  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.
