain Management, 13(2), 160-169.
Statista (2018), Number of smartphone users worldwide from 2014 to 2020 (in billions), Retrieved from https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/
Tan, K. H., Zhan, Z. Y., Ji, G., Ye, F., & Chang, C. (2015). Harvesting Big Data to Enhance Supply Chain Innovation Capabilities: An Analytic Infrastructure Based on Deduction Graph. International Journal of Production Economics, 165, 223-233. http://dx.doi.org/10.1016/j.ijpe.2014.12.034 .
Tan, K. C. (2001). A Framework of Supply Chain Management Literature. European Journal of Purchasing and Supply Chain Management, 7(1), 39-48.
Tarn, J. M., Razi, M. A., Wen, H. J., & Perez, A. A. (2003). E-fulfilment: Strategy and Operational Requirements. Logistics Information Management, 16(5), 359-372.
Terziovski, M. (2010). Innovation practice and its performance implications in small and medium enterprises in the manufacturing sector: are source-based views. Strategy Management Journal, 31(8), 892-902.
Tweney, D. (2013, June 10). Walmart scoops up Inkiru to bolster its ‘big data’ capabilities online. Retrieved from http://venturebeat.com/2013/06/10/walmart-scoops-up-inkiru-to-bolster-its-big-data-capabilities-online/.
Water Ford Technologies (2017). Big Data Statistics & Facts for 2017, Retrieved from, https://www.waterfordtechnologies.com/big-data-interesting-facts/
Wilkins, J. (2013, November 25). Big data and its impact on manufacturing, Design Products and Applications. Retrieved from http://www.dpaonthenet.net/article/65238/Big-data-and-its-impact-on-manufacturing.aspx.
Wong, D. (2012). Data is the Next Frontier, Analytics the New Tool: Five Trends in Big Data and Analytics, and Their Implications for Innovation and Organizations. London: Big Innovation Centre.
Yiu, C. (2012, July 3). The Big Data Opportunity: Making Government faster, smarter and more personal. Policy Exchange. Retrieved from https://policyexchange.org.uk/publication/the-big-data-opportunity-making-government-faster-smarter-and-more-personal/.
Zhan, Y. Z., Tan, K. H., Pawar, K., & Tan, K. C. (2014). Harvesting big data to support supply chain innovation. In K. S. Pawar & M. Nkhoma (Eds.), Proceedings of the 19th International Symposium on Logistics conference (pp. 138-145). Nottingham, UK: Centre for Concurrent Enterprise, Nottingham University Business School.
Zikopoulos, P., & Eaton, C. (2011). Understanding big data: analytics for enterprise class Hadoop and streaming data. New York: McGraw-Hill.
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