Modern alchemy: Use AI to spin your logistics data into gold

AI-powered freight analytics solutions are turning shippers’ own raw logistics data into the gold standard source for strategic, actionable insights. 

Keep reading to learn how your company can also leverage the technology to optimize costs, enhance revenue, and gain a competitive edge in an increasingly complex shipping landscape.

Key takeaways

• AI-powered logistics analytics transform raw data into actionable insights, optimizing costs and improving customer satisfaction.
• Effective analytics integrate data from various sources, providing a comprehensive view for informed decision-making.
• Many shippers still use manual methods, while modern analytics leverage cutting-edge technologies for deeper insights.
• AI analytics optimize expenses, enhance revenue, manage risks, and increase efficiency in logistics operations.
• The sooner AI-powered technologies are embraced by shippers, the more likely they are to secure competitive advantage.

What is logistics data analytics, and why is it important?

Logistics data analytics refers to the process of collecting, analyzing, and interpreting large volumes of data generated throughout the supply chain to optimize logistics operations. 

Large shippers can use the resulting goldmine of insights to make more informed decisions, improve efficiency, and enhance customer satisfaction. This data-driven approach transforms traditional shipping into a more agile, responsive, and intelligent process.

Unearthing logistics data

Data points come from a diverse range of sources that provide a comprehensive view of the entire logistics process. Here are some of the main ones:

• Enterprise resource planning (ERP) systems: Centralized databases that store and manage various business processes
• Warehouse management systems (WMS): Software that controls and manages warehouse operations
• Transportation management systems (TMS): Platforms for planning, executing, and optimizing the movement of goods
• Internet of things (IoT) devices: GPS trackers, RFID tags, smart sensors in vehicles and warehouses
• Electronic data interchange (EDI): Standardized format for exchanging business documents between companies
• Customer relationship management (CRM) systems: Databases containing customer interactions and preferences
• Public data sources: Weather databases, traffic information systems, economic indicators
• Supplier portals: Platforms where suppliers input data about their products and capabilities
• Mobile applications: Apps used by drivers, warehouse workers, or customers that generate data

The challenge lies in integrating these data streams and mining the resulting mass of information for meaningful guidance. Raw metrics alone don’t constitute a roadmap for improving operations and making customers happier.

How do shippers analyze logistics data?

Logistics analytics is at a crossroads. 

It’s no secret that rapidly evolving technological advances, like artificial intelligence (AI), are transforming the way businesses approach… well, everything. But transformation is never an immediate process, or even a particularly fast one in many industries. This is especially true for freight logistics, which by default seems to take an extended ‘wait-and-see’ approach to emerging technologies.

Regardless, the introduction of AI-powered logistics analytics solutions means a shift from old to new is already underway. During this transition period, there are two approaches to supply chain analytics. 

Manual approaches to freight analytics

On one hand, the majority of large shippers are following the same data analytics as always. These traditional methodologies often rely on:

• Labor-intensive manual data collection and analysis, often entailing error-prone human data entry and heavy dependence on spreadsheets
• Descriptive statistics that provide insights into past performance indicators with limited predictive capabilities
• Basic data visualization that help users better understand trends, but may not be able to uncover complex patterns

While these methods have been effective in the past, analysts today are being buried by newly available data sets. Sifting through it is time-consuming, and the end results are seldom completely accurate due to integration difficulties and old-fashioned human error.

The modern approach to logistics data analysis

Meanwhile, a growing number of pioneers in the food and beverage, manufacturing, retail, and consumer goods (CGP) industries use cutting-edge technologies to mine their own data more effectively. 

Instead of spreadsheets and data entry clerks, they rely on:

• Machine learning, which enables algorithms to learn from data and answer questions that users may not know to ask directly
• Predictive analytics powered by advanced pattern recognition capabilities, helping to forecast future trends and outcomes based on integrated, comprehensive historical data
• Prescriptive analytics that helps you identify the best path forward by reducing data overload down to insights that are most relevant and valuable for your operations
• Natural language processing, which allows non-technical users to analyze even unstructured data using queries structured like normal speech patterns

Instead of waiting for already-proven innovations to pan out, these companies are reaping the financial benefits that come with task automation, accuracy improvements, and unprecedented strategic control and oversight.

Good as gold: 6 key ways AI-powered analytics drive operational value

AI-powered logistics analytics solutions are transforming shippers’ freight data into a goldmine of valuable insights and opportunities. Here’s how they’re enriching logistics operations.

1. Optimizing expenses

• Route optimization and load consolidation: AI analyzes factors like traffic and weather to identify the most efficient routes. It also spots opportunities to combine partial loads, reducing overall shipping costs.
• Inventory management and warehousing efficiency: AI optimizes inventory levels based on demand forecasts and lead times. This, in turn, improves warehouse utilization and picking efficiency, decreasing storage and labor costs.
• Mode optimization: The technology suggests cost-effective alternatives to full truckload (FTL) shipments by analyzing trade-offs between cost, speed, and reliability across shipping modes.
• Accessorial cost evaluation: AI identifies accessorial charge patterns across carriers, lanes, origins, and destinations, and recommends strategies for lowering or eliminating them.

2. Enhancing revenue

• Improved service levels and reliability: By analyzing factors affecting on-time delivery, AI may improve your operational performance. More satisfied customers translate to increased business opportunities.
• Customer segmentation and targeted offerings: The technology can look for shipping patterns that inform the development of tailored service packages. By singling out high-value customers for personalized attention, you can increase customer loyalty and spend.

3. Managing risks

• Predictive maintenance: AI forecasts maintenance needs based on usage patterns and historical data to reduce unexpected downtime and extend the lifespan of your assets.
• Supply chain disruption forecasting: By identifying potential disruptions before they occur, you can mitigate their effects. 
• Fraud detection and prevention: Smart technologies can detect unusual patterns indicating fraudulent activity so you’re able to do something about it.

4. Increasing efficiency

• Backhaul opportunity identification: Modern analytics solutions match outbound and inbound shipments to reduce empty miles for improved efficiency and cost savings.
• Long haul analysis and facility relocation: Taking into account historical long-distance shipping needs, advanced technologies can suggest optimal distribution center locations.
• Lane contract opportunities: AI identifies high-volume lanes suitable for contracted rates, and offers guidance on agreement terms.
• Routing guide optimization: You can use data insights to update routing guides, and further streamline your operations.

5. Improving operational performance

• On-time pickup, delivery, transit, and lead time analysis: To help you reduce consistent delays or inefficiencies, the solution will offer process improvement suggestions that optimize transit times and enhance service levels.
• Carrier performance evaluation: AI assesses carrier performance across diverse categories to optimize load distribution in a way that ensures the best value.

6. Market intelligence

• Rate benchmarking: Comparing current rates to market averages, advanced analytics technologies can look for potential cost savings or revenue increases to keep you competitive.
• Trend identification and forecasting: By using emerging shipping data patterns to predict future market conditions, cutting-edge analytic solutions help you stay ahead.
• Continuous, real-time analysis: AI provides ongoing insights as market dynamics evolve and data is generated. These real-time suggestions for operational improvements enable rapid response to shifts in supply and demand, 

By leveraging AI-powered logistics analytics, you position your company to extract maximum value from your data. State-of-the-art solutions like Loadsmart’s FreightIntel AI, for example, dig deep to uncover hidden insights that make you more competitive within an increasingly complex shipping landscape.

Don’t miss a golden opportunity to gain competitive advantage

Like a precious metal, technology-based competitive advantage is a finite resource. While early adopters can use a given solution’s benefits to differentiate themselves and grow market share, the advantage tends to diminish as adoption spreads. Eventually (and inevitably), once-differentiating benefits get absorbed into operational assumptions. 

Fortunately, AI-powered freight analytics solutions are now sitting at the ideal juncture of this innovation lifecycle. The technology is proven, its benefits are undeniable, and best of all, not everyone is using it yet. They will, though, so it’s important to avoid sitting on the fence for too long and risk losing a potential pre-rush advantage. 

And definitely make sure you don’t linger there once the technology reaches its tipping point, because the future of freight will largely be based on AI. Companies that are slow to adapt could be in danger of waiting-and-seeing their operations into obsolescence.

FAQ