What Are the Different Kinds of Demand Forecasting?

While estimating things in the future seems complicated, demand forecasting software can conveniently predict the future accurately by considering specific aspects.  

Predicting future demand has become crucial for businesses to avoid incurring losses due to out-of-stock products. Several demand forecasting methods have been used in the past. Companies have multiple choices ranging from traditional forecasting methods to AI/ML-based forecasting through demand forecasting tools. If you want to know which one is suitable for your business, you can read on and review the limits, uses, and benefits of the different processes.  

Before getting into the details, let us understand the fundamentals of demand forecasting. 

What is Demand Forecasting?

Demand forecasting estimates and comprehends consumer demand for each product to make informed decisions about profit margins, capacity planning, supply chain operations, capital expenditures, etc. It assists businesses in predicting the total sales and income for a specified time in the future by getting insights from the past sales data. 

Demand forecasting tools can help businesses prevent overstocking and understocking. Demand forecasting tells you what present and future customers would want to buy, and therefore manufacturing units know what products they should produce and in what quantity. A correct estimate of demand is needed to produce goods in the right quantities. Producing little may lead to stock shortages and losing loyal customers. On the other hand, maintaining an additional stock of products can charge extra money.  

Demand forecasting is used to optimize the short-term supply chain and maximize revenue. It can predict product demand, production requirements, staff requirements, energy utilization, revenue, and web traffic.   

Types of Demand Forecasting

The demand forecasting methods can be divided based on the data they use. Quantitative forecasting methods rely on statistics, machine learning models that depend on complex data, and qualitative forecasting that uses market research and customer opinion. 

Different types of demand forecasting can be used based on:

  • Various stages of the product life cycle.
  • The requirements and abilities of your company, whether you are looking for a quick and affordable solution or need greater accuracy and can bear the necessary expense.
  • The required length of forecasting or how long you want to estimate the demand. Is it short-term demand (0-3 months), long-term demand (more than six months), or mid-term demand (3-6 months)?  

Qualitative Analysis

This method depends on the experience and expertise of stakeholders, experts, and the general public, and quantitative estimates are generated. Qualitative analysis can be comparative analyses, market research, or consultation with experts, and qualitative analysis may not always consider historical data while predicting demand. It is a valuable technique for market testing, product development, and places where R&D demand estimation is complex and newly launched products with little historical data.

Most demand forecasting methods use quantitative data, and implementing qualitative methods is costlier than quantitative methods. Moreover, expert opinions may be biased, and specific methods take months for implementation, such as Market Research and Delphi method, whereas quantitative methods can be applied within a day.

Statistical Methods

This method relies on historical data from approximately two years to predict demand. This method can detect seasonal variation, trends, and growth rates. The statistical time series method uses past data to generate an average. Complex processes use weighted data as recent data is more effective than past data, and these methods can be implemented quickly at a low cost. Still, the accuracy level is lower than those that are costly and time-consuming. As a result, statistical time series is influential for well-established products, has a stable demand, predicts the entire demand compared to the demand for individual items, and for mid to long-term forecasting.

This method follows predictable patterns and can fail to estimate the market saturation of a particular item and random fluctuations in demand, and sudden changes in customer behavior. 

Causal Models

Causal forecasting takes into consideration the relationship between demand and other variables. The causal method can consider various data sources with data sources such as social media activity, weather, product features, surveys, and competitor pricing. 

Causal models are best for generating medium to long-term forecasts for individual products and product categories, and multiple factors influence demand. The causal approach can identify changepoints as it can ingest several data sources. Any event that influences changes in demand can be detected, and it could be anything ranging from product replacements to price changes. 

Two causal models are:

  • The regression model that defines the connection between two variables and 
  • The econometric model considers the relation between internal sales data and external economic variables. 

These methods can be easily implemented, are more precise than the traditional qualitative and statistical analysis methods, and are inexpensive.

AI and Machine Learning Time Series

Time-series methods based on AI/ML use historical sales data to generate predictions like the statistical approach, but ML adds several benefits. You can discover trends and patterns that would not be recognized manually. These models are constantly learning and result in forecasts that can be altered in real-time with changes in market conditions. AI-based demand forecasting software can consider multiple variables, ranging from price changes to weather patterns and promotional offers.  

AutoML Approaches

The demand for various products varies greatly, and therefore a forecasting method that works for one may not work for another. In case of predictability doesn’t work with a single method, you can train your demand forecasting software with multiple methods and select one that yields accurate results. The machine learning ensemble approach is suited for products with little historical data, fast-changing environments, and short to mid-term forecasts. 

Forecasting is an integral part of a business, and different methods might be suitable for different stages in the business lifecycle. You must choose the right demand forecasting software and method. The AI-powered demand forecasting tool brings maximum accuracy, adjustment to changing situations, and ease and speed of implementation. 

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