Big data is a key component to realise the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The purpose of this paper is to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers.
The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP consultants and project managers. The qualitative data collected were then systematically analysed by using a thematic analysis approach.
A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers.
This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision making.
This study was supported by the grant funded by the Guangdong Natural Science Foundation (No. 2018A030313706) and the 100 Talent Grant offered by Sun Yat-sen University, China (No. 201603).
Li, S., Peng, G.C. and Xing, F. (2019), "Barriers of embedding big data solutions in smart factories: insights from SAP consultants", Industrial Management & Data Systems, Vol. 119 No. 5, pp. 1147-1164. https://doi.org/10.1108/IMDS-11-2018-0532
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