Show simple item record

dc.contributor.authorSharma, Parveen
dc.contributor.authorGhatorha, Kashmir Singh
dc.contributor.authorKang, Amardeep Singh
dc.contributor.authorČepová, Lenka
dc.contributor.authorKumar, Ajay
dc.contributor.authorPhanden, Rakesh Kumar
dc.date.accessioned2024-11-14T13:52:37Z
dc.date.available2024-11-14T13:52:37Z
dc.date.issued2024
dc.identifier.citationFrontiers in Mechanical Engineering. 2024, vol. 10, art. no. 1392543.cs
dc.identifier.issn2297-3079
dc.identifier.urihttp://hdl.handle.net/10084/155299
dc.description.abstractThe current study focuses on selecting the most suitable site location for a manufacturing industry using the Factor Rating Method (FRM). The study considers six key factors: Raw Materials Availability, Location, Availability of Labor, Transport, Availability of Utilities, and Environmental Impact. The FRM assign weights to each factor based on their relative importance. The results indicate that Raw Materials Availability holds the highest weight, suggesting its critical influence on site selection decisions. Subsequently, the Analytic Hierarchy Process (AHP) and Best Worst Method (BWM) are utilized to prioritize three available location alternatives through pairwise criteria comparisons. The analysis reveals that Location C emerges as the most favorable option, effectively meeting the manufacturing industry's requirements. The successful application of these methods demonstrates their value in aiding decision-making processes related to site location selection. By considering multiple factors and utilizing structured methodologies, organizations can make informed choices aligned with their specific needs and goals. This research contributes to the existing body of knowledge by providing insights into effective site selection strategies for the manufacturing industry. Further research opportunities exist in incorporating additional factors, addressing real-world constraints, and conducting sensitivity analyses to enhance the accuracy and applicability of site location decision-making.cs
dc.language.isoencs
dc.publisherFrontiers Media S.A.cs
dc.relation.ispartofseriesFrontiers in Mechanical Engineeringcs
dc.relation.urihttps://doi.org/10.3389/fmech.2024.1392543cs
dc.rights© 2024Sharma,SinghGhatorha,Kang,Cepova, Kumar and Phanden. This is an open-access article distributed under the terms of the Creative CommonsAttribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectanalytic hierarchy process (AHP)cs
dc.subjectdecision-makingcs
dc.subjectfactor rating methodcs
dc.subjectsite locationcs
dc.subjectbest worst methodcs
dc.titleStrategic insights in manufacturing site selection: a multi-method approach using factor rating, analytic hierarchy process, and best worst methodcs
dc.typearticlecs
dc.identifier.doi10.3389/fmech.2024.1392543
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume10cs
dc.description.firstpageart. no. 1392543cs
dc.identifier.wos001204933900001


Files in this item

This item appears in the following Collection(s)

Show simple item record

© 2024Sharma,SinghGhatorha,Kang,Cepova,  Kumar and Phanden. This is an open-access  article distributed under the terms of the  Creative CommonsAttribution License (CC BY).  The use, distribution or reproduction in other  forums is permitted, provided the original  author(s) and the copyright owner(s) are  credited and that the original publication in this  journal is cited, in accordance with accepted  academic practice. No use, distribution or  reproduction is permitted which does not  comply with these terms.
Except where otherwise noted, this item's license is described as © 2024Sharma,SinghGhatorha,Kang,Cepova, Kumar and Phanden. This is an open-access article distributed under the terms of the Creative CommonsAttribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.