Our content is reader-supported, which means that if you click on some of our links that we may earn a commission.     X

Demand Forecasting: Types, Methods, and Examples


Demand Forecasting is the process in which the historical sales data is used to develop an estimate of expected forecast of customer demand.

Demand Forecasting - Types, Methods, and Examples

Running a business is not a piece of cake. You have to know how every aspect of your business will turn out. There are a lot of calculations you have to get right from accounting to inventory management to financial projections. 

Every business manager should have an understanding of the demand for your products. Demand forecasting is one of the toughest metrics to get right because of the tendency of demand to fluctuate.

In this guide, you will learn the meaning of demand forecasting, the importance of demand forecasting, types of demand forecasting, demand forecasting methods, factors that influence the customer demand life cycle, how to forecast demand effectively, and examples of demand forecasting.

Let’s get started.

What is Demand Forecasting?

Demand forecasting is the use of historical sales data to predict the future demand for a product or service. It provides an estimate of the number of goods or services expected to be demanded by customers within a given period in the future. 

What current and future customers will want to buy is identified and purchase orders or manufacturing is optimized through this information.

Through demand forecasting, businesses also get to make informed decisions about their supply chain. 

Estimates of total sales and revenue in the future are the main results of demand forecasting. With these, decisions about inventory planning, future warehouse management needs, and sales become easier to make and more accurate.

Important estimations in running a business are also dependent on demand forecasting. These include inventory turnover, cash flow, profit margins, risk management, and capacity planning, among others.

Demand forecasting is an area of predictive analytics in business and deals with the optimization of the supply chain and overall inventory management. The past records of demand for a product are compared with current market trends to come to an accurate estimation.

Every company wants to be able to predict the amount of cost it has to bear to meet the demands of its customers. Demand forecasting is one of the methods of doing this.

What is Demand Forecasting
Source: Tutorstips

Importance of Demand Forecasting for Ecommerce Businesses 

Demand is undoubtedly one of the most important, flexible, and fragile factors that determine the success of a business. Forecasting your demand helps you a lot with running a business. Here are some of the benefits of demand forecasting.

Easier To Make Decision

Demand forecasting facilitates important management activities within a company. Decisions are easier to make and, for instance, performance evaluations are given enough context. 

Companies know how well the whole business, departments, or employees can cope with future expectations and make decisions accordingly.

Deciding how much resources are needed for future demands as well as whether a business is ready for expansion is also made easier. Companies have enough information to estimate and decide on financial and managerial needs for the future. 

Helps With Short and Long-Term Planning

Proper demand forecasting helps businesses to easily take care of important strategic plans for the future. 

Without knowledge of your demand, long-term business plans like budgeting, financial planning, and capacity planning, among others, are harder to create. These plans are also very much susceptible to inaccuracies and unproductivity.

Short and medium-term plans like contract creation and choosing a supplier are also difficult to make.

Demand forecasting gives businesses an idea of what to expect from customers within a period in the future. It helps managers set financial goals, create budgets, and allocate the company’s resources efficiently.

Reduces Cost

Proper knowledge of the expected future demand for goods and services enables businesses to avoid suffering massive losses or opportunity costs.

Costs of production, inventory purchase, and marketing are kept streamlined with estimated forecasts. With demand forecasting, profit margins are determined and financial resources are not overspent in a way that a profit margin is closed up.

Opportunity costs are also avoided. A company knows the opportunities for expansion or the potential for increased demand for goods in the future. Enough inventory is stocked in expectation for this demand and the amount of profit that would have been lost from a stock-out situation is saved.  

The staff required to take care of demand is easily determinable through demand forecasts. You ensure that you have enough manpower to deal with demand and excess wage is not paid to staff you don't need.

Pricing Strategy Is Easily Determined

The demand for a product determines the pricing strategy or the price you put on it for profit. 

Too much demand for a product without an adequate supply of it causes its price to increase. On the other hand, where the supply of a product becomes more than its demand, its price drops.

Demand forecasting takes this into account and determines the elasticity of demand as it relates to price. Prices are adequately determined according to future demands of goods. 

Businesses use demand forecasting to ensure that they do not place prices that are too high for customers and too low for them to generate profits.

Objectives of Demain Forecasting
Source: TheInvestorsBook

Types of Demand Forecasting

Demand forecasting is distinctly classified based on three different factors – the scope of the market considered (Macro and Micro-level demand forecasting), the number of details required (Passive and Active forecasting), and the length of time considered (Short-term and Long-term forecasting). 

1. Micro-Level Demand Forecasting

Micro-level demand forecasting involves estimations concerning the internal operations of a business. 

Demand forecasting at the microeconomic level is specific to a business and different segments of its internal operations. These segments may include particular product categories, customer groups, sales division, financial division, and other internal areas of business operations.

Micro-level demand forecasting also takes metrics like the cost of goods sold (COGS), cost of goods manufactured (COGM), net profit, and internal cash flow into consideration, among others.

2. Macro-Level Demand Forecasting

Macro-level demand forecasting deals with the broader macro-economic environment. It deals with external economic conditions and factors that affect a company's demand.

Some of the different factors considered with macro-level forecasting include general market research, customer preference change, inventory portfolio expansion, and other external macro-economic factors.

3. Passive Demand Forecasting

Passive demand forecasting is common with more stable internal and external economic environments. It involves and requires only historical data to predict future demand for goods and services.

With stable economic environments, past demand metrics can be directly used to predict future demand. Demand is expected to be the same as previous accounting periods, so other activities like trend analysis and crude statistical calculations are averted.

Passive demand forecasting is a rare but good model for businesses that aim for stability rather than growth.

4. Active Demand Forecasting

Active demand forecasting is used by startups or companies aiming for business growth and expansion. It involves extended marketing research, the study of trends, multiple calculations, assumptions, and plans for promotional campaigns and business expansion.

External factors are the main focus of active demand forecasting. Some of the factors that are typically considered include economic outlook, general market growth projections, and supply chain studies. 

Active demand forecasting is most especially important for startups that do not have historical data and are forced to rely on external factors.

5. Short-Term Demand Forecasting

Short-term demand forecasting is done with a period of 3 months to a year in mind. It considers the amount of demand that is expected within this short period. Short-term business decisions are made during this period.

6. Long-Term Demand Forecasting

Long-term demand forecasting deals with time lengths of between 12 months and possibly up to 4 years. It drives long-term business decisions regarding activities like financial planning, capital expenditure, and capacity investment planning, among a whole lot of others.

Types of Demand Forecasting
Source: Jelvix

Understanding Demand Forecasting Methods 

Before going on about demand forecasting, you need to know the different methods and which one is appropriate for you. 

Some of the most popular and crucial methods in demand forecasting include the Delphi technique, conjoint analysis, intent survey, trend projection method, and econometric forecasting. 

1. Delphi Technique

The Delphi method involves the use of a group of experts that provide their individual forecasts and justifications for their forecasts. 

Each forecast and explanation is then read out to other experts on the panel, with each of them influenced by the forecast of their counterparts. A subsequent forecast is then made by each expert with the new influenced knowledge and this process repeats itself until a consensus is reached.

A consensus exists when there is no significant difference between the forecasts of the different experts. 

The Delphi method is based on the idea that an individual cannot accurately or effectively predict future demands all on his or her own. When executed properly, the Delphi method is a very accurate technique of forecasting demands.

However, there are downsides to it. Apart from the need for highly knowledgeable experts on this panel to ensure accurate forecasts, the Delphi method is time-consuming.

2. Conjoint Analysis

The conjoint analysis involves the use of surveys to collect information about customer preferences as relating to a product. 

Surveys are typically in the form of questionnaires that seek preference information from customers. Consumers are asked about what they think of a particular product attribute and businesses make forecasts from their answers.

Information that surveys target to get from customers falls into personal, demographic, and economic information.

Conducting surveys helps a company to realize the most important selling point of their different products and services. The reasons why consumers choose a certain product over others is identified and a company gets to know which product or service feature consumers value the most.

Conjoint Analysis is a good demand forecasting method for products with no history. When a company wants to enter into another product category or increases its inventory portfolio, information about the preferred attributes allows it to start on the right track. 

Market preference and how consumers react to a product are collected and used accordingly.

3. Intent Survey

An intent survey aims to collect information about which product consumers are intending to buy in the future. This technique aims at understanding the factors that push a consumer to buy a product.

Intent surveys are usually conducted through the websites of companies and typically ask website visitors to rate their intent to buy a product on a scale of 0 – 10. 

Where intent is rated high, a company then decides on whether it should proceed to stock a product it was previously considering.

One point to note is that intent surveys only predict the likelihood of a product being purchased and not the actual consumer behavior. It is also better used to predict the purchase of existing products, durable products, and short-term forecasting periods.

4. Trend Projection Method

The trend projection method is effective for companies with large historical sales data. This sales data history typically spans more than 18 – 24 months.

A time series representing the past sales and demand for a particular product is then formulated. These different graphical trends are followed closely and used to determine the expected future demand for products. 

From the above, it is apparent that the trend projection method is only effective and feasible in generally stable economic environments. Uncertain environments usually do not have consistent graphical patterns over this long period and, therefore, are not effective to use.

5. Econometric Forecasting

Econometric forecasting involves the use of mathematical equations and various variables to come up with a demand forecast. It uses relationships among economic variables to forecast future developments.

Factors Influencing the Customer Demand Life Cycle

Demand forecasting is all about how the supply chain meets the demand for products. Numerous factors are influencing the customer demand life cycle such as seasonality, external competition, type of product, and geographical location.

1. Seasonality

Seasonality refers to the change in demand for products over a particular period. It involves the different periods and the volume of orders that are characteristic of them. 

A company that runs a highly seasonal business typically records highly distinct demand trends throughout the year. Demands are only received in a specific period or several limited periods of the year. Due to this, graphical demand trends show a spike in this period.

An example would be a company that manufactures and sells Christmas apparel. Demand for Christmas apparel is majorly received towards the end of the year, with a peak period in December.

Seasonality requires a company to optimize inventory storage following the expected demand trends. 

Inventory items and staff are kept very low during quiet periods while purchase orders, manufacturing activities, and inventory storage intensify towards periods of demand spikes.

2. External Competition

One unavoidable aspect of running a business is the competition for the attention of customers. The more competition you have in the market, the more options your potential consumers have to choose from other than you. 

With a lot of activities by external competitors to get the attention of consumers, the demand for your products will remain inconsistent and continuously dwindling. The effect of this factor is most especially noticeable when a new competitor comes into the market.

Competitor strategies largely affect how demand for a product shapes out to be and companies consider this while forecasting.

3. Type of Product 

The demand forecast of a product is different from the forecast for other products. Each product has its own market peculiarities and, therefore, should be given distinct attention.

Perishable goods have separate market characteristics as opposed to durable goods. Services that are paid for at the end of a monthly cycle are also different from services with spontaneous payment cycles.

Nonetheless, no matter what a product or service is, certain factors are crucial for consideration. These include the lifetime and purchase value of your customers for each product as well as the combination of products that are typically ordered.

Taking these into account helps you understand how you can group or bundle products and how the demand for one inventory item affects the demand for another.

4. Geographical Location

The location where you operate greatly determines both the demand for your products and how you meet up with demands. 

A lot of consumers prefer to buy items that can be immediately shipped to where they reside. Due to this, the location where your inventory is stored is very important. 

Order fulfillment centers can be placed at strategic locations that allow orders to be delivered quickly. You can also use reliable order fulfillment services to help you fulfill your products if your business does not have the resources to handle them.

With demand easily met and orders quickly fulfilled, consumers are encouraged to keep purchasing products.

Bad geographical locations greatly hinder the demand for products as well as the fulfillment of orders. Where your order fulfillment record is unsatisfactory, the number of customers and demand for products continuously decrease.

Factors Influencing Demand Forecasting
Source: eSwap

How to Forecast Demand Effectively?

Due to the flexible nature of demand, predicting future sales of a product is one of the most difficult tasks in economics. However, there are straightforward steps that businesses can follow to effectively predict future demands. 

Step 1. Establish A Plan

Every company runs in its peculiar business environment and has different economic factors specific to it. Demand forecasting activities must be in sync with the peculiarities of your own company for them to be effective.

A plan needs to be made according to your business goals and objectives. The period you want to consider, as well as the product and customer category you wish to focus on need to be established beforehand. 

Demand forecasting takes note of these factors to predict what your customers want, when they want it, and how much they want. It needs to fit your financial, marketing, operations, and logistics plans. It is important that these plans are established before proceeding with demand forecasting. 

You get to know which demand forecasting technique is best in achieving your goals and make appropriate decisions concerning it when you establish a plan.

Step 2. Compile And Record Data

After deciding on your business goals and the appropriate type of demand forecasting technique to be used, you then need to compile your historical and external analytics data.

Demand forecasting does not work without data. Even startup companies without historical data still need to make macro-level economic analyses to have enough information to work with. 

Historical sales data gives a great overview of how demand trends shape out to be in the future. Having knowledge of the usual time of demand spikes for a product, the number of stock-keeping units (SKUs) usually demanded, and the typical sales channel makes demand forecasting easier.

In stable business environments, internal historical data and trends are the only metrics required for accurate forecasts. General market data are, however, important key metrics for most companies and business types in very inconsistent business environments.

Step 3. Analyze Compiled Data

Demand forecasting does not end at just compiling internal and external economic data. Data still needs to be analyzed and converted to useful information.

Analyzing data can be done manually, using different economic equations and inferences. It can also be done with the use of automated software programs that are optimized for that exact purpose.

Your previous forecasts can be compared with the eventual demand and sales for that period under consideration to see areas for improvement. Variations between your prediction and actual occurrences help you measure the effects of miscalculations and opportunity costs suffered. 

For instance, a graphical spike in demand shows a company that demand for a product increases during that period. From this, the company has an idea of what it needs for that period to avoid stock-out situations. 

Of course, this spike could also be caused by other factors like the folding up of a competitor. Analyzing all the data compiled from sales history, internal operations, and the external general market environment helps you come up with the most appropriate forecast for the future.

Analyzing data helps you know how quickly products are selling and which items are slow-moving. It shows you how long your current inventory will take to run out, the profitability of each order, and where your customers from, among a whole lot of others. 

Data analysis for demand forecasts is relatively much easier in stable economic environments where trends are expected to always be the same as in previous periods. No complex calculations are required unlike in business environments with inconsistent variables.

Step 4. Create Your Budgets Accordingly

After making the appropriate analyses, it is then time to come up with a demand forecast. Demand forecasts are expected to follow the various inferences made from the study of historical data and the general market metrics.

You adjust your budget and other allocations to fill loopholes in previous forecasts and also take care of estimated future needs.

Hopefully, these inferences are accurate and comprehensive enough for demand forecasts to also be accurate. Accurate demand forecasts help you reduce overall inventory costs, optimize marketing strategies and costs, and maintain the appropriate number of staff to meet demand.

Examples of Demand Forecasting

Different organizations have different business objectives and plans for their future. While some may decide to pursue stable growth, some may pursue aggressive growth, while some may choose to maintain their current economic positions. 

Demand forecasting can be illustrated with the following examples.

Example 1

An online store checks out its sales trends from last year’s winter to prepare adequate inventory levels for the upcoming season. Sales of seasonal products like waterproof boots, winter gloves, scarves, and winter coats are looked at. Analyses show that there was a great seasonal sale for them. 

However, six months ago, a competing store opened close to it and, due to this, demand for products was expected to be skewed. However, at the same time, a lot of families continued to move into the neighborhood, and business growth remained at an average of 1% month-over-month since the competing store opened.

A plan to launch a few more promotional campaigns than last year is made and channels that have generated a good Return On Investment (ROI) are considered. Some new deals to position themselves as the go-to store are also proffered to customers. 

Projected forecasts for demand can be put at a 5% increase in sales from last year and budgets can be made accurately.

Example 2

A fast-growing direct-to-consumer (DTC) apparel brand starts off selling 10,000 units of inventory per month. Based on past sales data, upcoming promotional campaigns, and general market conditions in the industry, a plan to sell above 30,000 orders per month in the following year is then made.

Shortly after, a total of 30,000 inventory units were stocked up and at varying levels across their 5 different stock-keeping units (SKUs). The economic environment is considerably stable and order volume only fluctuates a bit based on their replenishment cycle. Inventory is also stocked at a rate of about every 90 days.

After reaching its 30,000 sales goal, a new plan to ship in another 50,000 units is then made based on historical sales data and the rate of demand is received.

With a long-term plan, the apparel brand plans to continuously grow at the same pace, so a longer projection of 75,000 units is made for the distant future. Other factors like the purchase of land, lease of a warehouse, or outsourcing of inventory fulfillment are also decided upon according to the projected demand.

Demand Forecasting FAQ

Why Is Demand Forecasting Important?

Demand forecasting helps the business make informed supply decisions that estimate the total sales and revenue for a future period. 

A huge part of a business’s operational strategy is based on demand forecasting. Through it, they can predict inventory turnover, profit margins, cash flow, product availability, and capital expenditure. 

Demand forecasting, for example, helps you to determine what products will have heavy traffic at a future date, like Christmas trees in the festive season.

How Do I Build A Demand Forecasting Model?

Building a demand forecasting model relies on many factors including the context of the forecast, the viability of available historical data, the degree of accuracy desirable, the period to be forecast, the benefits of the forecast to the company. 

Forecasting models can be generally differentiated into two groups based on whether they use qualitative or quantitative methods. 

Models such as a time series model or an econometric model will use quantitative methods because they need large amounts of data to predict future demand trends. 
On the other hand, qualitative research like the Delphi method or sales force composite will use human opinions where data is not available or applicable.

Quantitative methods are more data accurate but qualitative methods offer more flexibility. It can readily account for external factors like inflation and market competition, unlike the qualitative model.

Written by
Anastasia Belyh
Join our Free 7-Day Email Course for Beginners to
MAKE MONEY BLOGGING FAST
FREE 8,000 word email course. FREE WordPress blog launch checklist. FREE plan to $10k in 90 days.
JOIN NOW!
We respect your privacy. You can unsubscribe at any time.