PlanIQ

Hi All,   I have created some scenarios for PlanIQ & everything is working well. Output file is getting generated as per different algorithm etc.   Just wanted to know, what kind of calculation is being performed for these algorithm - I mean what is the calculation matrix for Anaplan AutoML vs Amazon AutoML. Is there a way to find it out? For the same dataset, I'm having different forecast which is expected because I have used different algorithm but how the calculation is happening basis on quantile etc. & how to identify the best forecast    One more thing, I have 2 past & 2 future years, however the time interval is only reflecting for 8 months (MAX). Is there a way to increase that?   Thanks BB     ... View more

Hi,   I am facing the mentioned error while setting up Forecast Actions. I checked the data types in both the source and target module, they are the same. Still getting the error.    Please let me know what can be the reason for this error. ... View more

Some of you might wonder, how can PlanIQ leverage related datasets that are external to company's data, for example custom holidays, demographic data, or projected industry growth. This article explains how such datasets can be added to your related data module and leveraged by PlanIQ. We will use Kaggle's Favorita dataset which is an open source dataset with sample of retail data from the Corporación Favorita Grocery chain. Setting up the dataset for PlanIQ ingestion The dataset contains, among other things, a file with daily oil price and it can be assumed that Ecuador's e conomical health is dependent to shocks in oil prices. You will note that this file has the time dimension and oil price, but does not have the item / SKU dimension that is required by related dataset in PlanIQ. This is the format of the file with oil prices: Our related data module is dimensionalized by time and SKU-store combination. In order to be able to use the old index data, we need to make sure that Anaplan does not require SKU-store as a dimension for the oil price. As you import the oil price data, you don't need to provide mapping to SKU-store combination. You will notice that this method not only allows you to skip creation of SKU-store combinations but also saves a great deal of workspace: Let us know what types of data you would like to leverage for your forecasts.   ... View more

This article  is meant to provide guidance that helps PlanIQ practitioners understand how the data in the "data collection" in PlanIQ is used in the training and running of a forecast model. This article also explains how PlanIQ users can change the data source for existing forecast models.    What is the "data collection"? Data collection is exactly what the name implies - a collection of data. Today, data collections reference data that is hosted in Anaplan and pulled into PlanIQ via their associated export actions. When data collections are created, PlanIQ exports the relevant data sets from Anaplan and analyzes them. This analysis allows PlanIQ to make sure that the data is properly formatted, to look for any possible issues or errors in the data, and to identify which algorithms and horizons can be supported for the given data. This process eliminates a significant chunk of manual data prep time that is required for more technical intelligent forecasting tools.  This checkpoint allows PlanIQ users to make sure everything is in order before running a forecast. If the process finds any issues, the user can always update the data in Anaplan and re-run data assembly of the data collection to resolve the problems.    What happens with the data when the "forecast model" is being created? When forecast models are created, PlanIQ is training the algorithms based on the historical data. During this process, PlanIQ pulls the latest data from the data collection. This means that even if the data collection was created some time ago, PlanIQ will always train its models on the most up to date actuals.  A tip for building PlanIQ into your planning process: If you want to perform a series of rolling forecasts, it's better to train all the models from the same "point in time" of the rolling forecast.   What happens with the data when the forecast action is run? When forecast actions are run, PlanIQ pulls the latest data from the data collection. So, as new actuals are added to the source model in Anaplan, the forecast will be generated using that data.  As part of the forecast action execution, PlanIQ compares its previous forecasts with new actuals to make sure that model accuracy stays consistent. You will receive a warning message when you run the forecast action if PlanIQ sees a degradation in the PlanIQ model quality metric (see Anapedia article on PlanIQ metrics).   What if I want to train a new forecast model? There might be several reasons to train a new forecast model, including:  A long time has passed since the previous model has been used and you want to retrain the model with the latest data. The engine noticed changes in forecast quality and you want to re-train the model to review and update the quality metrics. You want to use a new data collection for forecast and therefore need to retrain the model with the new data. To retrain the model, you must create a new version of the model. This can easily be done by duplicating the forecast model in the PlanIQ interface. PlanIQ will pre-populate all the configuration options so you only need to provide a new name for the model. If you'd like to change the data source for the model, you can point it to a different data collection. Once your new model has been trained, if your forecast action is already operationalized and/or scheduled, you can edit it and point it to the new forecast model.  Voila! ... View more

I wanted to share a few ideas on how to visualize forecasts.  I'd encourage everybody to not discount the value of visualization in any analysis.  How we present the data and how we allow users to interact with it will be critical in driving better decision-making.     Here are a few ways I've built PlanIQ visualizations into Anaplan apps to help users better understand what the data is telling us.   I hope this inspires you to do the same!     ... View more