How to Forecast Demand | Step-by-Step Guide with Examples

If your goal is to grow profitably, it's important that you know how to calculate demand in an appropriate way for your company.

The demand calculation allows you to understand how your market will behave, so that based on this data you can align your strategic areas and adjust your operations, from procurement and suppliers to distribution to your customers.

Here we'll share some key things to keep in mind about how to project demand for products or services.

What is the calculation of demand in a company?

The demand calculation process Analyze the number of products or services that consumers are willing to buy in the different combinations or hierarchies of your company, which may include different locations, channels, prices and other market conditions related to your company.

Why is it important to calculate demand?

By collecting information about the behavior of your consumers and calculating the demand for a product or service, your company will be able to know the needs and expectations of its customer, understand the competition, identify high and low seasons, and understand what situations affect the sales of a specific product.

Types of Demand

At the economic level, there are different types of demand in the market, and here are the most important ones:

• Direct demand: refers to the simplest type of demand where you identify how many people are planning to buy a product.
• Indirect demand: is related to products that are necessary for the manufacture of others. If demand for the final product increases, the one used for its manufacture will also be affected.
• Latent demand: in this case, it is not possible to meet the demand for a product or service because the consumer is unaware of the existence of the solution, cannot afford it or the product is simply not available.
• Joint Demand: If two products complement each other or are directly related, they affect your demand.
• Irregular demand: This varies unpredictably over time, whether due to fashion trends, events or seasonal factors.
• Negative Demand: presents itself with products or services that its potential consumers tend to avoid.
• Full demand: is when demand is equal to or greater than the production or supply capacity of a product or service.
• Over-demand: It occurs when demand is greater than the supply available in a market.
• Excessive demand: In this one, demand is also higher, but its level is not healthy or sustainable for society.

Information to consider in a demand analysis

To make a demand projection, there are multiple variables to consider, since clear and reliable results are sought. In order to achieve this, it is necessary to also consider the offer.

Therefore, we recommend that you consider the following information:

Factors affecting supply

The key information about the offer consists of 4 aspects, which your company must always be very clear about in order to achieve sustainable growth:

• Price: The price factor is fundamental because if it increases, the supply of the product or service will also increase, because producers will be more willing to participate in the market.
• Competitive or joint offer: Two particular cases are presented here, where on the one hand, if a competitor changes the manufacture of one product to another, the one that has been replaced will become less profitable. In the case of a joint offer, it occurs when the price increase in one product affects another.
• Cost of production: If production costs increase, supply is reduced, since manufacturing will be less profitable.
• Variation in resource availability: when a product that serves as a raw material becomes scarce, it will become more difficult to produce, so supply decreases.

Factors affecting demand

When it comes to factors that impact the demand for a product or service, there are a number of aspects that all companies must take into consideration, such as:

• Purchasing power of the target audience: it is clear that not all people have the same level of income, nor the ability to buy any type of products or services. This aspect must be well identified in your company.
• Preferences and tastes of your audience: a company must understand the needs of its consumers, this brings you closer to them.
• Prices for complementary or similar products: The demand for your product will be affected by the prices of products related to yours, whether they are substitutes or complementary. This is because consumers tend to evaluate the environment.
• Volume of potential customers: identify if your company has niche products or if, on the contrary, your product is for mass consumption.

How to calculate demand step by step

Then we will share with you The steps you must follow to calculate demand of a product or service, in order to understand the quantity of these that consumers would be willing to purchase and thus, better understand the market to achieve profitable growth.

1. Data Collection

Being able to define what your data sources are and how you will access them in order to process them is the first step when you are preparing to analyze the demand for a product.

Historical Data:

Examine your historical demand for understand patterns and trends, the amount of time to consider will depend on the technique you want to use.

Remember that demand is equivalent to the ratio between orders received and products invoiced. If you're not doing Proper monitoring of your exhausted people is important so you can find a way to capture this information for a more accurate calculation.

Generally, the basis for calculating the demand for products or services can be found in the ERP (Enterprise Resource Planning).

Other Internal Data Sources:

The acceleration of the digital transformation process has led companies to acquire new tools for their sales and supply chain, such as WMS (Warehouse Management System), MRP (Material Requirements Planning), TMS (Transportation Management System), CRM (Customer Relationship Management), among others.

Debes consider the tools that can help you to have a much more accurate calculation of the demand for products or services, depending on how you define your process.

External Data Sources:

It is becoming increasingly important for companies to be able to identify which external data sources may impact the behavior of their market, in order to obtain more accurate projections. It's key verify that your source of information is stable and reliable to be able to integrate it into the demand calculation process.

If your industry requires it, you can carry out market research such as surveys, interviews or analysis of secondary data related to your products or services to obtain information about market or consumer preferences and behaviors.

The more variables and data sources you want to integrate, the more robust your information processing method should be, as they are machine learning and deep learning models to be able to obtain valuable insights in the analysis of complex data and a more reliable demand projection.

Keep in mind that the quality of the data and your sources will depend on the quality of the results you obtain.

2. Identifying Variables

Defining the variables relevant to your company will help you establish mechanisms that contribute to improving accuracy when calculating demand for products or services.

• Price: Are you sure if price changes affect the quantity of products in demand? How volatile is the price behavior of your raw materials and how does it affect the price of your products?
• Revenue: Have you evaluated whether there is a relationship between consumer income and demand for your products?
• Tastes and Preferences: Is your demand sensitive to changes in consumer preferences? How can you get this information?
• External Factors: Do external variables such as the economy, politics, demographics and market trends affect the behavior of your demand? What are the most representative variables in your industry?

What other variable can affect the demand for your products or services and is considered representative in your industry?

3. Select the Demand Planning Model:

There are multiple models for projecting your demand and selecting the right model is essential in companies that want to achieve better results.

Have you noticed that the same product may behave differently depending on how you analyze it? That is, if you look at the behavior of the same product in different locations, channels or combinations, each of these may have a different behavior.

Some will have more stable behavior and others will be more variable, which is why you we recommend not relying on a single demand forecasting model for your products or services.

Causal Methods

Causal methods are techniques used by companies to project demand for products or services by identifying and analyzing the causes that influence that demand.

• Linear Regression Models: statistical techniques are based on to identify relationships between variables.

y = α + beta x

For example, imagine that you are going to predict demand for ice cream (y) based on temperature (x). Assuming that you have collected data and want to calculate linear regression.

• y = The dependent variable you want to predict, how many ice creams will I sell?
• x = The independent variable, in this case it will be temperature
• α = Source value, which represents the value of sales when the temperature is equal to zero.
• Β = Slope or variation of the independent variable, which indicates how much ice cream sales change for each unit change in temperature.

y (Quantity of Ice Cream to Sell) = α (50) + ß (2) * x (Temperature)

This means that, depending on the model, for each additional degree of temperature, ice cream sales are expected to increase by 2 units.

• Multiple Regression Models: involves multiple independent variables. Multiple regression is useful when you need to predict a dependent variable (such as ice cream sales) based on two or more independent variables (such as price, advertising and temperature).
  The multiple regression equation can be expressed as follows:
  

y = α + β1 x1 + β2 x2 + β3 x3

Based on the previous example, demand for ice cream (y) will be predicted based on price (x1), advertising (x2) and temperature (x3). Assuming that you have collected data and want to calculate the linear regression.

y = α + β1 (Price) + β2 (Advertising) + β3 (Temperature)

y (Ice Cream Demand) = α (1,000) — (50 Price) + (20 Advertising) + (2 * Temperature)

This means that, based on our model, sales are expected to decrease by 50 units for each increase in price, to increase by 20 units for each additional unit of investment in advertising, and to increase by 2 units for each additional degree of temperature.

Temporary Series

This is a series of data that represents observations collected sequentially, usually at uniform intervals.

• Simple Moving Average: statistical method used to analyze temporal data and smooth out short-term fluctuations in order to identify long-term trends or patterns in a time series.
  Projecting demand with a simple moving average requires calculating the average demand for a product in successive time intervals, with the following equation:
  

y = X (DemandI)

Where:

y = Ice Cream Demand Projection and is equivalent to the estimate of demand for the next period.

N = Number of periods included in the moving average.

Demand = Sum of sales in the period i.

For the case of the monthly ice cream demand projection, a simple 3-month average will be made.

y = Ventast-3 + Ventast-2 + Ventast
3

y (Ice Cream Demand Projection) = Demand January+ Demand February+ Demand March 3

y = 50 + 30 + 60 = 46.6 Average of 47 Ice-Creams/Month
3

The simple moving average is useful for smoothing seasonal fluctuations and highlighting medium-term trends in product demand or sales. You must adjust the value of N depending on the quality of the data and the frequency of the fluctuations that you want to smooth out.

• Exponential Smoothing: It is similar to the moving average, but weights are assigned to historical data to give greater relevance to the most recent information.

Ft+1 = α Yt + (1−α) Ft

• Ft+1 = Estimated demand for the next period.
• Yt = Demand in the current period.
• Ft = Demand estimate for the current period
• α = Exponential smoothing factor. It determines how quickly you react to new observations, with a value between 0 and 1.

When α is close to 1, more weight is given to the most recent data, causing the model to react quickly to changes in demand. On the other hand, when α is close to 0, the model tends to be more stable and less sensitive to recent variations. Here's an example:

If Yt = 100 Ice Cream and Ft = 90 Ice Cream for the current month, and if α = 0.2 is defined to give more weight to recent values, the calculation would be:

Ft+1 = α (0.2) Yt (100) + (1−α (0.2)) Ft (90) = 20 + 0.8 * 90 = 20 + 72 = 92

Ft+1 = 92 Ice creams are projected for the next period.

• Time Series Decomposition: is used to break down a time series into its fundamental components such as trend, seasonality and error in order to obtain more accurate projections.

Yt = Tt + St + Et

• Yt = Demand in the period T.
• Tt = Represents the direction of demand over time. (Ascending, Decreasing, Constant)
• St = Reflects repetitive patterns in the short term.
• Et = Variation between trend and seasonality.

The following case will serve as an example of calculating demand with time series decomposition:

• Yt = 100 Ice Creams for Time T.
• Tt = 90 Ice creams as a long-term trend.
• St = 20 Ice creams equivalent to a seasonal pattern.

Et = Yt — Tt — St = 100 — 90 -20 = -10 Ice creams that represent the residual error.

• Collaborative Forecasting: seeks collaboration between different stakeholders, such as internal areas, business partners, suppliers or even strategic customers, to generate more accurate forecasts based on different sources of information.
  In internal areas, it allows us to align the vision of various strategic areas within the supply chain, such as sales, marketing, operations and finance, including information such as negotiations, supplier restrictions, marketing campaigns, and other factors.
  

Let's look at an example below that provides us with the following data:

1. Marketing suggests that upcoming promotions may increase demand by 10%.
2. Sales provides information on market trends and expects an increase of 5% due to the Christmas season.
3. Production identifies restrictions in the supply chain that could negatively affect sales, estimating a 3% reduction.

Collaborative Forecasting = Base Forecasting + Marketing Input + Sales Input + Operations Input

If we start from a base forecast of 1,000 ice creams and taking into account the contributions of each department, we can calculate:

Collaborative Forecasting = 1,000 Ice Cream + (1,000 0.1) + (1,000 0.05) — (1,000 * 0.03) = 1,120 Ice Cream

With business partners or strategic clients, it is possible to include sellout, negotiations, participation in marketing campaigns, and others.

It allows providers to anticipate or take into account restrictions they may have for meeting service levels.

This demand calculation allows us to take into account, for example, negotiations that are being carried out with customers, marketing campaigns, and other factors that may affect the area of operations and the levels of service to customers.

Artificial Intelligence Models

If you need to analyze large volumes of information and include complex variables, the ideal is to resort to the use of artificial intelligence models due to the speed and precision with which they can analyze large volumes of information to project likely demand scenarios.

There are several artificial intelligence methods for processing demand forecasts which may include supervised and unsupervised learning, such as: machine learning, neural networks, deep learning and generative artificial intelligence. Allowing access to new levels of advanced analytics for companies, such as predictive, prescriptive or cognitive models.

Some of the most used are machine learning models, which are carried out supervised learning so that from historical data they can learn from patterns and make future demand projections for companies.

4. Establishment of Scenarios

Establishing scenarios is a fundamental step in the process of calculating demand for products or services. Al analyze different hypothetical situations or potential changes, companies can anticipate and be prepared for various situations that could affect demand.

• Price Scenarios: analyzes how different price levels affect demand.
• Economic Scenarios: considers diverse economic situations and their impact on demand.
• Price Elasticity of Demand: measures the sensitivity of demand to changes in price.
• Promotions and Events Scenario: evaluate the impact of promotions, discounts, or other marketing events on demand to optimize business strategies.
• Seasonality Scenario: consider seasonal patterns to anticipate predictable increases or decreases in demand, such as during vacation seasons or specific events.
• Trend Change Scenario: analyzes and projects changes in demand trends, whether due to external factors, changes in consumer preference or innovations in the market.
• New Product Introduction Scenario: projects demand when launching new products to the market, considering consumer acceptance and competition.
• Supply Shortage Scenario: anticipate the impact of potential supply chain disruptions on demand and take preventive measures.
• Scenario of Changes in Government Policies: evaluates how changes in government policies, such as business or tax regulations, may affect demand.

Conclusions

The calculation of demand is a dynamic process that requires a deep understanding of the market and a constant adaptation to changes in key variables.

By using analytical tools and mathematical models, companies can project the demand for a product or service to make decisions based on data and thus, meet the changing needs of consumers and remain competitive in the market.

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