Daily Pattern Arrivals

This Model Object automatically analyzes raw arrival data, creates distributions, and imports the restructured data into ProcessModel. You provide historical data, and this model object almost performs magic. The details of what it does are as follows:

1. Analyzes the data.
2. Shows a graphical summary of raw data.
3. Finds the arrival quantities for each day of the week.
4. Removes outliers.
5. Shows data needing conversion to a distribution.
6. Creates the daily distribution to spread arrivals over the hour of the day.
7. Automatically imports the data into ProcessModel.

Where to Find the Model Object

The following model object will be used to create daily pattern arrivals for healthcare and import data into ProcessModel:

• Arrivals \ Daily Pattern Arrivals.

Difficulty Level

• Ease of Use: Easy
• Ease of Modification: Easy

How to Use the Model Object

1. Open Your Model: Open your model or create a new blank model.
2. Locate this model object.
3. Insert the Model Object into Your Model:
   • Locate where you would like to place the model object.
   • Move the layout as needed, click the insert button for the model object, and click on the layout target location.
4. Connect to Your Model: Connect the route exiting from activity Priority_Calculator_# to the rest of your model.
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5. Open the Excel File: Right-click on the Daily Pattern Arrivals Sheet Excel icon and click on the third option from the bottom to open the Daily Pattern Arrivals v-x-x Excel file, where x-x represents the version number of the Excel file.
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   Before using this Excel file for the first time, or after changing your model name, ensure that a manual data export is done via ProcessModel. From your model, click Tools \ Export Data, click ‘Yes’ to all prompts until the data export file opens, close the data file, then use this Excel file. Click Enable Content if prompted.
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6. Enter Raw Data: The Input worksheet appears after opening the Daily Pattern Arrivals vx-x Excel file.
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   1. Copy your date and time data and paste it into cell A3. Use at least six months of data. Use the Excel date and time format. See the sample data included.
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   2. Copy your priority levels and paste them into cell B3, where 1 is the highest acuity. The priority values must be integers between 1 and 9. If priority is not needed, leave column B empty.
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   3. Enter holidays into cell C3. The holidays are optional and are only used to show the effect of holidays on volumes.
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      Notes:
      1. Sample data must be removed before entry of your data.
      2. You must provide at least 24 weeks’ worth of data.
7. Prepare Arrival Data for ProcessModel: Click the button titled Prepare Arrival Data for ProcessModel and allow 1 minute for the analysis (depending on the speed of your processor and the file size). Once completed, you will land on the Daily Pattern Daily Qtys worksheet and should see up to three new worksheets in the same Excel file.
   1. Daily Pattern Daily Qtys
   2. Daily Pattern Hourly Qty
   3. Daily Pattern Acuity / Priority
8. Review Your Data – Analyzing the Daily Pattern Daily Qtys
   The Daily Pattern Daily Qtys worksheet is divided into three parts.
   1. Raw Daily Arrivals: The first section of the Daily Pattern Daily Qtys is the Raw Daily Arrivals volume. Review the raw data and see how the outliers change how data is presented. The Whisker chart on the right helps review the outliers in more detail. Dots (•) represent outliers. If you have identified “dates of holidays” in the input data, the green bars (shown on the sheet below) identify potential holiday-related surges.
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   2. Daily Arrivals Outliers: This second section identifies outliers planned for removal in the next section.
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   3. Daily Arrivals without Outliers: This section shows the final arrivals data with outliers removed. Any outlier deemed to be correct may be re-added to the bottom of the column of numbers. This section contains three required steps highlighted in red.
      1. Step 1. Click the Copy button at the top of each column of data. Paste the copied data into Stat::Fit. Use Stat::Fit to select a distribution representing the data. See Convert Raw Data to Distributions to learn more about Stat::Fit.
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      2. Step 2. Copy/Export the distribution from Stat:Fit and paste it in the purple box.
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         Repeat the above steps for each day of the week.
      3. Step 3. Click the Send Data to ProcessModel button. This will import all the raw data into ProcessModel. Excel provides a confirmation the export to ProcessModel was successful.
         Notes:
         1. Leave the Track # field empty if your model only has one daily pattern arrival object.
         2. To add multiple daily pattern arrivals into your model, copy the model object multiple times into your model. You will need multiple copies of the Daily Pattern Arrivals vx-x to represent each arrival. The track numbers will be 1 for the first daily pattern added, 2 for the second, and so on.
9. Completion: The daily patterns have now been added to your model. You can now save and then simulate the model.

Analyzing the Daily Pattern Hourly Qty

The Daily Pattern Hourly Qty worksheet contains the hourly distribution of arrivals and has charts on the right for each day that show the hourly distribution of arrivals for each day of the week. Scroll down to review charts for all the days. No interaction is required on this worksheet as it only displays useful daily hourly patterns for review.

Analyzing the Daily Pattern Acuity / Priority

The Daily Pattern Acuity / Priority worksheet contains a table that shows the distribution of priority across all entities. No interaction is required on this worksheet as it only displays useful priority information for review.

Advanced Topic

Question: How was the Daily Pattern Arrivals model created, and why do you see an object instead of a separate entity and activities?

Answer: The Daily Pattern Arrivals model object has multiple Arrivals, Activities, and Routes that need to be updated with data. Since you don’t need to make changes to any of these items individually, they are grouped, then an object on top of them. The users may ungroup and inspect all elements as needed.

Daily Pattern Arrivals Exploded View