![]() The delay in total time, therefore, refers to the small delays that accumulate over time. The machine cannot compensate any available raw materials before the time of 1 minute, which ends up accumulating for the following parts. As a result, we get a final average time of 7.71 minutes. At the worst moment, the queue is around 24 parts, greatly delaying the measured time of the following parts entering the process. It is observed in the video that most of the time there is the formation of a queue of parts (the gray curve is always just above the green curve), due to the availability of parts at a pace greater than 1 parts by minute.Įventually (close to the minute 150), the availability of raw material cannot supply the production capacity and the green curve subtly changes its inclination to then, very quickly, get certain again slack in sending raw material. To improve understanding, set the Title field in the first data set to Machine Production, and the second data set to Delivery of Raw Material When showing results, it is important to be absolutely clear on the information in the charts. Repeat the procedure for the second data set, but define the field Data set: to dataset1 (which is the default name of the block we created in step 10) In the first data set, check the option Data set and define the field Data set: to dataset (which is the default name of the block we created in step 8) Note that the chart is filled with two sample data sets. To view the information generated by the datasets we created, drag a Time Plot graph from the Analysis palette to the work area Similarly, this instruction serves to count the parts that are generated by the source block (source of raw material). Repeat the procedure, creating a new Dataset block, but this time define the Vertical axis value field as: This instruction will count the number of parts that reach the sink block. With the new block dataset selected, locate the field Vertical axis value in the properties window and define it as: Locate the Dataset block in the Analysis palette and drag it to the work area. ![]() Now we are going to evaluate the number of parts produced. This graph will show a histogram with the distribution of the production times in the segment of the considered process. Size and position the generated graphic as desired. Right-click on the timeMeasureEnd block and choose Create Chart -> distribution. Observe the placement of the time blocks and also the timeMeasureEnd block correct configuration This will correctly define the stretch of the process to be considered for timing In the properties window, field TimeMeasureStart blocks, click the + button and select timeMeasureStart. Position the blocks in the work area in such a way that they are slightly more distant from each other, as shown in the figure below Make sure the connections are redone (happens automatically if you position the new block correctly) ĭo the same with the block Time Measure End, but this time place it between the blocks delay and sink With the Main referring to the des02 open, drag this block to the interconnection between the source and queue blocks. In the Process Modeling Library palette, locate the Time Measure Start block. Copy (Ctrl+c) and paste (Ctrl+v) in the Main of des02 Click and drag a selection rectangle, involving all blocks and links from des01. In your work area two Main tabs should appear: one blank (referring to des02) and another with the previously modeled process (referring to des01). ![]() Thus, we will use the process modeled earlier as a starting point. ![]() This process is identical to the process described in the previous article. ![]() Handle uncertainty Uncertainty in operations’ time and outcome can be easily represented in simulation models, which allows you to measure risk and find more robust solutions.It's easier to start from a 'V0', that is, from a known starting point and that we know is working.Increased accuracy A simulation model can capture much more details than an analytical model, which provides for increased accuracy and more precise forecast.E.g., you can check warehouse storage space utilization at any given date. Insight into dynamics Unlike spreadsheet- or solver-based analytics, simulation modeling allows observation of system behavior over time at any level of detail.Visualization Simulation models can be animated in 2D/3D, allowing concepts and ideas to be more easily verified, communicated, and understood.Save money and time Virtual experiments with simulation models are less expensive and take less time than experiments with real assets.Make the right decision before making real-world changes. Risk-free environment Simulation modeling provides a safe way to test and explore different “what-if” scenarios. ![]()
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