If you understand how demand fluctuates, you can optimize your inventory and stabilize your costs. When you underestimate demand for a product, you can lose out on potential sales. On the other hand, if you overestimate demand you’ll have products left that you are unable to sell which can wreak financial havoc on a company.
It’s vital to have appropriate demand forecasting models. Of course, there is no flawless model – what needs to be flawless are your data gathering methods and preparation. With good data mining and demand forecasting techniques, you’re better equipped to meet your customers’ actual demands.
Key Aspects of Demand Forecasting
Aspects of demand forecasting that are key to understanding your company’s sales patterns include:
Types of analytical tools
Analytical tools, such as time series analysis and data mining, allow you to explore the meanings behind your data. Analyzing time series uncovers patterns within your data and, once understood, extends a pattern into the future as a prediction. By setting various parameters to measure, you can spot trends linearly, quadratically, or constantly.
Data mining can help model even the most complex of data relationships. Data mining is one of the best-suited methods to use for demand forecasting since this type of forecasting has several intricate relationships.
Trends vs. Patterns
Usually, patterns of demand are of two basic types:
Patterns that tend to recur after a given period are an illustration of seasonal demand fluctuation. In other words, you’ll see this pattern again and again and usually at the same interval, such as every Black Friday, Valentine’s Day, etc., depending on the product.
A trend is a fluctuation in demand that may spike and then level off. It does not repeat.
Getting the Demand Forecast Right
As with many analysis efforts, you must properly prepare and gather the data you are going to analyze. Because data is your main resource, you must apply proper preparation and gathering methods before using a data mining tool or attempting to forecast. Think of it like this: if you input the wrong data, you will get the wrong results.
Your company makes major decisions from the results of a demand forecast. Any errors or unexplainable anomalies could impact the forecasting ability of the model. What does that mean?
- Bad forecasts.
- Financial losses.
One of the most common errors in data preparation stems from errors during the data entry portion. Methods such as graphing or statistical summaries can help point out these errors before you use them in any forecasting activities.
If you prepare your data correctly, you can make better, more informed decisions.
Communication in Demand Forecasting
A collaborative environment is crucial to proper demand forecasting. Each team that plays a role in management should understand how to use the advanced analytical strategies you’ve employed.
A large part of correctly forecasting lies within the types of communication found between departments, and the nature of the communication.
With good communication, advanced analytics strategies can identify trends and decipher which are seasonal, which are mere trends, and so on. You will begin to understand customer buying habits, which allows you to predict their future buying behavior, which in turn drives better, more strategic decisions.
Predictive analytics is a huge part of any company strategy. Employ these analytics and, over time, you’ll improve your customers’ experiences, streamline your operations, and your growth will become supercharged.
In fact, management can use these strategies internally by creating experimental systems. The end result? An embracing of innovation due to curiosity.
It is this curiosity that fosters continued experimentation and growth.
Better Demand Forecasting = Better Inventory Management
Employing the use of current demand forecasting models makes managing inventory much easier. Your forecast models provide insight into when you can expect shifts, but most importantly, how big these shifts will be.
Get It Right with Vanguard
A better demand forecast automatically improves your downstream planning initiatives, resulting in cost and inventory optimization and improved service levels. Vanguard’s unified platform enables better forecasting accuracy, which trickles down into a better supply plan and more optimized inventory.
About Vanguard Software
Vanguard Software introduced its first product for decision support analysis in 1995. Today, companies across every major industry and more than 60 countries rely on the Vanguard Predictive Planning platform. Vanguard Software is based in Cary, North Carolina.