Demand forecasting is an area of analytics that tries to understand and predict consumer needs. To optimize supply decisions with enterprise chain and business management. Demand forecasting includes quantitative methods, such as using historical sales data, as well as statistical methods. In addition, analytics can be used in production planning and inventory management, and sometimes in assessing future capacity requirements and in making decisions about entering a new market.
What is demand forecasting?
This is a process in which historical sales data is used to develop various estimates of the expected prediction of consumer needs. For enterprises, this analytics criterion provides information on the number of goods and services that its customers will buy in the foreseeable future. Critical business assumptions such as turnover, profit margins, cash flows, capital expenditures, risk reduction, etc. can also be calculated ahead of time.
Types
Demand forecasting can be broadly classified based on the level of detail that considers various time periods and all kinds of market volumes.
The following are the main types of needs that are most commonly used today:
- Passive study and forecasting of demand. It is held for stable enterprises with very conservative growth plans. A simple extrapolation of historical data is carried out with minimal assumptions. This is a rare type of forecasting, limited to small and local businesses.
- Active study. It is carried out to scale and diversify enterprises with aggressive growth plans, in terms of marketing activities, expanding the range of products and taking into account the work of competitors and the external economic environment.
- Short-term forecasting. It is carried out for a shorter period - from 3 to 12 months. In this perspective, the seasonal structure and the impact of tactical decisions on consumer needs are taken into account.
- Medium and long-term forecasting of population demand. As a rule, it is carried out for a period of 12 to 24 months (36-48 in some companies). The second option determines the planning of business strategies, sales and marketing, capital costs and so on.
External macro level
This type of forecasting is oriented towards wider market movement, which directly depends on the macroeconomic environment. An external macro level is conducted to assess all kinds of strategic business goals, such as expanding the product range, reaching new customer segments, technological failures, changing the paradigm in consumer behavior and risk reduction strategies.
Internal business layer
As the name implies, this type of forecasting no longer deals with external business operations, but with such as a product category, sales department or production group. These items include an annual trading forecast, an estimate of the cost of goods sold, net profit, cash flow, and so on.
Forecasting Examples
We bring to your attention some practical options.
Leading manufacturer, studying over the past 12 months the actual sales of their cars by model, engine type and color level. Based on expected growth, he predicts short-term demand for the next 12 months for procurement, production, and inventory planning.
The leading food company focuses on the actual sales of its seasonal goods, such as soups and mashed potatoes, over the past 24 months. Analysis of demand forecasting is carried out at the level of taste and packaging size. Then, based on market potential, an analysis is made for the next 12-24 months for the supply of key ingredients such as tomatoes, potatoes and so on, as well as capacity planning and an assessment of the need for external packaging.
The importance of miscalculations ahead
The concept of demand forecasting is the main business process around which strategic and operational plans of a company are developed. Based on analytics, long-term business plans are formed. These include financial planning, sales and marketing, assessment and forecasting of demand, risks and so on.
Short-term and medium-term tactical strategies, such as pre-assembly, custom manufacturing, contract manufacturing, supply planning, network balancing and so on, are performance-based. Demand forecasting also facilitates important management actions. It provides insights into performance evaluations, a reasonable allocation of resources in tight spaces, and business expansion.
It is important to know what demand forecasting methods are.
One of the most important steps in the process is the selection of the appropriate method. They can be applied using quantitative or qualitative methods for forecasting demand. Let's consider them in more detail.
Marketing research
This is the most important area of work, reflecting the specific state of affairs with a particular product. Using this method of forecasting the demand of market estimates, individual customer surveys are conducted to generate potential data. Such tests usually take the form of peculiar questionnaires that directly request personal, demographic, preferred and economic information from end users.
Since this type of research is based on random sampling, care must be taken in terms of regions, locations and demographics of the end customer. This type of activity can be useful for products that have virtually no demand history.
Trend Prediction Method
It can be effectively applied in enterprises with a long history of sales data, for example, exceeding 18-24 months. This historical information generates a “time series” that represents past trades and projected demand for a particular product category under normal conditions using the graphical construction method or least squares.
Barometric
This method of forecasting demand is based on the principle of recording events in the present for the future. In the process of demand analytics, this is achieved by analyzing statistical and economic indicators. Forecasters typically use parsing. An example of demand forecasting is the Leading, Concurrent series, or Lagging series.
Econometric analysis
It uses autoregressive integrated moving, average and complex mathematical equations to establish the relationship between demand and factors influencing it. The formula is derived and finely tuned to provide a reliable historical view. Predicted values of the influencing variables are inserted into the equation to create a prediction.
There are various models for forecasting demand. For example, a customized design may be developed based on the specific requirements of a business or product category. Such a model is an extension or combination of various qualitative and quantitative methods. The task of developing a custom circuit is often multiple, detailed, and experiential. It can be developed by implementing suitable demand management software.
Time Series Analysis
When historical data is available for a product and trends are clear, enterprises tend to use a time series analysis approach to forecast demand. It is useful for identifying seasonal variations, cyclical patterns, and key sales trends.
The time series analysis approach is most effectively used by well-established enterprises that have data for several years for work and relatively stable trend models.
The demand forecasting system is based on modeling. The causal model is the most complex tool for enterprises, because it uses specific information about the relationships between variables that affect market demand, such as competitors, economic opportunities, and other social factors. As with time series analysis, historical data is key to making a forecast of a causal model.
For example, an ice cream company can establish an analysis by looking at historical sales data, a marketing budget, promotions, any new ice cream stores in their area, their competitors' prices, weather, general demand in their area, even local unemployment.
Prediction of seasonality and trends
This term refers to fluctuations in demand that occur at certain times on a periodic basis (for example, on holidays). Trends can occur at any time and signal a general change in behavior (for example, an increase in the popularity of a particular product).
Successful demand forecasting is not a one-way task. This is an ongoing testing and learning process that should:
- Actively generate demand by optimizing customer service, product offerings, sales channels and so on.
- Providing an intelligent and flexible response to demand through the use and application of advanced analytics.
- Work on reducing systematic errors.
A good way to forecast demand is to anticipate what customers will expect from the business in the future. Therefore, an entrepreneur can prepare reserves and resources to meet these needs.
The automated stage of demand forecasting is the elimination of guesswork about growth.
Thanks to analytics, you can reduce retention costs and other operational expenses when they are not needed. At the same time, peak periods can be dealt with when they happen.
Traditional methods of manual manipulation and interpretation of data for forecasting demand are impractical for enterprises that are associated with rapidly changing expectations of customers and the market. In order for organizations to be truly flexible in making decisions based on relevant data, miscalculations in advance must take place in real time. This means using technology to do the hard work.
For example, TradeGecko's demand forecasting function uses key sales and inventory data to identify patterns. Information about future needs at the selected level of detail is obtained by product, option, location, and so on.
The demand forecasting system also launches automatic stock alerts with recommended changes in order and quantity based on analytics. In other words, an entrepreneur can know when to change the order of stocks and make business decisions based on data without having to make any forecasts manually. This means greater efficiency and time savings - two things that are an integral part of the success of any business.
The value of forecasts
Settlements in advance play a crucial role in managing any business. This helps the organization reduce the risks associated with business and make important decisions. Demand forecasting also provides insights into regulations on capital investment and organizational expansion.
The significance of analytics is shown in the following paragraphs:
1. Performing tasks. It is understood that each business unit begins with predefined goals. Analytics helps to achieve them. The organization evaluates the forecasting of demand for services in the market and is moving towards achieving goals.
For example, an organization has set a goal of selling 50,000 units of its products. In this case, it will forecast demand for this product. If it turns out to be low, the organization will take corrective actions to achieve the goal.
2. Budget preparation. It plays a decisive role in its formation by assessing costs and expected revenues. For example, the organization predicted that the demand for its product, which is estimated at 10 rubles, will be 100 thousand units. In this case, the total expected income is 10 * 100,000 = 1 million. Thus, forecasting demand allows organizations to calculate their budget.
3. Stabilization of employment and production. Helps organizations control their human resources. In accordance with the projected demand for products, planning helps to avoid the loss of organization resources. It also allows her to hire qualified staff. For example, if an organization expects an increase in demand for its products, it can use additional labor to meet increased needs.
4. Expanding companies. In this case, it is understood that demand forecasting helps in making decisions about expanding the business. If the expected flow of products is higher, then the organization can plan further expansion. If demand for products is expected to fall, the company may reduce investment in the business.
5. Making management decisions. Helps in the creation of global regulations, such as determining plant capacity, raw material requirements, and ensuring the availability of labor and capital.
6. Performance assessment. Helps to correct tasks and methods for solving them. For example, if the demand for the organization’s products is less, it can take corrective actions and increase the level by improving the quality of its products or spending more on advertising.
7. Assistance to the government. Allows the state to coordinate import and export activities and plan international trade.
8. The objectives of forecasting demand. Analytics is an important part of making business decisions. These goals are divided into short-term and long-term. The first include the following criteria:
- The formulation of production policy. Demand forecasting helps in assessing future raw material needs so that regular supplies can be maintained. It also allows maximum use of resources, as operations are planned based on forecasts. Human resource requirements are also easily met through analytics.
- The formulation of pricing policy. Refers to one of the most important tasks of forecasting demand. The organization sets prices for its products, focusing on the needs of the market. For example, if the economy enters a phase of depression or recession, the demand for products falls. In this case, the organization sets low prices for its products.
- Sales control. Helps in identifying sales goals that serve as the basis for evaluating performance. The organization makes demand forecasts for different regions and sets strategies for each of them.
- Organization of financing. It is understood that the cash needs of an enterprise are estimated using forecasting demand. This helps in providing adequate liquidity in the organization.
Long-term goals include the following:
- The choice of production capacity. It is understood that, using demand forecasting, an organization can determine the size of a plant needed for production. It must meet the requirements of the sales company.
- Long-term business planning. It implies that the calculation of demand forecasting helps in this aspect. For example, if the planned needs for the organization’s products are high, then consumers can invest in various expansion and development projects.
- Influencing factors. Demand forecasting is a proactive process that helps determine which products are needed, where, when and in what quantities. There are a number of factors that affect this setting.
Product Types
Goods may be manufacturer products, consumer goods, or services. In addition, they may be new or resold. Installed products are those that already exist on the market. And new ones are those that are not yet on sale.
Information on demand and the level of competition is known only in the case of identified products, since it is difficult to calculate the demand for new products. Therefore, forecasting for different types of goods is different.
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