Forecast Like a Futurist: The Top 10 Demand Forecasting Methods You Need to Know
Demand Forecasting is the basis of any supply chain planning process. Hence, it is important that correct demand forecasting methods are used to attain maximum accuracy as per business requirements.
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Key Takeaways
- A comprehensive highlight on demand forecasting methods.
- The necessity of forecasting for a business.
- The results forecasting bears.
- Common steps to undergo any demand forecast.
Demand forecasting is one of the most essential prerequisites for any supply chain planner. This forecast gives an idea of the possible demand trends in the future. It is not just guessing what future sales for any product or service might be but using scientific and objective methods to estimate the trends.
It hence helps planners devise a plan that aligns the resources, finances, and teams toward meeting the forecasted demand while maintaining the optimum inventory and minimizing costs. While predicting trends, it is important to ensure that the correct technique is being used.
Methods of Demand Forecasting in Supply Chain
Demand forecasting methods greatly impact the accuracy of predictions made for future trends. They help you make well-informed business decisions that drive profitability and productivity. There are many different methods of forecasting used by businesses that are explained further.
1. Statistical Method
When it comes to demand forecasting methods, the statistical method is the most commonly used approach, as it can provide accurate predictions based on historical data. Statistical methods come in many forms, some of them include Time series analysis, Regression analysis, ARIMA (Autoregressive Integrated Moving Average), and Exponential smoothing.
The choice of method depends on the nature of the data and the business problem. A combination of these may also provide a more robust forecast.
2. Survey Method
Conducting surveys is one of the most promising demand forecasting methods that involve gathering data directly from consumers, customers, or market participants to make predictions about future demand for a product or service. This can be done through various means, such as telephone calls, online, focus groups, or in-person interviews.
The data collected through surveys can provide valuable insights into consumers' opinions, attitudes, and buying behavior, which can be used to make informed predictions about future demand. The forecast accuracy will depend on the size and representativeness of the sample, the quality of the survey questions, and the ability to generalize the findings to the larger population.
3. Delphi Method
The Delphi method is a group-consensus-based demand forecasting method. It involves collecting predictions and opinions from a panel of experts and then using an iterative process to reach a consensus forecast. It involves identifying a panel of experts, collecting initial projections from each expert, compiling, and aggregating the initial estimates, providing expert feedback on the overall results, collecting updated forecasts from the experts, and creating a consensus demand forecast.
The Delphi method is helpful for complex forecasting problems where a single expert's forecast may need to be more reliable. It can help reduce the influence of personal biases and improve the accuracy of the final predictions.
4. Barometric Method
The barometric method of demand forecasting is a technique that predicts the future trend for a product or service based on an analysis of external factors such as economic indicators, market trends, and industry-specific variables. It assumes that consumers’ expectation for a product or service is closely related to changes in these external factors. It involves:
- Collect data on relevant indicators.
- Creating a model to establish the relationship between indicators and demand.
- Using the model to forecast future demand.
The accuracy of the forecast will depend on the quality of the data and the interdependence between external factors and demand, just as is the case with some other demand forecasting methods.
5. Econometric Method
The econometric approach is a statistical demand forecasting method to predict future sales for a product or service based on past sales data and relevant economic and market factors. It uses regression analysis and other statistical tools to establish relationships between demand and independent variables such as economic indicators, market trends, and other relevant variables.
The model is then used to predict future trends based on expected changes in the independent variables. The econometric method is considered highly reliable and accurate, especially for products or services with a long history of sales. However, it requires a large amount of data and a good understanding of statistical techniques to implement them effectively.
6. Expert Opinion Method
The expert opinion method of demand forecasting involves gathering opinions and insights from individuals with expertise in a particular product or market to make predictions about future demand. It typically relies on experts in a given field's experience, knowledge, and judgment, who can provide insights into market trends, consumer behavior, and other relevant factors.
Expert opinions can be collected through interviews, surveys, or by consulting industry publications and reports. This method can be useful when data is limited or consumer behavior changes rapidly. Still, it can also be subject to bias and error if the experts consulted do not have a broad or accurate understanding of the market. The forecast's accuracy will depend on the expertise of the individuals consulted and the validity of their assumptions.
7. Trend Projection Method
The trend projection method of demand forecasting is a statistical technique used to forecast sales based on past trends. It assumes that the demand will continue in the same direction as the past trend, with the same or a similar rate of growth or decline. This involves fitting a trend line to historical data and extrapolating the line into the future to estimate the trends.
Trend projection techniques use different mathematical models to fit the trend line to the data to minimize the difference between predicted and realized values. The trend projection method is best suited for forecasting demand in stable, predictable environments where past trends are expected to continue. However, it is important to consider external factors such as changes in the market, competitors, or government regulations that may impact demand and adjust the trend projections accordingly.
8. Sales Force Opinion Method
The Sales Force Opinion method of demand forecasting involves gathering forecasts from the company's sales force, such as sales representatives and managers. These individuals are in close contact with customers and have direct insight into current and future customer demand for a product or service. The sales force's opinions are combined and analyzed to create a forecast.
It can provide valuable information and help to identify regional, customer-specific, and product-specific trends. However, it may also be subject to biases and personal opinions, so it is often used in conjunction with other methods to increase accuracy.
9. Market Test Method
The Market Test method of demand forecasting involves conducting a limited-scale launch of a new product or service in a specific geographic area or customer segment to gather info. The data collected from the market test is then used to forecast the product or service in a larger-scale launch. This is advantageous when launching a new product or entering a new market.
This is one of the most prominent demand forecasting methods as it provides real-world information on customer demand, which can be difficult to estimate using other demand forecasting techniques. However, it can be expensive and time-consuming, and results may not represent trends for other regions. This is often used with other forecasting methods to increase accuracy.
10. Machine Learning Method
The demand forecasting method with machine learning involves statistical algorithms and models to analyze historical data and predict demand for a product or service. These models leverage large amounts of data for modern businesses to create accurate forecasts. Some popular ML methods for demand forecasting include Time series, Artificial Neural Networks, Decision trees, and Random Forest.
They can provide highly accurate forecasts, especially when combined with domain-specific knowledge and human insights. However, it is essential to carefully validate and test the accuracy of these models before implementing them in decision-making processes.
Additional Demand Forecasting Methods in Supply Chain
Here are some additional demand forecasting methods that can strengthen your forecast even further:
1. Simulation Methods
Simulation methods use computer generated models to simulate and test real-life hypothetical scenarios that may affect demand.
2. Seasonal Decomposition
Under the seasonal decomposition method, seasonal variations are analyzed after breaking down the demand into seasons, trends, and other components.
3. Casual Inferences
The casual inference method helps in establishing a cause-effect relationship between the demand and its impacting factors like ads, promotional campaigns, economic behaviors, and others to ascertain the impact of these factors.
4. Collaborative Planning, Forecasting, and Replenishment (CPFR)
Within CPFR different collaborative parties like suppliers, partners, and other stakeholders come together and share information to enhance forecasts.
5. Predictive Sales Analytics Method
The Predictive Sales Analytics Method helps in understanding customer behavior in different conditions for better demand estimates.
6. Forecasting with Purpose
The Predictive Sales Analytics Method helps in understanding customer behavior in different conditions for better demand estimates.
Finding the Right Demand Forecasting Methods for Your Supply Chain
Choosing the right demand forecasting method(s) can be tricky and overwhelming for businesses. Selecting the perfect forecasting method depends on factors including data availability, demand volatility, product life cycle, end-goals, seasonal patterns, and multiple others. Moreover, different forecasting methods are suitable for different scenarios.
For instance, the trend projection method will bore the maximum results in data-rich and stable business environments, whereas the Delphi method is perfect for consensus in complex situations. Hence, using a hybrid set that combines multiple forecasts can help you get ahead of this distinction and get the most accurate and reliable results for your business. By following this, you can easily outthink the competition with smarter strategies and lead the curve — not just follow it.
How can 3SC Help you Increase the Accuracy of Your Demand Forecast?
3SC's Intelligent Supply Chain Planning platform can help improve forecast accuracy by using different demand forecasting methods that leverage the power of advanced analytics, artificial intelligence, and machine learning algorithms. The platform can analyze various sources, such as historical sales data, market trends, consumer behavior patterns, and more, to generate accurate forecasts.
All you have to do is simply connect with us to know more!
Top FAQs on Demand Forecasting Methods -
You may have some questions related to demand forecasting and its methods. You can refer to the frequently asked questions below to find answers to your questions.
1. What is Short-Term and Long-Term Demand Forecasting?
Short-term forecasting is concerned with forecasting demands for a shorter period (a few weeks or a couple of months), to enhance the regular operations of the business. Whereas long-term forecasting addresses the forecasting of demands for a longer period (a quarter to several years) to plan investments or strategies and to broaden business capabilities.
2. What is Active and Passive Demand Forecasting?
Active demand forecasting uses real-time data to forecast demand while the passive demand forecasting method relies on already-established past data for forecasting.
3. What are the Three Types of Demand Forecasting?
Demand forecasting methods can be broadly divided into three categories: Qualitative, Quantitative, and Hybrid. The hybrid approach is a combination of both qualitative and quantitative approaches to forecasting demand.
4. What are the Five Basic Steps to Demand Forecasting?
Demand forecasting of any type includes five basic steps of analysis of past data, considering market trends, utilizing advanced options of analytics, monitoring third-party factors, and engaging with customers.
5. What are the Advantages of Demand Forecasting?
Demand forecasting offers immense advantages to the business including better decision-making, cash flow management, inventory management, production planning, customer satisfaction, and overall business planning paired with reduced costs and overall risks.
6. What are the Challenges Commonly Faced by Businesses During Demand Forecasting?
The most common challenges faced by a business during demand forecasting include data quality concerns, unpredictable demand volatility, shorter product life cycles, complicated product mix, supply chain disruptions, and many others.
7. What is the Best Approach to Demand Forecasting?
The best approach to demand forecasting is to use predictive analytics techniques to identify trends, setting up a machine learning structure that can adapt these trends, and finally deploying a generative AI and digital twins' system to run what if scenarios and comprehend their impacts.
