Benefits of Big Data in Logistics & Supply Chain Management

Logistics and supply chain professionals are responsible for ensuring goods arrive safely and on time to warehouses, stores, and end customers. But many factors can impact the movement and management of these goods.

To effectively optimize logistics and supply chain operations, decision-makers must consider customer demand, delivery route safety and reliability, fuel consumption, and more. Managing all these factors requires deep insights into store performance and traffic trends.

That’s where big data analytics can help. Big data on customer travel behaviors, route reliability, and more is helping logistics companies, supply chain vendors, and other businesses make smarter decisions that move goods efficiently, reduce resource consumption, and improve customer satisfaction. 

In this article, we’ll explore how businesses can use big data analytics to:

• Identify the safest, quickest, and most reliable routes for deliveries 
• Go beyond historical sales data to anticipate customer demand and manage inventory 
• Improve delivery experiences for drivers and customers 
• Manage risks to drivers, fleet vehicles, shipments, and store performance 
• Achieve sustainability and efficiency goals

What Is Big Data in Logistics and Supply Chain Management?

A big data approach to logistics and supply chain management helps businesses leverage massive datasets to understand everything from customer demand to route efficiency. 

While many types of big data may be helpful, transportation and mobility datasets are especially useful in understanding the most reliable, efficient, and cost-effective ways to get goods from point A to point B and meet consumer demand.

In this guide, we’ll focus on transportation big data, which uses information from connected vehicles, mobile devices, sensors, probe data, and other sources to generate insights into demand forecasting, risk management, supply chain operations, delivery routes and route optimization, fuel consumption, and more. 

Key Benefits of Big Data Analytics in the Logistics Industry

Big data analytics turn raw, unstructured data into decisions that improve performance. Pings from connected vehicles, mobile devices, sensors, probe data, and other sources are aggregated, processed, normalized, and validated before they’re delivered to users as metrics. These metrics are structured data that can be easily interpreted by business decisionmakers, such as vehicle volumes, trip distances, Origin-Destination (O-D) patterns, dwell times, and more.

Below, we’ll cover some of the key ways big data analytics helps drive strategic decision-making on logistics, from route optimization to fuel efficiency.

Route Optimization and Smarter Delivery Routes 

Identifying the best route for shipments and deliveries can be a high-stakes choice for businesses. Optimal routes can cut down on excess fuel usage, shorten delivery times, improve reliability, and protect the safety of shipments, freight vehicles, and drivers.

Often, the most optimal route isn’t necessarily the shortest distance between two points. Factors like traffic congestion, travel delays, road type, crash and safety risks, lane closures, and more can influence a route’s suitability for your logistical needs. 

Big data can give you aggregate insights to help you prioritize the best routes with metrics like:

• Origin-Destination patterns and Top Routes: These metrics tell you how other vehicles are getting from point A to point B.
• Travel times and travel time reliability: This tells you how long it takes other drivers to travel along a given route, and how much variance there is in travel times for that route. 
• Truck activity vs. personal vehicle activity: Zeroing in on truck activity specifically can help reveal the best freight routes instead of those mostly used by personal vehicles (e.g., commuting or tourist traffic). 
• Freight traffic by weight class, industry, route type, or fuel type: Some transportation analytics platforms, like StreetLight InSight®, can further break down truck traffic by weight class (heavy-, medium-, and light-duty), industry (e.g., construction vs manufacturing), fuel type (e.g., diesel vs. gasoline vs. electric), and route type (e.g., local vs. hub and spoke). This can help you identify the best routes for long-haul shipments vs. last-mile delivery and everything in between.
• Vehicle speeds: Speeds can be a useful metric to understand both safety and congestion on a given route. Low average speeds may indicate consistent congestion along a corridor, especially if these average speeds are lower than the posted speed limit. Meanwhile, looking at 85th percentile speeds and how they compare to posted speed limits can also help reveal corridors where speeding behaviors may pose a risk to your drivers, fleet vehicles, and shipments.

Demand Forecasting and Inventory Management

Transportation big data helps businesses analyze driving patterns to anticipate how consumer demand fluctuates over the course of a day, week, month, or year. These predictive analytics help drive smarter decisions about shipment timing, logistics infrastructure investments, and more.

These big data analytics are especially useful for demand forecasting:

• Traffic volumes with time trends: When traffic volumes are high, this often correlates with peak customer demand. Knowing when traffic tends to be highest and lowest over the course of a day, week, month, or year can help businesses anticipate when to make infrastructure investments, order inventory, or ship products from warehouses to store locations. 
• Aggregated traveler demographics: Understanding who travels and when helps businesses zero in on their target customers to understand how their mobility patterns impact demand for certain products. 
• Freight traffic to port/distribution hubs: Understand freight activity at major hubs by revealing true origin-destination patterns, truck stopping activity, industry mix, vehicle types, and high-impact routes to understand all truck activity in the region, not just your own fleets, to help you make informed decisions about your own logistics strategies and reveal opportunities to get a competitive edge. 

While transportation big data can’t help track your current inventory like many inventory management software do, in many cases, these demand forecasting insights can help supplement traditional inventory management methods. By forecasting how demand might fluctuate for different products at different times based on mobility patterns, retailers can act proactively to ensure they have the right products available to meet an expected rise in demand.

This is especially useful for convenience stores, fuel and charging retailers, fast casual restaurants, auto parts and repair businesses, and other companies whose store visitation patterns are particularly tied to driving behaviors.

Real-Time Visibility That Improves Customer Satisfaction

Transportation analytics platforms that offer real-time analytics, like StreetLight, can also deliver insights on current traffic patterns that can impact shipments and last-mile delivery. Though StreetLight’s real-time analytics are based on data from personal vehicles, not commercial freight vehicles, the insights they offer can still power timely decisions and communication that improve customer satisfaction.

Real-time vehicle data allows you to stay on top of road conditions that may cause delivery delays. These can include: 

• Road and lane closures: Whether a road is fully or partially closed, this information can help you efficiently reroute shipments and alert staff and consumers to potential delays, helping to manage customer expectations and improve the delivery experience. 
• Travel times and queuing: Similar to above, these metrics can help businesses anticipate and communicate potential delays and avoid routes where vehicles are backed up due to congestion, crashes, and other variables. 

Proactive Risk Management and Supply Chain Resilience

Travel delays, damage to shipments, and strain on your workforce can all create risk for your business. But transportation big data analytics can help you avoid problem routes and increase the likelihood your deliveries – and drivers – arrive safely and on time.

Metrics like traffic volumes, travel time reliability, routing, origin-destinations, real-time route monitoring, and safety indicators like traffic speeds can all help you anticipate where risks are high and opt for routes that are safer, more reliable, and less likely to be impacted by sudden or seasonal disruptions such as tourist events or natural disasters.

Sustainability and Efficiency Gains

Businesses that want to improve their climate impact and spend less on fuel can also leverage big data analytics to achieve these goals. 

Many of the same metrics used for route optimization can also help businesses understand the costs and benefits of fleet electrification, inform optimal siting of fleet charging hubs, and identify the best routes for shipments using electric vehicles.

Additionally, with certain transportation analytics providers like StreetLight, businesses can segment vehicle traffic by electric, gas, and diesel engines to reveal where EVs are driving today, further informing where to invest in fleet electrification and charging infrastructure.

Real-World Use Cases for Big Data and Data Analytics in Logistics

The benefits explored above aren’t just theoretical; businesses are already using transportation big data to make decisions that boost logistical efficiency, reliability, and customer satisfaction. Below, we explore some of the key use cases in data-driven logistics.

Freight Route Planning and Truck-Safe Corridor Selection

In our section above on Route Optimization and Smarter Delivery Routes, we discussed how businesses can use big data analytics like Top Routes and travel time reliability to identify efficient, safe, and reliable freight routes for long-haul and short-haul freight operations. 

Additionally, factors like road type (e.g., arterial vs. residential), truck mode share (the proportion of total traffic that is represented by commercial vehicles), and vehicle activity by weight class (heavy- vs. medium- vs. light-duty) can help signal whether a given corridor is safe and efficient for different types of truck traffic. Businesses can use these indicators to avoid roadways that are not suitable for larger vehicles or long-haul trips. 

Explore how public and private organizations are using mobility data for freight planning in our Freight Planning webinar. 

Last-Mile Delivery Optimization

Historical trends in truck volumes, speeds, O-D patterns, and travel times can all help with last-mile delivery planning. Understanding where and when delivery routes tend to encounter delays or safety risks can help logistics and supply chain professionals identify the most reliable routes, good alternate routes, and realistic delivery estimates to communicate with both drivers and customers to improve delivery experiences.

Port Activity Analysis

Understanding port activity by time of day, day of week, and time of year can help businesses navigate potential freight bottlenecks as efficiently as possible. Businesses can use metrics like truck volumes, dwell time, O-D, and top routes to identify when port traffic peaks, which ports are most busy at different times, how long freight vehicles are stopped, and which routes to and from port hubs might be most congested. 

Cold Chain Monitoring Support

Inefficient routes and unexpected delays can be particularly hazardous for perishable and temperature-controlled shipments. Identifying the most efficient and reliable routes at every stage of the shipment and delivery process can help businesses ensure products arrive in good condition while reducing costs and resources spent on refrigeration during the journey.

Data Quality Challenges in Big Data Analytics (And How to Avoid Bad Decisions)

Not all big data is created equal. Here are some common data quality challenges businesses should watch out for: 

• Disorganized data: Making sense of raw data inputs is a highly technical task requiring data science knowhow. Unstructured or poorly organized data can be difficult to use, especially for businesses that don’t have internal data science teams. Look for data vendors that deliver structured data organized into easily interpretable metrics such as travel times, trip lengths, top routes, and vehicle volumes. 
• Limited coverage: Big data vendors can offer different amounts of data coverage depending on their data collection methods, ranging from highly localized studies to nationwide insights. Likewise, short-term studies and outdated datasets can result in poor temporal data coverage that may limit your ability to make confident inferences. Before you sign with a data vendor, always ask about their geographic and temporal data coverage.
• Limited granularity: Similar to limited coverage, limited granularity can impact the types of inferences businesses can make from a given dataset. For example, some data sources may offer average traffic volumes by year while others allow businesses to drill down monthly, daily, or even 15-minute granularity.
• Unvalidated data: To power confident business decisions, you need data that has been tested and verified for accuracy. Look for a data vendor with a rigorous and transparent validation process. Even better if their data has been verified by third parties.

For more on data quality factors and questions to ask potential data suppliers, download our free Guide to the Transportation Data Revolution. 

Why StreetLight is the Standard for Logistics Mobility Analytics

StreetLight offers the most comprehensive mobility data repository in the industry, offering reliable and granular insights that are trusted by public agencies and private businesses alike. 

Our ability to deliver insights on both personal and commercial vehicles, including freight activity by weight class, helps logistics and supply chain professionals zero in on traffic patterns that impact their bottom line and offer a competitive edge.

Additionally, customers appreciate our rigorous validation and data quality assurance process, as well as our ability to deliver insights through convenient mechanisms that support existing workflows – whether it’s via API, CSV, our self-serve analytics platform, or custom services.

Businesses and agencies have used StreetLight to:

• Improve regional truck routing and coordinate freight corridor improvements
• Forecast demand and choose ridesharing hub locations for eVTOL air mobility
• Power smarter, quicker site selection decisions for retailers across the U.S.
• Get ahead of EV charging demand and inform charging infrastructure investments
• and lots more

To find out if StreetLight can help you achieve your logistics and supply chain goals, reach out to a team member today.