A Taxonomy and Archetypes of Business Analytics in Smart Manufacturing

TL;DR

This paper develops a comprehensive taxonomy and identifies archetypes of business analytics in smart manufacturing, providing a structured overview of the field's diversity and evolution through literature review and cluster analysis.

Contribution

It introduces a novel taxonomy with 52 characteristics and six archetypes, synthesizing existing knowledge and guiding future research and practice in smart manufacturing analytics.

Findings

Six archetypes of analytics in smart manufacturing identified

Deep learning dominates recent applications

Temporal analysis shows a shift beyond predictive approaches

Abstract

Fueled by increasing data availability and the rise of technological advances for data processing and communication, business analytics is a key driver for smart manufacturing. However, due to the multitude of different local advances as well as its multidisciplinary complexity, both researchers and practitioners struggle to keep track of the progress and acquire new knowledge within the field, as there is a lack of a holistic conceptualization. To address this issue, we performed an extensive structured literature review, yielding 904 relevant hits, to develop a quadripartite taxonomy as well as to derive archetypes of business analytics in smart manufacturing. The taxonomy comprises the following meta-characteristics: application domain, orientation as the objective of the analysis, data origins, and analysis techniques. Collectively, they comprise eight dimensions with a total of 52 distinct characteristics. Using a cluster analysis, we found six archetypes that represent a synthesis of existing knowledge on planning, maintenance (reactive, offline, and online predictive), monitoring, and quality management. A temporal analysis highlights the push beyond predictive approaches and confirms that deep learning already dominates novel applications. Our results constitute an entry point to the field but can also serve as a reference work and a guide with which to assess the adequacy of one's own instruments.