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
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