Big data streams in copious amounts of data coupled with powerful analytic applications. Logistics companies are scrambling like never before to make sense of it all and to revamp their operations for greater efficiency and leverage.

With a constant influx of data piling up from all over the world, third-party logistics (3PL) warehouses are struggling to sort through the information and organize everything. The ability to successfully leverage big data has never been more crucial to operations. The strain is monumental to make sense of the non-stop stream of data and to prevent the entire industry from imploding.

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The worldwide logistics network has gone through a transformation in the last year and a half as networks have struggled to meet the world's growing demand. The burgeoning market is fueled by ecommerce orders reaching record highs and with no end in sight.

The flow of data and information from multiple sources has caused 3PL providers to struggle to find some organization. They are tasked with formulating a clear-cut plan to make use of this information. Most are trying to figure out how the analytics impact business operations both now and in the future.

Improved data-driven decisions are necessary for future success. Big data is going to play a critical role in all aspects of third-party logistics. The 2022 Logistics Industry Report revealed that 81% of shippers and 84% of 3PLs must use Big Data to become more competent with their supply chains.

The Past and Future of Big Data for 3PLs

The advancement of Big Data for 3PLs continues to evolve-good things take time and rarely occur overnight. The use of data by logistics companies is not new. The companies have always relied on computers to advance and optimize their network efficiency.

Walmart and FedEx were leaders by launching changes with their package and retail delivery services as early as the 1970s when computers first appeared on the scene. However, those early days were just a drop in the bucket of what future usage would become. Yes, the 1970s brought computerization to the doorstep of logistic networks but 2010, was the gateway to data-BIG data.

Big data has revolutionized all aspects of the functions of 3PL warehouses, such as logistics analytics. Logistics is complex and dynamic. It relies on moving parts which often result in bottlenecks within the supply chain and create headaches and nightmares.

The use of big data provides the following advantages to both shippers and outsourced logistics providers:

  • Optimized routing
  • Streamlined factory functions
  • Visibility and transparency

Big Data and the Future of Logistics Companies

You don’t have to be a fortune teller with a crystal ball to tell that the logistics companies who will continue to grow and thrive are the ones who look to the future. They each must fully implement big data analytics trends in a constructive and positive manner. With a modern tech stack, a 3PL can grow stronger as they remain determined to effectively meet the needs of those in the industry.

Examining The Ins and Outs of Big Data

Let’s check out how big data and analytics are transforming the way third-party logistics providers perform.

Where Does the Data Come From?

Big data needs quality information to perform properly. However, you might wonder where the data used by 3PLs originates. Below is a list of some of the forms of data used by 3PLs to function on a daily basis, provide optimum service, and meet the needs of their customers:

  • Traditional operating systems and enterprises
  • Order data from marketplaces and shopping carts
  • Shipping carrier integrations
  • Digital barcodes
  • Robotics data
  • Vehicle diagnostics
  • GPS/location information
  • Traffic coverage
  • Weather obtained from forecasts, monitors, and sensors
  • Forecasts by financial business
  • Advertising data
  • Social media information
  • Website browsing patterns

All of the data sources above help push big data combined with automation technology to higher levels to aid in manufacturing, warehouse needs, logistics, and last mile deliveries.

Data gathering and management continues to grow. With the increase in SaaS business intelligence tools, most business users will access self-service business intelligence to truly empower their 3PL’s processes by selecting, filtering, comparing, visualizing, and analyzing data easily and in a noticeably clear manner without any specialized or advanced IT training requirements.

Speeding Up the Last Mile

The supply chain may operate like a well-oiled machine until the last mile when inefficiencies spring up. The last mile can end up costing around 28 percent of the entire delivery cost of the package.

Obstacles encountered in the last mile include:

  • A large delivery truck encounters many issues as they traverse urban areas-such as parking problems. Often a driver will have to park a long distance away and then walk the distance to deliver the package to its destination. Along the way, they may have to walk up flights of stairs or wait an excessive amount of time for an elevator in a high-rise building.
  • A delivery person has to take a great deal of care to ensure that the package arrives safely and is not damaged in transit along the last leg of the trip.
  • In some situations, items require a signature for delivery, and if the recipient isn’t home then the item cannot be left.
  • Upon arrival, the delivery person has to appear professional.

In addition to the above obstacles, a 3PL might not know what to expect on the last leg of the delivery. Logistics will often track up to the point of delivery which is why many call the last mile the ‘black box’ because it contains all of the necessary and important delivery data.

The advantage of big data promises to ease many of the problems faced during the last mile.

Benefits of Last-Mile Analytics

Last-mile analytics provide the following beneficial data with internet and GPS functional smartphones. Sensors and scanners also help shippers examine the delivery process from beginning to end-even the difficult and challenging last mile.

Here is an example of a scenario: A UPS delivery truck that is equipped with a GPS sensor heads to downtown St. Louis to make a delivery. The driver parks the vehicle close to the delivery destination and then takes the package to proceed on foot. The delivery person’s GPS-enabled smartphone then takes over and continues to stream data.

Everything is recorded, including how long the delivery takes and if it's successful. All of the data collected is valuable to the end consumer, the brand, and the logistics company.

The patterns created can be examined so that deliveries can be better optimized for speed and success, especially in big cities where multi-tiered systems are often used like smaller distribution centers that are spaced out throughout neighborhoods, predetermined parking spots for delivery drivers, and even areas where smaller delivery vehicles are more functional than large trucks.

Reliability and Transparency

Sensors are becoming commonplace for use in transportation vehicles in the service industry and that are used for shipping. The sensors are beneficial throughout the supply chain and offer a substantial amount of data for complete transparency.

The data gathered is used by carriers, shippers, and customers. They use the information collected to prevent bottlenecks that further hinder the supply chain. The data is a wonderful way to negotiate by showing exactly how shippers can deliver on time and more.

With sensors embedded in all delivery vehicles and using GPS smartphones, they can quickly check for accuracy and reliability plus map out timelines. They can then use all of the compiled data to bid on new and more lucrative contracts.

Improved Routes

A 3PL relies heavily on routes to ensure that the supply chain flows uninterrupted. Of course, no one can guarantee there won’t be a massive pileup on a freeway or some other logistics horror, but big data does help by giving an edge for route improvement.

Optimization helps save a logistics company money while avoiding late shipments, which can give them a bad reputation and impact their future bottom line. When managing any delivery system, everything is a juggling act. You cannot overcommit resources like vehicles, nor can you fail to meet the need.

If you flood a delivery route with an excessive number of vehicles and resources, then you risk spending too much money and cutting into your profitability. Even if you have the additional funds, the assets will prove more useful somewhere else.

Holding back vehicles might delay delivery and your client relationship will suffer and your brand integrity starts to slip.

Factors you have to constantly analyze and optimize for include:

  • Cost of fuel
  • Freeway and highway closures whether temporary or permanent
  • New roads
  • Number of usable vehicles
  • Vehicles that are not currently being used to due to repairs or other problems
  • Weather conditions (weather is constantly changing so real-time data is highly beneficial)

Big data combined with predictive analytics provides logistics companies with the knowledge they need to face and overcome obstacles. Whether you use sensors on delivery trucks, road maintenance data, weather forecast information, fleet status, maintenance schedules, or other personal information like schedules, this data can all be integrated after examining past historical trends and providing advice.

UPS extensively uses big data logistics for savings. They examine everything, even the smallest details such as which way a delivery truck turns and determine if it costs the company additional money. As mentioned previously, the UPS data analysis is so extensive that they were able to figure out that turning into oncoming traffic often causes their delivery truck delays, wastes time, and presents a real safety risk.

The Conversation recently released the following information that UPS gained after they started to ensure that their drivers turn left only 10 percent of the time:

  • 10m gallons less fuel used
  • Emits less carbon dioxide at 20,000 tons
  • Effectively delivers 350,000 packages per year

Clearly, by simply turning right or going straight, there is a sizable advantage. The UPS “left turns only when absolutely necessary” strategy benefits the company.

Using data compiled, UPS also opted to eliminate 1,110 trucks which lowered the distance traveled by 28.5 million miles thus saving the company a sizable amount of money.

Greater Care During Shipping for High Quality Items

Meal preparation services offering fresh foods have become extremely popular. The need to keep perishables fresh during transit has never been more important. It is also proving to be a logistics nightmare. Luckily, big data is providing the solution for 3PL warehouses and delivery drivers. Big data is helping to prevent excessive costs that result from perished goods.

Example: A truck is en route transporting ice cream, the driver can monitor the temperature of the cargo via a temperature sensor installed within the truck. The data collected is even transmitted along with roadwork and traffic data straight to a central routing computer. The driver’s best route is then planned out to ensure that the perishable items reach their destination in a timely manner.

The computer system monitors everything and alerts the driver if a different route will work better to prevent the ice cream from melting. The alternative route is then mapped out and provided to the driver’s GPS system.

Big Data to Improve Customer Service

All businesses can benefit from improved customer service. Big data technology provides a window into what the customer is thinking and feeling about a business. The feedback can then be used to enhance the brand or organization.

Ongoing customer feedback should always be used to improve the company’s customer experience first and foremost. Companies often ask their customers via social media sites what time they expect their products to be delivered after ordering. The data compiled can then be used as a goal to ensure that the orders are delivered within the time period specified by the customers. Meeting customer expectations helps companies build strong relationships, grow their business, and boost loyalty.

Automation of the Supply Chain and 3PL Warehouses

Big data coupled with automation technology is gearing towards the goal of making logistics a completely automated operation process. With big data, automated systems coupled with warehouse management system (WMS) software function effectively through a variety of data streams and sets. Amazon, who remains a leader in supply chain logistics, already has automation present at all of their fulfillment centers and runs the orange KIA robots to grab and gather things from the shelves.
Amazon is even operating automated drones to deliver items if you live within 30 minutes of the Amazon center. Uber is another leader exploring automation with self-driving vehicles.

The future of delivery and automation go hand-in-hand as innovation evolves. Humans will always remain a part of the last mile of delivery-especially in urban areas where delivery drivers can use scooters or bikes to navigate the congested city streets. Not to mention, having a human make the deliveries ensures the human touch remains.

Suburban areas might end up focusing on a combination of drones and self-driving trucks to ensure efficient delivery.

The possibilities remain endless and with the use of sensors, the internet, business intelligence software, reduction in costs, customer service satisfaction, and innovation, big data continues to change things and ensures that automation continues to become commonplace.

Big Data and Automation

A successful logistics company knows that customer satisfaction is pivotal. To understand if clients are truly satisfied, every experience is noted either positively or negatively. An artificial intelligence (AI) powered bot can provide quick and efficient effectiveness for simple tasks.

They respond rapidly to the most common asked questions. However, on occasion a customer requires the skill of a real person. An AI cannot express empathy but by making the entire process quicker and efficient, they do provide high marks for customer satisfaction.

AI has the ability to help improve the understanding of copious quantities of data. You can use the information gathered by AI applications to predict a customer’s unique needs. With AI, you can span the entire customer journey from start to finish. You can rapidly understand conversations through all engagement channels, so your agents are empowered with greater and more efficient workflow.

Artificial intelligence will predict and even recommend the next best option based on a combination of factors like your customer’s preference and your business goals. With AI, you’ll know when to reach out to customers directly versus which interactions can readily be managed by a bot.

Integrating AI throughout the employee and customer journey ensures greater connections and insight so your employees can engage better using predictive analytics combined with machine learning to build a firm foundation for customer and brand loyalty.

Discover the latest trends in third-party logistics through Extensiv’s lens –  stay ahead of the curve in 2024 with our best practice recommendations.

AI engages consistently across channels bringing in relevant data both real-time, historical, and asynchronous engagements within marketing, sales, service, voice control, and digital coupled with CRM and other internal systems. AI remembers so a customer is never forgotten.

With logistics, customer expectations are intense, and competition is fierce. AI understands and recognizes prospects or customers to determine what way to engage.

Coupling Big Data with Warehouse Management

Customers often want to order a product online via a company’s website, but they then learn the product is out of stock. Rarely will the would-be customer wait for the item to be restocked. Instead, they will go to the competition to make the purchase. However, what if that item was actually in stock but someone at the 3PL warehouse had dropped the ball and not updated the inventory. In such a situation, the company lost the potential sale, the business of a return customer.

Big data technology helps a company and their 3PL warehouse partners maintain their stock of supplies far more efficiently than humans who often make errors. Nowadays, companies such as Amazon, DHL, and Alibaba are embracing smart warehouses.

Smart warehouses are workhorses that improve effectiveness, promptness, efficiency, and performance. A traditional warehouse operating system cannot compete. The use of real time insights generated by big data technology lets a logistics company implement the data throughout to monitor and track the current and future supply of stock. Without a doubt, big data technology improves every aspect of warehouse management.

What is WMS?

WMS stands for ‘warehouse management system.’ It is a software solution that supplies complete inventory visibility and offers management for all supply chain fulfillment operations that span from the distribution center all the way to consumer delivery. A technology stack encompasses a collection of various technologies that all function together in order for a logistics company to improve their operations.

Types of WMS Software

When looking at WMS systems, you’ll find that there are four types:

  1. A cloud-based WMS. A cloud-based WMS provides access to warehouse software applications over the Internet using shared computing resources. These warehouse systems update in real time as new features are released. These are often flexible and integrate easily with ecommerce technology.
  2. On-premises WMS. On-premises WMS software is installed on the company’s local servers and may require extensive one time set up.
  3. Supply chain modules (SCM) do not overlap with your existing software but work singularly.
  4. Enterprise Resource Planning (ERP) can combine warehouse functions with accounting and other system functions but may require significant cost, system implementation time, and dedicated resources to support.

The best WMS for a 3PL is hard to determine because of a variety of factors, including the size and complexity of your company, warehouse, inventory requirements, workflows, and your own unique needs. You’ll need to carefully assess your current needs, then decide which WMS will meet the needs of your third-party fulfillment warehouse today and tomorrow while letting your company make use of a large amount of big data to improve your analytics.

The Growing Need for WMS

The growth of the supply chain and logistics industry is astounding, and with growth comes the need for changes. 3PLs are scrambling to embrace innovative technology and artificial intelligence to meet not only their current needs but also the changes that are forecasted for the near future. To grow, a WMS has to join with Big Data to work seamlessly, which means evolving and changing when needed. You can’t get stuck using an old system that does not afford growth potential.

At this time, ecommerce sales are driving growth, in part due to the pandemic but also a change in shopping attitude. Many individuals face a serious time crunch in their daily lives. They are juggling thriving careers and families with extraordinarily little time left over. Going to a store and wasting hours shopping the aisles and at the checkout line has become far too much of a time thief. Nowadays, it's easier to grab your smartphone and make an order for necessities online using ecommerce. You can then have the items delivered directly to your door. Why shop in person or wait in long lines?

At this stage, ecommerce is not forecasted to slow down but instead continue growing at a steady rate. Even brick-and-mortar retailers have experienced an unprecedented surge in online orders. Sales are projected to continue to grow. As such, it is imperative that 3PLs embrace big data to help solidify customer relationships to create a differentiated experience for both their customer and the end consumer.

Leading Challenges of Using Big Data in the Supply Chain

Big data is providing an impressive range of logistics management solutions, but much of the data collected is hardly analyzed. Big data is a ‘bulk’ supply of information that provides no value if it is not effectively used. The sheer volume and density are often overwhelming to conventional optimization software.

Unfortunately, consumer demand, order data, inventory fluctuations, fuel price fluctuations, traffic updates, news information, national statistics, and weather reports are of no value if they are not properly analyzed. A strong fleet management and WMS system has the tools needed to transform the data collected into something useful to field strategic planning and day-to-day decisions.

In the Accenture Global Operations Megatrends Study, it was shown that logistics companies often had high expectations associated with big data analytics. Despite the high hopes of a radical change, 97% of executives were unable to fully adopt the information provided. A study shows that only 12% reported that they had implemented any changes or functions as a result of the data analytics.

The report went on to share that big data is being integrated in the following ways:

The Use of Geoanalytics

The Boston Consulting Group reported that geoanalytics is being used to optimize and merge delivery networks for route optimization, driving accuracy, and reducing wait times.

With the help of big data analytics, geoanalytics is assisting with the following:

  • Visualizing delivery routes using millions of data points to effectively model hundreds of scenarios to better plan a route and its many stops.
  • Pinpointing future demand by examining competitive analysis, external factors, and pricing positions from software in a variety of languages so that warehouse managers can better visualize input.
  • Simplifying of distribution networks to enable warehouses to form interrelationships to better solve complex problems via interconnected business systems.

The six greatest achievements of big data include:

  • Improved customer service
  • Greater demand fulfillment
  • Supply chain efficiency
  • Faster reaction time when a supply chain issue arises
  • Integration
  • Optimization of inventory

Taking Warehouse Management to the Next Level

Warehouse management systems connected with devices (e.g. mobile barcode scanners) and automation takes a company to another level as the system provides valuable insights on things such as receipts, picking, packing, shipping, loading, carrying, and delivery.

An example of how this is being used is by planning routes for trucks and forklifts to carry outbound and inbound freight to enhance the cost of fuel and improve safety. Another example is optimizing inventory placement across the warehouse to create the fastest pick paths and to cluster frequently purchased items together in nearby bins to ensure the fastest pick process.

Customers want and need real-time updates on product availability and manufacturing details. They also demand delivery dates. Big data analytics help trace all of the changes effectively. Using AI algorithms, logistics companies gain insights into shipment locations, shipment methods, and insights into products.

Here are a few ways to truly embrace and use big data analytics:

  • Use automatic data collection
  • Transmit all data to the cloud
  • Integrate GPS trackers, mobile scanners, Wi-Fi, and wearable devices
  • Evaluate the data quality
  • Implement cybersecurity
  • Define action algorithms using the data collected


Big data analytics in logistics is the future. 3PLs simply must learn to analyze and interpret the data the correct way to gain insight. Data provides wisdom and optimal solutions to improve all aspects of the supply chain’s functions from warehousing all the way to last-mile delivery. The transformations will undoubtedly improve efficiency and welcome in an era of strong customer satisfaction.

To learn more about Extensiv’s first-party data on the third-party logistics industry at large, download our 3PL Warehouse Benchmark Report.

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Big Data & Analytics FAQs

What is the Role of Data Analytics in Logistics?

Predictive analysis is crucial and a major component of data analytics in logistics. Big data lets companies analyze the behavioral patterns of machines to look for anomalies. Predictive analytics will also play a critical role in creating balance between the process of supply and demand. An example is the ability to study weather patterns so that suppliers can change routes when necessary and make other alterations thanks to these predictions.

Predictive analytics also lets companies examine upcoming changes to supply and demand. Behavioral patterns of machines can uncover anomalies in the inventory management function. Companies can then deal with those issues and make real improvements to warehouse and transportation functions.
Predictive analytics help create balance between both supply and demand. Past data analysis lets shippers generate reports on consumption so they can predict future demand which can then be used to accelerate delivery and reduce waste.

How Can Big Data Affect Logistics?

The use of big data impacts logistics in a multitude of ways such as reducing inefficiencies during the last mile, optimizing bin placement or storage for high-volume inventory, or allowing for more precise placement of purchase orders to reduce out of stock situations. It also offers transparency, protects perishable goods, automates the supply chain, and enhances deliveries.

How is Data Analytics Transforming the Supply Chain?

Here are just a few ways that data analytics transforms the supply chain:

•    Validating data
•    Benchmarking operations
•    Detecting anomalies
•    Improving demand forecasts
•    Inventory management 
•    Mobile reporting and forecasting
•    Visibility into global logistics
•    Real-time route analytics

What is the Main Impact Area of Big Data for Freight Transport?

Big data is helping freight transport manage, predict, and estimate the volume of freight into the near and distant future, whether days, weeks, or months. Business owners can then estimate and define the complete cost structure to help with future forecasting and business operations.

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