Business Owner's Guide to Demand Forecasting Methods & Steps

Modern companies collect large volumes of data from daily processes, such as inventory procurement and customer transactions across all sales channels.

Collectively, this data creates an enormous database filled with valuable information. However, without practicing demand forecasting, this insightful data just sits there and slowly begins to decay with time.

Demand forecasting software gathers and analyzes sales data to detect dependent variables and trends within specific markets. With this quantitative evidence, the solution can predict future outcomes, enabling businesses to prepare their processes for the ebbs and flows of customer demand.

Companies can use several types of demand forecasting, depending on their market, inventory, and customers. Therefore, organizations should consider how implanting demand forecasting solutions can improve their functionality and adaptability.

What is Demand Forecasting?

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Demand forecasting is the process of using historical and current data to predict fluctuating customer demand over a set period. A thorough demand forecast considers the market, sales, and inventory trends so businesses can improve their decision-making for various strategies, such as procurement and pricing products.

Without demand forecasting, companies remain unaware of emerging trends that can significantly impact their sales and profits. This can lead to poor inventory, marketing, and supply chain management decisions, often resulting in inflated expenses.

For example, a retailer that is unaware of the decreasing demand trend for a particular clothing brand may continue reordering stock based on previous sales. This leads to increased procurement, carrying, and storage fees, diminishing the profit margin. If this product becomes obsolete, they will be forced to run discounts to boost its turnover and access tied up cash flow.

Demand forecasting is essential for other business processes as well. By understanding potential sales patterns-

  • Businesses can optimize inventory to increase turnover rates and minimize storage expenses.
  • Managers can identify and resolve bottlenecks in the sales pipeline to ensure smooth workflow throughout the supply chain.
  • Supervisors can create accurate staff schedules to ensure that there are enough employees during busy seasons.

Types of Demand Forecasting

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Demand forecasting is not a blanket-solution, there are many different types of forecasting models that depend on the timespan and market scope businesses want to focus on.

Passive Demand Forecasting

Passive demand forecasting is typically used by businesses with a long history of stable income and steady expansion. This model uses past sales trends to predict future demand with little to no assumptions, as long as the company's record remains consistent.

The passive forecasting model is best for small to medium-sized businesses that want to focus on stability rather than rapid expansion.

Active Demand Forecasting

Active demand forecasting incorporates market research, campaigns, and expansion efforts into their economic predictions. This additional research factors in external elements that could affect cost savings and growth projections.

This model is for startups and companies in their growth phase that do not have much data to generate forecasts from.

Short-Term Demand Forecasting

Short-term demand forecasting makes projections 3 to 12 months into the future. Therefore, it is typically used to prepare the supply chain for upcoming demand fluctuations.

This narrow look into the future focuses less on past trends and more on real-time sales so businesses can make snap decisions based on current customer demand.

The short-term forecasting model is critical for unexpected and severe demand changes that need immediate attention.

Medium to Long-Term Demand Forecasting

Medium to long-term demand forecasting can make predictions from one to four years into the future. This makes it useful for improving a brand's longevity and growth trend.

By considering sales and market data, the long-term forecasting model helps companies enhance their-

  • Capacity planning
  • Marketing planning
  • Financial planning
  • Capital expenditure

External Macro Level Demand Forecasting

External macro forecasting focuses on the broader economy to interpret how large market trends will impact a business's goals. This helps stakeholders adapt their strategies to ensure they meet objectives.

This model can improve stability and expansion, as it considers both current and emerging market trends.

Internal Business Level Demand Forecasting

Internal business forecasting sheds light on businesses' limitations that could potentially reduce their growth, such as inventory capacity. It also defines strengths that companies can promote to counteract inefficiencies.

To do this, the internal business model gathers financial data, such as cash-on-hand, profit margins, operational expenses, and labor wages, to make accurate predictions.

Demand Forecasting Methods

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There are many methods of demand forecasting, most of which fall under two broad categories.

Qualitative Methods

Qualitative forecasting methods rely more on the experience and input of experts, employees, or customers to generate predictions rather than defined datasets.

  • The Delphi Technique
The Delphi Technique uses a board of experts to generate forecasts for a specific segment, based on their expertise. Each appointee explains their predictions so the other experts can consider the findings in their own projections.

With these notes, the experts improve their predictions through another round of forecasting. This process is repeated until the majority of the panel generates the same prediction.

  • Sales Force Opinion
With the Sales Force Opinion Method, sales managers simply ask for the input of their representatives on the expected demand.

Each salesperson assesses their department, personal sales, and region to predict customer demand. Once all employees give their input, management consolidates the information to generate a final demand forecast.

  • Market Research
The Market Research Method uses customer surveys to ask consumers directly what products they want. Most surveys ask for their personal, demographic, preference, and financial information.

Since questionaries are often given out randomly, companies need to focus on specific stores, regions, and demographics when developing the final demand forecasts.

Quantitative Methods

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On the other hand, quantitative forecasting methods only use data collected by management systems to generate accurate predictions.

  • Trend Projection Method
The Trend Projection Method only works for businesses that have between 18 and 24 months' worth of sales data, as it uses historical data to create a time series of trends.

By defining patterns, the projection method can continue the trend to determine probable future outcomes. However, this technique is only accurate under normal business conditions, as trends typically omit anomalies and unexpected changes.

  • Barometric Technique
The Barometric Technique focuses less on past data and more on current events to make predictions, by analyzing statistical and economic indicators. The typical statistical analyses used in this method include the leading, concurrent, and lagging series.

  • Econometric Forecasting Technique
The Econometric Forecasting Technique uses complicated algorithms to define relationships between different supply and demand variables. The equations use historical data to ensure its reliability and forecast accuracy.

Once the algorithm is finetuned and tailored to the specific business, variables are simply inserted to generate the forecast.

Factors that Impact the Customer Demand Life Cycle

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Demand forecasts need to consider a multitude of external factors, so companies can adapt their internal operations to fulfill customer demand. The influencing elements that businesses should focus on include-


Seasonality refers to the fluctuation in sales volume over a specific timeframe, typically seen during holidays and other busy shopping events. However, retailers often experience their own unique slow and busy seasons that depend on their type of inventory.

For example, office supplies stores often see an influx in sales during the late summer months due to back-to-school shopping.

Seasonal demand is often followed by a slow period, requiring businesses to reduce their inventory procurement to save storage costs. Otherwise, companies can lose much of their bottom line to maintaining excessive stock levels.


The higher the competition, the more options consumers have to choose from. Therefore, businesses need to evaluate their direct and indirect competitors to determine their likely demand. This ensures that companies aren't taken aback when new companies enter the market.

Types of Goods

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Demand forecasting looks vastly different between different types of inventory, as some products are perishable, making their lifespans relatively short.

By understanding the clientele's lifetime value, average order value (AOV), and buying behaviors, businesses can better understand how to bundle products to boost turnover rates.

For example, stores can bundle popular items with slow-moving products to drive demand and create recurring revenue.


Where a business's customers, manufacturers, warehouses, and fulfillment centers reside directly impacts how well they meet the demand. Facilities that are greatly distanced from each other can significantly affect lead, delivery, and wait times.

Companies must strategically choose their locations to reduce the risk of supply chain disruptions and streamline order fulfillment. Keeping locations close together also minimizes operational and logistical costs, such as manufacturing, shipping, and stocking.

4 Steps to Demand Forecasting

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Demand forecasting is a challenging process but is critical for maintaining profitability and ensuring future success. Therefore, organizations must understand how to generate their own forecasts in order to adequately adapt to changing trends.

1. Set Clear Objectives

First, businesses need to set short- and long-term goals. These objectives should predict what items customers will need and when. Based on these products, management should be able to determine what subset of customers they are targeting.

For example, a business can set a short-term goal for increasing flower sales during Valentine's Day. This venture would include targeting people 18 years and older who are in a relationship.

By understanding the products, timeline, and buyer personas, companies can optimize their budgets and capacity planning.

2. Gather and Record Data

It is then time to collect all of the relevant sales data from each channel to determine the current demand. For businesses that have an automated point-of-sale (POS) system, managers can simply run a POS report that shows-

  • Time and date of orders
  • Units ordered
  • Product returns
  • Orders per sales channel
  • Sales trends

Managers should cross-examine this information with previous demand forecasts to determine the accuracy of their model and algorithms.

3. Measure and Evaluate Data

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Now, analysts need to assess the data to generate forecasts. This can be an expensive, time-consuming task for businesses that still perform manual calculations.

However, companies can streamline this process by implementing demand forecasting software. Automated forecasting software uses predictive analysis to compare forecasted and actual sales. This machine learning technology enables the solution to continuously improve its algorithms, providing the most accurate forecasts.

Organizations can even integrate demand forecasting software with their other management solutions to update projections with real-time transactions.

4. Create a Budget

A company's forecasting capabilities become enhanced with each recorded transaction and prediction analysis. Once the forecast accuracy is up to company standards, management must update their budget plans to diversify funds for expansion efforts.

When planned accurately, demand forecasts can minimize inventory and labor costs and improve overall decision-making to expand the bottom line.