Year: 2022
Type: article
Abstract: Structured problem-solving techniques have emerged as a critical approach for enhancing manufacturing yield and optimizing production processes.This review explores the application of structured problem-solving methodologies, such as Six Sigma, Lean Manufacturing and the Theory of Constraints, in the context of manufacturing datasets.The importance of data collection, preparation and analysis techniques, including statistical process control, regression analysis, machine learning algorithms for anomaly detection, clustering techniques and time-series analysis, is discussed.This study also highlights the role of data visualization tools in facilitating problem-solving and decision-making processes.Case studies from diverse manufacturing sectors, including semiconductor, automotive, pharmaceutical and electronics assembly, have demonstrated the successful implementation of structured problem-solving approaches and their impact on yield enhancement.This review delineates how Industry 4.0 technologies such as real-time data analysis and predictive maintenance, enhance traditional structured problemsolving techniques by enabling more proactive and data-driven decision-making processes.Challenges and limitations, including data quality issues, resistance to change and the need for cross-functional collaboration, were addressed.The paper concludes by discussing future trends in manufacturing data analysis, such as the application of artificial intelligence and edge computing and the growing importance of sustainability in manufacturing problem solving.This review underscores the significance of structured problem-solving techniques in driving continuous improvement and maintaining competitiveness in the evolving manufacturing landscape.
Author: Tarun Parmar
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4407056548