@TGagnon no those models are not needed. The model is already staged with generic data or you can clear and import a new data set from .csv files following the data setup process if needed.
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@imerchant here are the responses to your questions: - You can setup an Import staging module that the properties are loaded into first, this module can have logic/booleans setup to validate all applicable data is there before it gets imported into the actual List. - In the import staging module, line items can be setup with conditional formatting to validate required data was loaded, if not the missing/blank lines will be flagged. This view can be published to a dashboard so end user can review and upate missing data. - Example of this process is below. We setup a staging module that Product Hierarchy data is imported into to first check if all required data was imported (Product Name and Product Code). If it’s missing these are highlighted yellow using conditional formatting for end user to update before executing full import action which then imports data from staging module into product hierarchy Lists. If “View Missing Instances” option is selected it brings user to data clean up dashboard: Once missing data is populated then user can execute “Add Products to Hierarchy” action which will import data from staging module into Lists. - Erin & Sam
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@priyanka1029 here are the answers to your questions: Repsonse to Question 1: A parent is required, if there is no actual parent for the numbered list then you can create a placeholder parent list without any list items (just “All”) and make this the parent hierarchy Reponse to Question 2: - Selective Access can be used to give employees access to only certain List items. - Dynamic Cell Access can be used so employees can only read/write, view or have no access to certain Line items. We setup an example of both below: Step 1: Setup Cost Center and Employees list, in Cost Center list select “Selective Access Enabled?” Boolean Step 2: Update access for employees, under “Settings” -> Users, find employee and give them read/write access to applicable cost center. Step 3: Employee 1 who has Write access to Africa cost center will now only see Africa cost center in the list dropdown (Note: USA Cost Center is setup in list but Employee 1 does not have access) Step 4: Selective Access can also be used to give employee read/write access to certain line items applicable to the Africa Cost Center. Setup Boolean line item then use this Boolean as the “Read Access Driver” Step 5: Publish module to a dashboard, now Employee 1 can only view “Actuals” line item and can edit “Inputs” line item and enter data here - Erin & Sam
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@NicolasCadierhere are the responses to your questions: Question 1 Response: COLLECT() is a function used in conjunction with Line Item Subsets to dimensionalize line items back to a list. For example, in our Statistical Forecasting Application, we have a Calculation module that with line items that calculate the final forecasts for each algorithm method. Step 1: Identify Line Items to Dimensionalize/Collect Step 2: Create a Line Item Subset and mark line items for inclusion Step 3: In your list, create a property called Connect to Subset whose format is your line item subset: Step 4: Create a module dimensionalized by this Line Item Subset and relevant properties, and call COLLECT() Step 5: Create a module dimensionalized by your list, and reference the Subset module with a Lookup from the Connect to Subset property Another example and more details on COLLECT() can be found here: https://help.anaplan.com/anapedia/Content/Calculation_Functions/All/COLLECT.html Question 2 Response: Refer to Rob Marshall’s example (link below), he gave a great response and we agree with it. Create a concatenated list of the intersections and use this flat list in your model. Create a module with properties to translate the Flat List to other levels of your hierarchy https://community.anaplan.com/t5/Best-Practices/Best-practice-for-Organisation-Hierarchy-in-Matrix-Style/m-p/40534 - Erin & Sam
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@MarkWarren - here are the responses to your questions: Response to Question 1: Dimensionality and sparsity — fix with modules and line items to include only the relevant dimensions. This means taking out “Applies To” on certain line items when it’s not relevant for that line item to help reduce sparsity and module size (i.e. removing Time dimension if it’s not relevant). Can be helped by following DISCO design, designing up front to not build redundant and/or unnecessary functionality. Use line item subsets when needed and break long formulas into multiple line items if required to help with performance. Response to Question 2: - Article on Community - DISCO & How to Model - Two great previous AMAs -- Mark Shemaria - Model Sparsity Principles & Center of Excellence Best Practices Duncan Pearson - Designing for Performance and Scale - Erin & Sam
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The following is a g uide for the new Statistical Forecasting Calculation Engine Models (monthly and weekly). It includes enablement videos, practice data import exercise, model documentation, and specific steps when using the model for implementations .
1. Enablement Videos & Practice Exercise
Intro and Overview Video
Model overview and review of new key features.
Initial Model & Data Import Steps
Steps on how to setup model, product hierarchy, customer list and multi-level forecast analysis.
Practice Exercise—Import data to setup stat forecast
Two sets of load files included to practice setup for single level product set or multi-level product set w/ customers, product and brand level.
Start on "Initial App Setup" dashboard and load either Single OR Multi Level files into model, and use Import video as guide if needed.
.Zip File Attached
Lucidchart Process Maps
Lucidchart Process Map document includes High-Level process flow for end-user navigation and detailed tabs for each section.
**Details & links also on "Training & Enablement" dashboard.
High-Level Process Map PDF
High-level process map PDF format.
Forecast Methods PDFs
High-level version with forecast algorithms list and overview.
Detailed version which includes a slide for each forecast method, m ethod overview, advantages/disadvantages, equation and graph example output.
**These slides are also included on "Forecast Methods Overview & Formulas" dashboard.
3. Implementation Specifics
Training & Enablement Dashboard
Training & Enablement dashboard contains details on process map navigation.
Initial Model Setup
Initial Setup: current model staged with chocolate data from data hub, execute CLEAR MODEL action prior to loading customer-specific data.
Changing Model Time Scale— align Native & Dynamic Time Settings
If a Time Settings change is required, need to review Initial App Setup dashboard to align Native Time with Dynamic Time setup in model.
Monthly Update Process
After initial setup, use Monthly Data History Upload dashboard to update prior period actuals and settings .
Single Level vs. Multi-Level Forecast Setup
Two implementation options & when to use:
Single Level Forecast: Forecast at one level of product hierarchy (i.e. all stat forecasts calculated at Item level). Most use cases will leverage single level forecast setup.
Multi-Level Forecast : Ability to forecast at different levels of the product hierarchy (i.e. Top Item | Customers, Item and Brand level can all have stat forecast generated). This requires a complex forecast reconciliation process, review "Multi-Level Forecast Overview" dashboard if this process is needed.
Follow troubleshooting tips on Training & Enablement dashboard if having issues with stat forecast generating before reaching out for support.
Model Notes & Documentation
Module Notes—includes DISCO classification and module purpose.
"Do Not Modify" Items
Module notes contain DO NOT MODIFY for items that should not be changed during the implementation process.
User Roles & Selective Access
Demo, Demand Planner, Demand Planning Manager ro les can be adjusted
After Selective Access process run on Flat List Management dashboard; then users can be given access to certain product groups/brands etc.
Details on daily batch processing and how to prepare a roadmap of your batch processes – files, queries, import actions/processes in Anaplan (see attachment).
Intro & Model Intro and Overview Video.
Data Import and Setup Steps.
5. Model Download Links
Monthly Statistical Forecasting Calculation Engine
Weekly Statistical Forecasting Calculation Engine
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