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Understanding customer demand allows businesses to prepare their operations for the influx of orders. A company knows what customer trends are happening now and may happen in the future as they prepare to offer products and services to meet those demands. Demand forecasting is a process companies use to anticipate future demand by looking at past data.
Demand forecasting is an analytical method where a company gathers historical customer sales data. Then they use algorithms to look for any trends as they develop and estimate future customer demands. With demand forecasting, a company may evaluate previous sales during busy periods, such as the holidays, to understand the types of products or services that will have high turnover rates due to large demand.
By having these estimates, the company has a better idea of how many products to make without running short or producing merchandise that nobody wants. They also can make better corporate decisions regarding how they invest their capital in their supply chains and stock for the next fiscal year to stimulate more business growth. Companies have estimates for their cash flow, profit margins, capital expenditures, and capacity planning so they can adjust quickly to reach milestones and goals.
The estimates may also be used for risk management purposes. If historical sales data shows that customers will request a certain product from a supplier that would not be able to meet demand, the company may partner with additional suppliers as backup. Then the company may plan out their logistical strategies to ensure new products flow smoothly to destinations. When it comes to offering services, the company may hire and train seasonal workers early to have them ready for the upcoming demand to avoid being short-staffed.
For example, Nestle relied on demand forecasting technologies for their business operations. With improved forecasting capabilities, they were able to reduce inventory stock numbers by 14%-20% while still meeting customer demand. The company was able to save on inventory costs while still meeting revenue goals.
There are several types of demand forecasting analytical methods that companies may use for their specific operations. The types used may depend on various factors, such as the market scope for the company, how long of a period that the forecasting will cover, and the amount of analytical reporting that is desired from evaluated data.
Below are six different demand forecasting models. Companies are not restricted on which to select as they may use several of these methods to gain different insights regarding customer shopping behavior and their supply chain capabilities.
This model is the simplest and easiest to implement. A company takes past sales data, typically during a certain sales period, and uses this information to predict future sales during the same sales periods. The model allows companies with fluctuating seasonal sales periods to gain a bit more stability, so they can prepare for future trends.
Companies that use this method usually have strong sales numbers to extrapolate the required data. It is an ideal forecasting methodology when yearly sales are the same for each year as it does not require looking at economic trends to form estimates. Small companies and local companies may find benefits with this method when they are more focused with having stable operations versus trying to stimulate operational growth.
Active demand forecasting methods are useful when a company is looking to start out as they need to further understand their economic outlook in their specific market. This method also helps companies who are looking to grow their companies and need to understand competitors' activities. Active demand forecasting methods use marketing research, expansion plans, and market campaigns to provide estimates. Other considerations used in this forecasting include economic outlooks, projected supply chain cost savings, and market sector growth projections.
Active demand forecasting is often used by companies who have no historical sales data to use since they are just now opening their business doors. They instead look at competitor and industry benchmarks to estimate how well their products and services will sell in the same market segment. Then they can develop and implement the right supply chain strategies to meet predicted demand.
In some instances, a corporation may have multiple brands in their portfolio. These brands may sell similar products or dissimilar products under various market segments. Quantitative demand forecasting takes a look at all the data from a specific company. This data may involve website analytics, financial reports, revenue figures, and sales numbers. With this information, the company can figure out future sales and services activity by using trend analysis and statistical modeling.
Besides using quantitative demand forecasting for brands under a corporate portfolio, companies may also use this method to evaluate competitors in their market segment. This strategy allows them to develop marketing and promotional campaigns to gain new customers.
Qualitative demand forecasting takes a broader approach to anticipating future customer sales. It develops projections and estimates in regard to the economic landscape as a whole that could change product and service demand. This demand forecasting type takes into account expert opinions, emerging technologies, hard data, and recent innovations to develop future demand forecasting estimates. All of these factors may have an impact on various product and service aspects such as pricing, product availability, service changes, product upgrades, and product life cycles.
Companies large and small may use qualitative demand forecasting to have a holistic approach to their market segments. They can determine what new innovations or economic circumstances may occur that could have a dramatic influence on customer demand trends.
Since customer demand can change in an instant, companies may want a greater clarity on how present sales and supply chains are doing. Short-term demand forecasting focuses on a smaller historical sales data time span and uses it to make short demand projections, such as three months to 12 months. Using real-time sales data, the method allows companies to see how short-term demand is impacting the supply chain.
Short-term forecasting is ideal for companies that are using just-in-time lean methodologies. They can use the estimates to constantly adjust product demand projections to balance their inventory levels and to prevent making too many products that lead to waste. This forecasting may also help companies that are always changing their product lineups to appease present customer demand trends.
Unlike short-term demand projections, long-term demand forecasting looks at data to make estimates that will cover a one year to four year future sales period. The projections will be based on market research and historical sales data. This forecasting type may help a company better predict operational growth.
Many companies use long-term demand forecasting to plan out their marketing and investment strategies. They are able to place their capital in the best places to handle any changes and weather through possible risks. They may also seek to presently improve supply chain operations to reach established goals based on the demand forecasting predictions.
There are various methods used to create the above demand forecasting projections. Selecting the right method will be based entirely on the company's business needs. In addition, the methodology that may be chosen in the beginning could change later based on how operations change. For example, a company may use methods to perform active demand forecasting when they are new and then move on to passive demand forecasting once they have a solid sales figure. Evaluating each method first allows a company to select the best one for their operations.
The sales force opinion method takes information from the sales team to assist with demand forecasting predictions. The sales manager will gain the team’s opinion regarding customer demand depending on several factors, such as sales in certain product categories and customer buying behaviors in specific regions. This feedback from the sales team is aggregated to develop data regarding customer trends, product trends, and competitor activities. The forecast is then developed using all this input from the sales force. The sales force method is considered a qualitative demand planning technique.
The sales force opinion provides a firsthand view into customer demand through ordering. It makes your sales team an active participant in creating sales forecasts. However, a company must be able to trust the decision making of their sales team to provide accurate information and opinions. This method also may not work for companies who are just starting and do not have a sales team to draw these opinions from, such as a small e-commerce business solely using a marketplace to gain orders.
Customer surveys look to aggregate the opinions directly from the customers regarding everything from the products they purchased to what they’ll consider buying in the future. They are considered qualitative demand forecasting techniques and are typically lumped into the market research method. These surveys are often based on customer demographic as well as economic factors. They are an ideal method to obtain customer feedback to better understand their shopping behaviors.
There are many types of customer surveys to use, such as sample surveys, end-use surveys, and complete enumeration surveys.
Customer surveys are a great way to learn about buying behaviors directly from customers. They may provide possible insights into future demands and trends that may not have been anticipated. However, customer surveys are only good when asking the right questions at the best possible times. It can also be difficult to gain feedback from customers if they are not rolled out correctly, as some customers are reluctant to give out any personal information.
Trend projection methods project future sales by using previous sales numbers. It is a quantitative method that takes into account previous historical anomalies and evaluates what spurred such trends to see if they may happen again or be replicated. It may also show what occurred when sales fell. Usually this method relies on historical sales data history from about 18 months up to 24 months. Then the data is used to generate a time series that gives a deeper snapshot into supply chain and sales operations. Trend projection works for companies that have existing sales history to draw the historical data from, which may not work for companies that are just starting out.
As a qualitative method, market research relies on customer survey data to determine future customer buying behavior. The customer's opinions are gathered and tabulated to look for possible buying trends and service demands. This type of method allows a company to have a greater understanding about the types of customers who want their products and services, the types of pricing that customers are willing to pay, and where the customers are located. It also examines the customer's economic factors.
Market research is a great way to gather information from a random sampling of customers. However, care must be taken so that the random sampling reflects the entire customer base that may do business with the company in order for the estimates to be accurate. A company that is just starting their business can gain valuable insights using this method.
The barometric method differs in many ways from other methods. Instead of relying on past historical sales data, this quantitative method uses current information and data to predict future sales demands. The current events are recorded, as these events may consist of economic or statistical indicators, and the data is analyzed for possible trends.
There are several economic indicators used for this method: leading indicators, lagging indicators, and coincident indicators. Leading indicators is a performance indicator to determine future events. Lagging indicators look at past events and their impacts. Coincident indicators look at current events in real-time or during a short period.
The Delphi technique is a qualitative method that relies on expert opinions to create a demand forecast. Demand forecast experts will answer survey questionnaires to glean their opinions regarding products and market segments. These responses are placed into a summary and provided to the expert panel as the summary institutes another round of advice and opinions from the experts. The process keeps going until the majority of experts reach a consensus as the demand forecast is completed.
The advantages to the Delphi technique are that experts may be drawn from various levels of expertise as this strategy offers a more well-rounded way to develop sound and unbiased answers. However, the process may take some time with coming to a consensus with the experts.
Statistical methods are quantitative techniques that rely on trend projection and regression analysis to form future demand forecasting. Regression analysis takes different variables and looks for possible relationships as it analyzes them. Trend projection creates performance history using past sales trends to look for present and future trends. Trend projection methods are ideal for inventory management while regression analysis may help a company figure out how to allocate their resources in the best way.
While this method can help with providing stability and growth, a company needs to be ready to act on the data when it is gathered and analyzed. Waiting too long may lead to missing current trends as they are happening.
The econometric method is considered a quantitative method that uses complex mathematical formulas. These mathematical formulas are then used to predict future demand. The sales data and other influencing variables that have a direct influence on product and service demand are combined to create an equation. Then this equation is modified to represent historical sales representation and is combined with projected values.
The method relies on equations and crunching mathematical numbers to create a demand forecast. It is appropriate when a company has the solid sales data available to glean the appropriate numbers but may not work as well for companies with little to no sales data to rely on.
One of the biggest issues when performing demand forecasting is obtaining accurate data. However, there are many factors that may produce errors in forecast predictions, including the wrong sales order numbers, inaccurate inventory levels, or other factors. There may also be other factors that could impact sales data that are not considered when predicting future demand. Some common pitfalls and problems may involve the following:
Dealing with intermittent demand of products has become another leading factor to inaccurate demand forecasting. Even with these traditional methods, sometimes the methods do not apply to every company's operation. You’ll want to evaluate the type of forecasting as well as how data will be gathered and analyzed, where the data will come from, and what actions you’ll take with the data.
Also, have the right systems in place to create more error-free data. Inventory management systems, economic order quantity modeling, and methods to automate processes provide greater transparency regarding inventory levels.
There are various techniques and technologies that may be used to provide accurate demand forecasting. A Gartner survey found that 45% of companies are already using machine learning technologies to perform demand forecasting. These technologies may involve the use of supply chain management systems and demand forecasting software to gain accurate future predictions.
When analyzing demand forecasting software, a company wants to ensure it has the right features for their needs. Look for applications that can improve communication with sales teams, has machine learning capabilities, can be easily integrated into existing network systems, is easy to implement and use, provides hypothetical modeling, and offers performance measurements.
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