Demand Forecasting: Methods and Types

# Demand Forecasting: Methods and Types

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Jaya Sharma
Assistant Manager - Content
Updated on May 30, 2024 18:50 IST

While forecasting is crucial for every business, it is not full proof and has the scope of errors. Demand forecasting is useful for evaluating those factors that affect the demand to allow management understand factors that are important for demand behavior.

In this article, we will be discussing the types and methods of demand forecasting. We will also discuss the advantages of this concept.

## What is Demand Forecasting?

It is the process of making future estimations related to customer demand over a time period. Using the predictive analysis of historical data to estimate future demands of customers for products and services. Demand refers to the requirement of a product, and forecasting refers to the estimation for future events.

Through this technique, the probable demand for products and services is estimated for the future. Based on the analysis of past demands for products and services in the current market condition, demand forecasting is performed. Analysts gather information on different aspects of market and demand based on past for estimating future demand.

## Types of Forecasting of Demand

The following are the two main types:

### 1. Qualitative Demand forecasting

It is an approach for the prediction of future sales using immeasurable data, including intuition and opinions. This method is also known as the salesforce/grassroots forecast. It depends on the direct interaction of people with customers. Through this interaction, analysts can understand the requirements of customers with accuracy.

### 2. Quantitative Demand Forecasting

This is a data-based mathematical process in which the sales team assesses performance and predicts future revenue based on historical data. Through this type of forecasting, businesses can make informed decisions on strategies and processes. This method is objective in nature that focuses on subjective information such as insights and opinions. This method involves techniques such as the native method, seasonal indexing, historical growth rate, revenue run rate, and linear regression.

## Uses

Demand forecasting is mostly used in cases when there is minimum data available that can be analyzed. Let us consider the following use cases:

• Introducing a new product in the market.
• Making important supply decisions for optimizing business profitability.
• Assessing requirements related to the future capacity.
• Making decisions on entering a new market.
• Formulating pricing and advertising strategies.

### Advantages of Demand Forecasting

The following benefits can be achieved by forecasting demand:

• Reduction in risks related to business activities by making wise business decisions.
• Better business goal planning is possible through a forecast of demand.
• Helps in the appropriate process selection, production planning, capacity planning, inventory management and facility layout planning.
• Plays a critical role in functional areas of accounting.
• Provides relevant data that is useful in expansion-related decisions and capital investment.
• Helps in making wise financial decisions related to operating cost, inventory accounting, cash flow, profit margin, and resource allocation.
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### Methods for Forecasting Demand

To forecast a demand, statistical and econometric analysis is used. This method is completed in the following seven stages:

1. Statement of Hypothesis: In the first stage, analysts determine objectives to derive business strategies based on set of hypotheses. These hypotheses are derived from previous empirical studies and economic theory.
2. Choosing a Forecasting Model: Econometric models are chosen based on observations and the type of data obtained. At this stage, analysts define variable types that will be used in demand forecasting. For this purpose, they use regression analysis. Here, the analysis of the impact of multiple variables on dependent variables is measured.
3. Data Collection: After choosing a model, the data collection method and data are specified. In general, cross-section data and time series data are used by analysts. Time series data is based on historical observations that are taken sequentially in time to derive statistics and insights from data. Cross-section data is the data collected on single entity at different time periods that result in the final data point.
4. Estimating parameters: After collecting data, values are computed for determining the impact of independent variables on dependent variable in focus. For this purpose, analysts use a linear regression model.
5. Calculating demand forecast accuracy: This determines the accuracy of forecasts related to customer demands for a product. This is helpful for logistics management and supply chain management so that they can maintain inventory without any stock-outs. This accuracy check helps the team to be prepared for actual demand since it ensures an effective supply chain. In the supply chain, analysts use the Mean Absolute Percent Error (MAPE) for checking accuracy.
6. Testing the Hypothesis: The chosen model is now used for testing the hypotheses that were stated in the first stage. The expected results should describe what it aims to achieve and determine whether the hypothesis is true or false. The model should explain the relationship between the demand elasticity of market and its correlation to past sales. Based on this testing, analysts can make pricing and production quantity decisions for new products.
7. Forecasting demand: Here, demand is forecasted based on data set and chosen model. Analysts use the estimation of the chosen variable to understand its impact on demand. A regression model is used for qualitative as well as quantitative assessments.

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### Conclusion

The usefulness of demand forecasting varies according to the operated area of firm. Based on the time and cost incurred for acquiring information and reaping its benefits helps in determining the scope of forecasting demand.

## FAQs

Why is demand forecasting important?

Demand forecasting is crucial because it enables businesses to anticipate market needs, reduce holding costs, optimize inventory levels, improve customer satisfaction, and make informed strategic decisions.

How does seasonality affect demand forecasting?

Seasonality refers to regular and predictable fluctuations in demand based on the time of year. It affects demand forecasting by requiring adjustments to account for seasonal patterns, such as increased demand during holidays or specific seasons.

What is the role of technology in demand forecasting?

Technology plays a significant role by providing advanced tools as well as software for data collection, analysis, and modeling. Techniques like machine learning and artificial intelligence enhance accuracy and efficiency in forecasting.

What data is used in demand forecasting?

Data used in demand forecasting includes historical sales data, market trends, economic indicators, customer preferences, promotional activities, and external factors such as weather or political events.

How do businesses validate their demand forecasts?

Businesses validate demand forecasts by comparing predicted demand with actual sales data, conducting backtesting, using statistical metrics (e.g., mean absolute error, root mean square error), and regularly reviewing and adjusting forecasting models.