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Introducing the EPP house (topological space) method to solve MRP problems

The problem of product and process planning analysed so far is how we can take advantage of our strategy in planning. Among the principles of manufacturing and service management concepts is that after planning demand, planning transformation is one of the key steps of integrated efficiency; it make...

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Autores principales: Gyenge, Balázs, Kasza, László, Vasa, László
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8219129/
https://www.ncbi.nlm.nih.gov/pubmed/34157038
http://dx.doi.org/10.1371/journal.pone.0253330
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author Gyenge, Balázs
Kasza, László
Vasa, László
author_facet Gyenge, Balázs
Kasza, László
Vasa, László
author_sort Gyenge, Balázs
collection PubMed
description The problem of product and process planning analysed so far is how we can take advantage of our strategy in planning. Among the principles of manufacturing and service management concepts is that after planning demand, planning transformation is one of the key steps of integrated efficiency; it makes it possible to save costs that are not value adding and are not necessary from the customer’s point of view. Currently, the methods of material requirements and capacity planning can be seen as classic solutions that are based on dependency relations between different resources, which can be dynamic in space and time. Measuring and recording capacities raise several problems in addition to the fact that our planning methods are not always satisfying. In the literature, the methods of material requirements planning or manufacturing resource planning (MRP) are not typically optimization methods, so they do not guarantee the best solution, and even if our planning methods were satisfying, several manufacturing restrictions (the time allowed, the complexity of the planning process, the lack of testing opportunities, etc.) could prevent us from reaching satisfying application. It is necessary to create a simple planning algorithm that can give the planner a greater degree of freedom and that would be simple and algorithmic in order to maintain continuous conscious control, putting an end to planning uncertainty and leading us to the best solution under the given conditions. The aim of our research is to introduce a novel, simple planning algorithm, similar to heuristic methods that eliminates the problem of defining the order quantity when applying traditional methods, which prevents us from determining in advance which method is desirable (causing unnecessary planning steps); computer-based solutions hide the causal relations of the methodology from the planner (causing unreliability uncertainty).
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spelling pubmed-82191292021-07-07 Introducing the EPP house (topological space) method to solve MRP problems Gyenge, Balázs Kasza, László Vasa, László PLoS One Research Article The problem of product and process planning analysed so far is how we can take advantage of our strategy in planning. Among the principles of manufacturing and service management concepts is that after planning demand, planning transformation is one of the key steps of integrated efficiency; it makes it possible to save costs that are not value adding and are not necessary from the customer’s point of view. Currently, the methods of material requirements and capacity planning can be seen as classic solutions that are based on dependency relations between different resources, which can be dynamic in space and time. Measuring and recording capacities raise several problems in addition to the fact that our planning methods are not always satisfying. In the literature, the methods of material requirements planning or manufacturing resource planning (MRP) are not typically optimization methods, so they do not guarantee the best solution, and even if our planning methods were satisfying, several manufacturing restrictions (the time allowed, the complexity of the planning process, the lack of testing opportunities, etc.) could prevent us from reaching satisfying application. It is necessary to create a simple planning algorithm that can give the planner a greater degree of freedom and that would be simple and algorithmic in order to maintain continuous conscious control, putting an end to planning uncertainty and leading us to the best solution under the given conditions. The aim of our research is to introduce a novel, simple planning algorithm, similar to heuristic methods that eliminates the problem of defining the order quantity when applying traditional methods, which prevents us from determining in advance which method is desirable (causing unnecessary planning steps); computer-based solutions hide the causal relations of the methodology from the planner (causing unreliability uncertainty). Public Library of Science 2021-06-22 /pmc/articles/PMC8219129/ /pubmed/34157038 http://dx.doi.org/10.1371/journal.pone.0253330 Text en © 2021 Gyenge et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gyenge, Balázs
Kasza, László
Vasa, László
Introducing the EPP house (topological space) method to solve MRP problems
title Introducing the EPP house (topological space) method to solve MRP problems
title_full Introducing the EPP house (topological space) method to solve MRP problems
title_fullStr Introducing the EPP house (topological space) method to solve MRP problems
title_full_unstemmed Introducing the EPP house (topological space) method to solve MRP problems
title_short Introducing the EPP house (topological space) method to solve MRP problems
title_sort introducing the epp house (topological space) method to solve mrp problems
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8219129/
https://www.ncbi.nlm.nih.gov/pubmed/34157038
http://dx.doi.org/10.1371/journal.pone.0253330
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