Lesson 2.2 — Traditional Forecasting vs AI Forecasting

Lesson 2.2 — Traditional Forecasting vs AI Forecasting

Traditional Forecasting Approaches

Historically, many organisations relied on:

• Historical averages
• Moving averages
• Manual planner judgement
• Spreadsheet models
• Static forecasting rules

These approaches often have limitations:

• Heavy reliance on historical assumptions
• Slow response to change
• Limited data processing capability
• Human bias
• Difficulty managing complex demand patterns

Traditional forecasting often assumes that future demand will behave similarly to the past.

Modern supply chains rarely behave this predictably.

AI Forecasting Approaches

AI forecasting systems use:

• Machine learning
• Predictive analytics
• Pattern recognition
• Automated model selection
• Continuous learning algorithms

AI systems analyse:

• Historical demand
• Promotions
• Weather data
• Economic indicators
• Social media trends
• Supplier performance
• Web traffic
• Customer behaviour
• Transport disruption data

Unlike static spreadsheets, AI systems continuously adapt and improve.

For example:
A traditional forecasting spreadsheet may simply average the previous 12 months of sales.

An AI forecasting engine may simultaneously analyse:

• Regional weather patterns
• School holidays
• Fuel prices
• Competitor promotions
• Historical buying behaviour
• Online search activity
• Supply disruption signals

This allows significantly more intelligent forecasting decisions.