How Predictive Analytics Is Transforming Shipping Cost Forecasting

No matter how extensive your planning is, you’ll always have trouble predicting the costs associated with a shipment. “Changes in fuel prices, labor shortages, and port congestion are just a few things companies must consider,” warns JTM Cargo Management, a Sydney freight forwarder. 

Back in the day, teams used reactive decision-making and trends to estimate expenses. Unfortunately, given the nature of global shipments, these methods are no longer suitable. Businesses now rely on machine learning and predictive analytics to derive valuable insights from big data. 

In this article, we will explain the importance of predictive analytics and present some real-world use cases. 

What Is Predictive Analytics in Logistics?

As the name implies, predictive analytics is a form of analytics that relies on advanced technology to anticipate trends and outcomes. By analyzing massive amounts of historical data, logistics companies can detect patterns that will help them reduce expenses. 

Among other things, AI software will take into account seasonal demand, geopolitical risks, and weather conditions to make an accurate cost assessment. ML technology will process vast amounts of available data to discover correlations and patterns. Keep in mind that, while predictive analytics is accurate in most cases, it doesn’t cover all unpredictables.

For example, software can compare fuel prices during different seasons and during the last year or so. A steady increase or decrease makes it easy to determine how much the price will fluctuate in the upcoming period. Armed with this information, companies can change their budgets, make proactive purchases, and minimize potential risks. 

5 Benefits of Predictive Analytics

These are the five biggest reasons why your company should use predictive analytics for shipping cost forecasting:

Increased Accuracy

Unlike traditional forecasting, which was often perceived as rigid, today’s models account for the volatility of global shipping. ML technology uses a combination of different datasets, including fuel prices, delivery times, freight rates, weather conditions, transportation risks, and more. 

According to McKinsey, the right logistics program can reduce errors by at least 20%, making it easier for management to predict future expenses. Enhanced accuracy is vital for preventing budget overruns, funneling resources into the right activities, and ensuring your projects are sustainable. Overall, the technology increases the resilience of your supply chain while increasing stakeholders’ trust. 

Cost Optimization 

The best thing about predictive technology is that it allows you to optimize processes along the logistics chain. Machine learning can dissect every phase of the transport process, helping you improve aspects such as procurement policies and inventory levels. For example, you can minimize unnecessary spending and wait for a better time to make a specific purchase.

Predictive models also do a great job optimizing container usage. That way, you can better utilize available resources and minimize back-and-forth. Reducing waste will not only help you improve your financial balance, but it is also great for sustainability. Ultimately, you’ll be able to improve performance, specifically delivery times, without sacrificing service quality. 

Adjustable Planning 

Perhaps the best thing about predictive analytics is that it changes how we react to certain situations. Specifically, it allows us to make decisions in advance, before sustaining financial losses. For example, predictive analytics marks port congestion before you plan a ship’s course. This allows you to change the destination port and hasten delivery times.

Basically, the technology increases your reaction windows. Instead of making last-minute adjustments, which are usually costly, you can plan each transport slowly and meticulously. This makes it especially important for large companies that have massive vehicle fleets. 

Risk Management

Having all of this in mind, ML has become an invaluable part of the logistics process. The software identifies numerous relevant risks in advance, giving you enough time to react. For example, it can predict labor strikes, account for regulatory changes, consider currency fluctuations, and other factors that might be a bit harder to quantify.

By going through historical data and assessing the current market situation, predictive analytics can spot the biggest vulnerabilities. Besides changing their current plans, companies can also create better contingency plans, such as deciding on alternative routes or changing suppliers. 

Client Satisfaction

Besides affecting your finances, predictive analytics improves your relationship with clients and other stakeholders. It ushers in a new era of transparent, reliable logistics, where customers will always have precise delivery times and stable pricing. Ultimately, this will improve trust between the two sides and increase retention.

Speaking of transparency, it ensures that clients always feel confident about the pricing. They never get the feeling that they’re being scammed by the company, which is particularly important for logistics businesses operating in highly competitive markets. Overall, predictive analytics is a tool that gives you a massive competitive advantage, and in this day and age, it is vital for staying afloat. 

Real–World Use Cases

Despite being a relatively new technology, many logistics businesses have already adopted predictive analytics software. As such, we have numerous data points on how these programs assist businesses and make their daily operations better: 

• Fuel Prices: Fuel is one of the biggest tickets when it comes to shipping. Changes in global politics and economy can easily affect its price, leading to constant and sometimes sharp fluctuations.
• Route Optimization: Busy traffic, road works, and other factors can delay your shipment. So, modern businesses have started using predictive software to expedite deliveries and reduce fuel consumption.
• Demand Predictions: Big brands such as Nike have successfully used predictive software to optimize their inventories. During the recent disruption in China, their sales only dropped by 5%, which is a big win, especially compared to other brands. 
• Dynamic Pricing: Dynamic models enable companies to adjust prices based on supply and demand. That way, they can make great profits during surges and maintain the same number of units sold when interest declines. 

The Future of Shipping Cost Forecasting

Although predictive analytics shows incredible promise, this likely won’t be the only effective technology for shipping cost forecasting. As AI software improves, we will likely see similar solutions that help logistics businesses.