Walk by your local florist a day before Valentine’s Day or Mother’s Day and you’ll likely find a colorful sea of extra bouquets ready for sale. Owners of flower shops and plant stores know there will be a surge in customer demand for flowers on those days.
This is a simple example of demand forecasting, the process of predicting future customer purchases. It gets more complicated with manufactured products like automobiles, cameras, or computers with intricate global supply chains.
There are many types of demand forecasting, from examining accumulated internal data points to highly analytical modeling. Each one is suitable for different business models. Forecasting demand helps business owners make educated guesses about future demand, highlighting opportunities while satisfying customers.
What is demand forecasting?
Demand forecasting is the process of using statistical analysis, historical buying patterns, and market research to predict consumer behavior and future sales.
For example, a swimwear boutique might forecast demand for more bathing suits in hotter months. A deeper dive into active demand forecasting—studying both external trends and internal data—might reveal what type of bathing suits will be the hot summer item. Seasons, national holidays, cultural and social trends, and events can affect sales of a wide range of businesses across the country.
Making educated guesses about customer demand can mean the difference between running out of stock due to poorly planned supply chain management and having a plentiful supply of products on the shelves. Business owners can also plan and budget for hiring additional workers during peak seasons and running special promotions during slower periods.
Factors that influence demand forecasts
Seasons and holidays
Business owners can predict which products and raw materials will be in demand during certain seasons—like lace and sheer organza fabrics during the peak springtime wedding season, costumes and bite-size candies during Halloween, and champagne and party favors around New Year’s Eve.
Events and trends
Proper demand forecasting helps inform sales, manufacturing processes, supply chain management, and logistics. It’s smart to be prepared for future customer demand when market trends around social, cultural, and political movements have an impact on businesses. Broadly speaking, the state of the economy also influences consumer demand.
Weather and geography
Why does nearly every store in a touristy area suddenly have umbrellas for sale when it starts to rain? Because they’re familiar with a longstanding consumer behavior pattern. Business owners can use demand forecasting methods to determine needs when hot, cold, or wet weather arrives.
Demand forecasting methods
- Data analysis
- Regression analysis
- Market research
- Social media analysis
- Seasonal forecasting
- Sales force composite method
- Geographic forecasting
- Delphi method
Different demand forecast methods work better for particular industries, sectors, products, services, and business models. Here are a few to consider:
Data analysis
Data analysis, also known as passive demand forecasting, boils down to reviewing internal transaction charts and looking for patterns of high and low sales. Older businesses have a leg up with this method because years of past sales data provide a clearer picture of consumer behavior than a few months.
Regression analysis
In the context of demand forecasting, regression analysis is used to predict future demand by modeling the relationship between demand and one or more independent variables, such as time, price, promotions, or other relevant factors. This holistic approach to demand forecasting relies on robust internal retail metrics and data analysis to make better decisions about where to allocate funds and how to boost future sales.
Market research
Market research is conducted through physical or online surveys, questionnaires, interviews with a cross-section of the public, or more targeted groups like potential or actual customers. Market research can reveal micro and macro purchasing patterns.
Social media analysis
Monitoring and analyzing when a video, post, or meme gets robust engagement shows what topics strike a chord with social media users. Emerging patterns can show what products, like designer sneakers, have attained mainstream acceptance and are not a passing fad.
Seasonal forecasting
Predicting when customers might shop for a pair of sandals, graduation dress, snow shovel, or beach umbrella can be done with some certainty. Seasonal demand planning delves into the logistics of manufacturing, fabrication, supply chain operations, and storage during certain seasonal surges.
Sales force composite method
Sales teams are constantly in discussion with potential and actual clients; they have a wealth of valuable consumer behavior information to share with managers and executives. Sales forecasting is the crux of sales force composite forecasting, which adds insight to market growth projections. This method can be as simple as adding together how much each rep predicts they can sell in their area.
Geographic forecasting
Customers in New England or Southern California, for example, might have somewhat predictable consumer needs, from snow tires to LA Lakers paraphernalia. Geographic forecasting also encompasses when and where to manufacture, warehouse, and retail to best meet customer demand.
Delphi method
The Delphi demand forecasting method engages a group of experts with a designated facilitator to discuss a specific consumer trend. A questionnaire is typically sent to the experts, and their anonymous answers are reviewed by an internal team conducting the forecasting initiative. Once the answers are discussed by the internal team, they make a second set of questions for the experts. This cycle continues until there is consensus among the internal team.
Benefits of demand forecasting
Demand forecasting can’t predict the future with 100% accuracy, but it does allow business owners to make informed decisions that are both financially rewarding and satisfying to their customers. Consider some of the potential benefits:
Product availability
Demand forecasting enables business owners to have as many products or raw materials for customers to purchase as possible.
Budgeting
Accurate demand forecasting helps business owners budget for materials, manufacturing, storage space, delivery, and extra staff when needed.
Pricing
Price strategies on products are easier to formulate when business owners know there will be a rush on particular items.
Inventory and storage
Raw materials or manufactured products need to be stored in-house or warehoused; adequate forecasting leads to smarter inventory management and storage decisions.
Backorders and unscheduled restocking
Customers often cancel purchases when depleted stock forces the brand to rely on backorders or restocking. Forecasting helps business owners avoid unplanned restocking, which is often expensive and requires rush delivery.
Challenges in demand forecasting
- Lack of historical data
- Changing consumer behavior
- Limited external data
- Short- vs. long-term forecasting
- Selecting the right forecasting method
There are several common challenges business owners face when employing demand forecasting methods:
Lack of historical data
A new product, sector, manufacturing facility, or business will not have a long data history to analyze. Note: it’s never too late to start collecting data.
Changing consumer behavior
Social norms come and go, and it can sometimes be difficult to predict which will remain. While business owners try to keep one step ahead of demand with forecasting, some trends are impossible to predict.
Limited external data
There is scant data available collected by academics, analysts, and think tanks for new and emerging sectors, such as the various artificial intelligence (AI) applications.
Short- vs. long-term forecasting
Short-term demand forecasting is more accurate than longer forecasting time frames because there is more room for error and variables.
Selecting the right forecasting method
It can take time to choose suitable demand forecasting models for products or businesses, and the process often requires some trial and error. If your sales turn out to be fairly close to the predictions of a chosen method, it’s a good one. If not, try different demand forecasting techniques and compare the results with sales.
Demand forecasting methods FAQ
What are the most traditional demand forecasting methods?
Analyzing accumulated historical data, market research, and sales force composite method.
What are advanced demand forecasting methods?
The Delphi method involves surveying experts over the course of many rounds to reach a consensus. The econometric demand forecasting method combines economic theory, statistical techniques, and data to forecast demand for goods and services.
How can machine learning improve demand forecasting?
Advanced planning and scheduling software (APS), such as PlanetTogether and The Fulfillment Lab, typically are used by manufacturing businesses. Demand forecasting software like Blue Ridge or GMDH helps plan for heavier production periods, inventory options, delivery methods, or when to hire extra staff.
What role does big data play in demand forecasting?
Big data can give businesses insight into broad economic, seasonal, and behavioral trends.
Can demand forecasting eliminate the risk of stockouts or overstocking completely?
No. Business always involves risk, including the possibility of stockouts and overstocking, but demand forecasting can reduce their frequency and give indications of which products to stock.
