How long would your demand forecasting solution last?

Your demand forecasting technology may end up being ad hoc and expensive unless it is underpinned by elements of a robust data architecture — data lakes, data pipelines, and automated ML pipelines.

In our experience with demand forecasting and demand planning solutions, we find that the following three are the pillars of the underlying data architecture: Do you have these working well in your business?

Three pillars of a robust Application Architecture

1. Data Lake, which consolidates all your data into a single data repository, across all your business units and enterprise databases, creates a single source of data that is strategic to all analytics, BI, and AI/ML intelligence.
2. Data Pipelines: Data pipelines automate the extraction of data reliably. Once the selected data enters the pipeline, it is pre-processed, cleaned, and treated to make it suitable for creating forecasting models, before it is placed in the Data Lake. Data pipelines are critical to providing high-quality data to forecasting models.
3. Auto ML Pipelines: Data placed in the data lakes is available to train machine learning (ML) models, and the sequence of steps executed is often referred to as ML Pipelines. We have automated this process. This means that, based on the data patterns, models are automatically selected, trained, validated, and tested. These models are saved and used to generate forecasts. Now, as the external conditions change, the accuracy of the forecasting models takes a dip. Therefore, if we find an error rate breaching a certain threshold, e.g., WMAPE is more than 30%, the system raises an alert, and models are retrained with new data.

AutoML pipelines keep the forecasting algorithms relevant and accurate even as market conditions and consumer preferences change.