Impacts of Big Data Analytics and Absorptive Capacity on Sustainable Supply Chain Innovation : a Conceptual Framework

• Autorzy: Lineth Rodriguez , Catherine Da Cunha

Warianty tytułu

Einfluss der Big Data-Analyse und der Absorptionsfähigkeit auf die Innovation Einer Nachhaltigen Lieferkette : ein Konzept

Wpływ analizy big data oraz zdolności absorpcyjnej na innowacyjność zrównoważonego łańcucha dostaw : koncepcja

• Języki publikacji: EN

Abstrakty

EN

Background: Big data and predictive analytics could improve the ability to help with the sustainability of sourcing decisions. Sustainability has become a necessary goal for businesses and a powerful strategy for competitive advantage. There's a need for sustainable innovations along the supply chain to enable companies to have a strong market presence. Developing absorptive capacity both in firms and in supply chains are also integral to responding to dynamic markets and customer needs. The main objective of this paper is to identify the features of big data and predictive analytics applied to sustainable supply chain innovation, and to analyze the role of absorptive capacity.
Methods: A literature review investigates how absorptive capacity affects the impact of the utilization of big data and predictive analytics on sustainable supply chain innovation.
Results: This paper proposes a conceptual framework linking the different elements. It also proposes a synthesis of the existing definitions of the used concepts. In particular, the role of absorptive capacity as enabler on Big Data and Predictive Analytics on sustainable supply chain innovation is stressed.
Conclusions: The paper investigates the emerging paradigm of big data and predictive analytics. The conceptual framework use theoretical foundation of absorptive capacity, and the extant literature on Big Data and predictive analytics. This framework will help us to build a research model for sustainable supply chain innovation applications. Further work is required to develop an action research methodology for validating the framework in depth within a company. (original abstract)

Wstęp: Zastosowanie analizy big data oraz estymacji umożliwiają lepsze zrównoważenie decyzji wykorzystania zasobów. Rozwój zrównoważony stał się niezbędnym celem biznesowym i potężną strategią uzyskania przewagi konkurencyjnej. Można zaobserwować rosnące zapotrzebowania na zrównoważone innowacje w obrębie łańcucha dostaw, umożliwiające przedsiębiorstwom silny wpływ na rynek. Rozwój zdolności absorpcyjnej zarówno w firmach jak i w łańcuchach dostaw jest zintegrowane z potrzebami konsumentów oraz dynamicznych rynków. Głównym celem tej pracy było zidentyfikowanie cech analizy big data oraz estymacji istotnych dla zrównoważonych innowacji w obrębie łańcucha dostaw oraz analiza roli zdolności absorpcyjnej.
Metody: Podstawą pracy był przegląd literatury, umożliwiający analizę wpływu zdolności absorpcyjnych na zastosowanie analizy big data oraz estymacji dla osiągnięcia zrównoważonej innowacyjności w obrębie łańcucha dostaw.
Wyniki: Zaproponowano koncepcję rozwiązania łączącą różne elementy. Zaproponowano również syntezę istniejących definicji stosowanych koncepcji. W szczególności, rolę zdolności absorpcyjnych jako elementu umożliwiającego stosowanie analizy big data oraz estymacji dla zrównoważonej innowacyjności w obrębie łańcucha dostaw.
Wnioski: W pracy badano pojawiający się paradygmat analizy big data oraz estymacji. Koncepcja oparta jest na zastosowaniu zdolności absorpcyjnej oraz istniejących danych literaturowych i ich wpływu na analizę big data. Praca pomaga zbudować model badawczy dla zrównoważonych innowacji w obrębie łańcucha dostaw. Zwrócono uwagę na potrzebę kontynuowania badań w tym zakresie. (abstrakt oryginalny)

Słowa kluczowe

EN

Supply chain; Innovative character; Big Data; Sustainable supply chain

PL

Łańcuch dostaw; Innowacyjność; Big Data; Zrównoważony łańcuch dostaw

• Czasopismo: LogForum
• Rocznik: 2018
• Tom: 14
• Numer: nr 2
• Strony: 151--161

Twórcy

autor

Lineth Rodriguez

• LS2N-Ecole Centrale de Nantes, Nantes, France

autor

Catherine Da Cunha

• LS2N-Ecole Centrale de Nantes, Nantes, France

Bibliografia

• Addo-Tenkorang R., Helo P.T., 2016. Big Data Applications in Operations/Supply-Chain Management: A Literature Review. Computers & Industrial Engineering, 101, 528-543. http://dx.doi.org/10.1016/j.cie.2016.09.023
• Arlbjørn J.S., de Haas H., Munksgaard K.B., 2011. Exploring supply chain innovation. Logistics Research, 3(1), 3-18.
• Arunachalam D., Kumar N., Kawalek J.P., 2017. Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E-Logistics and Transportation Review, 1-21. http://dx.doi.org/10.1016/j.tre.2017.04.001
• Assink M., 2006. Inhibitors of disruptive innovation capability: a conceptual model. European Journal of Innovation Management, 9(2), 215-233 http://dx.doi.org/10.1108/14601060610663587
• Bansal P., 2005. Evolving sustainably: A longitudinal study of corporate sustainable development. Strategic Management Journal, 26(3), 197-218. http://dx.doi.org/10.1002/smj.441
• Beske P., 2012. Dynamic capabilities and sustainable supply chain management. International Journal of Physical Distribution & Logistics Management, 42(4), 372-387. http://dx.doi.org/10.1108/09600031211231344
• Brown B., Chui M., Manyika J., 2011. Are you ready for the era of "big data"? McKinsey Quarterly, 4(October), 24-35.
• Business_Wire, 2017. Global RFID Market Projected to be Worth USD 15 . 84 Billion by 2021 : Technavio. BUSINESS WIRE, 5. Available at: http://www.businesswire.com/news/home/20170420006281/en/Global-RFID-Market-Projected-Worth-USD-15.84 [Accessed April 9, 2017].
• Butlin J., 1989. Our Common Future. By World Commission on Evironment and Development. Journal of International Development, 1(2), 284-287. http://dx.doi.org/10.1002/jid.3380010208
• Carter C.R., Rogers D.S., 2008. A framework of sustainable supply chain management: moving toward new theory, International Journal of Physical Distribution & Logistics Management, 38, 5, 360-387 http://dx.doi.org/10.1108/09600030810882816
• Chae B., Olson D.L., 2013. Business analytics for supply chain: a dynamic-capabilities framework. International Journal of Information Technology & Decision Making, 12(1), 9-26. http://dx.doi.org/10.1142/S0219622013500016
• Čiutienė R., Thattakath E.W., 2014. Influence of Dynamic Capabilities in Creating Disruptive Innovation. Economics & Business, 26(December), 15-21. http://dx.doi.org/10.7250/eb.2014.015
• Columbus L., 2015. Ten Ways Big Data Is Revolutionizing Supply Chain Management. Forbes, 2015, 1-8.
• Deloitte & MHI, 2016. 2016 MHI Annual Industry Report - Accelerating change: How innovation is driving digital, alwayson supply chains, Available at: https://www.mhi.org/publications/report.
• Dobrzykowski D.D., Leuschner R., Hong P.C., Roh J.J., 2015. Examining absorptive capacity in supply chains: Linking responsive strategy and firm performance. Journal of Supply Chain Management, 51(4), 3-28.
• Downes L. Nunes P., 2013. Big Bang Disruption. Harvard Business Review, 12.
• Ebner K., Buhnen T., Urbach N., 2014, January). Think big with Big Data: Identifying suitable Big Data strategies in corporate environments. In System Sciences (HICSS), 2014 47th Hawaii International Conference on, 3748-3757. http://dx.doi.org/10.1109/HICSS.2014.466
• Elastic.com, 2017. The Open Source Elastic Stack, 1-5. Available at: https://www.elastic.co/products [Accessed October 20, 2017].
• Fainshmidt S., Pezeshkan A., Lance Frazier M., Nair A., Markowski E., 2016. Dynamic Capabilities and Organizational Performance: A Meta-Analytic Evaluation and Extension. Journal of Management Studies, 53(8), 1348-1380. http://dx.doi.org/10.1111/joms.12213
• Fawcett S.E., Wallin C., Allred C., Fawcett A.M., Magnan G.M., 2011. Information technology as an enabler of supply chain collaboration: a dynamic capabilities perspective. Journal of Supply Chain Management, 47(1), 38-59. http://dx.doi.org/10.1111/j.1745-493X.2010.03213.x
• Fawcett S.E., Waller M.A., 2014. Supply Chain Game Changers-Mega, Nano, and Virtual Trends-And Forces That Impede Supply Chain Design (i.e., Building a Winning Team). Journal of Business Logistics, 35(3), 157-164. http://dx.doi.org/10.1111/jbl.12058
• Gao D., Xu Z., Ruan Y.Z., Lu H., 2017. From a systematic literature review to integrated definition for sustainable supply chain innovation (SSCI). Journal of Cleaner Production, 142, 1518-1538. http://dx.doi.org/10.1016/j.jclepro.2016.11.153
• Gunasekaran A., Papadopoulos T., Dubey R., Wamba S.F., Childe S.J., Hazen B., Akter S., 2017. Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317. http://dx.doi.org/10.1016/j.jbusres.2016.08.004
• Gupta M. George J.F., 2016. Toward the development of a big data analytics capability. Information and Management, 53(8), 1049-1064. http://dx.doi.org/10.1016/j.im.2016.07.004
• Hahn G.J. Packowski J., 2015. A perspective on applications of in-memory analytics in supply chain management. Decision Support Systems, 76, 45-52. http://dx.doi.org/10.1016/j.dss.2015.01.003
• Hassini E., Surti C. Searcy C., 2012. A literature review and a case study of sustainable supply chains with a focus on metrics. International Journal of Production Economics, 140(1), 69-82. http://www.sciencedirect.com/science/article/pii/S0925527312000576
• Hazen B.T., Skipper J.B., Ezell J.D., Boone C.A., 2016. Big Data and predictive analytics for supply chain sustainability: A theory-driven research agenda. Computers & Industrial Engineering, 101, 592-598. http://dx.doi.org/10.1016/j.cie.2016.06.030
• Helfat C.E., Finkelstein S., Mitchell W., Peteraf M., Singh H., Teece D., Winter S.G., 2009. Dynamic capabilities: Understanding strategic change in organizations. John Wiley & Sons.
• Huisingh D., 2017. How would big data support societal development and environmental sustainability? Insights and practices. Journal of Cleaner Production, 142, 489-500.
• Kabir N., Carayannis E., 2013. Big data, Tacit Knowledge and Organizational Competitiveness. Journal of Intelligence Studies in Business, 3(3), 54-62.
• Lin Y., Wang Y., Yu C., 2010. Investigating the drivers of the innovation in channel integration and supply chain performance: A strategy orientated perspective. International Journal of Production Economics, 127(2), 320-332.
• Linton J.D., Klassen R., Jayaraman V., 2007. Sustainable supply chains: An introduction. Journal of Operations Management, 25(6), 1075-1082. http://dx.doi.org/10.1016/j.jom.2007.01.012
• Mandal S., Scholar V., 2011. Supply Chain Innovation: A dynamic Capability Perspective. In American Council of Supply Chain Management Professionals.
• Manyika J., Chui M., Bughin J., Dobbs R., Bisson P., Marrs A., 2013. Disruptive technologies: Advances that will transform life, business, and the global economy (Vol. 180). San Francisco, CA: McKinsey Global Institute.
• Oliveira M.P.V. De McCormack K., Trkman P., 2012. Business analytics in supply chains: The contingent effect of business process maturity. Expert Systems with Applications, 39(5), 5488-5498.
• Richey Jr R.G., Richey Jr R.G., Morgan T.R., Morgan T.R., Lindsey-Hall K., Lindsey-Hall K., ... & Adams F.G. (2016). A global exploration of big data in the supply chain. International Journal of Physical Distribution & Logistics Management, 46(8), 710-73. http://dx.doi.org/10.1108/IJPDLM-05-2016-0134
• Roberts N., Galluch P.S., Dinger M., Grover V., 2012. Absorptive capacity and information systems research: Review, synthesis, and directions for future research. MIS quarterly, 36(2).
• Sanders N.R., 2016. How to use big data to drive your supply chain. California Management Review, 58(3), 26-48.
• Schoenherr T., Speier-Pero C., 2015. Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 36(1), 120-132. http://dx.doi.org/10.1111/jbl.12082
• Seuring S., Muller M., 2008. From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699-1710. http://dx.doi.org/10.1016/j.jclepro.2008.04.020
• Sikdar S.K., 2003. Sustainable development and sustainability metrics. AIChE Journal, 49(8), 1928-1932. http://dx.doi.org/10.1002/aic.690490802
• Teece D.J., 2007. Explicating Dynamic Capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Manag, 28, 1319-1350. http://dx.doi.org/10.1002/smj.640
• The_Economist, 2010. The data deluge. The Economist, 3-4.
• Tien J.M., 2015. Internet of connected ServGoods: Considerations, consequences and concerns. Journal of Systems Science and Systems Engineering, 24(2), 130-167. http://dx.doi.org/10.1007/s11518-015-5273-1
• Touboulic A., Walker H., 2015. Theories in sustainable supply chain management: a structured literature review, International Journal of Physical Distribution & Logistics Management, 45, 1/2, 16-42. http://dx.doi.org/10.1108/IJPDLM-05-2013-0106
• Ulusoy G., 2003. An assessment of supply chain and innovation management practices in the manufacturing industries in Turkey. International Journal of Production Economics, 86(3), 251-270. http://dx.doi.org/10.1016/S0925-5273(03)00064-1
• Walker H., Di Sisto L., McBain D., 2008. Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sectors. Journal of Purchasing and Supply Management, 14(1), 69-85. http://dx.doi.org/10.1016/j.pursup.2008.01.007
• Waller M.A., Fawcett S.E., 2013. Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
• Waller M.A., Fawcett S.E., Beane B., 2013. Click Here for a Data Scientist : Big Data, Predictive Analytics, and Theory Development in the Era of a Maker Movement Supply. Journal of Business Logistics, 34(4), 249-252. http://dx.doi.org/10.1111/jbl.12024
• Wamba S.F., Gunasekaran A., Akter S., Ren S. J.F., Dubey R., Childe S.J., 2017. Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
• Wamba S.F., Akter S., 2015. Big data analytics for supply chain management: A literature review and research agenda. In Lecture Notes in Business Information Processing. 61-72. http://dx.doi.org/10.1007/978-3-319-24626-0_5
• Wang W., Liu L., Feng Y., Wang T., 2014. Innovation with IS usage: Individual absorptive capacity as a mediator. Industrial Management & Data Systems, 114(8), 1110-1130. http://dx.doi.org/10.1108/IMDS-05-2014-0160
• Wamba S.F., Akter S., Edwards A., Chopin G., Gnanzou D., 2015. How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. http://dx.doi.org/10.1016/j.ijpe.2014.12.031
• Wang Y., Kung L.A., Byrd T.A., 2015. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change. http://dx.doi.org/j.techfore.2015.12.019
• Wu K.J., Liao C.J., Tseng M.L., Lim, M. K., Hu, J., Tan K., 2017. Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties. Journal of Cleaner Production, 142, 663-676. http://dx.doi.org/10.1016/j.jclepro.2016.04.040
• Zhao R., Liu Y., Zhang N., Huang T., 2017. An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085-1097. http://dx.doi.org/10.1016/j.jclepro.2016.03.006

Identyfikatory

DOI

10.17270/J.LOG.2018.267