Simulation and Evaluation of Production Factors in Manufacturing of Fireplaces

Onofrejová, Daniela; Janeková, Jaroslava; Grinčová, Anna; Šoltysová, Zuzana

doi:10.2507/ijsimm19-1-504

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…The elimination of activities that represent waste, by simulating the total costs, determining the contribution of the implemented measure to the reduction in lead time, and, above all, reducing costs and thus the economic efficiency of the production process can be achieved with this approach. Onofrejova et al [18] proposed a qualitative approach to the generation of the data needed to support decision-making on the economic efficiency of the production system. In order to maximise profit from sales, she proposed the use of linear programming methods.…”

Section: Literature Reviewmentioning

confidence: 99%

Decision-Making Techniques of the Consumer Behaviour Optimisation of the Product Own Price

2022

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Giving an offer to a consumer and knowing the average cost of the product (ACP) for the product along the entire production process, depending on the size of the order, can be a very important indicator which also has an impact on the lean production process. This paper presents a mathematical model to analyze the impact of customer changes (changes in the product type, quantity and/or quality) on the lean production process, and thus on the average production price (ACP). Any change in the process could mean a deviation from the lean process, which means an increase of the ACP for the company, which is the main goal of Industry 4.0. The measurements can be performed on all of the operations of the production process in a company, or only the most critical operations in terms of waste can be selected. The model was tested on an example of a product from a Slovenian company. The average cost of the product in the selected work operation turned out to be 71% higher than the optimal case due to an excessive stock of semi-finished products. Excessive stock can lead to a situation in which the product’s own price even exceeds the selling price of the product, which of course leads to the loss of the company.

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Section: Literature Reviewmentioning

confidence: 99%

Decision-Making Techniques of the Consumer Behaviour Optimisation of the Product Own Price

2022

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“…Li [10] proposed a new data-oriented bottleneck detection method based on mathematical evidence for mass production systems. Onofrejova et al [11] proposed the simulation model for production of fireplaces, considering production key parametersproductivity and throughput. Yang et al [12] proposed to efficiently optimize a changed production layout and configuration using a modelling and optimization method based on DES.…”

Section: Literature Surveymentioning

confidence: 99%

A Simulation Approach for Transition to JIT Production System

Kilic¹,

Erkayman²

2021

Int. j. simul. model.

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Consumption is increasing day by day due to rapid developments in technology and increasing world population. Companies should review their production processes in order to keep up with the consumption rate and increase their competitiveness as well as profit margins. This research shows a case study of simulation application to improve solar panel manufacturing process. The aim is to achieve goals such as increasing system efficiency, reducing inventory and shortening delivery time. Efficient manufacturing process design may reduce work-in-process (WIP), waste, rework and faults, which may reduce the total number of products and yields an increase in the system cost. Inefficiency points were determined in the process based on the obtained observations of the manufacturing system. Various scenarios have been proposed to design such an efficient production system. According to the proposed scenarios, an increase in the utilization rates of machinery, personnel and the total number of final products has been achieved.

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“…With current developments in computational science and engineering, the main practical problem is a better understanding of the real-world processes becomes time-consuming, errorprone due to limitations of human perception since both simulation experiments and simulations as software are usually created manually. For this reason, evaluating the quality of experiments becomes necessary, and the correctness of the simulators that execute the simulation models is a significant aspect of this evaluation [2]. In this sense, defining simulation models at a germane level of abstraction and accuracy, and designing experiments with ample information would leverage the quality of experimentation.…”

Section: Introductionmentioning

confidence: 99%

Hypothesis-Driven Simulation Experiments with an Extension to SED-ML

Cam¹,

Oğuztüzün²,

Yılmaz³

2022

Int. j. simul. model.

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Most of the frameworks or assistance systems for experiment specification do not provide a process explicitly based on formally specified hypotheses. This deficiency leads to inaccurate or insufficient record of an experiment, decreasing the trustworthiness and reproducibility of the experiment. Moreover, the wide variety of models, metamodels, tools, and data for experimentation requires Global Model Management (GMM) that is utilizing Model-Driven Engineering techniques, facilitates documentation, sharing, reusability, and replicability of simulation experiments. In this study, we strive to illustrate how to support simulation experimentation with hypotheses as a scientific workflow through GMM with an extension to the Simulation Experiment Description Mark-up Language (SED-ML). In particular, we present a megamodel built to serve as a repository to manage the artefacts employed in a simulation experiment. Based on the SED-ML, and enriched with hypothesis handling, our megamodel attempts to address all the phases of a simulation experiment, including specification, input data generation, execution, and output data analysis.

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Simulation and Evaluation of Production Factors in Manufacturing of Fireplaces

Cited by 9 publications

References 14 publications

Decision-Making Techniques of the Consumer Behaviour Optimisation of the Product Own Price

Decision-Making Techniques of the Consumer Behaviour Optimisation of the Product Own Price

A Simulation Approach for Transition to JIT Production System

Hypothesis-Driven Simulation Experiments with an Extension to SED-ML

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