Dynamic Simulation Tool for Planning and Optimisation of Supply Process

Grznár, Patrik; Gregor, Michal; Gašo, Martin; Biňasová, Vladimíra; Schickerle, Marek; Burganová, Natália

doi:10.2507/ijsimm20-3-552

Cited by 17 publications

(14 citation statements)

References 15 publications

(15 reference statements)

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“…Verification and validation are an integrated part of developing simulation models and appears, alone or together, in each stage of the modelling process. Verification means checking the model's internal logic, examining whether all necessary details are displayer for proper running of simulated process while validation focus on checking the conformity of the simulation model with the real process [21]. They are performed in a series of white-and blackbox validations.…”

Section: Conceptual and Computer Modelmentioning

confidence: 99%

Combining Simulation and Data Analytics for OEE Improvement

Lindegren¹,

Lunau²,

Mafia³

et al. 2022

Int. j. simul. model.

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Overall Equipment Effectiveness (OEE) is a productivity performance metric widely used in industry to support production control decisions. However, there is still a gap in organisational procedures to systematically identify and address the most promising opportunities to improve the production setup. In this study, we propose and demonstrate a data-driven approach for increasing OEE by combining the strengths of discrete-event simulation with data analytics tools and methods, which provides a risk-free test environment that forms the basis for data-driven decisions and supports revealing production interdependencies. Therefore, this approach eases the process for practitioners to proactively identify production losses and forecast the outcome of the most promising selected improvement measures. A case study is performed to illustrate the potentialities of the proposed approach, demonstrating the interdependence between the processes and the improvement measures, and the knock-on effect both upstream and downstream. The results yield substantial insights and facilitate operational decisionmaking for managers.

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Section: Conceptual and Computer Modelmentioning

confidence: 99%

Combining Simulation and Data Analytics for OEE Improvement

Lindegren¹,

Lunau²,

Mafia³

et al. 2022

Int. j. simul. model.

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“…• more accurate capture of relevant material and component movements and movements in real-time, • access to different demand patterns that can be used for efficient inventory, order and delivery planning [10].…”

Section: Data Logistics and Supplymentioning

confidence: 99%

Postpandemic innovative trends in logistics, distribution and supply chain

Lachvajderová

Kádárová²,

Kopec³

et al. 2022

ALS

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This scientific output describes the weaknesses of material flows and the lack of resilience of supply chains, which resulted in the outbreak of the COVID-19 pandemic. This unpleasant situation has presented the whole of logistics with new challenges and problems, which society and industry have tried with their approach to achieving non-stagnant logistics processes, which were therefore threatened mainly by the pandemic. The article describes old but mainly new trends and innovations in logistics, distribution and supply chain, which are also the result of accelerated digital transformation.

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“…You [ 8 ] constructed the technical framework of service-oriented composition evolution method for large-grained web services and proposed the determination method of evolution type and influence scope for large-grained web service composition. Ojstersek and Buchmeister [ 9 ] and Grznar et al [ 10 ] applied simulation technology to study the production optimization strategy of enterprises under resource constraints. Hwang et al [ 11 ] studied the performance monitoring of message server and system based on MQTT.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Collaboration Model of Production Network Based on Cloud Service Bus

Zhao

Liu

2022

Computational Intelligence and Neuroscience

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The IT system of manufacturing enterprises usually has many problems, such as complex industrial software, different development languages, diverse communication protocols, and complex operation environment. Cloud service bus (CSB) technology based on service model encapsulates various applications existing in enterprises by means of the integration of cloud service bus and micro services, which can realize the rapid cloud migration and deployment of heterogeneous industrial application software. Theoretically, after the production system is connected to CSB, it can be arranged arbitrarily by service choreography technology to produce any possible products. However, in the context of industrial Internet, the production system connected to CSB corresponds to the equipment, materials, personnel, and other resources on the production line one by one. These production nodes need to consider the production capacity and cost of production service nodes and the output value of the whole production network, and cannot be combined arbitrarily. Therefore, when integrating production service nodes, we should not only consider the technical integration, but also consider whether the production conditions support this integration. To solve the problem of production node integration in CSB, a dynamic collaboration model of production network based on cloud service bus is proposed in this paper. The model takes the capacity, cost, and production relationship of production nodes as constraints, and the overall efficiency of production network as the optimization goal. The model can calculate the new creation, modification, deletion, and other scheduling operations of production line services in real time and give the production plan with the highest resource utilization and the greatest value in the current production network. The model can improve the rationality and economy of service choreography and give full play to the value of production network. Taking an enterprise with 11 production nodes and 5 production lines as an example, this paper discusses in detail how to use this model to calculate the optimal production organization scheme and the maximum output value of the enterprise.

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Dynamic Simulation Tool for Planning and Optimisation of Supply Process

Cited by 17 publications

References 15 publications

Combining Simulation and Data Analytics for OEE Improvement

Combining Simulation and Data Analytics for OEE Improvement

Postpandemic innovative trends in logistics, distribution and supply chain

Dynamic Collaboration Model of Production Network Based on Cloud Service Bus

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