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Company leaders face volatile markets and highly demanding consumers. So the ability to estimate current market demand has become a central factor in success of any company.
Previously, variations in demand were minimal, allowing for errors without serious consequences. Nowadays, fluctuations in demand are significant and unexpected, which translates into costly problems, which generate millions of dollars in losses.
In this article, we will explore in detail what data-based demand analysis involves, its importance in the current landscape, methods and tools available for calculating demand and how companies can gain a competitive advantage thanks to this information.
Demand forecasting focuses on predicting the quantity or value of products that consumers will purchase in the short, medium and long term.
It is based on the analysis of historical data, information from marketing campaigns, out of stock products and exogenous data such as economic indicators, holidays, weather, currencies and inflation, among others. Using this internal and external data allows for a broader and more accurate understanding of future demand.
This analysis is crucial for companies to optimize their purchasing strategies and their production, allowing them to acquiring and manufacturing the right quantities of products avoiding excess inventory that affects the company's cash flow or shortages that could result in lost sales.
Having an optimal balance sheet maximizes profitability, ensuring efficient resource management in companies that seek to anticipate market needs strategically and based on data.
An accurate demand forecast is essential, as it enables companies to formulate strategies for maximize profits while meeting the needs of their consumers.
Being able to adapt quickly to market changes has become an indispensable skill, however, not everyone is ready to do so.
Accuracy in estimation not only does it prevent losses, but it also opens up opportunities of growth. For this reason, organizations that manage to take advantage of their historical data and include environmental variables in their planning, are positioned one step ahead in competitiveness.
But, to accurately review demand, it is important that companies have advanced forecasting tools and not simple calculations in Excel that lead to errors in the forecast. Technology offers a lot of alternatives that will facilitate decisions in your company.
It is common for companies to confuse what demand forecasting is with demand estimation, so in this article an important point is to help you understand the differences.
In the following table you will have a clear visibility of what the main differences are:
Demand estimationDemand Forecast/DefinitionAnalysis and quantification of current demand. Prediction of future demand.Temporary FrameworkRetrospective, focused on past demand. Prospective, anticipating future demand.PurposeUnderstand existing demand patterns. Prepare for future market conditions.Methods and DataHistorical data analysis. Advanced forecasting techniques, neural networks, machine learning.ScopeCloser view of current market conditions. Broader approach, focused on future market changes.FocusStill photo that focuses on current demand-Dynamic model, focused on predicting future trends.Difference between Demand Forecast and Demand Estimation
When an analysis is required, it is always crucial to consider the key variables, to achieve more reliable results. In this case, when we approach a measurement of demand, we cannot ignore the factors that influence both supply and demand same.
Many times, when carrying out a demand analysis, we fall into the error of not considering the factors that influence the supply of that product or service. This is of great importance, because then you can understand the Quantity of that good that is in the market and their variations.
For this, the following factors are relevant:
To assess the demand for a product or service in the market, it is necessary to understand the factors that influence people to decide to purchase it.
These are some of the most used methods for forecasting demand
It uses traditional statistical techniques, such as regression analysis, to analyze historical sales data, considering factors such as price, revenue and other variables that influence demand. It is useful for identifying relationships between variables and estimating future demand based on historical patterns.
They directly engage consumers through questionnaires, interviews, or focus groups to gather information about their preferences, purchase intentions, and perceptions. These surveys provide qualitative and quantitative insights into consumer behavior.
It focuses on analyzing demand patterns over time to identify trends, seasonal variations and cyclical changes. This method helps predict future demand based on historical data.
Complex statistical models that combine economic theory with statistical methods to estimate and forecast demand. They integrate multiple variables to provide a more complete understanding of demand relationships.
Controlled experiments in specific markets or test groups that allow us to observe and measure changes in demand due to changes in prices, promotions or other marketing strategies.
The large amount of data has driven the use of machine learning algorithms and neural networks to analyze data and improve demand forecasts. Their ability to handle complex information is key to dynamically anticipate consumer trends and to efficiently optimize market strategies.
This method of Deep Learning with AI is the model we use at Datup to help demand planning teams to have forecasts with more than 95% accuracy.
These are the 8 different types of demand that we can find in the Supply Chain:
It refers to consumer demand for final products, which is not directly dependent on demand for other products. For example, consumer demand for cars.
It is the demand for components or raw materials needed to produce a final product. This demand is directly related to the demand for the final products. For example, demand for tires is dependent on demand for cars.
It combines several individual or dependent demands into a single forecast to simplify planning and replenishment. It is useful for macro-level management in the supply chain.
It varies depending on the time of year, holidays or seasons, affecting specific products. Companies must anticipate and adapt to these changes to meet demand efficiently.
It is characterized by being irregular or unpredictable, often due to unexpected external factors such as changes in market trends, economic events or natural disasters.
It occurs when companies produce or store products in anticipation of future demand, based on forecasts or historical trends.
It refers to fluctuations in demand that follow a cyclical pattern, typically related to the business cycle or long-term market trends.
It is created through promotions, discounts or sales incentives, temporarily increasing the demand for a product.
The concepts of Unrestricted and Restricted Demand are often overlooked and are essential when making decisions in the supply chain:
The unrestricted demand forecast predicts the demand for a product assuming zero operational or external limitations. This theoretical approach assesses maximum interest in the market at a given price, useful for identifying growth opportunities without considering supply or production barriers.
Instead, the restricted demand forecast considers real limitations such as productive capacity, logistics and exogenous factors (climate, inflation). This method anticipates the demand that can be met given these restrictions, essential for effective operational planning.
Artificial intelligence empowers demand planners because they have access to detailed analysis and accurate forecasts based on the processing of large volumes of data.
This ability to identify hidden trends and patterns allows planners to anticipate changes in demand, thus optimizing the organization's key performance indicators and improving inventory management.
In addition, AI enriches the decision-making process by enabling the simulation of diverse future scenarios, giving planners a powerful tool for evaluating the potential repercussions of different supply chain strategies.
This empowerment with relevant and up-to-date information ensures that decisions are well-informed, aligning operations with strategic objectives and effectively adapting to market changes.
Now that you know the factors that influence the demand for a product, let's address what they are The most used demand forecasting methods in companies, what they consist of and when it may be the most suitable for a particular need.
It uses traditional statistical techniques, such as regression analysis, to analyze historical sales data, considering factors such as price, income and other variables that influence demand. It is useful for identifying relationships between variables and estimating future demand for a product or service based on historical patterns.
They directly engage consumers through questionnaires, interviews, or focus groups to gather data about their preferences, buying intentions and perceptions. These surveys provide qualitative and quantitative insights into consumer behavior, which will be a great help when analyzing the demand for a product or service.
This method focuses on analyzing demand patterns over time based on data that allows us to identify trends, seasonal variations and cyclical changes. This method helps predict future demand based on historical data.
Complex statistical models that combine economic theory with statistical methods to estimate and forecast demand. They integrate multiple data variables to provide a fuller understanding of demand relationships from data.
Controlled experiments in specific markets or test groups that allow us to observe and measure the changes in demand due to price changes, promotions or other marketing strategies.
The large amount of data has driven the use of machine learning algorithms and neural networks, to analyze data and improve demand forecasts. Their ability to handle complex information is key to dynamically anticipate consumer trends and to efficiently optimize market strategies.
This method of Deep Learning with AI is the model we use at Datup to help demand planning teams to have forecasts with more than 95% accuracy.
Now that you know about methods for forecasting demand and the factors that can influence it, it's time to address the types of demand we can find in the supply chain.
It refers to the demand for final products on the part of consumers, who are not directly affected or dependent on the demand for other products. For example, consumer demand for cars.
It's the demand for components or raw materials necessary to produce a final product. This demand is directly related to the demand for the final products. For example, demand for tires is dependent on demand for cars.
Combine data from several individual or dependent demands in a single forecast to simplify planning and replenishment. It is useful for macro-level management in the supply chain.
For example, a furniture company sells X quantity of products to end consumers, X quantity of furniture to companies and exports X furniture. The aggregate demand would be the sum of the sales result of each channel.
It is a key type of demand, because it allows us identify variations depending on the time of year, holidays or seasons, affecting specific products. Companies must anticipate and adapt to these changes to meet demand efficiently. For example, the sale of fritters at Christmas.
It is characterized by being irregular or unpredictable, often due to unexpected external factors such as changes in market trends, economic events or natural disasters. For example, the use of masks required during the pandemic.
It occurs when companies produce or store products in anticipation of future demand, based on forecasts or historical trends, as for example, when the launch of the new X company cell phone model is going to take place.
It refers to the fluctuations of the demand for a product or service that follows a cyclical pattern, typically related to the business cycle or long-term market trends.
An example of cyclical demand would be: a luxury car brand observes a correlation between the business cycle and the sale of its vehicles. When there is a boom in the economy, there is greater confidence in the final consumer to invest in this type of vehicle. On the other hand, in recession environments, sales decline and consumers prefer to buy used vehicles.
It is created through promotions, sales discounts or incentives, temporarily increasing demand for a product, such as when food festivals take place and several restaurants meet to present a special dish at a promotional price.
So that you can better understand how the measurement of current demand is established in a company, we will see an example focusing on one type of demand, in this case, aggregate demand.
Remember that this refers to the total sales or orders that a company receives for its products in a given period of time, usually in a specific market.
You may also be interested in: How to Calculate Demand | Step by Step and Examples
Aggregate demand for a company is made up of the sum of all individual demands, such as consumer, business, government and foreign market demand who are willing to buy the products or services offered by that company. Let's look at a case below:
The company āFine Oak Furnitureā manufactures a wide variety of furniture, such as sofas, tables, chairs, wardrobes, etc. The aggregate demand for āFine Oak Furnitureā would be determined by the sum of all requests or purchases for your products by consumers, businesses and the government.
For a given month, āFine Oak Furnitureā receives the following orders:
Then, based on this data, we can calculate the aggregate demand for āFine Oak Furnitureā during that month:
Aggregate Demand = Consumer Orders + Business Orders + Government Orders + Exports
Aggregate demand = (500+300+200+100) + (100+50+200) + (50+50) +50 = 1550+350+100+50 = 2050 furniture units.
When applying the sum of the orders, we know that āFine Oak Furnitureā has an aggregate demand of 2050 units of furniture. With this data, the company will be able to make decisions about manufacturing, inventory and the supply chain.
The concepts of unrestricted and restricted demand are often overlooked and are essential when making decisions in the supply chain:
The unrestricted demand forecast predicts the demand for a product assuming zero operational or external limitations. This theoretical approach assesses maximum interest in the market at a given price, useful for identifying growth opportunities without considering supply or production barriers.
Instead, the restricted demand forecast considers real limitations such as productive capacity, logistics and exogenous factors (climate, inflation). This method anticipates the demand that can be met given these restrictions, essential for effective operational planning based on data.
Artificial intelligence andIt empowers demand planners because they have access to detailed analysis and accurate forecasts based on the processing of large volumes of data.
This ability to identify hidden trends and patterns allows planners to anticipate changes in demand, thus optimizing the organization's key performance indicators and improving inventory management.
In addition, AI enriches the decision-making process by enabling the simulation of diverse future scenarios, giving planners a powerful tool for evaluating the potential repercussions of different supply chain strategies.
This empowerment with relevant and up-to-date information ensures that decisions are well-informed, aligning operations with strategic objectives and effectively adapting to market changes.
In a volatile market, where adaptability is indispensable for any company, it is considered crucial to achieve the highest possible accuracy in the estimation of demand, due to the significant and frequent variability in consumption patterns.
It is clear that theerrors in predicting demand, however small they may seem, can be costly and generate millions of dollars in losses for a company. Therefore, demand forecasting must be based on the analysis of historical data, marketing information and external data such as economic indicators, in order to anticipate future market needs.
In this way, a company can optimize its purchasing and production strategies, avoiding both excess inventory and scarcity, and maximizing profitability and efficiency in resource management. On the other hand, you can formulate strategies that not only help you prevent losses but also that open up growth opportunities for your company.
The implementation of advanced data-based forecasting tools, such as machine learning algorithms and artificial intelligence, guarantees even greater accuracy in the estimation of demand, as is the case with DATUP, which offers forecasts of +95% accuracy. This allows companies to have a competitive advantage in an increasingly dynamic and challenging business environment.
