Discrete event simulation aids new Lean Production System at Mimeo.com

Author(s):  
Paul Babin ◽  
Gozde Agirbas
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Zhenmin Yuan ◽  
Yaning Qiao ◽  
Yaru Guo ◽  
Yaowu Wang ◽  
Chen Chen ◽  
...  

Component factories are experiencing the problems associated with lean production, especially the accuracy of production time prediction and the unnecessary waste in terms of time and resource utilization. In order to solve these problems, a discrete event simulation- (DES-) based lean planning and optimization method for precast component production is proposed by integrating the complexity assessment (CS), discrete event simulation (DES), and lean management (LM). The method includes three submodels: improved production planning, DES, and lean analysis and optimization. In the submodel of improved production planning, a complexity evaluation index system for precast components is established through investigating five component factories, consulting seven domain experts and analysing relevant literature. In the submodel of DES, the DES technique is adopted to simulate and analyse the production activities of precast components. The submodel of lean analysis and optimization provides multidimensional analysis, comparative analysis, and suggestions. Finally, a detailed production case is selected to simulate and test the proposed method. The important findings are as follows: (1) this method can minimize the difference between the processing time of each workstation to avoid bottleneck stations as much as possible; (2) this method can capture the uncertainty during precast component production, and the most likely production time calculated by the method is 12.05 hours instead of the 11.50 hours originally estimated by the component factory; (3) this method can identify some unnecessary waste in the production process of precast components, including less than 50% utilization of workstations and unnecessary equipment purchases; (4) this method also provides some suggestions regarding production optimization. Due to the particularity of precast component production, it further expands the boundary of lean production methodology from the perspective of the construction industry rather than the manufacturing industry. The proposed method assists component factories in planning and optimizing the precast component production when they make detailed production plans.


2021 ◽  
Vol 16 (3) ◽  
pp. 335-347
Author(s):  
M. Jurczyk-Bunkowska

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 financial resources, they rely on internal competencies. The proposed approach that does not require mastery of mathematical modelling but allows streamlining capacity planning decisions. It uses the reasoning of throughput accounting (TA) supported by data obtained based on discrete event simulation (DES). Using these related tools in the design and analysis of change scenarios, make it possible for SME managers to make a rational decision regarding the development of the production system. Case studies conducted in a roof window manufacturing company showed the methodology. The application example presented in the article includes seven change scenarios analyzed based on computer simulations by the software Tecnomatix Plant Simulation. The implementation of the approach under real conditions has shown that a rational decision-making process is possible over time scale and with the resources available to SMEs for this type of decision.


Author(s):  
C. Standridge ◽  
M. Wynne

<p>The throughput potential of a production system must be designed and validated before implementation.  Design includes creating product flow by setting the takt time consistent with meeting customer demand per time period and the average cycle time at each workstation being less than the takt time.  Creating product flow implies that the average waiting time preceding each workstation is no greater than the takt time.  Kingman’s equation for the average waiting time can be solved for the variation component given the utilization, and the cycle time.  The variation component consists of the variation in the demand and the variation in cycle time.  Given the variation in demand, the maximum allowable variation in cycle time to create flow can be determined.  Throughput potential validation is often performed using discrete event simulation modeling and experimentation.  If the variation in cycle time at every workstation is small enough to create flow, then a deterministic simulation experiment can be used.  An industrial example concerning a tier-1 automotive supplier with two possible production systems designs and various levels of variation in demand assumed is used to demonstrate the effectiveness of throughput validation using deterministic discrete event simulation modeling and experimentation.</p>


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