Artificial‐Intelligence‐Based Lean Biomanufacturing

To achieve lean biomanufacturing, which leads to waste reduction, efficiency improvement, and production of high-quality biopharmaceuticals in large quantities, it is necessary to adopt the philosophy of lean. The advent of Industry 4.0 will bring a new dominant paradigm of integrating artificial intelligence (AI) into lean bioprocessing that brings about the power to make real-time decisions while continuously improving the processes. The chapter explores the improvements in lean manufacturing processes with AI applications in both the upstream and downstream, including inventory of raw materials, bottle release, and so forth. It describes a combination of the application of machine learning, neural networks, and predictive analytics that can be used to tune process parameters, energy consumption rates, downtimes, and yields. Some of the critical instruments, which are digital twins, anomaly detection systems, and AI-based scheduling, are explained. Data integrity, model validation, and compliances with regulations in GMP settings are also the issues considered in the chapter. Real-life examples of the biopharma manufacturing lines include how AI-enabled lean transformation has improved objectively measurable productivity and sustainability. After the discussion of the key points that need to focus on the integration of the AI into the lean, agile, and scalable biomanufacturing systems, this chapter leaves us with the roadmap towards adopting the AI as a strategic enabler of the lean, agile, and scalable biomanufacturing systems.