Reducing production losses in additive manufacturing using overall equipment effectiveness

Basak, Shreeja; Baumers, Martin; Holweg, Matthias; Hague, Richard; Tuck, Christopher

doi:10.1016/j.addma.2022.102904

Cited by 10 publications

(7 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Another study [15] introduces an AM-specific OEE framework, addressing production losses in the AM workflow through simulation analysis. It emphasizes adaptability but is limited to single-machine AM operations and OEE scope.…”

Section: Related Literaturementioning

confidence: 99%

See 1 more Smart Citation

Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability

Zehra,

Mirjat,

Shakih

et al. 2024

Sustainability

View full text Add to dashboard Cite

In the face of pandemic-induced emergencies and unpredictable natural disasters, industries are compelled to implement rescue plans to mitigate unexpected risks. In this context, Overall Equipment Effectiveness (OEE) is considered as a key metric, followed by sustainability efforts to manage unforeseen risks, encompassing social, environmental, and economic aspects. OEE is considered as a lean tool to determine the efficiency of equipment or processes on par with the world class OEE standard, i.e., 85%. Performance, Availability and Quality as three main drivers of OEE. This research study explores the implementation of OEE in conjunction with sustainability principles in an auto sector manufacturing firm, aiming to enhance operational efficiency and sustainability practices. The research involves a 12-week initial session from April to June 2022, followed by an analysis of July to September 2022, resulting in an impressive OEE value of 48%. Notable improvements in Availability (89.75%), Performance (72.68%), and Quality (73.82%) contribute significantly. The analysis reveals enhancements in scrap rework (17%), training (16%), maintenance (13%), material availability (12%), and production capability (11%). Achievements include improvements in green profile (25%), health and safety (20%), and energy efficiency (25%), along with reductions in carbon dioxide emissions (21%), waste management (17%), and scrap (15%). This research underscores the commitment of the case study industry to sustainable development and economic growth, showcasing significant enhancements in product quality and efficiency. The integration of sustainability principles into OEE initiatives is pivotal for modern industrial optimization. The study results highlight the profound significance of this synergistic relationship, particularly within the blending section, driving substantial positive outcomes in manufacturing processes and operational excellence. The implementation of sustainability efforts not only mitigates risks and fosters growth for automotive manufacturers but also yields environmental benefits. Based on findings of this study, a roadmap for automotive manufacturers is devised to achieve robust OEE while concurrently reaping economic and environmental rewards by employing sustainability principles.

show abstract

Section: Related Literaturementioning

confidence: 99%

“…To overcome limitations, expanded metrics and consideration of simulation constraints are proposed. The research provides insights into OEE implementation in AM, stressing the need for further investigation, especially in the auto sector's manufacturing sustainability [15].…”

Section: Related Literaturementioning

confidence: 99%

Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability

Zehra,

Mirjat,

Shakih

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…In the automotive industry, AM's tool-free nature helps to simplify supply chains and provide more agile and sustainable opportunities [16]. Besides the main benefits of AM mentioned above, the automotive industry can reduce the weight of a vehicle by replacing heavy metal parts with AM fabricated composite materials that have stiffness and strength-to-weight ratios.…”

Section: Introductionmentioning

confidence: 99%

Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS

et al. 2023

View full text Add to dashboard Cite

For many different types of businesses, additive manufacturing has great potential for new product and process development in many different types of businesses including automotive industry. On the other hand, there are a variety of additive manufacturing alternatives available today, each with its own unique characteristics, and selecting the most suitable one has become a necessity for relevant bodies. The evaluation of additive manufacturing alternatives can be viewed as an uncertain multi-criteria decision-making (MCDM) problem due to the potential number of criteria and candidates as well as the inherent subjectivity of various decision-experts engaging in the process. Pythagorean fuzzy sets are an extension of intuitionistic fuzzy sets that are effective in handling ambiguity and uncertainty in decision-making. This study offers an integrated fuzzy MCDM approach based on Pythagorean fuzzy sets for assessing additive manufacturing alternatives for the automotive industry. Objective significance levels of criteria are determined using the Criteria Importance Through Inter-criteria Correlation (CRITIC) technique, and additive manufacturing alternatives are prioritized using the Evaluation based on Distance from Average Solution (EDAS) method. A sensitivity analysis is performed to examine the variations against varying criterion and decision-maker weights. Moreover, a comparative analysis is conducted to validate the acquired findings.

show abstract

“…Occurrence (O), severity (S), and detection (D) of a failure are the based point of RPN [18]. The engine efficiency, and the size of the ratio of the six elements of the six most losses equipment failure, set-up, and adjustments, idling and minor stoppages, reduced speed, scrap and rework, and startup losses [19].…”

Section: Introductionmentioning

confidence: 99%

Analysis of sag mill machine performance using overall equipment effectiveness and failure model and effects analysis method

Fuadiya

Widjajati²

2022

Int. J. Ind. Optim.

View full text Add to dashboard Cite

The mining company uses a variety of grinding machines to process minerals, whereas the most common type of machine is the Semi-Autogenous Grinding SAG Mill machine. This machine is employed for the mining process of hard rock as raw material into gold, copper, and silver. However, the SAG Mill machines are often broken, even suddenly not working, with an average loss time of 97.30 hours which impacts a decrease in efficiency and production quality of up to 40%. It can cause losses that do not reach the production target. This research aims to measure the effectiveness of the SAG Mill machine and determine the failure using the OEE and FMEA methods. The results showed that the SAG Mill machine is still under standardized based on the Japan Institute of Plant Maintenance (JIPM), which is 85%. The FMEA method and RPN value apply to analyze downtime losses, and idling is the loss that highly affects the effectiveness of SAG Mill machines. Recommendations for the company are to increase the number of equipment that aims to prolong the machine's age and accelerate production. This research contributes to another solution to help maintenance managers by measuring the effectiveness and determining the failure of the SAG Mill machine

show abstract

Reducing production losses in additive manufacturing using overall equipment effectiveness

Cited by 10 publications

References 41 publications

Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability

Optimizing Auto Manufacturing: A Holistic Approach Integrating Overall Equipment Effectiveness for Enhanced Efficiency and Sustainability

Additive manufacturing process selection for automotive industry using Pythagorean fuzzy CRITIC EDAS

Analysis of sag mill machine performance using overall equipment effectiveness and failure model and effects analysis method

Contact Info

Product

Resources

About