2021
DOI: 10.14743/apem2021.3.404
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Tactical manufacturing capacity planning based on discrete event simulation and throughput accounting: A case study of medium sized production enterprise

Abstract: The article presents the application of the original methodology to support tactical capacity planning in a medium-sized manufacturing company. Its essence is to support medium-term decisions regarding the development of the production system through economic assessment of potential change scenarios. It has been assumed that the developed methodology should be adapted to small and medium-sized enterprises (SMEs). Due to their flexibility, they usually have limited time for decision-making, and due to limited f… Show more

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Cited by 4 publications
(2 citation statements)
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“…SPSS 18.0 software was used to process and analyse the data, and t -tests were used to compare the means to see if there were significant differences between the data. The questionnaires were categorized and valid questionnaires were numbered after excluding invalid questionnaires [ 16 , 17 ]. A database was created using Epidata 3.0 software, and statistical processing was carried out using SPSS 19.0 software.…”
Section: Objectives and Research Methodologymentioning
confidence: 99%
“…SPSS 18.0 software was used to process and analyse the data, and t -tests were used to compare the means to see if there were significant differences between the data. The questionnaires were categorized and valid questionnaires were numbered after excluding invalid questionnaires [ 16 , 17 ]. A database was created using Epidata 3.0 software, and statistical processing was carried out using SPSS 19.0 software.…”
Section: Objectives and Research Methodologymentioning
confidence: 99%
“…The plant simulation activity is closely associated with productivity and, hence, has implications for production economics. The existing literature shows that this connection is effective, for example, in shortening the production cycle [32]; production line balancing [33]; logistics flow performance [34]; production scheduling [35,36]; the theory of constraints modeling (different improvement scenarios are used to solve the bottleneck [37]); the analysis of customer-perceived value networks using DES [38]; supply chain logistics (multi-product and multi-echelon, using DES [39], different optimization algorithms [40], and software comparison [41]). There are numerous other examples of the application of DES to all manner of production layout, freight, logistics, and queueing problems.…”
Section: Plant Simulationmentioning
confidence: 99%