ABSTRACT
The increased amount of data being generated in virtually every context provides huge potential for a variety of organisational application fields, one of them being Supply Chain Management (SCM). The possibilities and use cases of applying this new type of data, i.e. Big Data (BD), is huge and a large body of research has already been conducted in this area. The current paper aims at identifying the understanding and the applications of BD not from an academic but a practitioners’ point of view. By applying expert interviews, the main aim is to identify (i) a definition of Big Data from SCM practitioners’ point of view, (ii) current SCM activities and processes where BD is already used in practice, (iii) potential future application fields for BD as seen in SCM practice and (iv) main hinderers of BD application. The results show that Big Data is referred to as complex data sets with high volumes and a variety of sources that can't be handled with traditional approaches and require data expert knowledge and SCM domain knowledge to be used in organisational practical. Current applications include the creation of transparency in logistics and SCM, the improvement of demand planning or the support of supplier quality management. The interviewed experts coincide in the view, that BD offers huge potential in future SCM. A shared vision was the implementation of real-time transparency of Supply Chains (SC), the ability to predict the behavior of SCs based on identified data patterns and the possibility to predict the impact of decisions on SCM before they are taken.
- A. de Mauro, M. Greco, and M. Grimaldi, “A formal definition of Big Data based on its essential features”, Library Review, vol. 65, no. 3, pp. 122–135, 2016.Google ScholarCross Ref
- R. Iqbal, F. Doctor, B. More, S. Mahmud, and U. Yousuf, “Big data analytics: Computational intelligence techniques and application areas”, Technological Forecasting and Social Change, vol. 153, p. 119253, 2020.Google ScholarCross Ref
- Awwad, Mohamed, Pranav Kulkarni, Rachit Bapna, and Aniket Marathe, Ed., Big Data Analytics in Supply Chain: A Literature Review, 2018.Google Scholar
- J. T. Mentzer , “Defining supply chain management”, Journal of Business logistics, vol. 22, no. 2, pp. 1–25, 2001.Google ScholarCross Ref
- S. Tiwari, H.-M. Wee, and Y. Daryanto, “Big data analytics in supply chain management between 2010 and 2016: Insights to industries”, Computers & industrial engineering, vol. 115, pp. 319–330, 2018.Google ScholarCross Ref
- J. Liu, M. Chen, and H. Liu, “The role of big data analytics in enabling green supply chain management: a literature review”, Journal of Data, Information and Management, pp. 1–9, 2020.Google Scholar
- Feki, Mondher, Imed Boughzala, and Samuel Fosso Wamba., Ed., Big data analytics-enabled supply chain transformation: A literature review: IEEE, 2016.Google Scholar
- P. Brandtner, “Requirements for Value Network Foresight-Supply Chain Uncertainty Reduction”, in ISPIM Conference Proceedings, pp. 1–12, 2020.Google Scholar
- C. Joseph Udokwu, F. Darbanian, T. N. Falatouri, and P. Brandtner, “Evaluating Technique for Capturing Customer Satisfaction Data in Retail Supply Chain”, in 2020 The 4th International Conference on E-commerce, E-Business and E-Government, Arenthon France, pp. 89–95, 2020.Google ScholarDigital Library
- L. van Audenhove and K. Donders, “Talking to People III: Expert Interviews and Elite Interviews”, in The Palgrave Handbook of Methods for Media Policy Research, H. van den Bulck, M. Puppis, K. Donders, and L. van Audenhove, Eds., Cham: Springer International Publishing, pp. 179–197, 2019.Google ScholarCross Ref
- S. Döringer, “‘The problem-centred expert interview’. Combining qualitative interviewing approaches for investigating implicit expert knowledge,” International Journal of Social Research Methodology, vol. 1, no. 4, pp. 1–14, 2020.Google Scholar
- I. Mergel, N. Edelmann, and N. Haug, “Defining digital transformation: Results from expert interviews,” Government Information Quarterly, vol. 36, no. 4, p. 101385, 2019.Google ScholarCross Ref
- B. Flyvbjerg, “Five Misunderstandings About Case-Study Research,” Qualitative Inquiry, vol. 12, no. 2, pp. 219–245, 2006.Google ScholarCross Ref
- A. Kurz, C. Stockhammer, S. Fuchs, and D. Meinhard, “Das problemzentrierte Interview,” in Qualitative Marktforschung: Konzepte ‐- Methoden ‐- Analysen, R. Buber and H. H. Holzmüller, Eds., Wiesbaden: Gabler, pp. 463–475, 2007.Google ScholarCross Ref
- H. O. Mayer, Interview und schriftliche Befragung, 4th ed. München: Oldenbourg, 2009.Google Scholar
- A. Witzel, “The problem-centered interview. FQS Forum: Qualitative Sozialforschung,” in Forum: Qualitative Social Research, p. 22, 2000.Google Scholar
- S. Albers, D. Klapper, U. Konradt, A. Walter, and J. Wolf, “Methodik der empirischen Forschung. 3., überarbeitete und erweiterte Auflage,” Wiesbaden and sl: Gabler Verlag, 2009.Google ScholarCross Ref
Index Terms
- Applications of Big Data Analytics in Supply Chain Management: Findings from Expert Interviews
Recommendations
Big data and predictive analytics for supply chain sustainability
Big data/predictive analytics (BDPA) impacts financial/strategic performance in SCM.We suggest that BDPA can also be used to enhance and enable sustainable SCM.We review extant theories that can inform research in this area.A theory-based research ...
Big data optimisation and management in supply chain management: a systematic literature review
AbstractThe increasing interest from technology enthusiasts and organisational practitioners in big data applications in the supply chain has encouraged us to review recent research development. This paper proposes a systematic literature review to ...
Data Preprocessing in Supply Chain Management Analytics - A Review of Methods, the Operations They Fulfill, and the Tasks They Accomplish.: Data Preprocessing in Supply Chain Management Analytics.
ICCMB '23: Proceedings of the 2023 6th International Conference on Computers in Management and BusinessData preprocessing is thought of as one of the most important steps in data analytics. This is especially true for the field of Supply Chain Management (SCM), in which the handling of huge data sets is the norm. Data preprocessing consists of multiple ...
Comments