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This guide was updated in December 2025.
In this article, we will discuss various qualitative and quantitative forecasting methods supply chain that can streamline your future predictions, inventory planning, and overall supply chain performance. Unfulfilled orders, lost revenue, missed opportunities, and dissatisfied customers – these supply chain-related discrepancies can be easily alleviated. But not when demand planners are buried in spreadsheets, pulling data, and correcting someone else’s errors.
Modern supply chain teams rely on forecasting methods not just to predict sales, but to protect service levels, working capital, and operational efficiency. The right method can reduce stockouts, improve inventory turns, and support better S&OP alignment.
Qualitative and Quantitative Forecasting Methods
Forecasting is important to planning the future of the business. There are two main types of forecasting methods i.e, Qualitative and quantitative forecasting methods. The Qualitative methods are based on expert opinions and intuition. They are useful when there’s little historical data. Examples include the Delphi method and market research.
Quantitative methods help with numerical data and statistical analysis. These forecasting methods use past data to predict future trends. These techniques include time series analysis and regression models. By using both methods of forecasting, businesses can make better decisions and improve their strategic planning.
In practice, most supply chain teams blend these approaches to build a unified forecast used across operations, finance, sales, and procurement. This reduces alignment gaps and eliminates “multiple versions of the truth” commonly found in spreadsheet-driven planning.
1. Quantitative Methods
There are 4 best practices for the quantitative forecasting approach:
- Naïve Forecasting:
Naïve Forecasting method, you simply consider the previous sales year’s data and use it to forecast future sales; e.g., if you sold 100 smartphones last sales season, then this season, your ideal sales goal is 100 smartphones.
Real-world note: In operational planning, naïve forecasting is often used as a baseline metric to compare against more advanced models. If a model can’t outperform naïve forecasting, planners know it’s not reliable. - Moving Average Method:
Consider the average of past sales periods and apply it to forecast upcoming periods; e.g., if the average sales of the last four sales periods is 140, then the coming period will be in that range as well.
Planning insight: Moving averages work well for stable, low-variability items. However, they lag behind trend changes and cannot account for seasonality — which is why most teams prefer exponential smoothing or multi-model forecasting for faster-moving or seasonal SKUs. - Exponential Smoothing Method:
Applies the weighted average method when looking at moving averages; e.g., if you are selling ice cream, you should weigh January–March differently than July–September.
Why planners like it: Exponential smoothing adapts more quickly to demand changes, making it ideal for short-term forecasting in volatile markets. - Trend Projection:
Trend Projection forecasting Method is all about predicting future trends based on market situations and your historical data. Companies with sufficient data on their past sales can efficiently utilize this method. For example, if you sell 200–300 stationery units during the new educational period, then you should maintain that inventory in your warehouses.
Operational example: Trend models are often combined with near-term demand sensing to avoid overreacting to one-time spikes or promotions.
2. Qualitative Methods
Qualitative methods also have a fourfold approach to efficient forecasting.
- Executive Opinion:
Executive Opinion forecasting methods, top-level executives come together to discuss the future of the company and its upcoming business period. For instance, the CEO, COO, and VPs of sales and marketing meet to discuss and decide where the company's sales are headed.
S&OP relevance: Executive opinion is a core component of consensus forecasting during the monthly S&OP cycle. - Delphi Method:
Delphi Method of forecasting is a trustworthy advisor in the industry that provides opinions about future movements, then another group of experts compiles and interprets the analysis to the decision-makers.
Practical usage: This method is common in long-range planning, new product forecasting, or when historical data is limited. - Salesforce Prediction:
Sales executives are the ones who work on the ground level of any thriving market, which is why their opinions are the most essential. In this method, sales teams gather their data and experience to project future sales opportunities.
Planning insight: Most organizations combine sales input with statistical forecasting to reduce optimism bias and improve forecast accuracy. - Customer Surveys: In the Customer Surveys method, businesses ask customers about their experience and valuable feedback about products and services; e.g., customers are provided with a survey form on new or existing products to observe their behavior towards the products.
Closer Look: Naïve Forecasting, Moving Average, and Exponential Smoothing
- Naive Forecasting
Naïve forecasting is an easy-to-implement approach that relies on your business’s historical data. This method utilizes your past year’s actual data as current period forecasting data. This way, you can quickly predict your future strategy based on your previous data. Due to its simplicity, it has various benefits such as being easy to implement, needing limited data, not being tricky for system integration, being an ideal technique for steady demand, and being appropriate for small businesses.
Although this method is crucial for many organizations, it has its own limitations. For instance, it does not provide real-time data, lacks accuracy, is challenging to predict seasonal changes, and gives a more reactive approach than proactive decision-making.
Planners often use naïve forecasting as a benchmark to test whether more advanced models (Holt-Winters, Croston’s, machine learning) actually improve accuracy. - Moving Average
Moving Average forecasting method is one of the most accessible practices for supply chain forecasting. It evaluates data points by creating an average series of subsets for complete data. The average is used to develop a prediction for the coming period and then reevaluated each month, quarter, or year. For example, if you begin your commercial activities at the start of Q1 and want to predict sales for Q4, you can pull the sales average of the past three quarters combined to calculate the next quarter’s sales projections.
The moving average method does not consider that recent data may be a better future benchmark and should be given more weight. It also does not reflect seasonality or major trends shifts. As a result, this forecasting method is best suited for inventory with low order volume.
Operational example: For high-volume, high-variability SKUs, planners often switch to exponential smoothing or multi-model AI forecasting to avoid lag and capture demand shifts earlier. - Exponential Smoothing
Exponential Smoothing technique works by separating the time series into several components. Exponential smoothing forecasting methods is a knowledgeable approach to supply chain management. This process uses weighted averages, assuming that past trends and events mirror the imminent future.
When it comes to comparing this method with other quantitative methods, it makes it easier to come up with data-driven predictions without analyzing multiple data sets. With the appropriate tools and expertise, this method can be easy to apply and ideal for short-term forecasting.
Practitioner note: Exponential smoothing forms the foundation for more advanced models like Holt-Winters and damped trend, commonly used in demand planning software for seasonality and trend-heavy items.
How Modern Supply Chain Teams Forecast Today
While the methods above are foundational, most planning teams no longer rely on a single technique. Today’s best-in-class operations use:
- Multi-model forecasting that tests 100–350+ algorithms to find the best fit for each SKU
- Weekly or daily reforecasting for volatile products
- Demand sensing using recent sales, open orders, and POS data
- Scenario planning (“What-If Mode”) to simulate supplier delays, demand shocks, and new product launches
- A single consensus forecast used across S&OP, finance, sales, and operations
These practices reduce forecast error, improve service, and lower working capital requirements.
Take the Next Step with TransImpact
TransImpact provides state-of-the-art demand forecasting software that gives you complete visibility into your future demand and optimal inventory control. Our demand planning and inventory management solutions deliver accurate reports, so you keep just the right amount of inventory at all times.
Schedule a demo or connect with our experts to learn more about our solutions.
