A Framework for Analyzing Forecast Accuracy Metrics

Abstract

Demand Planning forecasts at Nike, Inc. are used by many groups: Supply Planning/Materials Planning, Sourcing, Categories/Merchandising, Finance, S&OP, and Sales. These groups take forecasts as an input to make key decisions. Forecasts, by nature, will be inaccurate. There are two big unknowns to answer as Nike considers how to improve forecast accuracy: 1) how accurate can or should forecasts become (target setting) and 2) what are the causes and impacts of inaccuracy. However, the first step to addressing these questions is to understand and measure forecast accuracy metrics in a consistent way across Nike's various Demand Planning groups. This project investigates the following through the design of a Tableau dashboard * which metrics should be reviewed (accuracy, bias, volatility, etc.) * how they should be computed (what to compare) * at what level of aggregation for which groups * at what level of detail for which groups (category, classification, etc.) * over how many seasons * with which filters In addition to aligning on forecast accuracy metrics, the project also focuses on the dashboard design (determining the most appropriate structure/views, how information is laid out or presented, and the use of labels and color) and on setting the long-term vision for viewing and using forecast accuracy metrics through researching and outlining the process for root cause analysis and target setting.