  In recent decades, it has become a significant tendency for industrial
manufacturers to adopt decentralization as a new manufacturing paradigm. This
enables more efficient operations and facilitates the shift from mass to
customized production. At the same time, advances in data analytics give more
insights into the production lines, thus improving its overall productivity.
The primary objective of this paper is to apply a decentralized architecture to
address new challenges in industrial analytics. The main contributions of this
work are therefore two-fold: (1) an assessment of the microservices'
feasibility in industrial environments, and (2) a microservices-based
architecture for industrial data analytics. Also, a prototype has been
developed, analyzed, and evaluated, to provide further practical insights.
Initial evaluation results of this prototype underpin the adoption of
microservices in industrial analytics with less than 20ms end-to-end processing
latency for predicting movement paths for 100 autonomous robots on a commodity
hardware server. However, it also identifies several drawbacks of the approach,
which is, among others, the complexity in structure, leading to higher resource
consumption.
