Demand Forecasting: From Spreadsheets to AI-Driven Sensing
Explore the spectrum of demand forecasting approaches — from simple statistical methods to AI-driven sensing — and how accuracy affects supply chain performance.
Every supply chain decision is an implicit bet on future demand. The quality of your forecasting approach determines how often you win that bet — and how costly the losses are when you get it wrong.
The Options
Statistical Forecasting
Using historical sales data and quantitative methods — moving averages, exponential smoothing, ARIMA models — to project future demand. This is the baseline for most organisations, well understood and easy to audit. Its weakness is that it is inherently backward-looking and struggles with new products, promotions or market discontinuities.
Collaborative Forecasting
Incorporating inputs from sales, marketing, customers and suppliers into a structured process (often called Sales & Operations Planning, or S&OP). This blends quantitative signals with qualitative intelligence and tends to outperform pure statistical models, particularly at the SKU level. It requires organisational discipline and cross-functional trust to work well.
AI-Driven Demand Sensing
Using machine learning models that incorporate a wide range of signals — point-of-sale data, social media trends, search data, weather, economic indicators — to generate near-real-time demand forecasts. These models can detect demand shifts days or weeks earlier than traditional approaches. The investment in data infrastructure and capability is significant, but the accuracy gains in complex, high-SKU environments can be substantial.
Why It Matters in Practice
Forecast accuracy has a direct and measurable impact on inventory levels, service rates and operational costs. A 10% improvement in forecast accuracy can reduce safety stock requirements by a similar proportion, releasing working capital whilst maintaining or improving service levels. Organisations that invest in forecasting capability consistently outperform peers on inventory turns and order fill rates.
The right approach depends on data availability, product complexity and the cost of forecast error in your specific context. Many organisations operate on a maturity curve — starting with statistical methods and progressively incorporating collaborative and sensing capabilities.
In the Simulation
In SPPIN Sim, your forecasting approach affects forecast accuracy, which in turn influences your inventory efficiency and service level KPIs each turn. Investing in AI-driven sensing reduces your exposure to demand-side shock events and improves your inventory cost score, but requires a higher capability investment that affects your cost base in the early turns of the simulation.