Prediction of Equipment Effectiveness using Hybrid Moving Average-Adaptive Neuro Fuzzy Inference System (MA-ANFIS) for decision support in Bus Body Building Industry

Sivakumar, Appa Iyer; Singh, N. Bagath; Arulkirubakaran, D.; Raj, Praveen Vijaya Raj Pushpa

doi:10.1590/0001-3765202220210552

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…In general, AI-based neural network algorithms have been demonstrated to be an excellent predictive tool (Patel et al 2017), as evidenced by particle size prediction (Gautam et al 2021), equipment effectiveness prediction (Sivakumar et al 2022), and paper industry performance prediction (Jauhar et al 2022). Likewise, the robust search algorithm of Back Propagation Neural Network (BPNN) also a good technique for predicting facilities (Zhang and Qu 2021).…”

Section: Review Of Related Studiesmentioning

confidence: 99%

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Sivakumar¹,

Singh

Arulkirubakaran

et al. 2022

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The pandemic recession has caused enormous disturbances in many industrialized countries. The massive disruption of the supply chain of production is affecting manufacturing companies operating in and around India. Particularly the medium-sized bus body building works have been reduced, due to its compound anomalies. The integrated view of the production facility priorities is not an easy task. Since it is difficult for available labour to conduct an entire project, the completion of a production process is delayed. But still, the dilemma remains as to how production managers can correctly interpret the priorities of the facility. Indeed, this is a problem missing from the previous study. Fortunately, in the current competitive environment, it is essentially needed. This study has been used Back Propagation Neural Network (BPNN) approach for predicting production facility priorities. The experimental results confirm the suitability of the model for predicting priorities. A real-world problem is taken into account in making use of the model output. In this sense, this total solution facilitates production managers in assessing and enhancing the production facilities. The findings emphasize the priority of "equipment effectiveness, labour scheduling and communication" in order to strengthen the post-pandemic production facility.

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Section: Review Of Related Studiesmentioning

confidence: 99%

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Sivakumar¹,

Singh

Arulkirubakaran

et al. 2022

Qual Quant

Self Cite

View full text Add to dashboard Cite

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Proposal for Gemba-Kaizen Implementation to Enhance Efficiency in the Medium-Scale Bus Body Fabrication Industry

Balakrishnan,

Senthilkumar,

Rajkumar

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">The basic needs of people are met by the building, fabric, and farming sectors. In addition, the automobile industry significantly contributes to human mobility and is essential to India’s economic expansion. There are numerous research strategies available to improve the bus body building industries. Several investigative approaches for enhancing bus body building industries are available. However, several of these studies merely look at it from the perspective of shop floor activity. Accordingly, when it comes to the execution of process design approaches, there is little practical evidence for accepting Gemba kaizen’s attitude. Hence, the purpose of this article is to present a continuous improvement redesign framework tailored to a specific bus body building industrial sector. The proposed model is structured after a critical examination of Gemba and Kaizen. The results showed that by implementing the improvement initiatives, the number of process activities decreased from 44 to 25 (of which 43% were wasteful), and the cycle time decreased from an average of 112 hours to 68 hours, or 39% faster. The outcomes also show how the suggested model helped organizations reduce resource usage and enhance organizational effectiveness.</div></div>

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Prediction of Equipment Effectiveness using Hybrid Moving Average-Adaptive Neuro Fuzzy Inference System (MA-ANFIS) for decision support in Bus Body Building Industry

Cited by 2 publications

References 31 publications

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

Proposal for Gemba-Kaizen Implementation to Enhance Efficiency in the Medium-Scale Bus Body Fabrication Industry

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