Strategic logistics: Re-designing companies in accordance with Lean Principles

Carrasqueira, M.; Cruz‐Machado, V.

doi:10.1080/17509653.2008.10671056

Cited by 8 publications

(1 citation statement)

References 3 publications

(3 reference statements)

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“…The word lean in manufacturing means the efficient use of the available resources by cutting the NVA activities or wastes (Carrasqueira & Machado, 2008). Lean manufacturing represents a collection of tools that work together synergistically to create a streamlined, high-quality system that produces finished products at the same pace of the customer demand (Shah & Ward, 2007).…”

Section: Lean Manufacturingmentioning

confidence: 99%

Lean thinking for a maintenance process

Mostafa

Lee

Dumrak

et al. 2015

Production & Manufacturing Research

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The maintenance process shares significant operating costs in an organisation. Lean thinking can be incorporated into maintenance activities through applying its principles and practices/tools. Lean maintenance (LM) is a prerequisite for lean manufacturing systems. This research proposes a new structure for LM process based on a systematic literature review of a significant number of related articles that were published on LM. The process structure is designed based on the five lean principles to guide and support organisations to pursue maintenance excellence. This study establishes a scheme for LM tools that are structured into 2 level 4 bundles and 26 lean practices/tools and develops a House of Waste (HoW) to demonstrate the association between maintenance wastes and the LM tools. With a successful accomplishment of the proposed scheme, the performance of a maintenance department can create more improvement opportunities over time to reach the maintenance excellence status.

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Section: Lean Manufacturingmentioning

confidence: 99%

Lean thinking for a maintenance process

Mostafa

Lee

Dumrak

et al. 2015

Production & Manufacturing Research

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Using machine learning and deep learning algorithms for downtime minimization in manufacturing systems: an early failure detection diagnostic service

Shahin,

Chen,

Hosseinzadeh

et al. 2023

Int J Adv Manuf Technol

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Accurate detection of possible machine failure allows manufacturers to identify potential fault situations in processes to avoid downtimes caused by unexpected tool wear or unacceptable workpiece quality. This paper aims to report the study of more than 20 fault detection models using Machine Learning (ML), Deep Learning (DL), and Deep Hybrid Learning (DHL). Predicting how the system could fail based on certain features or system settings (input variables) can help avoid future breakdowns and minimize downtime. The effectiveness of the proposed algorithms was experimented with a synthetic predictive maintenance dataset published by the School of Engineering of the University of Applied Sciences in Berlin, Germany. The delity of these algorithms was evaluated using performance measurement values such as accuracy, precision, recall, and the F-Score. Final results demonstrated that Deep Forest and Gradient Boosting algorithms had shown very high levels of average accuracy (exceeded 90%).Additionally, the Multinominal Logistic Regression and Long Short Term Memory based algorithms have shown satisfactory average accuracy (above 80%). Further analysis of models suggests that some models outperformed others. The research concluded that, through various ML, DL, and DHL algorithms, operational data analytics, and health monitoring system, engineers could optimize maintenance and reduce reliability risks.

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