The Importance of the Right Demand Forecasting Technique

Demand forecasting is a highly important business activity, and it’s imperative you choose the right technique to do so. Otherwise, no matter how good your data is, it won’t lead to an accurate forecast!

What Are Forecasting Techniques?

Forecasting techniques, or forecasting models, are methods that aim to accurately represent possible future outcomes for your business using the data available. This data could come from your business’s historical performance, the performance of other businesses in similar spaces, or from models themselves.

Types of Forecasting Models

There are many forecasting models out there, but these are the most common ones.

Quantitative Models

Regression Model: This model uses a statistical technique that creates a regression curve. This curve is based on an equation that minimizes the squared errors.

Econometric Model: The econometric model tests the relationships between variables such as sales, promotional campaign, and customers over time in order to form regression curves that are interdependent. This is a more comprehensive application of the regression model.

Time-Series Model: This model discovers patterns in historical data and extrapolate it into forecasts. To do this, it uses exponential smoothing, ARIMA, and trend analysis. However, it’s highly dependent on the quality of the historical data.

Qualitative Models

Delphi method: This method takes expert opinions from a questionnaire and turns them into numerical values, allowing them to be analyzed as quantitative data.

Components & Characteristics of Forecasting in a Supply Chain

Supply chain forecasting is a primary activity in supply chain planning. Demand forecasting is one of the most important business activities, as it will allow you to determine how to place orders and manage your entire supply chain for a given time period.

Without it, you can’t reliably do business.

So what does forecasting require in your supply chain? Here are some sources you could use in your forecasting model:

● Historical sales data for your business
● Planned advertising and marketing efforts
● Planned pricing discounts and rebates
● Macroeconomic factors
● Operating business scenario
● Competitive analysis
You may not want to use all of these in your model, as some of them may be more or less relevant to your supply chain.

In Summary

● Demand forecasting depends on having a proper model.
● The quality of your data is a primary consideration for demand forecasting.
● You can choose which data you want to include in your forecasting model.

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