The following is a guide for the 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 and practice exercise
Intro and overview video
Model overview and review of new key features
Initial model and data import steps
Steps on how to setup model, product hierarchy, customer list and multi-level forecast analysis
Practice exercise: import data to set up stat forecast
Two sets of load files included to practice setup for single level product set or multi-level product set with customers, product and brand level
Start on "Initial App Setup" dashboard and loadeither Single OR Multi Level filesinto 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 version with forecast algorithms list and overview.
Detailed version which includes a slide for each forecast method, method overview, advantages/disadvantages, equation and graph example output.
These slides are also included on "Forecast Methods Overview & Formulas" dashboard.
3. Implementation specifics
Training and 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 and dynamic time settings
If a Time Settings change is required, 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 and 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 and Enablement dashboard if there are issues with stat forecast generating before reaching out for support
Model notes and 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 and Selective Access
Demo, Demand Planner, Demand Planning Manager roles 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.
The content in this article has not been evaluated for all Anaplan implementations and may not be recommended for your specific situation.
Please consult your internal administrators prior to applying any of the ideas or steps in this article.