About timothybrennan

Hi @Sampriti.Anand ,   PlanIQ pricing is fixed, and based on the number of expected predictions that will be running each month. Determining the number of expected monthly predictions typically requires a discussion with a customer to better understand the desired outcomes of the forecasting process. To oversimplify... If a customer needs a monthly forecast for 1000 SKUs across 300 different stores, they may need 300,000 forecasts per month.  With that said... 1. We're making an assumption that every SKU is sold in every store, and often times that isn't the case.  This is something you want to address in a conversation with the business to have a better understanding of the # of forecasts required. 2. Just because we may need forecasts at the SKU*Store level, that does not necessarily mean we are building our forecast models and making predictions at that level.  Sometimes the data is very sparse at a granular level and it makes more sense to build forecast models at a higher hierarchical level (i.e. build a forecast model that predicts Product Category x Store) and then disaggregate to the SKU.  Figuring out which level to build the forecast models at requires a deeper understanding of the data.  Knowing the level is important, because PlanIQ pricing is based on the number of predictions forecast models make, not the final level a forecast ends up at in the planning process. 3. Most of the time we're also building multiple forecast models to make predictions, so we can then determine which model is best at predicting any individual SKU (or category).  At the extreme case, if we want to test all 5 models offered in PlanIQ (ARIMA, ETS, Prophet, DeepAR, CNN), we'd be looking at 1.5m predictions per month (1000 SKUs * 300 stores * 5 models). 4. You also want to think about building in a prediction buffer for testing out models.  Don't try to back into # of monthly predictions by only accounting for what happens during scheduled production runs.  A big part of the value of PlanIQ is your ability to make better predictions by testing different models on your own (it can be a fun exercise too!).  Sometimes you don't know which features will add value to your forecast predictions until you test them out. Long winded answer but I hope that is helpful. Figuring out the # of monthly predictions required by a customer is a nuanced process.  You don't have to be exact, but hopefully considering some of the things I mentioned above helps get you a decent estimate. ... View more

Hi @ankit_cheeni ,   Thanks for the question.  One minor point of clarification around pricing before jumping into the crux of your question. PlanIQ pricing is based on estimated monthly usage patterns. Based on the estimate, the cost is then fixed, which is different than traditional usage based pricing that many cloud services leverage. You are correct that running 3 separate models (i.e. CNN, DeepAR, Prophet) would count as 3 distinct forecast runs whereas the AutoML options count as 1. We try to account for this during initial pricing conversations with customers.   In terms of how the algorithms run, yes, AutoML is a superset of all the other algorithms.  However, AutoML will only produce forecasts using the single model that is the "best" across the entire time series on average. This means that AutoML results are always derived from a single algorithm. For example, if Anaplan AutoML finds that the ETS model produces the best MASE, on average, across the entire dataset, AutoML will produce forecasts for each series using ETS (even if ARIMA or a different algorithm was the better model for a particular series). So with AutoML, you are not necessarily receiving a prediction for each individual time series that is based on the model that is the best for that series.   We recognize that in the majority of situations a user will want the best model for each individual series and you'll likely see some valuable enhancement coming to AutoML in the future.   With that said, I actually prefer running each algorithm separately in PlanIQ and storing the results in Anaplan. I can then use the flexibility of Anaplan to assess each model based on whichever evaluation metric, forecast horizon or lag period I prefer. This approach may require a bit more effort but the outcome can be incredibly valuable.   I hope this helps! ... View more