In the attached model we use a Timetable and two MTBF/MTTR objects to define Schedule Loss, Availability Loss (breakdowns) and an element of Performance loss due to short stops (state Down). The processor sends 'bad' items to port 2 based on the send to percentage which account for QualityLosses. The processor's 'best' processing time per part (5 seconds) is stored as a label, while the processing time itself is a triangular distribution with the minumum as 5 seconds - so it also contributes to performance loss. When the Type of the item changes a setup time occurs which is the final contributor to performance loss. Two state profiles were added to the processor - one to track production time and another for availability. An object process flow on the processor detects production profile state changes (between On and off shift) and regular Flexsim state changes and determines the availability state that should prevail. A user command getOEEstat is used to access the values which it calculates on demand and stores in a label on the processor called statsMap. The syntax for this command is: getOEEstat(myMachine,"OEE") The list of stats: "ScheduleLoss","AvailabilityLoss","PerformanceLoss","QualityLoss","IdealProdTime","AvailabilityRatio", "QualityRatio","PerformanceRatio", "IdealProdTime", "RunTime", "OEE", "TEE". A group was used to indicate which objects have their OEE tracked, and a stats Collector reads the group members and adds rows at reset. Finally Performance Measures were added for the stats for processor 1. Processor_OEE_2.fsm 2023-08-22 Update: Added 'TEE' stat.

Many simulation models need to show operator or machine utilization by time period of day, or per shift: shiftutilization.fsm Under Construction Summary Until this article is completed, here is the basic idea. I use a process flow to create one token per operator in the group. Each operator token creates one token per shift. The shift token creates its own Categorical Tracked Variable (which is how state information is stored on all objects). The shift token just remains alive, holding the tracked variable. The oeprator token goes into a loop, listening to OnChange of the 3D Operator's state profile node. Whenever the state changes, the token sets the shift token's state label to the same state. This essentially copies the state changes onto another tracked variable. Another child token listens for when the shift changes. When the shift changes, the operator token puts the current shift profile into some unused state (STATE_SCHEDULED_DOWN, in this case), and then applies new state changes to the correct shift token's label. A Statistics Collector listens for when the Operator token starts the main loop at the beginning of each shift. When this happens, the collector saves a row value that is an array, composed of the operator and the shift token. The row mode is set to unique, so that each unique combo of operator/profile gets its own row. Each column in the collector is made to show the data from the shift corresponding to that row. The result is the table shown in the article, which can be plotted with a pie chart as shown above.

Tokens and Flow Items can be very difficult to add to a chart. This is true because they don't exist on Reset, making them difficult to select. This article shows how you can use a Process Flow to allow a Statistics Collector to record a token's changing label value, and also to chart that value over time: The model for this example is attached (graphlabeldata.fsm). It is a very simple model: The Scheduled Source creates three tokens, each of which create a label called data. This label is created by choosing "Add Tracked Variable" for the value, which opens this dialog box: The reason we want the label to be a Tracked Variable is that Tracked Variables emit an OnChange event. We want to listen to that event. If you use the time interval collection method, discussed later in this article, you don't need to make the label a Tracked Variable. Each token then goes through a loop, where it waits, and then updates the value. This is meant to represent a much more complicated model, where the token travels through many activities, any of which could change its label value. For this example, the model randomly changes the value on the label. Now that we have a token and a label whose value is changing as the model runs, we can work on making a chart. We want to eventually make a Statistics Collector, but Statistics Collectors can only listen to events of objects that exist after the model has been reset. Tokens and FlowItems (along with their labels) are destroyed on reset, and so we can't listen directly to them. However, some Process Flow activities can listen to events on tokens and flowitems, and the Statistics Collector can listen to those events. For that reason, we make a second Process Flow: This flow has an Event-Triggered Source, which listens for tokens to leave the "Init Tracked Variable" activity in the first flow. When that happens, the source creates a token, and that token immediately gets a reference to the label node (note that this is different than the value of the label node). Next, the new token goes to a Start activity. The Start activity called "Log Change." This activity is just a placeholder. While you could technically live without this activity, it makes things a little clearer, as we will discuss later. Other than providing OnEntry and OnExit events, the Start activity has no internal logic whatsoever. After passing through the Log Change activity, the new token waits for the label value to change: In order to listen to this event, you can first sample a Tracked Variable in the Toolbox. This provides the OnChange event. Then you can update the Object field to the code shown above. Notice that every time this event happens, the token simply passes through the Log Change activity, and then resumes listening. When the original label value changes, it emits an OnChange event. When that event fires, the token listening to that even travels through the Log Change activity, which emits OnEntry and OnExit events. We can use these events in the Statistics Collector. The key to this technique is that we used Process Flow, which is good at listening to token and flowitem events, to generate Activity events, which can be used in the Statistics Collector. In the attached model, the first Statistics Collector is configured like this: It simply listens to the On Entry of the Log Change activity. The columns are defined as follows: The first two columns are simpler; the Time column uses the Model Date/Time option: The second column gets the ID of the token as an integer: The third column gets the current value of the Data label: Now that the Statistics Collector is set up, we can configure the chart to use this collector, and split by the Token ID. The process to record the label value every interval (rather than on every change) is very similar. The downside is that the data is less granular, but the upside is that a label doesn't have to be a Tracked Variable to be charted. The example model simply uses a Split activity to copy the data from the Event Triggered Source, and sends it to a similar listening loop: Instead of waiting for the value to change, the second token waits for a fixed time interval. A similar Statistics Collector will allow you to create the following chart: This approach works for every token created by the scheduled source. No matter how many tokens you create, each will show up on the chart:

O link que segue leva a um vídeo no qual é apresentado uma introdução à nova ferramenta do FlexSim, o Statistics Collector. No vídeo, explica-se quais são as configurações básicas da ferramenta. Também pode-se acompanhar um exemplo simples, desde a coleta de uma estatística básica, até a apresentação dos dados em um gráfico, do tipo histograma. Vídeo Tutorial: Overview sobre Statistics Collector Esperamos que aproveitem mais este vídeo tutorial.

In version 2018 and forward, you can make this chart using a Chart Template. You can simply drag and drop the chart from the dashboard library. This article may help you understand how the chart template works. You can use the Install button on the chart template to view the Process Flow, Statistics Collector, and Calculated Table that make the chart. This article reviews how to use the Zone, along with the Statistics Collector, to create a bar chart of the current work-in-progress (WIP) by item type. The method scales to as many types as you need (this example uses 30 types), and can easily adapted to text data, like SKU. An example model ( zonecontentdemo.fsm) demonstrates this method. Creating the Process Flow To create this chart, we first need to gather the data for this chart. In this case, it is easiest to build off the capabilities of the Zone. In particular, we will use Zone Partitions to categorize all of our object. After we set up the Zone, we'll use a Statistics Collector to gather the data that we need. Create a new General Process Flow. You can have as many General Process Flow objects as you want, so let's one that just deals with gathering statistics. This way, gathering statistics will not interfere with the logic in our model. The process flow should look something like this: Here's how it works. The Listen to Entry is configured to listen to a group of objects. In this case, the group contains all the sources in the model, and it's listening to the OnExit of the sources. However, it could be OnEntry or OnExit of any group of 3D objects. If you want to split the statistics by Type or SKU, then any flowitem that reaches the entry group already has the appropriate labels. In this example model, when a flowitem leaves any source, a token gets created. The token makes a label called Item that stores a reference to the created item, as shown in the following picture. The next step is to link the flowitem with the token that represents it. The Link Token to Item is configured like this: Now, the Item has a label that links back to the token. The token then enters a zone. The Zone is partitioned by type: At last, the token comes to a decide activity. The decide is configured not to release the token. The token will be released by the second part of the flow. Once the token is released, it exits the zone, and goes to a sink. The second part of the flow also has an event-triggered source, that is configured to listen to all the sinks in the model. Again, the entry objects and exit objects are arbitrary; you can gather data for the entire model, or for just a small section of the model, using this method. The event triggered source also caches off the item in a label. At this point, we need to release the token that was created when items entered the system. To do this, the Release Token activity is configured as seen here: The token created on the exit side has a reference to the item, which has a reference back to the token created on the entry side. We release this token to 1, which means connector 1. Note: We could have used a wait for event activity in the zone, and then used the match label option to wait for the correct item to leave the system. However, this method is much, much faster, especially as the number of tokens grows. Creating the Bar Chart Statistics Collector The next step is to create a statistics collector that gathers data appropriate for a bar chart. Note that this method will grow the number of rows dynamically, so that it won't matter how many types (or SKUs) your model has; you will still get one bar per type/SKU. In order to make the number of rows dynamic, we need to listen to the OnEntry and the OnExit of the zone activity: Notice the shared label on this collector. Because this label is shared, both the OnEntry and OnExit events will create this label on the data object. The value of this label is the item's type. Next, we move to the Data Recording tab. Set the Row Mode to Unique Row Values, and set the row value to Partition. This means that whenever an event fires, the statistics collector will look at the partition label on the data object. If the value is new to the statistics collector, the collector will make a new row for this value. If not, then the collector will use the row that is already present. Finally, we need to make our columns. We only need two columns: one for the Partition, and one for the Content of that partition. Both of these columns can use the Integer storage type, and raw display format. However, if your partition value was text-based, like an SKU, you should use the String storage type. The value for Partition is just the data object's Partition label: Notice that the Update option is set to When Row is Added. This way, the statistics collector knows that this value will not change, and that it's available at the time the row is created. The other column is a little harder, because we need to use the getstat command: The getstat command arguments depend on the stat you are trying to get. In this case, we are asking the zone (current, the event node) for the Partition Content statistic. We want the current value. Since this is a process flow activity, we pass in the instance as the next argument. Finally, we pass in which partition we want to get the data from, the row value. In this case, we could have identically passed in data.Partition. Also, notice that this column is updated by event dependency. To make sure this does what we want, we need to edit the event/column dependency table. We want the Content column to be updated when items enter and exit, so it should look like this: Now, open the table for the statistics collector. You should see two columns. When you run the model, rows will be added as items of different types are encountered. The table will look something like this: This screenshot came from early in the model, before all 30 types of item has been encountered, so it doesn't have 30 rows yet. Making the Bar Chart This is the easiest part. Create a new dashboard, and add a new bar chart. Point the chart at the statistics collector. For the Bar Title option, choose the Partition column. Be sure to include the Content column. Also, make sure that the "Show Percentages" checkbox on the Settings tab is cleared. The settings should look like this: The resulting chart looks something like the following image. You can set the color on the Colors tab. Ordering the Data Because the rows of this table are created dynamically, the order of the rows will likely change run to run. To force an ordering, you can use a calculated table. Since the number of rows on this table don't grow indefinitely, and the number is relatively small, it's okay to set the Update Mode on the calculated Table to always. Here's what the properties of that calculated table look like: We simply select all columns from the target collector (CurrentContent, in this case) and order it by the Partition column. That yields an ordered bar chart: Example and Additional Charts The attached example model demonstrates this method, as well as how to create a WIP By Type vs Time chart: Happy data collecting! zonecontentdemo.fsm

This article reviews one method for making a state Gantt chart for the default and alternate state profiles: Example Model You can download the model for this walkthrough ( stateganttdemo.fsm). The model has two multiprocessors, in a Group called Multiprocessors. Each multiprocessor has two processes: Process1 and Process2. To make the chart, we will first make a Statistics Collector, and then a Calculated Table. Making the Statistics Collector Make a new Statistics Collector. On the Event Listening tab, use the Sampler to listen to On State Change of the group of multiprocessors. You can leave the parameter names alone. However, we need to add a label, so we can record the profile number. Select the new event, and then use the green plus button in the Event Labels area to add a label for this event. Set its name to ProfileNum, and its value to the following code: data.StateProfileNode?.rank The event settings should look something like the following: Next we need to set the row mode. Make sure it's set to Add Per Event, with no row value. As the final configuration step for the statistics collector, we need to set up the columns. There should be four columns in this collector: Time - In the pick options, select Time, then Model Date/Time Object - In the pick options, select IDs, then ID of Event Object Profile - Type data.ProfileNum for the value. The default storage and display format are fine. State Type the following code: data.eventNode.as(Object).stats.state(data.ProfileNum).profile[data.ToState + 1][1] Set the Storage Type to String The code is necessary because On State Change occurs before the state is set to the new state. So the code is looking up the name of the future state in the profile table. When you reset and run this model, you will see a table like the following: Making the Calculated Table Make a new Calculated Table, and give it the following query: SELECT Object, Time as StartTime, LEAD(Time) OVER (PARTITION BY Object) AS EndTime, State FROM StatisticsCollector1 WHERE Profile = 1 This query creates an Object column as well as a Time column. To get the time that the current state ends, we look to when the next state begins. The LEAD() function looks ahead in the table, and the OVER(PARTITION BY Object) clause makes sure that LEAD() makes sure to look to the next row with the same Object. We also record the state column, and filter out the standard state profile, keeping the special multiprocessor state profile. Once you get this query to work, change the Update Mode to By Interval, and set the interval to 20 or 30. Since the Statistics Collector table will get longer and longer, the query will become more and more expensive as the model runs. To control how much time is spent running the query, we use an interval. The final configuration of the Calculated Table should look like this: You will need to set the Display Format of each column on the Display Format tab (Object, Date/Time, Date/Time, and Raw). Making the Chart Make a new dashboard, and create a Gantt chart. Point it at the Calculated Table. When you do that, the chart should fill in all the other columns correctly. Charting Both State Profiles for Both Objects In order to chart both profiles on the same chart, we first need to add a column to the Statistics Collector, and then update the query in the Calculated Table. The new column should be named ObjectAndProfile, and a Storage Type of String. Use the following code for a value: data.eventNode.name + " - " + string.fromNum(data.ProfileNum.as(int)) Then change your query to the following: SELECT ObjectAndProfile, Time as StartTime, LEAD(Time) OVER (PARTITION BY ObjectAndProfile) AS EndTime, State FROM StatisticsCollector1 With these changes, you should be able to view both profiles for both multiprocessors.

This article demonstrates how to use the Statistics Collector and Calculated Tables to create three utilization pie charts: a state pie chart an individual utilization pie chart a group utilization pie chart Example Model You can download that model (utilizationdemo.fsm) to see the working demonstration. The model has a Source, a Processor, a Sink, a Dispatcher, and several operators. The operators carry flow items to and from the processor, as well as operate the processor. The operators are in a group called Operators. State Pie Chart First, we need to make a Statistics Collector that collects state data for the operators. The easiest way to do that is to use the pin button to pin the State statistic for any object in the model. Use the pin button to pin a pie chart to a new dashboard. The pin button creates a new Statistics Collector, as well as a new chart. Open the properties for new Statistics Collector (double click on it in the toolbox), and change its name to OperatorStatePie. On the Data Recording tab, remove the object from the Enumerated Rows table. Using the sampler, add the Operators group (you can sample it in the toolbox). Now, when you reset and run the model, the state chart should work. Utilization Pie Chart Often, users need to combine sever states into a single value that can be used to determine the utilization of an object in the model. In order to gather this data, we can use a calculated table. Make a new Calculated Table, and give it the following query: SELECT Object, (TravelEmpty + TravelLoaded + Utilize) / Model.statisticalTime AS Busy, 1 - Busy AS NotBusy FROM OperatorStatePie This query sums the time in several states into a total, and then divides by the statistical time. Be sure to set the name of the table (the part after FROM) to the name of your Statistics Collector. Run the model for a little bit of time, and then click the Update button on the properties window. You should get a table like the following: Instead of viewing the data as just numbers, change the Display Format of each column to better represent the data. On the Display Format tab, set the Object column to display Object data. Then set the other two columns to display percentages. When you switch back to the Calculations tab, the data will be formatted: Once we get the query right, set the update mode to Always. This will updated the data in the table whenever the data is needed, including every time the chart draws. If updating the table is computationally expensive, you can use the By Interval or Manual options. Generally, a small number of rows (1-100) is small enough to use the Always mode. Regardless of the update mode, we can make a chart based on this table. In the dashboard, create a new Pie Chart. For the Data Source, select the calculated table. For the Pie Title, select the Object column. For the Center Data, select the Busy column. Be sure to include the Busy and NotBusy columns. This should show you a pie chart, comparing the operator's busy and not busy time. Group Utilization To make the final utilization chart, make a second calculated table. The query for the second table should be as follows: SELECT AVG(Busy) AS AvgBusy, AVG(NotBusy) AS AvgNotBusy FROM CalculatedTable1 Again, use the Update button to be sure the query is correct. Once it is, set the update mode to Always. Finally, you can make the pie chart for this data: Things to Try If you feel comfortable with this model, you can try a couple extra tasks, such as: Remove one of the operators from the group, reset, and run. The charts will update accordingly. Add the Processor to the group, reset, and run. The state chart should work automatically.

In version 2018 and on, you can make this chart by dragging the Throughput Per Hour by Type template from the dashboard library. If you install the template (available on the Advanced tab), you will see a Process Flow and a Statistics Collector appear in your toolbox. One of the most common questions from FlexSim users is as follows: How do I make a chart that shows the output every hour? You can make this chart in three steps. Configure the Statistics Collector First, you need a Statistics Collector. Make a new one in the toolbox (click the green plus button, select Statistics, and then select Statistics Collector). On the event listening tab, use the green plus button to add a timer event, and configure as shown here: This timer event will fire every hour (every 3600 seconds) in the model. Notice the shared label, that is storing all members of the Processors group as an array. We will use this label in the next step. Once you have configured the timer, then you need to set up the row mode for this collector. We want one row per processor, and we need to use the Processors label as the row value. Since the Processors label is an array, we will get three rows per timer event, each row corresponding to a processor. Finally, we can add the columns. The three columns are as follows: Time - use the pick list to select Model Date/Time from the Time menu Object - use the pick list to select ID of row value from the IDs menu Output - use the pick list to select Statistic by Object from the Object Statistics menu Use data.rowValue as the object value in the popup If you use the pick options to choose these options, then the storage type and display format options should be set automatically. With these three columns in place, we can watch the table populate. Reset and run the model at high speed. Every model hour, you should see a new set of rows appear, one for each processor in the group. The table will look something like this: Configure the Calculated Table The Statistics Collector table from the previous steps is close to what we want, except that the output value always increases as the model runs. But what about the output for just a single hour? To get that value, we can use a Calculated Table. Make a new calculated table, and give it the following query (in the Query field): SELECT Time, Object, ISNULL(Output - LAG(Output) OVER (PARTITION BY Object), 0) AS OutputPerHour FROM StatisticsCollector1 This query uses SQL window functions. Basically, it says that each row's value should subtract the previous row's value for the object. In addition, if that value is NULL (because it's the first row), then just use a value. If you reset and run the model, so that the collector table has at least a few rows in it, click the Update button to run the query. Notice that the Time and Object columns show numbers. This is because the Calculated Table can't infer the formatting of the column. To set the formatting, use the Display Format Tab. You may also wish the table to update every hour, with the Statistics Collector. Make the Chart Now that our data is correct, we can make a chart. Make a new dashboard, and create a Time Plot chart. Point the chart to the calculated table. Let's use the Time column for the X values, and let's use the OutputPerHour column for the Y values. In addition, make sure to split by the Object column. If the calculated table updates every hour, then running the model should create the chart shown at the beginning of the model. Here is the model used to create this chart (should work in 2017 Update 2 Beta or later; beta must be built on or after August 21, 2017). outputperhourdemo.fsm