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Model Load: A large and complex model such as 10B cells can take up to 10 minutes to load the first time it's in use after a period of inactivity of 60 minutes. The only way to reduce the load time is by identifying what formula takes most of the time. This requires the Anaplan L3 support (ask for a Model Opening Analysis), but you can reduce the time yourself by applying the formula best practices listed above. One other possible leverage is on list setup: Text properties on a list can increase the load times, and subsets on lists can disproportionately increase load times. It is best practice not to use List Properties but house the attributes in a System model dimensioned by the list.  See Best practice for Module design for more details. Model Save: A model will save when the amount of changes made by end-users exceeds a certain threshold. This action can take several minutes and will be a blocking operation. Administrators have no leverage on model save besides formula optimization and reducing model complexity. Using ALM and Deployed mode increases this threshold, so it is best to use Deployed mode whenever possible. Model Rollback: A model will roll back in some cases of an invalid formula, or when a model builder attempts to adjust a setting that would result in an invalid state. In some large models, the rollback takes approximately the time to open the model, and up to 10 minutes worth of accumulated changes, followed by a model save. The recommendation is to use ALM and have a DEV model which size does not exceed 500M cells, with a production list limited to a few dozen items, and have TEST and PROD models with the full size and large lists. Since no formula editing will happen in TEST or PROD, the model will never rollback after a user action. It can roll back on the DEV model but will take a few seconds only if the model is small.
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Details of known issues  Challenge Recommendations Performance issues with long nested formulas Need to have a long formula on time as a result of nested intermediate calculations. If the model size does not prevent from adding extra line items, it's a better practice to create multiple intermediate line items and reduce the size of the formula, as opposed to nesting all intermediate calculations into one gigantic formula. This applies to summary formulae (SUM, LOOKUP, SELECT). Combining SUM and LOOKUP in the same line item formula can cause performance issues in some cases. If you have noticed a drop in performance after adding a combined SUM and LOOKUP to a single line item, then split it into two line items. RANKCUMULATE causes slowness A current issue with the RANKCUMULATE formula can mean that the time to open the model, including rollback, can be up to five times slower than they should be. There is currently no suitable workaround. Our recommendations are to stay within the constraints defined in Anapedia. SUM/LOOKUP with large cell count Separate formulas into different line items to reduce calculation time (fewer cells need to recalculate parts of a formula that would only affect a subset of the data). A known issue with SUM/LOOKUP combinations within a formula can lead to slow model open and calculation times, particularly if the line item has a large cell count. Example: All line items do not apply to time or versions. Y = X[SUM: R, LOOKUP: R] Y Applies to [A,B] X Applies to [A,B] R Applies to [B] list formatted [C] Recommendation: Add a new line item 'intermediate' that must have 'Applies To' set to the 'Format' of 'R' intermediate = X[SUM: R] Y = intermediate[LOOKUP: R]  This issue is currently being worked on by Development and a fix will be available in a future release Calculations are over non-common dimensions Anaplan calculates quicker if calculations are over common dimensions. Again, best seen in an example. If you have, List W, X Y = A + B Y Applies To W, X A Applies To W B Applies To W This performs slower than, Y = Intermediate Intermediate = A + B Intermediate Applies To W All other dimensions are the same as above. Similarly, you can substitute A & B above for a formula, e.g. SUM/LOOKUP calculations. Cell history truncated Currently, history generation has a time limit of 60 seconds set. The history generation is split into three stages with 1/3 of time allocated to each. The first stage is to build a list of columns required for the grid. This involves reading all the history. If this takes more than 20 seconds, then the user receives the message "history truncated after x seconds - please modify the date range," where X is how many seconds it took. No history is generated. If the first stage completes within 20 seconds, it goes on to generate the full list of history.  In the grid only the first 1000 rows are displayed; the user must Export history to get a full history. This can take significant time depending on volume.  The same steps are taken for model and cell history. The cell history is generated from loading the entire model history and searching through the history for the relevant cell information. When the model history gets too large then it is currently truncated to prevent performance issues. Unfortunately, this can make it impossible to retrieve the cell history that is needed. Make it real time when needed Do not make it real time unless it needs to be. By this we mean, do not have line items where users input data being referenced by other line items unless they have to be. A way around this could be to have users have their data input sections, which is not referenced anywhere, or as little as possible, and, say, at the end of the day when no users are in the model, run an import which would update into cells where calculations are then done. This may not always be possible if the end user needs to see resulting calculations from his inputs, but if you can limit these to just do the calculations that he needs to see and use imports during quiet times then this will still help. We see this often when not all reporting modules need to be recalculated real time. In many cases, many of these modules are good to be calculated the day after. Reduce dependencies Don't have line items that are dependent on other line items unnecessarily.This can cause Anaplan to not utilize the maximum number of calculations it can do at once. This happens where a line items formula cannot be calculated because it is waiting on results of other line items. A basic example of this can be seen with line item's A, B, and C having the formulas: A - no formula B= A C = B Here B would be calculated, and then C would be calculated after this. Whereas if the setup was: A - no formula B = A C = A Here B and C can be calculated at the same time. This also helps if line item B is not needed it can then be removed, further reducing the number of calculations and the size of the model. This needs to considered on a case-by-case basis and is a tradeoff between duplicating calculations and utilizing as many threads as possible. If line item B was referenced by a few other line items, it may indeed be quicker to have this line item. Summary calculation Summary cells often take processing time even if they are not actually recalculated because they must check all the lower level cells. Reduce summaries to ‘None’ wherever possible. This not only reduces aggregations, but also the size of the model.
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Overview: A dashboard with grids that includes large lists that have been filtered and/or sorted can take time to open. The opening action can also become a blocking operation; when this happens, you'll see the blue toaster box showing "Processing....." when the dashboard is opening. This article includes some guidelines to help you avoid this situation.  Rule 1: Filter large lists by creating a Boolean line item.  Avoid the use of filters on text or non-Boolean formatted items for large lists on the dashboard. Instead, create a line item with the format type Boolean and add calculations to the line item so that the results return the same data set as the filter would. Combine multiple conditions into a single Boolean value for each axis. This is especially helpful if you implement user-based filters, where the Boolean will be by the user and by the list to be filtered. The memory footprint of a Boolean line item is 8x smaller than other types of line items. Known issue: On an existing dashboard where a saved view is being modified by replacing the filters with a Boolean line item for filtering, you must republish it to the dashboard. Simply removing the filters from the published dashboard will not improve performance. Rule 2: Use the default Sort. Use sort carefully, especially on large lists. Opening a dashboard that has a grid where a large list is sorted on a text formatted line item will likely take 10 seconds or more and may be a blocking operation. To avoid using the sort: Your list is (by default) sorted by the criteria you need. If it is not sorted, you can still make the grid usable by reducing the items using a user-based filter. Rule 3: Reduce the number of dashboard components. There are times when the dashboard includes too many components, which slows performance. Avoid horizontal scrolling and try and keep vertical scrolling to no more than three pages deep. Once you exceed these limits, consider moving the components into multiple dashboards. Doing so will help both performance and usability. Rule 4: Avoid using large lists as page selectors. If you have a large list and use it as a page selector on a dashboard, that dashboard will open slowly. It may take 10 seconds or more. The loading of the page selector takes more than 90% of the total time. Known issue: If a dashboard grid contains list formatted line items, the contents of page selector drop-downs are automatically downloaded until the size of the list meets a certain threshold; once this size is exceeded, the download happens on demand, or in other words when a user clicks the drop down. The issue is that when Anaplan requests the contents of list formatted cell drop-downs, it also requests contents of ALL other drop-downs INCLUDING page selectors. Recommendation: Limit the page selectors on medium to large lists using the following tips: a) Make the page selector available in one grid and use the synchronized paging option for all other grids and charts. No need to allow users to edit the page in every dashboard grid or chart. b) For multi-level hierarchies, consider creating a separate dashboard with multiple modules (just with the list entries) to enable the users to navigate to the desired level. They can then navigate back to the main planning dashboard. This approach also de-clutters the dashboards. c) If the dashboard elements don't require the use of the list, you should publish them from a module that doesn't contain this list. For example, floating page selectors for time or versions, or grids that are displayed as rows/columns-only should be published from modules that do not include the list. Why? The view definitions for these elements will contain all the source module's dimensions, even if they are not shown, and so will carry the overhead of populating the large page selector if it was present in the source. Rule 5: Split formatted line items into separate modules. Having many line items (that are formatted as lists) in a single module displayed on a dashboard can reduce performance as all of the lists are stored in memory (similar to Rule 4). It is better, if possible, to split the line items into separate modules. Remember from above, try not to have too many components on a dashboard; only include what the users really need and create more dashboards as needed.  
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Overview These dashboards are absolutely critical to good usability of a model. Dashboards are the first contact between the end users and a model. What SHOULD NOT be done in a landing dashboard: Display detailed instructions on how to use the model. See "Instruction Dashboard" instead. Use it for global navigation, built using text boxes and navigation buttons. It will create maintenance challenges if different roles have different navigation paths. It's not helpful once users know where to go. What SHOULD be done in a landing dashboard: Display KPIs with a chart that highlights where they stand on these KPIs, and highlight gaps/errors/exceptions/warnings. A summary/aggregated view of data on a grid to support the chart. The chart should be the primary element. Short instructions on the KPIs. A link to an instruction-based dashboard that includes guidance and video links. A generic instruction to indicate that the user should open the left-side sliding panel to discover the different navigation paths. Users who perform data entry need access to the same KPIs as execs are seeing. Landing dashboard example 1:   Displays the main KPI, which the planning model allows the organization to plan. Landing dashboard example 2:   Provides a view on how the process is progressing against the calendar. Landing dashboard example 3:   Created for executives who need to focus on escalation. Provides context and a call to action (could be a planning dashboard, too).  
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If you’re familiar with Anaplan, you’ve probably heard the buzz about having a data hub and wondered why it’s considered a “best practice” within the Anaplan community. Wonder no more. Below, I will share four reasons why you should spend the time to build a data hub before Anaplan takes your company by storm.   1. Maintain consistent hierarchies Hierarchies are a common list structure built by Anaplan and come in a variety of options depending on use case, e.g., product hierarchy, cost center hierarchy, and management hierarchy, just to name a few. These hierarchies should be consistent across the business whether you’re doing demand planning or financial planning. With a data hub, your organization has a higher likelihood of keeping hierarchies consistent over time since everyone is pulling the same structure from one source of truth: the data hub.   2. Data Optimization As you expand the use of Anaplan across multiple departments, you may find that you only need a portion of a list, rather than the entire list. For instance, you may want the full list of employees for workforce planning purposes, but only a portion of the employees for incentive compensation calculations. With a data hub, you can distribute only the pertinent information. You can filter the list of employees to build the employee hierarchy in the incentive compensation model while having the full list of employees in the workforce planning model. Keep them both in sync using the data hub as your source of truth.   3. Separate duties by roles and responsibilities An increasing number of customers have asked about roles and responsibilities with Anaplan as they expand internally. In Anaplan, we recommend each model have a separate owner. For example, an IT owner for the data hub, an operations owner for the demand planning model, and a finance owner for the financial planning model. The three owners combined would be your Center of Excellence, but each has their separate roles and responsibilities for development and maintenance in the individual models.   4. Accelerate future builds One of the main reasons many companies choose Anaplan is for the platform’s flexibility. Its use can easily and quickly expand across an entire organization. Development rarely stops after the first implementation. Model builders are enabled and excited to continue to bring Anaplan into other areas of the business. If you start by building the data hub as your source of truth for data and metadata, you can accelerate the development of future models since you already have defined the foundation of the model, the lists, and dimensions. As you begin to implement, build, and roll out Anaplan, starting with a data hub is a key consideration. In addition to this, there are many other fundamental Anaplan best practices to consider when rolling out a new technology and driving internal adoption.
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Problem to solve: As an HR manager, I need to enter the salary raise numbers for multiple regions that I'm responsible for. As a domain best practice, my driver-based model helps me to enter raise guidelines, which will then change at the employee level. Usability issue addressed: I have ten regions, eight departments in each, with a total of 10,000+ employees. I need to align my bottom-up plan to the down target I received earlier. I need to quickly identify what region is above/behind target and address the variance. My driver-based raise modeling is fairly advanced, and I need to see what the business rules are. I need to quickly see how it impacts the employee level. Call to action: Step 1: Spot what region I need to address.  Step 2: Drill into the variances by department. Steps 1 & 2 are analytics steps: "As an end user, I focus first on where the biggest issues are." This is a good usability practice that helps users. Step 3: Adjusting the guidelines (drivers) There are not excessive instructions on how to build and use guidelines, which would have cluttered the dashboard. Instead, Anaplan added a "view guideline instruction" button. This button should open a dashboard dedicated to detailed instructions or link to a video that explains how guideline works. Impact analysis: The chart above the grid will adjust as guidelines are edited. That is a good practice for impact analysis— no scrolling or clicking needed to view how the changes will impact the plan. Step 4: Review a summary of the variance after changes are made. Putting steps 1–4 close to each other is a usable way of indicating to a user that he/she needs to iterate through these four steps to achieve their objective, which is to have every region and every department be within the top down target. Step 5: A detailed impact analysis, which is placed directly below steps 3 and 4. This allows end users to drill into the employee-level details and view the granular impact of the raise guidelines. Notice the best practices in step 5:   The customer will likely ask to see 20 to 25 employee KPIs across all employees and will be tempted to display these as one large grid. This can quickly lead to an unusable grid made of thousands of rows (employees) across 25 columns. Instead, we have narrowed the KPI list to only ten that display without left-right scrolling. Criteria to elect these ten: be able to have a chart that compares employees by these KPIs. The remaining KPIs are displayed as an info grid, which only displays values for the selected employee. Things like region, zip codes, and dates are removed from the grid as they do not need to be compared side-by-side with other KPIs or between employees.  
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I recently posted a Python library for version 1.3 of our API. With the GA announcement of API 2.0, I'm sharing a new library that works with these endpoints. Like the previous library, it does support certificate authentication, however, it requires the private key in a particular format (documented in the code, and below). I'm pleased to announce, the use of Java keystore is now supported. Note:   While all of these scripts have been tested and found to be fully functional, due to the vast amount of potential use cases, Anaplan does not explicitly support custom scripts built by our customers. This article is for information only and does not suggest any future product direction. This library is a work in progress and will be updated with new features once they have been tested.   Getting Started The attached Python library serves as a wrapper for interacting with the Anaplan API. This article will explain how you can use the library to automate many of the requests that are available in our Apiary, which can be found at   https://anaplanbulkapi20.docs.apiary.io/#. This article assumes you have the requests and M2Crypto modules installed as well as the Python 3.7. Please make sure you are installing these modules with Python 3, and not for an older version of Python. For more information on these modules, please see their respective websites: Python   (If you are using a Python version older or newer than 3.7 we cannot guarantee the validity of the article)   Requests   M2Crypto Note:   Please read the comments at the top of every script before use, as they more thoroughly detail the assumptions that each script makes. Gathering the Necessary Information In order to use this library, the following information is required: Anaplan model ID Anaplan workspace ID Anaplan action ID CA certificate key-pair (private key and public certificate), or username and password There are two ways to obtain the model and workspace IDs: While the model is open, go Help>About:  Select the workspace and model IDs from the URL:  Authentication Every API request is required to supply valid authentication. There are two (2) ways to authenticate: Certificate Authentication Basic Authentication For full details about CA certificates, please refer to our Anapedia article. Basic authentication uses your Anaplan username and password. To create a connection with this library, define the authentication type and details, and the Anaplan workspace and model IDs: Certificate Files: conn = AnaplanConnection(anaplan.generate_authorization("Certificate","<path to private key>", "<path to public certificate>"), "<workspace ID>", "<model ID>") Basic: conn = AnaplanConnection(anaplan.generate_authorization("Basic","<Anaplan username>", "<Anaplan password>"), "<workspace ID>", "<model ID>")   Java Keystore: from anaplan_auth import get_keystore_pair key_pair=get_keystore_pair('/Users/jessewilson/Documents/Certificates/my_keystore.jks', '<passphrase>', '<key alias>', '<key passphrase>') privKey=key_pair[0] pubCert=key_pair[1] #Instantiate AnaplanConnection without workspace or model IDs conn = AnaplanConnection(anaplan.generate_authorization("Certificate", privKey, pubCert), "", "") Note: In the above code, you must import the get_keystore_pair method from the anaplan_auth module in order to pull the private key and public certificate details from the keystore. Getting Anaplan Resource Information You can use this library to get the necessary file or action IDs. This library builds a Python key-value dictionary, which you can search to obtain the desired information: Example: list_of_files = anaplan.get_list(conn, "files") files_dict = anaplan_resource_dictionary.build_id_dict(list_of_files) This code will build a dictionary, with the file name as the key. The following code will return the ID of the file: users_file_id = anaplan_resource_dictionary.get_id(files_dict, "file name") print(users_file_id) To build a dictionary of other resources, replace "files" with the desired resource: actions, exports, imports, processes.  You can use this functionality to easily refer to objects (workspace, model, action, file) by name, rather than ID. Example: #Fetch the name of the process to run process=input("Enter name of process to run: ") start = datetime.utcnow() with open('/Users/jessewilson/Desktop/Test results.txt', 'w+') as file: file.write(anaplan.execute_action(conn, str(ard.get_id(ard.build_id_dict(anaplan.get_list(conn, "processes"), "processes"), process)), 1)) file.close() end = datetime.utcnow() The code above prompts for a process name, queries the Anaplan model for a list of processes, builds a key-value dictionary based on the resource name, then searches that dictionary for the user-provided name, and executes the action, and writes the results to a local file. Uploads You can upload a file of any size and define a chunk size up to 50mb. The library loops through the file or memory buffer, reading chunks of the specified size and uploads to the Anaplan model. Flat file:  upload = anaplan.file_upload(conn, "<file ID>", <chunkSize (1-50)>, "<path to file>") "Streamed" file: with open('/Users/jessewilson/Documents/countries.csv', "rt") as f: buf=f.read() f.close() print(anaplan.stream_upload(conn, "113000000000", buf)) print(anaplan.stream_upload(conn, "113000000000", "", complete=True)) The above code reads a flat file and saves the data to a buffer (this can be replaced with any data source, it does not necessarily need to read from a file). This data is then passed to the "streaming" upload method. This method does not accept the chunk size input. Instead, it simply ensures that the data in the buffer is less than 50mb before uploading. You are responsible for ensuring that the data you've extracted is appropriately split. Once you've finished uploading the data, you must make one final call to mark the file as complete and ready for use by Anaplan actions. Executing Actions You can run any Anaplan action with this script and define a number of times to retry the request if there's a problem. In order to execute an Anaplan action, the ID is required. To execute, all that is required is the following: run_job = execute_action(conn, "<action ID>", "<retryCount>") print(run_job) This will run the desired action, loop until complete, then print the results to the screen. If failure dump(s) exits, this will also be returned. Example output: Process action 112000000082 completed. Failure: True Process action 112000000079 completed. Failure: True Details: hierarchyName Worker Report successRowCount 0 successCreateCount 0 successUpdateCount 0 warningsRowCount 435 warningsCreateCount 0 warningsUpdateCount 435 failedCount 4 ignoredCount 0 totalRowCount 439 totalCreateCount 0 totalUpdateCount 435 invalidCount 4 updatedCount 435 renamedCount 435 createdCount 0 lineItemName Code rowCount 0 ignoredCount 435 Failure dump(s): Error dump for 112000000082 "_Status_","Employees","Parent","Code","Prop1","Prop2","_Line_","_Error_1_" "E","Test User 2","All employees","","101.1a","1.0","2","Error parsing key for this row; no values" "W","Jesse Wilson","All employees","a004100000HnINpAAN","","0.0","3","Invalid parent" "W","Alec","All employees","a004100000HnINzAAN","","0.0","4","Invalid parent" "E","Alec 2","All employees","","","0.0","5","Error parsing key for this row; no values" "W","Test 2","All employees","a004100000HnIO9AAN","","0.0","6","Invalid parent" "E","Jesse Wilson - To Delete","All employees","","","0.0","7","Error parsing key for this row; no values" "W","#1725","All employees","69001","","0.0","8","Invalid parent" [...] "W","#2156","All employees","21001","","0.0","439","Invalid parent" "E","All employees","","","","","440","Error parsing key for this row; no values" Error dump for 112000000079 "Worker Report","Code","Value 1","_Line_","_Error_1_" "Jesse Wilson","a004100000HnINpAAN","0","434","Item not located in Worker Report list: Jesse Wilson" "Alec","a004100000HnINzAAN","0","435","Item not located in Worker Report list: Alec" "Test 2","a004100000HnIO9AAN","0","436","Item not located in Worker Report list: Test 2 Downloading a File If the above code is used to execute an export action, the fill will not be downloaded automatically. To get this file, use the following: download = get_file(conn, "<file ID>", "<path to local file>") print(download) This will save the file to the desired location on the local machine (or mounted network share folder) and alert you once the download is complete, or warn you if there is an error. Get Available Workspaces and Models API 2.0 introduced a new means of fetching the workspaces and models available to a given user. You can use this library to build a key-value dictionary (as above) for these resources. #Instantiate AnaplanConnection without workspace or model IDs conn = AnaplanConnection(anaplan.generate_authorization("Certificate", privKey, pubCert), "", "") #Setting session variables uid = anaplan.get_user_id(conn) #Fetch models and workspaces the account may access workspaces = ard.build_id_dict(anaplan.get_workspaces(conn, uid), "workspaces") models = ard.build_id_dict(anaplan.get_models(conn, uid), "models") #Select workspace and model to use while True: workspace_name=input("Enter workspace name to use (Enter ? to list available workspaces): ") if workspace_name == '?': for key in workspaces: print(key) else: break while True: model_name=input("Enter model name to use (Enter ? to list available models): ") if model_name == '?': for key in models: print(key) else: break #Extract workspace and model IDs from dictionaries workspace_id = ard.get_id(workspaces, workspace_name) model_id = ard.get_id(models, model_name) #Updating AnaplanConnection object conn.modelGuid=model_id conn.workspaceGuid=workspace_id The above code will create an AnaplanConnection instance with only the user authentication defined. It queries the API to return the ID of the user in question, then queries for the available workspaces and models and builds a dictionary with these results. You can then enter the name of the workspace and model you wish to use (or print to screen all available), then finally update the AnaplanConnection instance to be used in all future requests.
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As a model builder, you have to define line item formats over and over. Using a text expander/snippet tool, you can speed up the configuration of modules. When you add a new Line Item, Anaplan sets it by default as a Number (Min Significant Digits: 4, Thousands Separator: Comma, Zero Format: Zero, etc.). You usually change it once and copy it over to other line items in the module. Snippet tools can store format definition of generic formats (numbers, text, boolean, or no data) and by a simple shortcut, paste it in the format of the desired line items.  Below is an example of a number format line item with no decimal and hyphens instead of zeros. On my Mac, I press Option + X, I type "Num..." and get a list of all Number formats I pre-defined. I press Enter to paste it. It also works if several line items were selected. The value stored for this Number format is : {"minimumSignificantDigits":-1,"decimalPlaces":0,"decimalSeparator":"FULL_STOP","groupingSeparator":"COMMA","negativeNumberNotation":"MINUS_SIGN","unitsType":"NONE","unitsDisplayType":"NONE","currencyCode":null,"customUnits":null,"zeroFormat":"HYPHEN","comparisonIncrease":"GOOD","dataType":"NUMBER"} Here is the result of a text format snippet. {"textType":"GENERAL","dataType":"TEXT"} Or a Heading line item (No Data, Style: Heading 1). ---- false {"dataType":"NONE"} - Year Model Calendar All false false {"summaryMethod":"NONE","timeSummaryMethod":"NONE","timeSummarySameAsMainSummary":true,"ratioNumeratorIdentifier":"","ratioDenominatorIdentifier":""} All Versions true false Heading1 - - - 0  This simple trick can save you a lot of clicks. While we are unable to recommend specific snippet tools, your PC or Mac may include one by default, while others are easy to locate for free or low-cost, online.
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This article provides the steps needed to create a basic time filter module. This module can be used as a point of reference for time filters across all modules and dashboards within a given model. The benefits of a centralized Time Filter module include: One centralized governance of time filters. Optimization of workspace, since the filters do not need to be re-created for each view. Instead, use the Time Filter module. Conforms with the D.I.S.C.O. methodology as a 'System' module.  More on D.I.S.C.O. can be found here.   Step 1: Create a new module with two dimensions—time and line items. The example below has simple examples for Weeks Only, Months Only, Quarters Only, and Years Only. Step 2: Line items should be Boolean formatted and the time scale should be set in accordance to the scale identified in the line item name. The example below also includes filters with and without summary methods, providing additional views depending on the level of aggregation desired. Once your preliminary filters are set, your module will look something like the screenshot below.  Step 3: Use the pre-set Time Filters across various modules and dashboards. Simply click on the filters icon in the toolbar, navigate to the time tab, select your Time Filter module from the module selection screen, and select the line item of your choosing. Use multiple line items at a time to filter your module or dashboard view.  
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Assume the following Non-Composite list, ragged hierarchy, needs to be set to Production Data. We need to refer to the ultimate parent to define the logic calculation. In the example, we have assumed that children of Parent 1 and Parent 3 need to return the 'logic 1' value from the constants module below, and those under Parent 2 return 'logic 2,' and we apportion the results based on the initial data of the children. Select Proportion: Data / IF PARENT(ITEM('Non-Composite List')) = 'Non-Composite List'.'Parent 1' THEN Data[SELECT: 'Non-Composite List'.'Parent 1'] ELSE IF PARENT(ITEM('Non-Composite List')) = 'Non-Composite List'.'Parent 2' THEN Data[SELECT: 'Non-Composite List'.'Parent 2'] ELSE IF PARENT(ITEM('Non-Composite List')) = 'Non-Composite List'.'Parent 3' OR PARENT(ITEM('Non-Composite List')) = 'Non-Composite List'.'Child 3.1' THEN Data[SELECT: 'Non-Composite List'.'Parent 3'] ELSE 0   Select Calculation: Select Proportion * IF PARENT(ITEM('Non-Composite List')) = 'Non-Composite List'.'Parent 1' OR PARENT(ITEM('Non-Composite List')) = 'Non-Composite List'.'Parent 3' OR PARENT(ITEM('Non-Composite List')) = 'Non-Composite List'.'Child 3.1' THEN Parent Logic Constants.'Logic 1' ELSE IF PARENT(ITEM('Non-Composite List')) = 'Non-Composite List'.'Parent 2' THEN Parent Logic Constants.'Logic 2' ELSE 0   These “hard references” will prevent the list from being set as a production list. SOLUTION: Create a Parents Only list (this could be imported from the Non-Composite list).  As we don't need the sub-level parents, we do not need to include 'Child 3.1,' even though it is technically a parent. To calculate the proportion calculation without the SELECT, a couple of intermediate modules are needed:   Parent Mapping module This module maps the Non-Composite parents to the Parents Only list.  Due to the different levels in the hierarchy, we need to check for sub levels and use the parent of Child 3.1. In this example, the mapping is automatic because the items in the Parents Only list have the same name as those in the Non-Composite list. The mapping could be a manual entry if needed.   The formula and “applies to” are:   Non Composite Parent: PARENT(ITEM('Non-Composite List')) Applies to: Non-Composite List   Parent of Non Composite Parent: PARENT(Non-Composite Parent) Applies to: Non-Composite List   Parent to Map: IF ISNOTBLANK(PARENT(Parent of Non Composite Parent)) THEN Parent of Non Composite Parent ELSE Non Composite Parent Applies to: Non-Composite List    Parents Only List FINDITEM(Parents Only List, NAME(Parent to Map)) Applies to: Parents Only List   Parents Only subtotals An intermediary module is needed to hold the subtotals. Calculation: Parent Logic Calc.Data[SUM: Parent Mapping.Parents Only List]   Parent Logic? Module We now define the logic for the parents in a separate module. Add Boolean line items for each of the “logic” types. Then you can refer to the logic above  in the calculations. Lookup Proportion: Data / Parents Only Subtotals.Calculation[LOOKUP: Parent Mapping.Parents Only List]   Lookup Calculation: Lookup Proportion * IF Parent Logic?.'Logic 1?'[LOOKUP: Parent Mapping.Parents Only List] THEN Parent Logic Constants.'Logic 1' ELSE IF Parent Logic?.'Logic 2?'[LOOKUP: Parent Mapping.Parents Only List] THEN Parent Logic Constants.'Logic 2' ELSE 0 The list can now be set as a production list as there are no “hard references”.  Also, the formulas are smaller, simpler and now more flexible should the logic need to change.  If Parent 3 needs to use Logic 2, it is a simple change to the checkbox.     Appendix: Blueprints:      
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Anaplan API: Communication failure <SSL peer unverified:  peer not authenticated> This is a common error if a Customer Server is behind a proxy or firewall. The solution is to have the customer whitelist '*.anaplan.com' for firewall blocks.  If behind a proxy, use the '-via" or 'viauser" commands in Anaplan Connect. The other very common cause for this error is that the security certificate isn’t synced up with java. If the whitelist or via command solutions don’t apply or don’t resolve the error, uninstalling and reinstalling Java usually does the trick. Thanks to Jesse Wilson for the technical details.   jesse.wilson@anaplan.com Here are the commands available:  
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Making sure that production data lists are correctly marked within a model is a  key step to setting up and using ALM . This guide will provide a solution to how someone can make revisions to their model to allow for the tagging of a list as a production data list. Please note: this solution doesn’t work if there are hard-coded references on non-composite summary items. For more information on working with production lists and ragged hierarchies, please visit Production lists and ragged hierarchies logic. The issue arises as a model administrator needs to tag a production data list, but there are hard-coded references in the model that won’t allow the person to do so. When this occurs and the model administrator tries to tag it as a production list, they will get a warning similar to this: See  Formula Protection  for more details. To fix this issue, all direct formula references to production data lists need to be changed to be indirect references to lists using either LOOKUPs or Boolean formatted conditional logic.  Below, you will find a step-by-step guide to replacing these formulas. Identify formulas with hard-coded references There is now an easy way to identify all of the formulas which are hard-coded to production data lists. Check the 'Referenced in Formula' column in the General Lists section. This will show the line items where the list is used. Check the respective formula for hard-coded references.  If there are no hard-coded references, then it is OK to check the list as a production data list.  This is the recommended approach, as just setting the lists without prior checking may lead to a rollback error being generated, which could be time-consuming for large models (as well as frustrating). It is possible to just export the General Lists grid to help where there are multiple references for the same list and then use formulas and filters to identify all offenders in the same effort. This option will save significant amounts of time if there are many line items that would need to be changed. You are looking for direct references on the list members: [SELECT: List Name.list member] ITEM(List Name) =List Name.List member The following constructs are valid, but not recommended, as any changes to the names or codes could change the result of calculations: IF CODE(ITEM(List Name))= IF NAME(ITEM(List Name))= After following those steps, you should have a list of all of the line items that need to be changed in the model in order for production data list to be open to being checked. Please note: There may still be list properties that have hard-coded references to items. You will need to take note of these as well, but as per D.I.S.C.O., (Best practice for Module design) we recommend that List Properties are replaced with Line Items in System Modules. Replacing model formulas: The next step is to replace these formulas within the model. For this, there are two recommended options. The first option (Option 1 below) is to replace your SELECT statements with a LOOKUP formula that is referencing a list drop-down. Use this option when there are 1:1 mappings between list items and your formula logic. For example, if you were building out a P&L variance report and needed to select from a specific revenue account, you might use this option.  The second option (Option 2 below) for replacing these formulas is to build a logic module that allows you to use Booleans to select list items and reference these Boolean fields in your formulas. Use this option when there is more complex modeling logic than a 1:1 mapping. For example, you might use this option if you are building a variance report by region and you have different logic for all items under Region 1 (ex: budget – actual) than the items under Region 2 (ex: budget – forecast).  (Option 1) Add List Selections module to be used in LOOKUPs for 1:1 mappings: From here you should make a module called List Selections, with no lists applied to it and a line item for each list item reference that you previously used in the formulas that will be changed. Each of these line items will be formatted as the list that you are selecting to be production data. Afterward, you should have a module that looks similar to this: An easy and effective way to stay organized is to partition and group your line items of similar list formats into the same sections with a section header line item formatted as No Data and a style of "Heading 1." After the line items have been created, the model administrator should use the list drop-downs to select the appropriate items which are being referenced. As new line items are created in a standard mode model, the model administrator will need to open the deployed model downstream to reselect or copy and paste the list formatted values in this module since this is considered production data. Remove hard-coding and replace with LOOKUPs: Once you have created the List Selections module with all of the correct line items, you will begin replacing old formulas, which you’ve identified in Excel, with new references. For formulas where there is a SELECT statement, you will replace the entire SELECT section of the formula with a LOOKUP to the correct line item in the list selections. Example: Old Formula = Full PL.Amount[SELECT: Accounts.Product Sales] New Formula = Full PL.Amount[LOOKUP: List Selections.Select Product Sales] For formulas where there is an IF ITEM (List Name) = List Name Item, you will replace the second section of the formula after the ‘=’ to directly reference the correct line item in the list selections. Example: Old Formula = If ITEM(Accounts) = Accounts.Product Sales THEN Full PL.Amount ELSE 0 New Formula = IF ITEM(Accounts) = List Selections.Select Product Sales THEN Full PL.Amount ELSE 0   (Option 2) Modeling for complex logic and many to many relationship: In the event that you are building more complex modeling logic in your model, you should start by building Boolean references that you can use in your formulas. To accomplish this, you will create a new module with Boolean line items for each logic type that you need. Sticking with the same example as above, if you need to build a variance report where you have different logic depending on the region, start by creating a module by region that has different line items for each different logic that you need similar to the view below: Once you have the Boolean module set up, you can then change your hard-coded formulas to reference these Boolean formatted line items to write your logic. The formula may look similar to this: IF Region Logic.Logic 1 THEN logic1 ELSE IF Region Logic.Logic 2 THEN logic2 ELSE IF Region Logic.Logic 3 THEN logic3 ELSE 0   Here is a screenshot of what the end result may look like:   This method can be used across many different use cases and will provide a more efficient way of writing complex formulas while avoiding hard-coding for production data lists. Selecting production data list: After all of the hard-coded formulas have been changed in the model, you can navigate back to the Settings tab, and open General Lists. In the Production Data column, check the box for the list that you want to set as a production data list. Repeat for each list in the model that needs to be a production data list: For each list in the model that you need to make a production data list, you can repeat the steps throughout this process to successfully remove all hard-coded list references.
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Reducing the number of calculations will lead to quicker calculations and improve performance. However, this doesn’t mean combining all your calculations into fewer line items, as breaking calculations into smaller parts has major benefits for performance. Learn more about this in the Formula Structure article. How is it possible to reduce the number of calculations? Here are three easy methods: Turn off unnecessary Summary method calculations. Avoid formula repetition by creating modules to hold formulas that are used multiple times. Ensure that you are not including more dimensions than necessary in your calculations. Turn off Summary method calculations Model builders often include summaries in a model without fully thinking through if they are necessary. In many cases, the summaries can be eliminated. Before we get to how to eliminate them, let’s recap on how the Anaplan engine calculates. In the following example we have a Sales Volume line-item that varies by the following hierarchies: Region Hierarchy Product Hierarchy Channel Hierarchy City SKU Channel Country Product All Channels Region All Products   All Regions     This means that from the detail values at SKU, City, and Channel level, Anaplan calculates and holds all 23 of the aggregate combinations shown below—24 blocks in total. With the Summary options set to Sum, when a detailed item is amended (represented in the grey block), all the other aggregations in the hierarchies are also re-calculated. Selecting the None summary option means that no calculations happen when the detail item changes. The varying levels of hierarchies are quite often only there to ease navigation, and the roll-up calculations are not actually needed, so there may be a number of redundant calculations being performed. The native summing of Anaplan is a faster option, but if all the levels are not needed it might be better to turn off the summary calculations and use a SUM formula instead.  For example, from the structure above, let’s assume that we have a detailed calculation for SKU, City, and Channel (SALES06.Final Volume). Let’s also assume we need a summary report by Region and Product, and we have a module (REP01) and a line item (Volume) dimensioned as such. REP01.Volume = SALES06 Volume Calculation.Final Volume is replaced with REP01.Volume = SALES06.Final Volume[SUM:H01 SKU Details.Product, SUM:H02 City Details.Region] The second formula replaces the native summing in Anaplan with only the required calculations in the hierarchy. How do you know if you need the summary calculations? Look for the following: Is the calculation or module user-facing? If it is presented on a dashboard, then it is likely that the summaries will be needed. However, look at the dashboard views used. A summary module is often included on a dashboard with a detail module below; Effectively, the hierarchy sub-totals are shown in the summary module, so the detail module doesn’t need the sum or all the summary calculations. Detail to Detail Is the line item referenced by another detailed calculation line item? This is very common, and if the line item is referenced by another detailed calculation the summary option is usually not required. Check the Referenced by column and see if there is anything referencing the line item. Calculation and staging modules If you have used the D.I.S.C.O. module design, you should have calculation/staging modules. These are often not user-facing and have many detailed calculations included in them. They also often contain large cell counts, which will be reduced if the summary options are turned off. Can you have different summaries for time and lists? The default option for Time Summaries is to be the same as the lists. You may only need the totals for hierarchies, or just for the timescales. Again, look at the downstream formulas. The best practice advice is to turn off the summaries when you create a line item, particularly if the line item is within a Calculation module (from the D.I.S.C.O. design principles). Avoid Formula Repetition An optimal model will only perform a specific calculation once. Repeating the same formula expression multiple times will mean that the calculation is performed multiple times. Model builders often repeat formulas related to time and hierarchies. To avoid this, refer to the module design principles (D.I.S.C.O.) and hold all the relevant calculations in a logical place. Then, if you need the calculation, you will know where to find it, rather than add another line item in several modules to perform the same calculation. If a formula construct always starts with the same condition evaluation, evaluate it once and then refer to the result in the construct. This is especially true where the condition refers to a single dimension but is part of a line item that goes across multiple dimension intersections. A good example of this can be seen in the example below: START() <= CURRENTPERIODSTART() appears five times and similarly START() > CURRENTPERIODSTART() appears twice. To correct this, include these time-related formulas in their own module and then refer to them as needed in your modules. Remember, calculate once; reference many times! Taking a closer look at our example, not only is the condition evaluation repeated, but the dimensionality of the line items is also more than required. The calculation only changes by the  day, as per the diagram below: But the Applies To here also contains Organization, Hour Scale, and Call Center Type. Because the formula expression is contained within the line item formula, for each day the following calculations are also being performed: And, as above, it is repeated in many other line items. Sometimes model builders use the same expression multiple times within the same line item. To reduce this overcalculation, reference the expression from a more appropriate module; for example, Days of Week (dimensioned solely by day) which was shown above. The blueprint is shown below, and you can see that the two different formula expressions are now contained in two line items and will only be calculated by day; the other dimensions that are not relevant are not calculated. Substitute the expression by referencing the line items shown above. In this example, making these changes to the remaining lines in this module reduces the calculation cell count from 1.5 million to 1500. Check the Applies to for your formulas, and if there are extra dimensions, remove the formula and place it in a different module with the appropriate dimensionality .
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Dynamic Cell Access (DCA) controls the access levels for line items within modules. It is simple to implement and provides modelers with a flexible way of controlling user inputs. Here are a few tips and tricks to help you implement DCA effectively. Access control modules Any line item can be controlled by any other applicable Boolean line item. To avoid confusion over which line item(s) to use, it is recommended that you add a separate functional area and create specific modules to hold the driver line items. These modules should be named appropriately (e.g. Access – Customers > Products, or Access – Time etc.). The advantage of this approach is the access driver can be used for multiple line items or modules, and the calculation logic is in one place. In most cases, you will probably want read and write access. Therefore, within each module it is recommended that you add two line items (Write? and Read?). If the logic is being set for Write?, then set the formulas for the Read? line item to NOT WRITE? (or vice-versa). It may be necessary to add multiple line items to use for different target line items, but start with this a default. Start simple You may not need to create a module that mirrors the dimensionality of the line item you wish to control. For example, if you have a line item dimensioned by customer, product, and time, and you wish to make actual months read-only, you can use an access module just dimensioned by time. Think about what dimension the control needs to apply to and create an access module accordingly. What settings do I need? There are three different states of access that can be applied: READ, WRITE, and INVISIBLE or hidden. There are two blueprint controls (read control and write control) and there are two states for a driver (TRUE or FALSE). The combination of these determines which state is applied to the line item. The following table illustrates the options: Only the read access driver is set:   Read Access Driver Driver Status True False Target Line Item READ INVISIBLE Only the write access driver is set:   Write Access Driver Driver Status True False Target Line Item WRITE INVISIBLE Both read access and write access drivers are set:   Read Access Driver Write Access Driver Driver Status True False True False Target Line Item READ INVISIBLE WRITE Revert to Read* *When both access drivers are set, the write access driver takes precedence with write access granted if the status of the write access driver is true. If the status of the write access driver is false, the cell access is then taken from the read access driver status. The settings can also be expressed in the following table:   WRITE ACCESS DRIVER TRUE FALSE NOT SET READ ACCESS DRIVER TRUE Write Read Read FALSE Write Invisible Invisible NOT SET Write Invisible Write Note: If you want to have read and write access, it is necessary to set both access drivers within the module blueprint.  Totals Think about how you want the totals to appear. When you create a Boolean line item, the default summary option is NONE. This means that if you used this access driver line item, any totals within the target would be invisible. In most cases, you will probably want the totals to be read-only, so setting the access driver line item summary to ANY will provide this setting. If you are using the Invisible setting to “hide” certain items and you do not want the end user to compute hidden values, then it is best to use the ANY setting for the access driver line item. This means that only if all values in the list are visible then the totals show; otherwise, the totals are hidden from view.
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If you have a multi-year model where the data range for different parts of the model varies, (for example, history covering two years, current year forecast, and three planning years) then Time Ranges should be able to deliver significant gains in terms of model size and performance. But, before you rush headlong into implementing Time Ranges across all of your models, let me share a few considerations to ensure you maximize the value of the feature and avoid any unwanted pitfalls. Naming Convention Time Ranges As with all Anaplan models, there is no set naming convention, however, we do advocate consistency and simplicity. As with lists and modules, short names are good. I like to describe the naming convention thus—as short as practical—meaning you need to understand what it means, but don’t write an essay! We recommend using the following convention: FYyy-FYyy. For example, FY16-FY18, or FY18 for a single year Time Ranges available are from 1981 to 2079, so the “19” or the “20” prefixes are not strictly necessary. Keeping the name as short as this has a couple of advantages: It has a clear indication of the boundaries for the Time Range It is short enough to see the name of the Time Range in the module and line items blueprint The aggregations available for Time Ranges can differ for each Time Range and also differ from the main model calendar. If you take advantage of this and have aggregations that differ from the model calendar, you should add a suffix to the description. For example: FY16-FY19 Q (to signify Quarter totals) FY16-FY19 QHY (Quarter and Half Year totals) FY16-FY19 HY (Half Year totals only) etc. Time Ranges are Static Time Ranges can span from 1981 to 2079. As a result, they can exist entirely outside, within, or overlap the model calendar. This means that there may likely be some additional manual maintenance to perform when the year changes. Let’s review a simple example: Assume the model calendar is FY18 with two previous years and two future years; the model calendar spans FY16-FY20 We have set up Time Ranges for historic data (FY16-FY17) and plan data (FY19-FY20) We also have modules that use the model calendar to pull all of the history, forecast, and plan data together, as seen below: At year end when we “roll over the model,” we amend the model calendar simply by amending the current year. What we have now is as follows: You see that the history and plan Time Ranges are now out of sync with the model calendar. How you change the history Time Range will depend on how much historical data you need or want to keep. Assuming you don’t need more than two year’s history, the Time Range should be re-named FY17-FY18 and the start period advanced to FY17 (from FY16). Similarly, the plan Time Range should be renamed FY20-FY21 and advanced to FY20 (from FY19). FY18 is then available for the history to be populated and FY21 is available for plan data entry. Time Ranges Pitfalls Potential Data Loss Time Ranges can bring massive space and calculation savings to your model(s), but be careful. In our example above, changing the Start Period of FY16-FY17 to FY17 would result in the data for FY16 being deleted for all line items using FY16-FY17 as a Time Range. Before you implement a Time Range that is shorter or lies outside the current model calendar, and especially when implementing Time Ranges for the first time, ensure that the current data stored in the model is not needed. If in doubt, do some or all of the suggestions below: Export out the data to a file Copy the existing data on the line item(s) to other line items that are using the model calendar Back up the entire model Formula References The majority of the formula will update automatically when updating Time Ranges. However, if you have any hard-coded SELECT statements referencing years or months within the Time Range, you will have to amend or remove the formula before amending the Time Range. Hard-coded SELECT statements go against best practice for exactly this reason; they cause additional maintenance. We recommend replacing the SELECT with a LOOKUP formula from a Time Settings module. There are other examples where the formula may need to be removed/amended before the Time Range can be adjusted. See the Anapedia documentation for more details. When to use the Model Calendar This is a good question and one that we at Anaplan pondered during the development of the feature; Do Time Ranges make the model calendar redundant? Well, I think the answer is “no,” but as with so many constructs in Anaplan, the answer probably is, “it depends!” For me, a big advantage of using the model calendar is that it is dynamic for the current year and the +/- years on either side. Change the current year and the model updates automatically along with any filters and calculations you have set up to reference current year periods, historical periods, future periods, etc.  (You are using a central time settings module, aren’t you??) Time ranges don’t have that dynamism, so any changes to the year will need to be made for each Time Range. So, our advice before implementing Time Ranges for the first time is to review each Module and: Assess the scope of the calculations Think about the reduction Time Ranges will give in terms of space and calculation savings, but compare that with annual maintenance. For example: If you have a two-year model, with one history year (FY17) and the current year (FY18), you could set up a Time Range spanning one year for FY17 and another one year Time Range for FY18 and use these for the respective data sets. However, this would mean each year both Time Ranges would need to be updated. We advocate building models logically, so it is likely that you will have groups of modules where Time Ranges will fall naturally. The majority of the modules should reflect the model calendar. Once Time Ranges are implemented, it may be that you can reduce the scope of the model calendar. If you have a potential Time Range that reflects either the current or future model calendar, leave the timescale as the default for those modules and line items; why make extra work? SELECT Statements As outlined above, we don’t advocate hard-coded time selects of the majority of time items because of the negative impact on maintenance (the exceptions being All Periods, YTD, YTG, and CurrentPeriod). When implementing Time Ranges for the first time, take the opportunity to review the line item formula with time selects. These formulae can be replaced with lookups using a Time Settings module. Application Lifecycle Management (ALM) Considerations As with the majority of the Time settings, Time Ranges are treated as structural data. If you are using ALM, all of the changes must be made in the Development model and synchronized to Production. This gives increased importance to refer to the pitfalls noted above to ensure data is not inadvertently deleted. Best of luck! Refer to the Anapedia documentation for more detail. Please ask if you have any further questions and let us and your fellow Anaplanners know of the impact Time Ranges have had on your model(s).
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Thinking through the results of a modeling decision is a key part of ensuring good model performance—in other words, making sure the calculation engine isn’t overtaxed. This article highlights some ideas for how to lessen the load on the calculation engine. Formulas should be simple; a formula that is nested, or uses multiple combinations, uses valuable processing time. Writing a long, involved formula makes the engine work hard. Seconds count when the user is staring at the screen. Simple is better. Breaking up formulas and using other options helps keep processing speeds fast. You must keep a balance when using these techniques in your models, so the guidance is as follows: Break up the most commonly changed formula Break up the most complex formula Break up any formula you can’t explain the purpose of in one sentence Formulas with many calculated components The structure of a formula can have a significant bearing on the amount of calculation that happens when inputs in the model are changed. Consider the following example of a calculation for the Total Profit in an application. There are five elements that make up the calculation: Product Sales, Service Sales, Cost of Goods Sold (COGS), Operating Expenditure (Op EX), and Rent and Utilities. Each of the different elements is calculated in a separate module. A reporting module pulls the results together into the Total Profit line item, which is calculated using the formula shown below. What happens when one of the components of COGS changes? Since all the source components are included in the formula, when anything within any of the components changes, this formula is recalculated. If there are a significant number of component expressions, this can put a larger overhead on the calculation engine than is necessary. There is a simple way to structure the module to lessen the demand on the calculation engine. You can separate the input lines in the reporting module by creating a line item for each of the components and adding the Total Profit formula as a separate line item. This way, changes to the source data only cause the relevant line item to recalculate. For example, a change in the Product Sales calculation only affects the Product Sales and the Total Profit line items in the Reporting module; Services Sales, Op EX, COGS and Rent & Utilities are unchanged. Similarly, a change in COGS only affects COGS and Total Profit in the Reporting module. Keep the general guidelines in mind. It is not practical to have every downstream formula broken out into individual line items. Plan to provide early exits from formulas Conditional formulas (IF/THEN) present a challenge for the model builder in terms of what is the optimal construction for the formula, without making it overly complicated and difficult to read or understand. The basic principle is to avoid making the calculation engine do more work than necessary. Try to set up the formula to finish the calculations as soon as possible. Always put first the condition that is most likely to occur. That way the calculation engine can quit the processing of the expression at the earliest opportunity. Here is an example that evaluates Seasonal Marketing Promotions: The summer promotion runs for three months and the winter promotion for two months. There are more months when there is no promotion, so this formula is not optimal and will take longer to calculate. This is better, as the formula will exit after the first condition more frequently. There is an even better way to do this. Following the principles from above, add another line item for no promotion. And then the formula can become: This is even better because the calculation for No Promo has already been calculated, and Summer Promo occurs more frequently than Winter Promo. It is not always clear which condition will occur more frequently than others, but here are a few more examples of how to optimize formulas: FINDITEM formula The Finditem element of a formula will work its way through the whole list looking for the text item, and if it does not find the referenced text, it will return blank. If the referenced text is blank, it will also return a blank. Inserting a conditional expression at the beginning of the formula keeps the calculation engine from being overtaxed. IF ISNOTBLANK(TEXT) THEN FINDITEM(LIST,TEXT) ELSE BLANK Or IF BLANK(TEXT) THEN BLANK ELSE FINDITEM(LIST,TEXT) Use the first expression if most of the referenced text contains data and the second expression if there are more blanks than data. LAG, OFFSET, POST, etc. If in some situations there is no need to lag or offset data, for example, if the lag or offset parameter is 0. The value of the calculation is the same as the period in question. Adding a conditional at the beginning of the formula will help eliminate unnecessary calculations: IF lag_parameter = 0 THEN 0 ELSE LAG(Lineitem, lag_parameter, 0) Or IF lag_parameter <> 0 THEN LAG(Lineitem, lag_parameter, 0) ELSE 0 The use of formula a or b will depend on the most likely occurrence of 0s in the lag parameter. Booleans Avoid adding unnecessary clutter for line items formatted as BOOLEANS. There is no need to include the TRUE or FALSE expression, as the condition will evaluate to TRUE or FALSE. Sales>0 Instead of IF Sales > 0 then TRUE ELSE FALSE
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Creates the Java KeyStore required for Anaplan Connect 1.4
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Overview The Anaplan Optimizer aids business planning and decision making by solving complex problems involving millions of combinations quickly to provide a feasible solution. Optimization provides a solution for selected variables within your Anaplan model that matches your objective based on your defined constraints. The Anaplan model must be structured and formatted to enable Optimizer to produce the correct solution. You are welcome to read through the materials and watch the videos on this page, but Optimizer is a premium service offered by Anaplan (Contact your Account Executive if you don't see Optimizer as an action on the settings tab). This means that you will not be able to actually do the training exercises until the feature is turned on in your system. Training The training involves an exercise along with documentation and videos to help you complete it. The goal of the exercise is to setup the optimization exercise for two use cases; network optimization and production optimization. To assist you in this process we have created an optimization exercise guide document which will walk you through each of the steps. To further help we have created three videos you can reference: An exercise walk-through A demo of each use case A demo of setting up dynamic time Follow the order of the items listed below to assist with understanding how Anaplan's optimization process works: Watch the use case video which demos the Optimizer functionality in Anaplan Watch the exercise walkthrough video Review documentation about how Optimizer works within Anaplan Attempt the Optimizer exercise Download the exercise walkthrough document Download the Optimizer model into your workspace How to configure Dynamic Time within Optimizer Download the Dynamic Time document Watch the Dynamic Time video Attempt Network Optimization exercise Attempt Production Optimization exercise
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L'application Bring Your Own Key (BYOK) vous permet maintenant de vous approprier les clés de chiffrement de vos données de modèle. Si vous avez accès à l'outil Anaplan Administration, vous pouvez chiffrer et déchiffrer des espaces de travail sélectionnés à l'aide de vos propres clés AES-256. À la différence des clés principales système, les clés créées par BYOK vous appartiennent et vous en assurez la sécurité. Aucun mécanisme ne permet au personnel Anaplan d'accéder à vos clés. Bring Your Own Key (BYOK) - Guide de l'utilisateur  Bring Your Own Key (BYOK) est un produit complémentaire que votre organisation peut acheter si elle possède l'édition Enterprise.
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Deal with monthly dashboards Many FP&A dashboards will need to display all 12 months in the current year, as well as Quarter, Half, and Total Year totals. Doing this is likely to create a very large grid, especially if more than one dimension is nested on the rows. Avoid this: The grid displayed here is what may be requested when Anaplan is replacing a spreadsheet-based solution. The requirement being "At minimum, do what we could do in the spreadsheets". Avoid the trap of rebuilding this in Anaplan. Usually, this simply creates an extra requirement to export this into Excel ® , have users work offline, and then import the data back into Anaplan, which kills the value that Anaplan can bring. Instead, build the dashboard as indicated below: Have end users view the aggregated values on the Cost center (the first nested dimensions) that will provide an overview on where most OPEX are spent Have end users highlight a cost center, and enter its detailed sub-accounts Visualize the monthly trend using a line chart for the selected sub-account   
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