Understanding demand and the ability to accurately predict it is imperative for efficient manufacturers, suppliers, and retailers. Overestimated demand leaves the supplier with a surplus that can be a financial drain; underestimated demand means missed sales opportunities.
Demand forecasting is the area of predictive analytics dedicated to understanding consumer demand for goods or services. That understanding is harnessed and used to forecast consumer demand. Knowledge of how demand will fluctuate enables the supplier to keep the right amount of stock on hand. If demand is underestimated, sales can be lost due to the lack of supply of goods. If demand is overestimated, the supplier is left with a surplus that can also be a financial drain. Understanding demand makes a company more competitive in the marketplace. Understanding demand and the ability to accurately predict it is imperative for efficient manufacturers, suppliers, and retailers. To be able to meet consumers’ needs, appropriate forecasting models are vital. Although no forecasting model is flawless, unnecessary costs stemming from too much or too little supply can often be avoided using data mining methods. Using these techniques, a business is better prepared to meet the actual demands of its customers.
Understanding Consumer Demand
In demand forecasting, as with most analysis endeavors, data preparation efforts are critical. Data is the main resource in data mining; therefore it should be properly prepared before applying data mining and forecasting tools. Without proper data preparation, the old adage of “garbage in, garbage out” may apply: useless data results in meaningless forecast models. Major strategic decisions are made based on the demand forecast results. Errors and anomalies in the data used to create forecast models may impact the model’s ability to forecast. These errors give rise to the potential for bad forecasts, resulting in losses. With properly prepared data, the best possible decisions can be made.
There are several sources for problems with data. Data entry errors are one possible source of error that can adversely affect the demand forecasting efforts. Basic statistical summaries and graphing procedures can often make these types of error apparent. Artificial demand shifts are another error source. For example, consumer response to a promotional offer may temporarily boost sales of an item. Without a similar promotion, the same increase cannot be expected in the future. Some uncontrollable factors have the ability to influence consumer demand as well. A factor such as economic conditions may tend to impact demand. An unusually mild winter will likely cause lower energy demand. Accounting for these influences of demand can help fine tune forecast modeling.
Every business sees seasonal fluctuations. Holidays and weather changes influence products and services that consumers want. While it is extremely important to account for how seasonal changes affect demand, it may be possible to benefit further from this. Understanding how seasonal factors affect consumers helps businesses position themselves to take advantage.
Forecasting Consumer Demand
A wide variety of analysis tools can be used to model consumer demand – from traditional statistical approaches to neural networks and data mining. Using these demand models enables estimation of future demand: forecasting. Possibly, a combination of multiple types of modeling tools may lead to the best forecasts.
Time series analysis is a statistical approach applicable for demand forecasting. This technique aims to detect patterns in the data and extend those patterns as predictions. The ARIMA model, or autoregressive integrated moving average, in particular is used both to gain understanding of the patterns in data and to predict in the series. Different parameters are used to detect linear, quadratic, and constant trends.
Other approaches for building forecast models are Neural Networks and Data Mining, which are capable of modeling even very complex relationships in data. Demand forecasting is a very complex issue for which these methods are well suited. Multilayer Perceptrons and Radial Basis Function neural networks, Multivariate Adaptive Regression Splines, Machine Learning, and Tree algorithms can all generate predictive models for this application.
Systematic Patterns vs. Trends
Generally, demand patterns consist of some basic classes of components, seasonality, and trend. Seasonality refers to the portion of demand fluctuation accounted for by a reoccurring pattern. The pattern repeats systematically over time. Trend is the portion of behavior that does not repeat. For example, a trend may show a period of growth followed by a leveling off. In retail sales, seasonality will likely find patterns that repeat every year. With sufficient data, other seasonality trends may manifest across multiple years.
Once adequate predictive models are found, these models can then be used to forecast demand. A demand forecast model may actually be an ensemble of multiple models working together. This technique of combining models often results in better predictive accuracy. When one model gets off track, the ensemble as a whole counteracts.
As more data accumulate about consumer behavior, demand forecast models should be updated. This will be a continual effort monitoring and modeling demand in order to be constantly aware of changes. Failing to update forecast models and take advantage of all the information available will likely prove to be a costly mistake.
Using up-to-date demand forecast models, inventory management becomes a much simpler task. The forecast models offer insight into when shifts will occur, but more importantly, how big the shift will be. Using demand forecast models, inventory and human resources can be properly planned and managed well in advance and with fewer surprises.