Manufacturing System Optimization with Lean Methods, Manufacturing Process Objectives and Fuzzy Logic Controller Design

Drews, Tom; Molenda, Paul; Oechsle, Oliver; Koller, Jan

doi:10.1016/j.procir.2020.04.145

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…Labor performance influences production, with fatigue affecting indicators including service level (Dahlan & Widanarko, 2022). Measures such as stress reduction training and ergonomic methods are suggested (Drews et al, 2020). A solid production chain optimizes automatic and manual stages through lean tools, reducing bottlenecks and downtime (Duc & Thu, 2022).…”

Section: Background Informationmentioning

confidence: 99%

Implementing Lean Manufacturing to Optimize Service Level: An Industrial Lubricating Oils Processing and Marketing Company Case Study

Herrera Barberena,

Tomás Rivera

2024

Ing. ind. (Lima)

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This research aims to implement a lean manufacturing management model using tools such as value stream mapping (VSM), single-minute exchange of die (SMED) and autonomous maintenance (AM) to improve the service level within petroleum refining companies. Moreover, its objective is to mitigate the technical gap that leads to monetary losses for the company in question. In terms of methodology, the research used a case study design focused on pre-test and post-test analyses to identify the key improvements. The results revealed a 35 % reduction in company losses and an increase of 19 % in the service level.

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Section: Background Informationmentioning

confidence: 99%

Implementing Lean Manufacturing to Optimize Service Level: An Industrial Lubricating Oils Processing and Marketing Company Case Study

Herrera Barberena,

Tomás Rivera

2024

Ing. ind. (Lima)

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“…Fuzzy Logic provides an efficient way for designing a simulation based on various variable inputs that control the development for a proposed model [20]. The fuzzy approach is best used to identify crucial factors influencing machine selection and machine reconfiguration in a manufacturing environment to allow efficient decision-making [21]. Fuzzy Logic can be described as a human-like representation in software to produce almost accurate solutions when faced with unfamiliar tasks based on presented principles [19,20].…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic

et al. 2022

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Several research works have addressed the different aspects and technologies associated with Smart Manufacturing Systems (SMS); however, the evaluation challenges while establishing a new SMS that requires pre-implementation planning and assessment have given little attention. To overcome this limitation, this paper formulates an evaluation framework by identifying apparent evaluation factors to measure the effectiveness of a particular SMS configuration before implementation. Three factors from the literature studies have been used as inputs to control the final output of the configuration modal. Compositions were manipulated based on how factors affected the manufacturing cost justification in multiple setups. Different configurations were analyzed based on the trained Fuzzy Logic model by configurations and based on the trained Fuzzy Logic model using MATLAB’s Fuzzy Logic Designer tool. Results obtained from the evaluation performed by various configuration experiments were later presented to actual field engineers from the manufacturing industry to evaluate the satisfaction level of the evaluation framework. The result showed that this proposed configuration model has a satisfactory rate of 83.7%, as this was achieved by significant feedback from field engineers. This study has significantly facilitated the identification of influential factors and the measured relationship of the factors in the formulated configurations, enabling the best configuration approach to be identified. Therefore, it can be concluded that a visualized and measured configuration system can influence decision-making in the manufacturing industry, thus allowing manufacturers to stay competitive by making well-versed decisions proactively. Exclusively, this research has staged a framework for the industry to follow suit and adapt for future research work related to the SMS field.

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Development of a systematic approach to identify non-value-adding operations in the LBM process chain

2021

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Manufacturing System Optimization with Lean Methods, Manufacturing Process Objectives and Fuzzy Logic Controller Design

Cited by 9 publications

References 14 publications

Implementing Lean Manufacturing to Optimize Service Level: An Industrial Lubricating Oils Processing and Marketing Company Case Study

Implementing Lean Manufacturing to Optimize Service Level: An Industrial Lubricating Oils Processing and Marketing Company Case Study

An Evaluation Model of Smart Manufacturing System Configurations Prior to Implementation Using Fuzzy Logic

Development of a systematic approach to identify non-value-adding operations in the LBM process chain

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