Vanguard Uses Artificial Intelligence Forecasting to select the forecast method that is most likely to produce the lowest prediction error over the near-term horizon.
Forecasting is a decision-making tool used by many businesses to help in budgeting, planning, and estimating future growth. In the simplest terms, forecasting is the attempt to predict future outcomes based on past events and management insight. Artificial intelligence forecasting is the automatic attempt to predict future outcomes with given past data and insight.
There are two forecast types: judgment-based (e.g. “gut feel”) and quantitative (e.g. statistics). The most trustworthy forecasts combine both methods to support their strengths and mitigate their weaknesses.
Judgement forecasting uses only our intuition and experience. Our minds are able to make connections and understand the context in a way that no computer can. However, we’re also prone to certain biases that make analyzing large amounts of data difficult. Judgment forecasting is best where there is little to no historical data- such as new product launches, competitor actions, or new growth plans.
Quantitative forecasting uses analytics to analyze large amounts of historical data in order to discern trends and patterns. Quantitative forecasting is excellent at churning through large amounts of data and is less prone to bias. However, it is weakest when there is little to no historical data that can be analyzed. Quantitative forecasting relies, more or less, on identifying repeated patterns in your data so it may take a while to see the same pattern repeat more than once. Combining judgment and quantitative forecasting gets the best results. An example of quantitative forecasting is the Time Series Forecast.
Forecasting Methods and Associated Parameters
Time Series Forecast records use historical data to develop a model for generating forecasts. This record type generates forecasts automatically by analyzing the data for trends and patterns and applying the best method.
The historical data used in this analysis can be any of the following:
- Manually entered data
- Data imported directly from a data source
- Data contained in other records
Time Series Forecast records can also contain some or all of the following sections:
- Bill of Materials
- Historical data
- Comparable Items
- Adjustments and Overrides
- Monte Carlo Simulation
With an AI-enabled tool, business planners (and others who plan) can automate forecast method selection, among other routing and non-routine decisions, and focus on improving business outcomes.