Understanding Big Data Analytics for Manufacturing Processes: Insights from Literature Review and Multiple Case Studies. (November 2019)

• Record Type: Journal Article
• Authors: Belhadi, Amine; Zkik, Karim; Cherrafi, Anass; Yusof, Sha'ri M.; El fezazi, Said
• Is Part Of: Computers & industrial engineering. Volume 137(2019)
• Publication Date: 2019-11
• Subjects: Big Data Analytics -- Manufacturing process -- Big Data Analytics capabilities -- Business intelligence -- Literature review -- Multiple case study; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.106099
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Review of the ongoing research on BDA in manufacturing process. Proposition of a framework grouping BDA capabilities in manufacturing process. Identification of research trends in implementing BDA in manufacturing process. Conducting multiple case studies of the application of BDA in real-life context of the manufacturing process in a leading manufacturers of phosphate derivatives. Drawing a set of recommendations based on the findings of the multiple case study. Abstract: Today, we are undoubtedly in the era of data. Big Data Analytics (BDA) is no longer a perspective for all level of the organization. This is of special interest in the manufacturing process with their high capital intensity, time constraints and given the huge amount of data already captured. However, there is a paucity in past literature on BDA to develop better understanding of the capabilities and strategic implications to extract value from BDA. In that vein, the central aim of this paper is to develop a novel model that summarizes the main capabilities of BDA in the context of manufacturing process. This is carried out by relying on the findings of a review of the ongoing research along with a multiple case studies within a leading phosphate derivatives manufacturer to point out the capabilities of BDA in manufacturing processes and outline recommendations to advance research in the field. The findings will help companies to understand the big data analytics capabilities and its potential implications for their manufacturing processes and support them seeking to design more effective BDA-enabler infrastructure.