Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Explainable Artificial Intelligence (Xai) in Lean Manufacturing: Transparent and Trustworthy Machine and Deep Learning for Continuous Process Improvem

Explainable Artificial Intelligence (Xai) in Lean Manufacturing: Transparent and Trustworthy Machine and Deep Learning for Continuous Process Improvem

Chen, Tin-Chih Toly

Paperback

Series: Springerbriefs in Applied Sciences and Technology

DatabasesGeneral ComputersProbability & Statistics

PREORDER - Expected ship date January 18, 2027

ISBN10: 3032461537
ISBN13: 9783032461537
Publisher: Springer
Published: Jan 18 2027
Language: English

This book systematically reviews XAI techniques and introduces how these XAI techniques can be systematically applied to lean manufacturing, including methodology, system architecture, and applications. So far, artificial intelligence (AI) technologies have been widely used in the field of lean manufacturing for cycle time reduction, capacity balancing, transportation planning, demand and sales forecasting, just-in-time (JIT) control, bottleneck detection, etc. Some applications of AI technologies in lean manufacturing are not easy to understand or communicate, especially for managers or engineers with insufficient background knowledge of AI, which undoubtedly limits the practicality and credibility of these applications. To solve this problem, explainable artificial intelligence (XAI) is considered as a feasible strategy. However, most of the relevant research results are scattered in various journals or conference proceedings, and there is an urgent need to systematically integrate these results. In addition, although there have been many reviews on the possible applications of XAI in lean manufacturing, there are few systematic introductions, including methodology, system architecture, and case studies. Relevant references, examples, or cases are also used as supporting evidence.

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