Learning About Demand Forecasting Best Practices from Industry Examples

Demand forecasting is a critical process for businesses aiming to meet customer needs while optimizing resources. By leveraging historical data, exploratory data analysis (EDA) advanced analytics including machine learning algorithms, companies in sectors such as retail, automotive, fashion, pharmaceuticals, telecommunications, tourism and airlines can predict demand with greater accuracy. By analyzing historical data, market trends, and external factors driving customer demand, companies can predict future demand with measured uncertainty and adjust their operations accordingly. Learning from industry examples about of demand forecasting can help organizations improve accuracy, reduce waste, and enhance customer satisfaction. Here’s how various industries apply demand forecasting:

Retail Industry

In the retail industry, department store sales may be influenced by a number of regional economic variables such as a consumer price index, average weekly earnings, and the unemployment rate. Retailers may also feel that the number of shopping days between Thanksgiving and Christmas holidays has a major impact on the Christmas holiday sales volume, so their needs tend to be expressed by accurate disaggregated unit volume forecasts. Likewise, the shifting Ramadan holiday period will impact sales volumes reported for the month the holiday occurs.

Retailers like Walmart and Amazon use demand forecasting to manage inventory, pricing strategies, and promotions. For example, Amazon employs machine learning algorithms to predict customer purchasing behavior based on past activity and seasonality. This enables them to optimize stock levels and minimize out-of-stock situations, ensuring that the right products are available at the right time.

Manufacturing Sector

Manufacturers, such as Toyota, rely on demand forecasting to align production schedules with consumer demand. The automotive giant uses sophisticated forecasting models to predict which vehicle models will be in demand based on historical sales data, trends, and economic indicators. This approach helps reduce excess inventory, prevent overproduction, and improve supply chain efficiency.

Tourism and Airline Industry

Airlines like Delta and Lufthansa utilize demand forecasting to adjust ticket prices, plan flight routes, and allocate resources. By analyzing past travel patterns, seasonal factors, and economic conditions, airlines can predict passenger demand for specific routes and times, allowing them to optimize their fleet capacity and maximize revenue.

Food and Beverage Industry

Companies like Coca-Cola and McDonald's forecast demand to ensure consistent product availability. S supermarket chains use data analytics to predict sales fluctuations during holidays or weather events, allowing for efficient supply chain planning and reducing waste. Beverage manufacturers employ regional sales trends to adjust production and distribution to meet local demand fluctuations.

Technology and Electronics

Tech companies, such as Apple, often forecast demand for new product releases. By analyzing pre-order data, consumer sentiment, and market conditions, Apple can predict how many units of a new device like the smart phone will be needed. Accurate forecasting ensures they meet initial demand without overproducing.

The information on the industry would indicate whether this product is still a wise investment. The demand factors and consumption trends that need to be investigated include price, income, demographics, advertising, and regulation.

A predictive visualization of an electronics product shows historical data values, a trend/seasonal forecast profile with prediction limits based on the Additive Holt-Winters model, coded Error-Trend-Seasonal ETS (A,A,A) State Space Forecasting model (available in R forecast and fable packages).-It clearly shows a dominant seasonal pattern (reflecting consumer habits?) and a less pronounced trend pattern (reflecting consumer demographics?).

Using an exploratory year-month Seasonal-Trend-Irregular classification (STI) method, calculated with the “Two-way ANOVA without Replication” routine (Excel>Data>Data Analysis> ‘calculate SS column in percents’) in the Excel Data Analysis add-in, we can make the interpretation that the data variability constitutes about 51% Seasonality, 4% Trend, and 45% Irregular (other than seasonality or trend).

The three Seas/Trend/Irregular (STI) components can be visualized as pie- and cone charts to show the relative contribution of the trend and seasonal variation to the total variation.

As an exploratory step, the demand forecaster, using Exploratory Data Analysis (EDA)

could now make some insightful assumptions for the product. The dominant seasonality can be quantified by consumer habit factors, such as number of holidays and School openings/closings, driving the demand. The trend relates to the underlying growth of the population and can be quantified by the age-cohorts of consumers, for example. The “Other” component (45%) still contains information about everything else not attributable to consumer habits (seasonality) and consumer demographics (trend). This could include promotions, economic cycle, unusual events and random error. In a modeling environment, we first characterize the trend/seasonality with a time series forecasting model with a trend/seasonal forecast profile (e.g., Holt-Winters exponential smoothing or an ARIMA (011) (011)12 “airline” model; these are also in the same family of the State Space Forecasting models as exponential smoothing.

Key Takeaway

Across various industries, effective demand forecasting is a powerful tool for improving supply chain efficiency, reducing costs, and enhancing customer satisfaction. By adopting EDA (developed by John Tukey, shown above with yours truly), advanced forecasting models, utilizing real-time data, and learning from industry best practices, businesses can navigate uncertainty with agility and stay ahead of market trends.