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Autor
Rodriguez Lineth (LS2N-Ecole Centrale de Nantes, Nantes, France), Da Cunha Catherine (LS2N-Ecole Centrale de Nantes, Nantes, France)
Tytuł
Impacts of Big Data Analytics and Absorptive Capacity on Sustainable Supply Chain Innovation : a Conceptual Framework
Wpływ analizy big data oraz zdolności absorpcyjnej na innowacyjność zrównoważonego łańcucha dostaw : koncepcja
Einfluss der Big Data-Analyse und der Absorptionsfähigkeit auf die Innovation Einer Nachhaltigen Lieferkette : ein Konzept
Źródło
LogForum, 2018, vol. 14, nr 2, s. 151-161, bibliogr. 56 poz.
Słowa kluczowe
Łańcuch dostaw, Innowacyjność, Big Data, Zrównoważony łańcuch dostaw
Supply chain, Innovative character, Big Data, Sustainable supply chain
Uwagi
summ., streszcz., zfsg.
Abstrakt
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)

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)
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Bibliografia
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Cytowane przez
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ISSN
1895-2038
Język
eng
URI / DOI
http://dx.doi.org/10.17270/J.LOG.2018.267
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