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Glossary

Demand Forecasting

Also called: Sales Forecasting, Demand Planning
Demand forecasting is the process of predicting future customer demand for products based on historical sales data, open orders, market trends and other relevant factors, in order to guide purchasing, production and inventory planning decisions. Accurate demand forecasts help manufacturers order the right materials and schedule the right production capacity ahead of actual need, reducing both shortages and excess inventory. 

Quick facts

Category Predictive planning for materials and production
Used by Manufacturing, medical devices, pharmaceuticals, aerospace, automotive and other production-based industries
Also called None widely standardized
Related standards None specific
Related processes MRP, purchasing software, production scheduling, sales management
Semantic match demand forecasting, sales forecasting, demand planning, forecast-driven purchasing

What is Demand Forecasting?

Demand forecasting estimates how much of a product customers will need in the future, using historical sales patterns, existing open orders, seasonal trends and other available data. This forecast becomes a key input to decisions about what materials to purchase and how much production capacity to plan.

Forecasts can be built at different levels of detail, from a single product to broader product families, and can incorporate varying degrees of sophistication, from simple historical averages to more advanced statistical or machine-learning-based models that account for seasonality and trend shifts.

When integrated with material requirements planning, an updated demand forecast can automatically recalculate material needs, allowing purchasing teams to generate purchase orders with accurate quantities and timing already reflected.

Why is Demand Forecasting important?

Without demand forecasting, purchasing and production decisions rely on reactive ordering, responding to shortages only after they occur, which often leads to expensive rush orders and expedited shipping.

Accurate forecasts help manufacturers avoid over-ordering, which ties up working capital and increases carrying costs, while also reducing the risk of stockouts that delay production and disappoint customers.

Because forecasting feeds directly into purchasing timelines, it also supports better supplier negotiations, since planned, non-urgent orders typically secure better pricing than last-minute, expedited purchases.

How does Demand Forecasting work?

A typical demand forecasting process includes:

  1. Data collection. Gather historical sales data, open orders and relevant market information.
  2. Forecast generation. Apply a forecasting method, from simple trend analysis to statistical models.
  3. Review. Validate forecasts against sales team input and known upcoming changes.
  4. Integration. Feed the forecast into MRP to calculate material and production requirements.
  5. Monitoring. Track actual demand against forecast and adjust future forecasts accordingly.

Demand Forecasting vs. Material Requirements Planning (MRP)

Comparison Demand Forecasting MRP
Purpose Predict future customer demand Calculate material and production needs based on demand
Primary input Historical sales, trends, open orders Demand forecast, BOM, inventory levels
Output An estimate of future demand Specific purchase and production recommendations

Real-world examples of Demand Forecasting

A manufacturer uses historical sales data and seasonal patterns to forecast demand for a product line, feeding that forecast into MRP to generate purchase orders with appropriate lead time rather than reacting to demand as it arrives.

An automotive supplier adjusts its demand forecast after a customer signals an upcoming production ramp-up, allowing purchasing to secure materials before the increased demand actually hits.

An electronics company tracks forecast accuracy over time, refining its forecasting approach after noticing it consistently underestimated demand for a specific product category during certain months.

Regulations and standards related to Demand Forecasting

Demand forecasting is not a regulatory requirement in any quality management standard, but forecast accuracy indirectly supports broader supply chain reliability that regulated manufacturers depend on to avoid shortages affecting production commitments.

How QT9 helps with Demand Forecasting

QT9 demand forecasting capabilities

  • Embed forecasting directly within the ERP engine so planning drives purchasing and scheduling in real time.
  • Automatically recalculate material requirements when a forecast is updated.
  • Analyze forecasted demand alongside current inventory and open purchase orders.
  • Reduce emergency orders and expedited shipping costs through proactive planning.
  • Track supplier performance based on forecast-driven purchase orders.
  • Adjust lead-time assumptions and planning recommendations based on historical accuracy.

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Common mistakes with Demand Forecasting

Common mistakes include relying solely on historical averages without accounting for known upcoming changes, such as a new customer contract or a discontinued product, that will make the past a poor predictor of the future.

Other problems include never comparing forecast accuracy to actual results, missing the opportunity to refine forecasting methods based on demonstrated performance over time.

Frequently asked questions

Demand forecasting predicts future customer demand. MRP, or material requirements planning, uses that forecast, along with existing inventory and bills of materials, to calculate exactly what materials need to be purchased or produced and when.
Common inputs include historical sales data, current open orders, seasonal patterns, market trends and, in more advanced approaches, statistical or machine-learning models that account for multiple factors simultaneously.
Accurate forecasting reduces the need for expensive emergency orders and expedited shipping, while also preventing over-ordering that ties up working capital in excess inventory and increases carrying costs.
Yes. Forecasts should be updated as new sales data, orders or market information becomes available, and many systems automatically recalculate downstream material requirements whenever the forecast changes.
It should. Purely historical forecasting methods can miss known upcoming changes, such as a new customer contract or a product being phased out, so forecasts are often adjusted with input from sales or customer-facing teams.
Forecast accuracy is typically measured by comparing forecasted demand to actual demand over time, helping organizations identify systematic biases and refine their forecasting approach accordingly.

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Last reviewed: July 21, 2026