Demand forecasting is the practice of estimating future customer demand over a predetermined period. Effective demand forecasting provides firms with essential data about their potential in both their current and other markets for managers to make intelligent decisions regarding pricing, corporate growth plans, and market potential.
Without demand forecasting, companies risk making bad decisions about their products and target markets. Bad decisions can have significant negative implications on the expenses associated with maintaining inventory, the satisfaction of customers, logistics management, and profitability.
Innovative retail businesses join in the action by attempting to foretell the future using demand. Let’s explore different demand forecasting types, methods, examples, and more.
What Is Demand Forecasting?
Understanding and predicting consumer demand is the process of making informed judgments about the supply chain, profitability, working capital, CAPEX, capacity planning, and other factors. Often, but not always, by using historical records, demand forecasting aids companies in estimating the total sales and earnings for a future period.
Almost all businesses should use demand forecasting to prevent excessive production and underproduction. Analysts must decide what they are measuring and the temporal perspective, choose a type and methodology of demand forecasting, and then gather, evaluate, and interpret results to perform a systematic and objective demand forecast.
The Demand Forecasting Methods
Financial analysts utilize four basic forecasting techniques to project a company’s future sales, expenditures, and investment costs.
- Predictive Analysis
By evaluating consumer motivations, the predictive analysis goes beyond conventional demand forecasting. Just like traditional forecasting determines future demand, the predictive analysis identifies the cause of that need.
The methodology developed from this analysis is then utilized to pinpoint the elements that affect consumer purchases and confidence.
- Conjoint Analysis
Surveys are used in the conjoint analysis to learn which aspects of a product are most appealing to customers. Customers are questioned in these studies about how they would utilize and react to particular product qualities.
Recognizing the key factors that buyers weigh before making a purchase is essential when selling a product. By identifying the products that appeal to customers the most, conjoint analysis can assist the business in going beyond demand forecasting.
Customers’ ranking of feature preferences is used to do this, and an analysis of the results produces a report outlining the aspects that customers find most appealing. To assess the market potential for new items or features, the approach produces a forecast of consumer preferences.
- Client Intent Survey
A buyer’s intentions survey gets data on the consumer’s future purchasing plans. This method is employed to ascertain the factors that influence a customer’s decision to buy a particular product.
While client intent surveys might aid in predicting the possibility of a purchase, it is crucial to remember that they do not always precisely reflect actual purchasing behavior.
However, research shows that purchase intent is a stronger predictor of actual purchasing for durable products, established products as opposed to new products, and short-term as opposed to long-term forecasting horizons.
This method is still helpful for predicting demand since it incorporates customer feedback—the ones who will purchase the product.
- Delphi Technique
The Delphi method was developed based on the idea that group forecasts are typically more accurate than individual projections. A demand forecast is to be made by a group of experts.
Each expert aims to create a prognosis for the section they have been given. Following the initial round of forecasting, each expert reads out their prediction while being influenced by the predictions of the other experts. After then, each expert makes a new prediction, and so on, until every expert has come to a nearly unanimous conclusion.
Examples of Demand Forecasting Methods
While company A could grow or diversify with ambitious growth plans, company B might follow a cautious growth strategy. The examples of demand forecasting that follow go over a few distinct possibilities.
Example 1
To plan proper inventory levels for the upcoming season, a supermarket examines sales statistics during the Festive week of the previous year. They examine purchases of seasonal goods like berries, turkeys, and casseroles during the previous year.
For them, the holiday sales were great. However, a competing grocery shop opened up a few blocks away three months earlier, so they’re worried about how Festive demand will change and whether local shoppers will buy ingredients from their competition.
Nevertheless, since the competitor business opened, the number of families living there has increased significantly—by an average of 3% per month.
To establish themselves as the go-to Festive destination, they want to run a few more advertisements than they did last year, using channels that previously provided a high return on investment. They will also offer a few fresh bargains. According to their forecasts, revenues will be up 8% from the previous year.
Example 2
Growing quickly is a new direct-to-consumer beauty brand. At present, they deliver 5000 orders per month. They anticipate having more than 10,000 orders each month at this time next year, based on their historical sales statistics, planned advertising initiatives, and general industry market circumstances.
They currently have 35,000 units in stock throughout their 4 SKUs at varying levels. Based on their replenishment cycle, their product demand varies considerably, and they restock goods at the SKU level at a rate of roughly every three months.
While the cadence won’t change, the average number of units they store will increase quickly. Their primary SKU’s most recent run contained 5000 units. Their subsequent run will consist of 35,000 units, and they are about to ship another 20,000 units.
They intend to maintain expanding at that rate. Therefore they are deciding whether to buy land, rent a warehouse, or subcontract fulfillment to meet demand.
The 5 Types of Demand Forecasting
Numerous techniques can be used to forecast demand. Based on the forecasting model you employ, your forecast can be different. Let’s discuss the various demand forecasting types so you can decide which to utilize depending on the situation.
- Short-Term Demand Forecasting
Short-term demand forecasting examines a brief window of time to provide insight into the day-to-day. It might also benefit from running a just-in-time (JIT) supply chain or a constantly-changing product lineup. Most companies, meanwhile, will only combine it with longer-term estimates.
- Long-Term Demand Forecasting
In a long-term forecast, projections are made for the following one to four years. This forecasting model aims to determine your company’s growth path. It can help with supply chain management, capital investments, and the company’s marketing planning.
You may get ready for future demand with the aid of long-term forecasting. The success of your business depends on your ability to handle growth.
- Passive Demand Forecasting
Utilizing historical sales data to estimate future sales data does not require statistical techniques or an examination of economic trends. Therefore, even while this renders passive data forecasting very simple, it only benefits companies with a wealth of previous data.
Only businesses that want stable sales growth instead of explosive sales growth should utilize the passive model, which relies on the premise that this year’s sales data will be comparable to last year’s sales data.
- Active Demand Forecasting
It is wise if your business is just starting or growing. The active approach considers the industry’s whole competitive situation, including the overall economy, market growth estimates, and other considerations. It also considers aggressive expansion strategies, like marketing or product development.
- Macro & Micro Demand Forecasting
At the macro level, demand forecasting considers external factors that affect business, such as the economy, competition, and consumer behaviors. Understanding these dynamics enables companies to forecast potential financial difficulties or shortages of raw materials, find chances for product or service expansion and more.
Even if your business prioritizes stability above development, keeping an eye on outside market dynamics helps keep you informed about potential problems that could affect your supply chain.
Final Word
Businesses benefit from demand forecasting because it enables them to create data-driven plans and make better business decisions. Market conditions and future expectations are considered while making financial and operational decisions based on the forecasts.
To learn more about how demand forecasting is used by businesses to predict sales accurately, tune into ClicData for all the latest information.
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