Optimize your freight management processes for best efficiency and cost control by using data analytics.
Optimize your freight management processes for best efficiency and cost control by using data analytics.
According to a 2021 survey, worldwide, businesses suffer economic losses of roughly $184 million annually due to supply chain disruptions. With global trade expanding and consumer expectations rising, logistics managers are under increasing pressure to deliver products faster, cheaper, and more efficiently.
In this regard, managing logistics is challenging work, and it’s becoming even more challenging due to fluctuating costs, inconsistent delivery times, and operational disruptions. As international trade grows and regulations evolve, these obstacles are likely to intensify.
The good news is that data analytics provides an effective way to pinpoint inefficiencies and take action. Companies that invest in predictive freight analytics find more efficient routes, enhance customer satisfaction, and significantly reduce costs. Freight analytics simplifies transportation processes and improves decision-making across the supply chain.
In this blog, we’ll explore the role of predictive analytics in shipping, covering key areas like inventory optimization, disruption mitigation, route efficiency, and more.
Let's learn the power of data-driven logistics.
The power of data analytics in freight management
According to an IBM survey, nearly 93% of shippers believe that big data analytics empowers them to make smart, data-driven decisions. This highlights a major opportunity for excellence, leveraging the vast amount of data generated throughout the logistics lifecycle.
Advanced transportation management systems (TMS), like those of Pando.ai, play a crucial role in this transformation. These systems provide valuable insights that help optimize freight management processes, enabling shippers to make more informed decisions.
Let's dive deeper.
Predict demand fluctuations and optimize inventory levels
Data analytics significantly improves another key pillar of the modern agile supply chain: inventory management. Do you know that you could be wasting 15% to 30% of your overall inventory value on carrying costs? That's a significant amount. But with data-driven logistics and predictive analytics in shipping, you can reduce it by a huge margin.
For instance, big data in supply chains can help you identify non-moving products that are going to be obsolete. With freight management solutions, you can shift this inventory to areas with higher demand. Additionally, big data analysis helps you foresee product seasonality, regional demand variations, and even competitor trends. Such modern freight management helps you optimize inventory to release cash flow by cutting excess and obsolete inventory and guarantees the contentment of your clients.
Mitigate disruptions with predictive analytics
Freight management isn't just about moving goods; it's about ensuring they arrive on time despite external factors. Predictive analytics in shipping helps you anticipate disruptions caused by changing regulations, labor issues, geopolitical events, or weather conditions. It also provides proactive solutions to mitigate these risks.
For example, various challenges across the Middle Corridor, including the conflict between Russia and Ukraine, have affected trade routes and political relationships. By using predictive analytics in shipping, businesses can redirect goods before delays occur, avoiding costly disruptions. Such early warnings about potential delays will allow those businesses to adjust quickly and minimize risks.
Optimize transportation routes and cut fuel costs
Transportation costs are a major factor in the overall cost of goods. According to McKinsey, logistics expenses account for 12–20% of e-commerce revenues, and this number is expected to grow. In this regard, big data in supply chains can help optimize shipping routes and cut fuel costs. Big-data-powered freight analytics processes large data sets, including traffic patterns, fuel prices, and carrier performance, to suggest the most efficient routes.
With tools like geocoders, IoT devices, and GPS tracking systems, freight companies gather data for advanced route planning. AI and machine learning then identify the best routes. This leads to on-time deliveries, lower fuel costs, and a competitive edge in the market.
Check data quality
While data is a powerful tool, using poor-quality data can lead to inaccurate forecasts and bad decisions, disrupting the entire logistics process. For instance, using pre-COVID-19 data for e-commerce trends would produce irrelevant results since customer demand has shifted significantly post-pandemic.
Low-quality data can cause several issues, such as:
- Shipping goods to incorrect addresses
- Incomplete client records, leading to missed sales opportunities
- Incorrect compliance reporting, resulting in heavy fines
Major companies using data analytics to boost efficiency and reduce cost
Let's talk about two major companies that are seeing big benefits from using big data in their supply chains.
Walmart: To maximize the efficiency of its distribution hubs and stores, Walmart uses inventory management systems powered by artificial intelligence. These algorithms analyze past data and make predictions about where products will be sold. Customers can be assured that they will receive the necessary materials efficiently and at cheap prices using this approach.
Efficiency in item delivery, customer satisfaction, and operational efficiency have all been greatly improved with this data management system, which has streamlined the overall shopping experience.
UPS: UPS uses its ORION system to find the most efficient routes for deliveries. In order to determine the optimal routes for its fleet, this system takes into account data from sensors in the vehicles, information about the packages, and outside factors.
ORION's data-based route optimization has cut down on yearly miles driven by almost 100 million, leading to substantial fuel savings, decreased maintenance expenses, and improved delivery efficiency.
Minding data security: a challenge in data analytics
The logistics industry relies heavily on digital platforms, making data security a top priority. A data breach can result in significant losses. For example, in November 2023, DP World, a major global port operator, faced a cyberattack that caused a three-day suspension and a 30,000-container backlog. That said, even a minor breach of your freight data can harm your business, erode customer trust, and lead to legal trouble.
According to an IBM study, 40% of data breaches are due to multi-environment data storage. To avoid this, you should use strong data validation tools and secure your analytics systems. This ensures that the insights you rely on are both accurate and safe. In this regard, a single, unified freight management system helps safeguard your data in one secure location.
Leveraging data analytics with Pando's unified freight management system
Data-driven freight management is the future. Businesses that adopt analytics today will be better prepared to overcome the dynamic challenges of the logistics industry in the future.
According to Gartner, nearly 50% of logistics companies will use data analytics to optimize transportation networks by 2025.
And why not? Big data and predictive analytics allow companies to foresee market changes, reduce risks, and ensure smooth operations.
If you want to stay ahead of the curve and transform your supply chain, explore how Pando's unified freight management system platforms can help you optimize your freight operations from start to finish. Book a demo today!
