In the manufacturing industry, there are claims about a novel system or paradigm to overcome current data interpretation challenges. Anecdotally, these studies have not been completely practical in real-world applications (e.g., data analytics).
This article focuses on smart manufacturing (SM), proposed to address the inconsistencies within manufacturing that are often caused by reasons such as:
(i) data realization using a general algorithm,
(ii) no accurate methods to overcome the actual inconsistencies using anomaly detection modules, or
(iii) real-time availability of insights of the data to change or adapt to the new challenges.
A real-world case study on mattress protector manufacturing is used to prove the methods of data mining with the deployment of the isolation forest (IF)-based machine learning (ML) algorithm on a cloud scenario to address the inconsistencies stated above.
The novel outcome of these studies was establishing efficient methods to enable efficient data analysis.
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