The 3 Strategies For Proper Forecasting In The Trucking Industry

Accurate predictions help you use resources wisely, cut costs, and provide better services in any industry, but the trucking industry needs accuracy more than most. The industry has many challenges, like changing fuel prices, seasonal demand shifts, and economic ups and downs. Without good forecasting, you could face inefficiencies and losses.

For good forecasts, there are different techniques to use, from simple approaches to advanced ones using machine learning and the Internet of Things. Knowing these methods can help you spot trends in the trucking industry so you can make better decisions.

In this article, we will go over some of the best strategies to help you prepare for the future of your trucking business.

1 - Qualitative methods

There is a lot you can learn from data and by using AI to help you forecast. However, nothing beats the human touch. Qualitative methods are forecasting techniques that rely on human judgment, experience, and insights rather than just data.

One common method is to lean on expert opinion. This involves talking to people who know a lot about the industry. Their expertise and experience can give you some insights to help you see trends and potential problems that data alone might miss.

Market research is also important. Proper market research involves gathering and analyzing information about market conditions, what customers want, and what competitors are doing. You can collect this information through surveys, interviews, and focus groups. Market research helps you understand future market trends, anticipate changes in demand, and make strategic decisions.

2 - Quantitative methods

Humans can only be accurate to a limited degree. There is a place for using data to help paint a picture. Quantitative methods use numbers and statistics to make accurate forecasts.

One main quantitative method is time series analysis. This looks at data collected over time to understand patterns and trends. It’s useful in trucking and shipping, where things like demand and fuel prices change over time.

Within time series analysis, moving averages are a common technique. This method smooths out short-term changes by averaging data points over a set period. For example, a three-month moving average takes the average of the past three months to predict the next value so you see the overall trend without being confused by random short-term changes.

3 - Use machine learning

Machine learning is a way for computers to learn from data and make predictions. It uses advanced algorithms to find patterns and make decisions without being explicitly programmed for each task.

Machine learning techniques use advanced algorithms to analyze data and make predictions. These methods are very good at finding complex patterns and handling large amounts of data, which a human would never be able to do.

Support vector machines (SVMs) are a very helpful machine learning technique. SVMs work by finding the best way to separate data into different groups which is good for classifying problems, like deciding if a shipment will be on time or late.