A demand forecasting model turns a client-facing business need into a measurable prediction task. Its value depends not only on accuracy, but also on acceptable error, run time, and delivery constraints. section 12.1.2
Choosing an acceptable trade-off
Clients accept forecasts within a ±5% error margin. Rather than optimize accuracy indefinitely, the team compares a computationally intensive deep learning approach with a faster gradient boosting algorithm, measuring both run time and performance across datasets. section 12.1.2
A practical forecasting discussion should:
- Start with the business problem instead of prescribing a technical solution. section 12.1.2
- Define a business-driven benchmark such as a 5% error margin. section 12.1.2
- Compare accuracy and run time against the needs of clients and the product. section 12.1.2
- Leave room for data-science creativity while making trade-offs explicit. section 12.1.2
