Explaining Simplistic Forecasts to Management

Recall this kind of thing coming up many times.   We addressed it by installing a clickable pop up to anticipate the question and make the case for the forecast.    Agree, its about selling the forecast,in context to management.  Good thoughts on the issue below from SAS:

How do I explain a flat-line forecast to senior management?   By Charlie Chase   SAS

How do you explain flat-line forecasts to senior management? Or, do you just make manual overrides to adjust the forecast?   

When there is no detectable trend or seasonality associated with your demand history, or something has disrupted the trend and/or seasonality, simple time series methods (i.e. naïve and simple exponential smoothing) will often generate a flat-line forecast reflecting the current demand level. Because a flat-line is often an unlikely reflection of the future, delivering a flat-line forecast to management may require explanation. And sometimes, explaining is not enough.

Today, we have large scale automatic hierarchical statistical forecasting systems to automatically build statistical models up/down a business hierarchy for hundreds of thousands, and in some cases millions, of data series. As you add more historical data, and causal factors (i.e., price, promotions, advertising, in-store merchandizing, economic data and others), the system re-diagnoses this information and rebuilds (tweaks) the models automatically. They also automatically identify and correct for outliers and other anomalies in the demand history.

The ability to use stacked neural network (NN) plus time series models have proven to be the best forecasting method according to the recent M4 competition. Stacked NN + time series ensemble models are just another statistical method that can be used along with traditional methods (e.g. naïve, exponential smoothing, ARIMA, ARIMAX, dynamic regression, unobserved components models, weighted combined models and others).

We all know how hard it is to beat a naïve model over time. As a result, naïve models are now the benchmark for evaluating forecasts. If your forecast can’t beat a naïve model, then why are you spending so much time developing and adjusting (manual overrides) statistical forecasts?
Subsequently, we all know that not all products are forecastable using statistical methods because of sparse data, randomness, lack of historical demand data, and no access to causal information. However, it’s not just a matter of forecast accuracy, but also whether you can sell the forecast to senior management. ..... "