Life Cycle Method

This method is useful for retailers wanting to account for price elasticities and markdowns. This is a causal approach and goes through multiple stages to generate the final forecast. Typically, the data requirement for this method is more than one year of sales, inventory, receipts, and prices. This is among the best methods in AIF that can produce reliable forecasts at the lowest levels of merchandise and location hierarchies accounting for prices and markdowns but with a larger breadth of data needs.

The stages for Life Cycle method:

  1. Preprocessing filters bad data points that represent unusual sales behavior, low inventory, or irregular prices so that the demand forecast does not replicate undesirable patterns. The filtering of historical data defines a subset of data to produce reliable demand parameters for the next process steps.

  2. Event Lift Estimation involves heavy data mining of the relationships between historical sales and events. This estimates the traffic lift during promotional or holiday events. The estimation of traffic lifts occurs at different Escalation levels which are aggregated sales at different merchandise and location hierarchy levels.

  3. Elasticity Estimation is generated from calculations of historical demand sensitivity based on markdown or promotional prices changes. The estimation of elasticity occurs at different Escalation levels which are aggregated sales at different merchandise and location hierarchy levels.

  4. Seasonality Estimation utilizes various machine learning techniques to understand the time series in which data experiences regular and predictable changes that recur every calendar year. This reflects a sales pattern that repeats based on the time of year (season). The estimation of seasonality occurs at different Escalation levels which are aggregated sales at different merchandise and location hierarchy levels.

  5. Base Demand Estimation calculates the average demand signal after the filtering of data, accounting for traffic lifts, and removal of elasticity and seasonality effects. This is generated at the same merchandise and location hierarchy level as the required forecast.

  6. Forecast Generation calculates the demand forecast combining the output of all previous steps. This forecast multiplies base demand, seasonality, elasticity, and traffic lifts. In this manner, changes in seasonality, elasticity, and events can be evaluated and combined with the de-seasonalized base demand.