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Principles of Industry 4.0 direct us to predict how pharmaceutical operations and regulations may exist with automation, digitization, artificial intelligence (AI), and real time data acquisition. Machine learning (ML), a sub-discipline of AI, involves the use of statistical tools to extract the desired information either through understanding the underlying patterns in the information or by development of mathematical relationships among the critical process parameters (CPPs) and critical quality attributes (CQAs) of biopharmaceuticals. ML is still in its infancy for

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The therapeutic and immunological properties of biopharmaceuticals are governed by the glycoforms contained in them. Thus, bioinformatics tools capable of performing comprehensive characterization of glycans are significantly important to the biopharma industry. The primary structural elucidation of glycans using mass spectrometry is tricky and tedious in terms of spectral interpretation. In this study, the biosimilars of a therapeutic monoclonal antibody and an Fc‐fusion protein with moderate and heavy glycosylation, respectively, were employed as representative biopharmaceuticals for released glycan analysis using liquid chromatography–tandem mass spectrometry instead of conventional mass spectrometry‐based analysis. SimGlycan® is a software with proven ability to process tandem MS data for released glycans could identify eight additional glycoforms in Fc‐fusion protein biosimilar, which were not detected during mass spectrometry analysis of released glycans or glyco‐peptide mapping of the same molecule. Thus, liquid chromatography–tandem mass spectrometry analysis of released glycans not only complements conventional liquid chromatography–mass spectrometry‐based glycan profiling but can also identify additional glycan structures that may otherwise be omitted during conventional liquid chromatography–tandem mass spectrometry based analysis of mAbs. The mass spectrometry data processing tools, such as PMI Byos™, SimGlycan®, etc., can display pivotal analytical capabilities in automated liquid chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry‐based glycan analysis workflows, especially for high‐throughput structural characterization of glycoforms in biopharmaceuticals.