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