Common Forecasting Techniques Used by Businesses
Currently, demand forecasting is becoming an increasingly important task for companies. It takes a lot of planning to ship the right amount of products at the right time; otherwise, companies could end up with excess stock or missing out on valuable sales opportunities. When faced with such critical decisions, it's always good to have several techniques up your sleeve to help you make better forecasts. Here are four forecasting techniques you can apply in your business:
1) Moving Average (MA) Method
The moving Average (MA) method uses average demand data from multiple months/years/quarters to predict future demand trends. First, calculate the moving average by adding the most recent month's total number of units sold and dividing by one less than the total number of months used in the average. Then, determine the demand for next month by adding the moving average to the current month's total number of units sold. Using a series of monthly data points makes this method more effective than taking into account just one past data point or relying on high or low data from a given period.
2) Exponential Smoothing (ES) Method
Demand Smoothing: This statistical technique averages inventory levels over time to better predict future demand and avoid stock-outs. It's based on the idea of smoothing out peaks and dips in sales to maintain an acceptable level of stocks without having too much or too little inventory. In summation, it uses a three-step method to forecast future demand:
i) Calculate the prior period's demand average, known as the smoothing constant
ii) Calculate today's total number of units sold
iii) Calculate the next period's demand by multiplying today's total number of units sold by the smoothing constant for this data set.
If you plot these values on a graph, the series will show an exponential move toward the projected value. The higher the volatility in your sales, the lower the constants you use should be. Although this technique is easy to use, it doesn't consider seasonality or special events outside normal patterns. Therefore, even though it smooths out some volatility, it fails to capture other important factors to boost sales.
3) Trend-based Method
Trend-based forecasting or index number method uses time series analysis to make forecasts. It converts historical data into indices for making future projections. Under this method, the users will assume that past trends will continue in future unless there are definite changes in economic conditions. These changes are reflected in the index values, which then become the basis of forecasts. Index numbers combine several attributes (such as forecasting average prices and quantities demanded) into one measure so that you can gauge their overall effect to arrive at a forecasted value. For example, if you're selling skis, you might find that sales during the end-of-season sales are always higher than expected due to new inventory coming into stores from next season's line. However, this insight will only work if you have enough historical data going back several years.
4) Regression Analysis
A statistical approach similar to factor analysis, regression analysis, focuses on specific variables (such as prices and promotions) while considering ancillary factors like seasonality and economic trends. It also uses past data and trends to predict future demand. Businesses use this technique with a high product variety or multiple outlets. It's particularly useful when demand is driven by factors that are difficult to measure, like customer satisfaction and perceptions of quality. However, this method can take longer than some other methods to set up and use.