Demand Forecasting

In supply chain management, it is a foundational function that directly influences inventory planning, production scheduling, capacity planning, and procurement. Accurate demand forecasts enable businesses to optimize stock levels, reduce costs, and improve service levels.

Concept Explanation (Theory)

Demand forecasting involves projecting future demand by analyzing patterns such as seasonality, trends, cyclical behaviors, and external factors like promotions or economic shifts. There are two primary types of forecasting methods:

1. Qualitative Forecasting – Used when historical data is scarce or unreliable, relying on expert judgment, market research, or analogical reasoning.
2. Quantitative Forecasting – Based on numerical data and statistical methods. Common models include:
   • Time Series Models (e.g., Moving Averages, Exponential Smoothing, ARIMA)
   • Causal Models (e.g., Linear Regression, econometric models)
   • Machine Learning Models for complex patterns or large datasets

Forecast accuracy is critical. Over-forecasting leads to excess inventory and high holding costs; under-forecasting results in stockouts and lost sales.

Operationalization

In practical terms, demand forecasting integrates with multiple supply chain functions:

• Procurement: Suppliers are aligned with expected demand, minimizing stockouts and overstock.
• Production Planning: Manufacturing schedules match forecasted demand, avoiding underutilization or bottlenecks.
• Distribution: Logistics capacity is optimized by anticipating shipment volumes and delivery timelines.
• Sales & Operations Planning (S&OP): Forecasts align cross-functional goals, balancing demand with supply capabilities.

A forecast is typically disaggregated by product SKU, location, and time period. It should be continuously monitored and refined using forecast error metrics like Mean Absolute Percentage Error (MAPE) or Root Mean Squared Error (RMSE).

Leveraging ChatGPT for Demand Forecasting

ChatGPT can significantly accelerate demand forecasting by aiding in model selection, formula writing, and data interpretation. Use prompts like:

General Prompt

"Suggest a suitable demand forecasting model for a seasonal product with three years of monthly sales data. What steps should I follow?"

Structured Prompt

"Given this monthly sales data for Product X [paste data], generate an Excel formula or model using exponential smoothing. Also, suggest how to calculate and interpret forecast accuracy using MAPE."

Advanced Prompt

"Create a demand forecasting Excel model for 20 SKUs across 5 regions, incorporating trend and seasonality, and output confidence intervals."

Final Thoughts & Business Reflection

Demand forecasting is not just a statistical exercise—it is a strategic discipline that shapes the agility and resilience of the supply chain. By aligning forecasts with operational plans, businesses can reduce waste, improve responsiveness, and enhance customer satisfaction. The key is to adopt a structured, data-driven approach, continuously refine models, and ensure cross-functional alignment.

💡How accurately are your current forecasts driving operational decisions—and what would be the impact of a 10% improvement in forecast accuracy across your supply chain?