Integrating Machine Learning and Databricks for Work Order Automation in Pharmaceutical Manufacturing

The operation of manufacturing pharmaceuticals and drugs requires high efficiency, accuracy and definitive compliance with standards set by regulatory bodies. Work order management, which is a critical and generic area addressing how manufacturing tasks are planned, assigned and executed, is still a difficult area given its dependence on both human and automated factor inputs. This paper aims to look at the implementation of ML algorithms and Databricks within SCM and work order management of a pharmaceutical manufacturing firm. This research achieves better task scheduling, anomaly detection, and predictive maintenance by using Databricks’ distributed computing features and ML models. The resulting work describes a case study applying both supervised and unsupervised learning to historical data, process modeling and bottleneck detection, and static and dynamic scheduling. The experimental study employing real-world datasets suggests a specific 30% times saving regarding task completion and a general 20% augmentation of compliance with regulations. This paper thus discusses the impact ML-driven automation can bring to the pharmaceutical Industry while considering issues like data privacy and model interpretability. Keywords: Work Order Automation, Pharmaceutical Manufacturing, Machine Learning, Databricks, Predictive Maintenance, Data Analytics.