Identifying Meaningful Targets for Complex Lean 4.0 Manufacturing Using Business Analytics: A Case Study in Biopharmaceutical Manufacturing

Abstract

Traditional lean manufacturing (LM) material waste and immaterial (time and effort) reduction targets may not be of significant value for complex manufacturing. A competitive advantage in complex manufacturing lies in the accumulation of process knowledge and leveraging this knowledge to improve performance metrics, such as yield. The study demonstrates how business analytics (BAs) using cross-industry standard process for data mining can extract process knowledge from human experts and historical manufacturing data to provide actionable insights. The study explores how established LM tools, such as standard work and 5s, can be adapted to deploy the s recommendations on the manufacturing floor, leading to Lean 4.0. The proposed approach is validated on a case study in biopharmaceutical manufacturing, resulting in a 6% increase in product yield. The study discusses how the successful combination of BAs and LM can provide useful process knowledge insights in complex manufacturing through an adapted Lean 4.0 framework to target non-traditional performance measures such as yield.