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Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network.

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Title: Improving operations through a lean AI paradigm: a view to an AI-aided lean manufacturing via versatile convolutional neural network.
Authors: Shahin, Mohammad, Maghanaki, Mazdak, Hosseinzadeh, Ali, Chen, F. Frank
Source: International Journal of Advanced Manufacturing Technology; Aug2024, Vol. 133 Issue 11/12, p5343-5419, 77p
Subject Terms: CONVOLUTIONAL neural networks, PATTERN recognition systems, VALUE stream mapping, ALGORITHMIC bias, WASTE minimization, LEAN management
Abstract: Integrating Lean Manufacturing tools with artificial intelligence (AI) is emerging as a revolutionary approach to optimize production processes, reduce waste, and enhance efficiency. Traditional Lean practices focus on waste reduction and process improvement, often relying on human expertise for problem identification and resolution. AI algorithms, on the other hand, excel in pattern recognition, data analysis, and decision-making. Lean tools and AI can offer more precise, data-driven solutions for common manufacturing challenges when integrated. AI algorithms can automate and refine Lean techniques like value stream mapping, Kanban, and 5S by providing real-time, actionable insights drawn from big data. This fusion of Lean and AI aids in predictive maintenance, quality control, and optimization, enhancing the efficiency and responsiveness of the manufacturing process. Moreover, AI's capability for machine learning allows the system to adapt and improve autonomously over time, further aligning with Lean's continuous improvement ethos. Seven case studies were conducted to show how this alignment might aid Lean Manufacturing. However, successful implementation necessitates overcoming data quality and algorithmic bias challenges. Despite these hurdles, integrating Lean tools and AI can redefine best practices in manufacturing, setting new standards for operational excellence. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Integrating Lean Manufacturing tools with artificial intelligence (AI) is emerging as a revolutionary approach to optimize production processes, reduce waste, and enhance efficiency. Traditional Lean practices focus on waste reduction and process improvement, often relying on human expertise for problem identification and resolution. AI algorithms, on the other hand, excel in pattern recognition, data analysis, and decision-making. Lean tools and AI can offer more precise, data-driven solutions for common manufacturing challenges when integrated. AI algorithms can automate and refine Lean techniques like value stream mapping, Kanban, and 5S by providing real-time, actionable insights drawn from big data. This fusion of Lean and AI aids in predictive maintenance, quality control, and optimization, enhancing the efficiency and responsiveness of the manufacturing process. Moreover, AI's capability for machine learning allows the system to adapt and improve autonomously over time, further aligning with Lean's continuous improvement ethos. Seven case studies were conducted to show how this alignment might aid Lean Manufacturing. However, successful implementation necessitates overcoming data quality and algorithmic bias challenges. Despite these hurdles, integrating Lean tools and AI can redefine best practices in manufacturing, setting new standards for operational excellence. [ABSTRACT FROM AUTHOR]
ISSN:02683768
DOI:10.1007/s00170-024-13874-4