Demand forecasting, also known as demand planning, is a key tool for business decision-making, as it lets you anticipate your customers’ needs and improve their satisfaction while optimizing your production processes and stock flows.
In this article, we explain everything you need to know about demand forecasting, from the basic concepts to the most advanced methods, along with the tools that can help you and the common mistakes you should avoid.
Factors to consider in demand forecasting
To produce an accurate demand forecast, you need to take into account a range of factors that can influence it. Some will depend largely on you and how you run your business, while others will be beyond your control.
Below, we look at some of the main factors to consider:
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Internal factors: Among the internal factors that can affect demand are the availability of products or services, their quality, the prices and promotions on offer, and the quality of your customer service.
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External factors: These include economic factors, such as the state of the market, consumers’ income levels and the unemployment rate; social factors, such as culture, fashion and trends; and political factors, such as laws and regulations.
Demand forecasting methods
Demand forecasting methods can be divided into two main categories: quantitative methods and qualitative methods.
Quantitative demand forecasting methods:
Quantitative methods allow you to carry out objective, accurate analyses based on numerical and statistical data, which is useful for predicting numerical trends and making long-term projections across various fields, such as economics and finance. However, it’s important to bear in mind that these methods can be limited in their ability to account for subjective or unpredictable factors that may influence the final results. That’s why it’s necessary to complement quantitative methods with other, more subjective approaches, such as qualitative research and hands-on experience.
For example: a restaurant uses quantitative methods to predict the number of customers it will have on a given day. However, it doesn’t take into account subjective factors such as the weather, local competition, or how popular a new dish on the menu is. If there’s a rainy day or a popular new restaurant opens in the same area, demand could be much lower than predicted, and the restaurant could end up with a surplus of food that goes unsold. This illustrates the limitation of quantitative methods in not accounting for the subjective factors that can influence demand.
The main quantitative demand planning methods are:
- Trend analysis:
This method is based on the assumption that future demand will follow a linear, exponential or logarithmic trend derived from historical data. The data is fitted to a trend line and used to project future demand. For example, if a company wants to forecast demand for a product over the next 6 months, it can use historical sales data from the previous 12 months and fit a trend line to predict future demand. When using this method, it’s important to account for seasonality factors that can create patterns of ups and downs within an overall trend. It’s also important to bear in mind that in certain sectors “fads” have a significant impact, and that some trends can be radically interrupted by the emergence of a new “fad”.
- Statistical models:
One of the most widely used statistical models in demand forecasting is the moving average, which uses the mean of a set number of previous periods to predict future demand. For example, if you use a three-month moving average, you take the average sales of the last three months to forecast demand for the next month. This model is useful for predicting short-term demand and adjusting inventory levels accordingly.
Another common statistical model is time series analysis, which is used to analyze seasonal, cyclical and trend patterns in historical data and project them into the future. This model is useful for forecasting long-term demand and planning production and inventory levels accordingly.
- Historical data analysis:
This method uses historical sales data and other economic indicators to project future demand. For example, if a company wants to predict demand for a particular product, it can use the historical sales data for that product, along with relevant economic indicators such as GDP or the Consumer Price Index (CPI), to forecast future demand.
- Time series analysis:
This analysis is based on historical sales data. The data is analyzed to find seasonal patterns and long-term trends, and statistical models are then used to forecast future demand. This method is very useful for forecasting demand for products with seasonal sales patterns, such as products for the Christmas season.
- Regression models:
The regression model uses historical sales data and other external factors to forecast future demand. External factors can include changes in the economy, competition and market trends. Statistical models are used to analyze the data and forecast future demand. For example, a beverage company can use a regression model to forecast its demand based on the economy, health trends and changes in the competition.
- Social media data analysis:
This method involves analyzing social media data. The data can include mentions of products or brands on social media, customer comments and market trends on social networks. For example, a fashion company can use this type of analysis to forecast future demand for a new clothing line based on current social media trends.
A success story is SHEIN, the fashion platform that has revolutionized the retail world by implementing a fast fashion production model based on analyzing social media trends. SHEIN uses social media data analysis to predict future fashion trends and adapt its production in real time, becoming the go-to platform for “real time retail”. For example, if a fashion trend starts to gain popularity on social media, SHEIN can respond quickly by producing and selling garments that fit that trend.
Qualitative demand forecasting methods:
Qualitative methods have as their main advantage the ability to obtain detailed, in-depth information about consumers and their needs. These methods allow greater flexibility and adaptability to changing market situations, and they can also be useful in developing new products and services. However, their main drawback is that they can be subjective and may not provide an accurate quantitative measure of demand. Some examples:
- Opinion surveys:
This method involves conducting surveys to gather information about the opinions and expectations of consumers and experts. For example, a food company can run a survey to find out consumer preferences regarding flavors or packaging appearance.
- Delphi analysis:
The Delphi method involves bringing together a group of experts alongside a moderator and a set of structured techniques to discuss and reach a consensus on expectations for future demand.
The process is carried out anonymously to avoid the influence of opinion, and it’s used as a method for exploring and analyzing future scenarios in order to make informed decisions and choose the best strategies for the company.
3. Scenario analysis:
This is a technique in which different possible future scenarios are considered and demand is estimated for each one. For example, a company can consider several possible economic scenarios and estimate demand in each, and then make decisions based on the results.
Common demand forecasting mistakes you should avoid
Making mistakes in demand forecasting can lead you to accumulate unnecessary stock or to suffer the dreaded stockouts.
Below, we list some of the most common mistakes people tend to make when trying to plan demand:
- Planning without enough historical data:
Let’s say a clothing store has decided to add a new sportswear line. However, the store has no historical data on sportswear sales, which makes it hard to forecast demand for this new line. In this case, the store could gather market information, run surveys and look for competitor data to get a clearer idea of potential demand. It’s important not to jump to hasty conclusions based on too small a data sample, or to extrapolate from realities that don’t match our own.
- Failing to account for seasonality
A toy store can expect a significant increase in demand during the Christmas season. If the store doesn’t take seasonality into account, it could plan its production and inventory management poorly. To avoid this, the store should factor in seasonality when planning production and inventory management to make sure it has enough stock during high-demand periods. It’s vital to reverse-engineer your plans, factoring in the start of these high-demand periods and the production and shipping times, so you can plan your orders and stock up sufficiently.
- Making decisions based on assumptions or intuition:
Let’s say an electronics store assumes that demand for flat-screen TVs will decline because of the introduction of new technologies. However, demand for flat-screen TVs remains high thanks to their relatively low cost compared to the sector’s latest additions. If the store relies on this assumption, it could plan its orders poorly and pile up stock of a product that isn’t in demand while running short of the products its customers actually want, leading to the worst situation of all: overstock and stockouts at the same time.
- Lack of communication between departments:
A clothing store might plan a promotion in its winter clothing section to clear out the remaining inventory, but if the purchasing department isn’t aware of this offer, they could interpret it as a spike in demand and decide to keep buying more stock of that same collection, resulting in excess inventory and a financial loss for the store. To avoid this, it’s important for departments to work together and share relevant information in order to produce an accurate, consistent forecast.
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How can you improve the accuracy of your demand forecasts?
Although forecasting demand is never an easy task, there are various strategies that can help companies improve the accuracy of their forecasts and reduce the risks associated with inventory management and production planning. In this section, we’ll show you some of the most effective ways companies can improve demand forecasting accuracy and ensure their operations always stay one step ahead.
- Improving data quality:
Digitized stock management is the first step to making sure you have accurate turnover data. Take inventory frequently and record transfers between stores, thefts, losses and discounts.
Having tools such as a people counter can also be useful for understanding not only your stores’ sales but also their footfall and your sales team’s visitor-to-sale conversion.
- Integrating data from different sources:
For example, an electronics store wants to improve the accuracy of demand forecasting for its products. To do so, it could combine internal data, such as sales histories and production data, with external data, such as market trends and consumer behavior. By integrating this data, the business could gain a more complete view of demand and improve forecasting accuracy.
- Continuous monitoring and adjustment of the demand forecast: Let’s say a sports store wants to improve the accuracy of demand forecasting for seasonal products, such as swimsuits in summer and coats in winter. To do so, it could use analytical tools and techniques to monitor deviations from the actual forecast and adjust accordingly. By doing this, the store could improve forecasting accuracy over time and keep as close as possible to actual market demand.
Discover how Stockagile can help you forecast your demand
Stockagile uses analytics and machine learning techniques to generate accurate demand forecasts and provides real-time information on sales performance and inventory levels.
What’s more, Stockagile lets you continuously adjust and refine your forecasts as more data is gathered and new sales are made, which can help ensure that inventory levels are optimized and that products are available for customers when they need them.
Finally, having software like Stockagile will help you adapt your purchasing and replenishment strategy to that demand, improving your business’s efficiency.
In conclusion, demand forecasting is a crucial task for any business that wants to optimize its supply chain and maximize its profitability. With so many factors that can affect demand, it’s important for companies to use a combination of quantitative and qualitative methods to predict future trends more accurately.
At the same time, it’s important for companies to watch out for the common mistakes when making their forecasts and to work on continuously improving data quality and monitoring their businesses so they can adjust their forecasts accordingly.