When we say forecasting, weather forecasting comes to mind for most people. We are very acquainted with the term, thanks to years of suit-clad weather forecasters gracing our breakfast time every morning.
Just like the weather, forecasting is undeniably a significant part of the supply chain process. And just like weather forecasting, it refers to predicting upcoming business changes based on old data.
Supply chain forecasting helps businesses estimate their sales and product performance and use that data to plan their supply chain smartly. When you can predict your supply chain, you can shape your production based on available products, manage inventory, and ensure availability.
You can maximize your revenue while minimizing the losses in the supply chain. In the long run, it enables you to maintain a positive experience for your customers, enhance brand presence, and increase your credibility.
What are forecasting methods?
Forecasting methods are techniques used to estimate demand in the future based on your past sales, experts’ opinions, and more. There are multiple forecasting methods used by professionals, depending on their needs.
They are essentially divided into two types – the quantitative and the qualitative methods. Where quantitative methods are objective and create forecasts based on data, qualitative methods are subject and rely on personal insights and observations.
Quantitative forecasting is data-driven and based on facts. It is an unbiased process that utilizes historical data and analysis to create forecasts. They are based on mathematical models and calculations and are useful for short-term forecasts.
Here are a few quantitative forecasting techniques given below.
- Naive Approach
The naive approach relies on considering the past period’s actual sales as the forecasted sales for the coming period. It doesn’t account for any major variable that affects your demand. You can use a seasonal approach where you consider the actual sales from the same season or period from the last year as the forecast for the coming period. For example, you can use your past year’s October sales as the forecast for the October sales of the current or coming year.
- Moving Average
In the moving averages method, you take the average of sales in past periods as the forecast for the coming period; say you use the sales data for the first four months to forecast demand for the fifth month, then the data from month 2 to 5 to forecast demand for the sixth month and so on. With time, as you remove old values and introduce new ones, you can move the average, hence the name. This method isn’t accurate and only useful for inventory control for low-volume since it doesn’t consider recent data’s significance in forecasting demand.
- Exponential Smoothing
This method is similar to moving average, but takes a more weighted approach, adding significance to demand-altering parameters. The method focuses on recent data more while also considering old data. It is beneficial for short-term forecasting, but lags in successfully predicting future trends. Due to its reliance on historical data, it might predict demand patterns similar to the past or current ones. It also doesn’t account for seasonality during forecasting.
- Trend Projection
Trend projection helps you successfully predict repetitive or seasonal trends by using your past sales data. It enables you to establish the relationship between various variables that affect demand. But it requires a large amount of long-term data to identify a pattern and properly analyze it. The method assumes that past trends will continue to be relevant and likely repeat themselves. But in reality, past data may not be apt to create a forecast for the future.
- Regression Analysis
Regression analysis helps you establish a relationship between the independent and dependent variables that affect your demand. It helps you better understand your future demand based on the variables that affect them. You can add as many variables as you want to the equation. But typically, this method works best when few variables are involved, and any correlation between independent variables can affect your forecasts.
A few significant qualitative forecasting techniques are discussed below.
- Executive Opinion
The executive opinion method relies on the expertise of executive-level professionals to generate a forecast. The method is easy to perform and requires industry experts to form a collective opinion on how likely demand is to change. But it is highly subjective, relies majorly on the personal insights of the experts involved, and is found to have only poor to fair accuracy.
- Delphi Method
Similar to the last method, the Delphi method also involves expert insights to generate a forecast. But, here, the forecasts are reviewed and discussed amongst the experts until they reach a common consensus. To avoid bias, personal insights are collected anonymously. It involves multiple discussions and is one of the most reliable qualitative forecasting methods available. But the process can be extremely cumbersome and time-consuming for the experts as well as the observer.
- Market Research
Market research is extremely useful for businesses without historical data or during a new product launch. It uses competitor analysis, consumer surveys, polls, and questionnaires to create future forecasts. It gathers information to understand consumer expectations, bottlenecks, and potential issues to help you cater to your audience. Though useful, this method can be expensive, time-consuming, and can only target a small audience due to a lack of respondents.
- Historical Analysis
This method suggests that new products would have a similar sales pattern to old ones. You can use sales data from your products or similar products from the competitors. Though it’s a good forecasting method for the long term, it won’t help you forecast trends or sales for the short term.
- Sales Force Estimates
This method relies on the experience of salespeople to forecast demand based on the trends they’ve noticed in the sales over time. The process is simple and requires an estimate based on experience and expectations. The method has fair accuracy and is useful for short-term forecasting. But it requires managerial judgment to be useful, and human bias can affect the forecast accuracy.
How to find the best fit?
Both quantitative and qualitative forecasting methods play a significant role in accurate forecasting. The ideal way is to use both moderately, but the methods differ according to your business goals. The best way to do that is to use demand planning software. It uses both qualitative and quantitative methods to give you precise demand forecasts for your business.
TransImpact provides one of the best demand planning software in the market. It uses 250+ forecasting algorithms to give you accurate forecasts for up to five years. It uses historical data, competitor analysis, seasonality, trend analysis, market analysis, and more to ensure all demand-altering factors are considered. A thorough what-if analysis ensures that your business is prepared for any situation.
Get in touch to learn how to optimize your supply chain with our software.