Evidence map›Paper›PMID 35382094›Full record

ArticleQuality & quantity2023

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

A Sivakumar, N Bagath Singh, D Arulkirubakaran, P Praveen Vijaya Raj

Abstract read
In one paragraph

Article in Quality & quantity, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

A SivakumarDepartment of Mechanical Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu 638060 India.
N Bagath SinghDepartment of Mechanical Engineering, Kurinji College of Engineering and Technology, Manapparai, Trichy, Tamil Nadu 621307 India.ORCID 0000-0002-6413-437X
D ArulkirubakaranDepartment of Mechanical Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, Tamil Nadu 641114 India.
P Praveen Vijaya RajCenter for Digital Economy, Indian Institute of Management Raipur, Atal Nagar, Raipur, Chhattisgarh 493661 India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BPNNBus bodybuildingCompetitivenessFlexibilityProduction facility priorities

Identifiers

PMID35382094
PMCPMC8970063

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.