With rapid advancements in machine learning and artificial intelligence, businesses across all sectors increasingly adopt these technologies to solve complex challenges. But how do data analytics, AI, and machine learning for transportation impact the logistics industry and your supply chain operation?
The Roles of Data Analytics, Machine Learning, and AI in Logistics
1. Machine Learning in Transportation
Machine learning algorithms analyze vast historical transportation data (shipment details, carrier performance, etc) to identify correlations and trends. This allows businesses to predict potential delays, optimize routes, and forecast demand.
Machine learning involves:
- Building models that recognize patterns in data.
- Making predictions based on those data patterns.
Two examples of using machine learning in transportation and logistics:
- Analyze past carrier performance data to predict which carriers will likely deliver on time for a specific lane or shipment type.
- Identify optimal transportation routes, minimizing costs and transit times.
Watch our webinar with Kevin LoGuidice and Steve Blough to learn more about how data analytics, machine learning, and AI for transportation are transforming the industry.
2. Data Analytics in Logistics
Data analytics is crucial for understanding operational performance and identifying areas for improvement across the supply chain.
However, specific challenges persist when making data-driven decisions.
- Data quality: In many cases, data is inaccurate, incomplete, or outdated. This leads to flawed insights and poor decision-making.
- Human bias: We believe we decide based on factual information, but we can err and unconsciously make decisions based on beliefs.
- Complexity: Supply chains are complex operations with many nuances. Even with advanced analytics tools, it can be challenging to understand the system and derive meaningful information—such as ETA calculations, which consider many factors.
- Mindset shift: Reporting tells us what’s happening, but analytics explain why. However, humans are wired for reporting, not analytics. It can be hard to leverage the correct information.
3. Advancing the Supply Chain with AI
Stakeholders can focus on more strategic elements, enabling faster and more informed decisions. At the same time, businesses can forecast demand, optimize transportation routes, and manage inventory levels, improving efficiency and cost savings.
By leveraging real-time data and predictive analytics through machine learning and AI for transportation-powered systems such as the TMS, these businesses also track shipments in real time.
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Leveraging Data Analytics, Machine Learning, and AI with MercuryGate
As AI and machine learning for transportation continue to evolve, supply chain processes will evolve with solutions like predictive analytics that support:
- Vehicle maintenance.
- Real-time visibility and tracking.
- Carrier selection.
- Dynamic pricing and route optimization.
- Customer service support.
MercuryGate provides Smart Transportation solutions that help customers achieve rapid time to value, avoid disruption, and scale growth by integrating machine learning and AI for transportation processes.
Leading manufacturer Facil significantly improved logistics efficiency by implementing MercuryGate’s Transportation Management System (TMS). By addressing manual processes and visibility challenges in less-than-truckload (LTL) shipping, Facil optimized routes and carrier selection. The result: 12% annual cost avoidance in LTL expenses and enhanced overall operational efficiency.
Want a partner who cares about the minor aspects of your transportation process? Request a demo.
