Barriers of embedding big data solutions in smart factories: insights from SAP consultants

Abstract

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Closing the Gap between Smart Manufacturing Applications and Data Management

“…As we discussed, an appropriate data management concept depends on the specific use of data [14]. While most publications focus either on data management or smart manufacturing applications, bridging both is rather scarce in research [17,18]. We address this deficit by providing a catalog of requirements and a design of a framework for data management that fosters the implementation of smart manufacturing applications.…”

“…In general, scientific publications either prioritize the elaboration of a particular smart manufacturing application scenario [15] or the development of a reference architecture for data management [16]. Bridging both worlds has been mainly neglected by research [17,18]. Smart manufacturing brings along specific challenges, which must be considered by a sufficient data management concept.…”

Smart Production Planning and Control Model

“…Cloud manufacturing services can be used within manufacturing systems for big data and analytics tools running, which correspond to big data processing of machines, products, processes for production patterns detection and solving problems [47]. The analytics part refers to the ability to obtain information from data, applying statistics, mathematics, econometrics, simulations, optimizations, and other techniques that can support organizations in managerial decision-making [37].…”

Guest editorial

“…The current literature on supply chain finance involves many aspects, batch ordering (Shang et al , 2009), buyer intermediation (Tunca and Zhu, 2018), factoring and reverse factoring (Kouvelis and Xu, 2021), sourcing and risk management, (Tang et al , 2018) etc. At present, the development of mobile Internet makes the communication between members of the supply chain more timely (Akpakwu et al , 2017; Zhang et al , 2019), and the data of enterprises, consumers and banks also present massive and complex data (Li et al , 2019, 2018). Given that the previous literature rarely studies supply chain finance from these two dimensions, this special issue aims to fill this gap.…”