Demand forecasting is an area of analytics that attempts to understand and predict consumer needs. To optimize supply chain decisions through 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 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 expected customer demand predictions. For businesses, this analytics criterion provides information about the amount of goods and services that its customers will buy in the foreseeable future. Critical business assumptions such aslike turnover, profit margin, cash flow, capital cost, risk mitigation, etc. can also be calculated ahead.
Types
Demand forecasting can be broadly classified based on the level of detail that considers different time periods and market sizes.
The following are the main types of needs that are used most often today:
- Passive study and demand forecasting. It is conducted for stable enterprises with very conservative growth plans. 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 learning. It is carried out to scale and diversify an enterprise with aggressive growth plans, in terms of marketing activities, expanding the product range 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. This perspective takes into account the seasonal structure and the impact of tactical decisions on buying needs.
- Mid-term and long-term forecasting of demand of the population. 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 expenditures, and so on.
External macro level
This type of forecasting is focused on morebroad market movement, which is directly dependent on the macroeconomic environment. The external macro level is conducted to assess all sorts of strategic business objectives such as product expansion, new customer segments, technology disruptions, paradigm shifts in consumer behavior and risk mitigation strategies.
Internal business layer
As the name implies, this type of forecasting no longer deals with the external operations of the business, but with those such as the product category, sales force, or production team. These items include annual trade forecast, cost of goods sold, net income, cash flow, and so on.
Forecast examples
Give you some practical options.
Top manufacturer looking at actual sales of their vehicles over the past 12 months by model, engine type and color level. Based on expected growth, he forecasts short-term demand over the next 12 months for purchasing, production, and inventory planning purposes.
The leading food company is looking at the actual sales of its seasonal items such as soups and mashed potatoes over the past 24 months. Demand forecasting analysis is carried out at the level of taste and package size. Then, based on the 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, andalso for capacity planning and outer packaging needs assessment.
The importance of miscalculations in advance
The concept of demand forecasting is the core business process around which a company's strategic and operational plans are developed. Based on analytics, long-term business plans are formed. These include financial planning, sales and marketing, demand assessment and forecasting, risk assessment and so on.
Short to medium term tactical strategies such as prefabrication, customization, contract manufacturing, supply chain planning, network balancing and so on are based on performance. Demand forecasting also facilitates important management activities. It provides insight into performance evaluations, smart resource allocation 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 choosing the right method. They can be applied using quantitative or qualitative demand forecasting techniques. 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. This market valuation demand forecasting technique uses individual customer surveys to generate potential data. These tests usually take the form of questionnaires that directly ask for personal, demographic, preference, and economic information from end users.consumers.
Because this type of research is based on a random sample, care must be taken in terms of regions, location and demographics of the end customer. This type of activity can be useful for products that have little to no history of demand.
Trend forecasting method
It can be effectively applied to enterprises with a long history of sales data, such as more than 18-24 months. This historical information generates a "time series" that represents past trades and projected demand for a particular category of product under normal conditions using plotting or least squares.
Barometric
This demand forecasting method 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. As a rule, forecasters use graphic analysis. An example of demand forecasting is the Leading series, Concurrent series or Lagging series.
Econometric analysis
It uses autoregressive integrated moving averages and complex mathematical equations to establish relationships between demand and the factors that influence it. The formula is derived and fine-tuned to provide a reliable historical representation. The predicted values of the influencing variables are inserted into the equation to createpredictions.
There are different demand forecasting models. For example, a customized schema can be developed based on specific business requirements or a product category. Such a model is an extension or combination of various qualitative and quantitative methods. The task of designing a custom circuit is often repetitive, detailed, and experience-based. 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, businesses tend to use a time series analysis approach to forecast demand. It is useful for identifying seasonal fluctuations, cyclical patterns and key selling trends.
The time series approach is most effectively used by established businesses that have several years of data to work with and relatively stable trend patterns.
The demand forecasting system is based on simulation. The causal model is the most complex tool for businesses because it uses specific information about relationships between variables that affect market demand, such as competitors, economic opportunities, and other social factors. As with time series analysis, historical data is the key to making a causal model forecast.
For example, an ice cream business might base the analysis by consideringhistorical sales data, marketing budget, promotional activities, any new ice cream shops in their area, their competitors' prices, weather, general demand in their area, even the local unemployment rate.
Forecasting seasonality and trends
This term refers to fluctuations in demand that occur at certain times on a periodic basis (eg holidays). Trends can occur at any time and signal a general change in behavior (such as an increase in the popularity of a particular product).
Successful demand forecasting is not a one-sided task. It is an ongoing process of testing and learning that should:
- Proactively generate demand by optimizing customer service, product offerings, sales channels and more.
- Ensure intelligent and agile demand response through the use and application of advanced analytics.
- Work on reducing systematic errors.
A good way to predict demand is to anticipate what customers will expect from a business in the future. Therefore, the entrepreneur can prepare supplies and resources to meet these needs.
Automated Demand Forecasting step is the elimination of growth guesswork.
With analytics, you can reduce your retention and other operating expenses when you don't need them. In doing so, peak periods can be de alt with when they occur.
Traditional methods of manual manipulation and data interpretation for forecastingdemand impractical for businesses that deal with rapidly changing customer and market expectations. For organizations to be truly agile in their data-driven decision making, forward thinking must happen in real time. It means using technology to get the job done.
For example, TradeGecko's demand forecasting feature uses key sales and inventory data to determine patterns. Get information about future needs at the selected level of detail by product, variant, location, and so on.
The demand forecasting system also triggers automatic stock alerts with recommended order and quantity changes based on analytics. In other words, an entrepreneur can know when to reorder inventory and make data-driven business decisions without having to make any manual forecasts. This means more efficiency and time savings, two things that are integral to the success of any business.
Meaning of forecasts
Calculation up front plays a crucial role in running any business. This helps the organization reduce business risks and make important decisions. Demand forecasting also provides insight into capital investment and organizational expansion regulations.
The importance of analytics is shown in the following paragraphs:
1. Complete tasks. It is understood that each business unit begins with predetermined goals. Analytics help in achieving them. The organization is evaluating the demand forecast for services in the market and is moving towards achieving the objectives.
For example, an organization has set a goal of selling 50,000 units of its products. In this case, it will forecast the demand for this product. If it is low, the organization will take corrective action so that the target can be achieved.
2. Preparing the budget. Plays a decisive role in its formation by estimating costs and expected revenues. For example, an organization predicted that the demand for its product, which is estimated at 10 rubles, would be 100 thousand units. In this case, the total expected income is 10100,000=1 million. Thus, demand forecasting allows organizations to calculate their budget.
3. Stabilize employment and production. Helps an organization control its HR activities. According to the forecasted demand for products, planning helps to avoid wasting resources of the organization. It also allows it to hire qualified personnel. For example, if an organization expects an increase in demand for its products, it may use additional labor to meet the increased demand.
4. Expanding companies. In this case, it is assumed that demand forecasting helps in the decision to expand the business. If the expected flow to products is higher, then the organization can planfurther expansion. If demand for products is expected to fall, the company may cut investment in the business.
5. Management decision making. Helps to create global regulations such as plant capacity, raw material requirements, and ensuring the availability of labor and capital.
6. Performance evaluation. Helps to correct tasks and methods for solving them. For example, if there is less demand for an organization's products, it can take corrective action and level up by improving the quality of its products or by spending more on advertising.
7. Assistance to the government. Allows the government to coordinate import and export activities and plan international trade.
8. Demand forecasting goals. Analytics are an important part of business decision making. These goals are divided into short-term and long-term. The former include the following criteria:
- Formulation of production policy. Demand forecasting helps in assessing future raw material requirements so that a regular supply of products can be maintained. It also allows maximum utilization of resources as operations are planned based on forecasts. Human resource needs are also easily met through analytics.
- Formulation of pricing policy. Refers to one of the most important tasks of demand forecasting. The organization sets prices for its products, focusing on the needs of the market. For example, if the economy enters a depression or recession, demandfalls on products. In this case, the organization sets low prices for its products.
- Sales control. Assists in setting sales targets, which serve as the basis for performance evaluation. The organization makes demand forecasts for different regions and sets strategies for each of them.
- Organization of financing. It is understood that the monetary needs of the enterprise are estimated using demand forecasting. This helps in providing proper liquidity to the organization.
Long-term goals include:
- Choice of production capacity. It is understood that through demand forecasting, the organization can determine the size of the plant required for production. It must meet the sales requirements of the enterprise.
- Planning for the long term. It implies that the calculation of demand forecasting helps in this aspect as well. For example, if the planned demand for an organization's products is high, then customers can invest in various expansion and development projects.
- Influencing factors. Demand forecasting is a proactive process that helps determine what products are needed, where, when, and in what quantities. There are a number of factors that affect this parameter.
Product types
Goods can be manufacturer's products, consumer goods or services. In addition, they may be new or resold. Established products are those that already exist on the market. And the new ones are those that have not yet been introducedon sale.
Information about demand and level of competition is only known for established products, as it is difficult to calculate the demand for new products. Therefore, forecasting for different types of goods is different.
In a highly competitive market, the demand for products depends on the number of competitors that exist at the moment. Moreover, there is always the risk of new participants appearing. In this case, it becomes even more difficult to predict anything.
The price of a commodity acts as the main factor that directly affects the demand forecasting process. Any analytical activity of organizations is highly dependent on changes in their pricing policy. In such a scenario, it is difficult to calculate a perfectly accurate demand for products.
The state of the art is also an important factor in obtaining reliable demand forecasts. In the event of a rapid change in technology, existing inventions or typical products may become obsolete. For example, there is a significant decline in demand for floppy disks with the advent of CDs and various drives for storing data on a computer. With constantly evolving technology, it is difficult to predict the demand for existing products in the future.
The economic point of view plays a major role in obtaining demand forecasts. For example, if there is a positive development in the economy, then the analytics of any company will also be positive.
