10 Examples of How Big Data in Logistics Can Transform The Supply Chain
Table of Contents
1) Benefits Of Big Data In Logistics
Big data is revolutionizing many fields of business, and logistics analytics is no exception. The complex and ever-evolving nature of logistics makes it an essential use case for big data applications.
You can use big data analytics in logistics, for instance, to optimize routing, improve factory processes, and create razor-sharp efficiency across the entire supply chain. These applications are designed to benefit logistics and shipping companies alike.
Did you know? The big data market is expected to exceed $68 billion in value by 2025, a testament to its growing value and necessity across industries.
According to studies, 92% of data leaders say their businesses saw measurable value from their data and analytics investments. Applied to logistics and supply chain processes, you will see your productivity levels soar while consistently exceeding client or customer expectations, and ultimately, boosting your bottom line.
To work effectively, big data requires a large amount of high-quality information sources. Where is all of that data going to come from?
- Traditional enterprise data from operational systems
- Traffic and weather data from sensors, monitors, and forecast systems
- Vehicle diagnostics, driving patterns, and location information
- Financial business forecasts
- Advertising response data
- Website browsing pattern data
- Social media data
As for many other industries, data gathering and data management are becoming overwhelming, and professionals may need help. The rise of SaaS business intelligence tools is answering that need, providing a dynamic vessel for presenting and interacting with essential insights in a way that is digestible and accessible.
The future is bright for logistics companies that are willing to take advantage of big data. In this article, we’re going to examine examples and benefits of big data in logistics industry to fuel your imagination and get you thinking outside of the box.
Benefits Of Big Data In Logistics
Before we look at our selection of practical examples and applications, let’s look at the benefits of big data in logistics - starting with the (not so) small matter of costs.
- Financial efficiency: One of the key benefits of big data in supply chain and logistics management is the reduction of unnecessary costs. Using the right dashboard and data visualizations, it’s possible to hone in on any trends or patterns that uncover inefficiencies within your processes. Interacting with powerful data sets will empower you to drive down operational costs by optimizing delivery routes, predicting machinery or delivery vehicle maintenance, and weaving the whole supply chain together fluently.
- Transparency: With the ability to monitor the movements of goods and delivery operatives in real-time, you can improve internal as well as external efficiency. With dynamic data alerts, you can pick up potential issues or delays swiftly, notify your colleagues, suppliers, or customers, and manage expectations. This level of responsiveness and transparency will improve communication, create a better customer experience, and boost your brand reputation.
- Proactivity: Another key benefit of big data in the logistics industry is that it encourages informed decision-making and proactivity. Working with a mix of historic (trend-based), real-time, and predictive insights, everyone on your team will be able to make valuable strategic suggestions, take active measures to spot any spiraling trends before they cause organizational damage, and keep on top of every process or operation with pinpoint precision.
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10 Essential Big Data Use Cases in Logistics
Now that you’re up to speed on the perks of investing in analytics, let’s look at some practical examples that highlight the growing importance of data in logistics, based on different business scenarios.
1) The last mile of shipping can be quickened
The last mile of a supply chain is notoriously inefficient, costing up to 28% of the overall delivery cost of a package. There are many obstacles that lead to this, including:
- It can be challenging for large delivery trucks to park near their destination in urban areas. Drivers often have to park far away and then walk the package to its final address. Then, they may have to go up many flights of stairs or wait for an elevator in a high-rise building.
- Some items must be signed for, and if a customer isn’t home, the item can’t be delivered.
- Delivery personnel have to take extra care not to damage the package during this last leg, and they must present themselves in a professional way to the recipient.
Adding to these challenges, it can be very difficult to know exactly what’s going on during the last leg of delivery. Packages are often tracked up until this point, leading some to say that the last mile is the “black box” of delivery data.
Big data aims to address many of these challenges. In an interview with the Wall Street Journal, Matthias Winkenbach, director of MIT’s Megacity Logistics Lab, details how last-mile analytics are yielding useful data. Because of the low cost and ubiquity of fast mobile internet and GPS-enabled smartphones, as well as the spread of the Internet of Things through sensors and scanners, shippers are able to see how the delivery process goes from start to finish - even during the last mile.
Imagine this: a UPS delivery truck with a GPS sensor on it makes a delivery in downtown Chicago. After parking nearby, the delivery man's phone GPS continues to stream data to the UPS center, giving a constant account of how long the delivery is taking. This isn’t just valuable for the customer - it allows logistics companies to see patterns at play that can be used to optimize their delivery strategies. For example, Dr. Winkenbach said that his data showed that “deliveries in big cities are almost always improved by creating multi-tiered systems with smaller distribution centers spread out in several neighborhoods, or simply pre-designated parking spots in garages or lots where smaller vehicles can take packages the rest of the way.”
2) Reliability is more transparent
As sensors become more prevalent in transportation vehicles, shipping, and throughout the supply chain, they can provide data enabling greater transparency than has ever been possible.
This transparency is valuable to shippers, carriers, and customers. If a shipment is going to be late, carriers want to know as soon as possible so that they can prevent bottlenecks further down the supply chain. And carrier companies can use this data in the aggregate to negotiate with shippers by showing how often they deliver on time.
Imagine this: logistics companies have embedded sensors in all of their delivery vehicles, with GPS-enabled smartphones covering any gaps. A third party validates these sensors for accuracy, and then the reliability and timeliness data from these sensors is used when logistics companies are bidding for new contracts.
This kind of open-source, radically transparent information could change how business is conducted in the logistics world.
3) Routes will be optimized
40% of forward-thinking organizations use big data to optimize their daily operations, while 35% say the biggest benefit is cost reduction. A testament to the rising role of optimization in logistics.
Why are logistics companies so interested in optimization? For two reasons: it helps them save money and avoid late shipments. When you’re managing a delivery system or supply chain, you have to walk a fine line between overcommitting resources and vehicles and under-committing them. If you put too many vehicles and resources on one delivery route, then you are spending more money than you have to, and possibly using assets that could be better utilized elsewhere.
However, if you underestimate how many vehicles a particular route or delivery will require, then you run the risk of giving customers a late shipment, which negatively affects your client relationships and brand image.
To add to the challenges of optimization, the factors involved in effectively allocating resources are constantly changing. For example:
- Fuel costs can change
- Highways and roads can be temporarily shut down or new ones can be built
- The number of vehicles at your disposal may change due to repairs or new acquisitions
- Weather conditions, both seasonal and immediate, are constantly changing
Big data and predictive analytics give logistics companies the extra edge they need to overcome these obstacles. Sensors on delivery trucks, weather data, road maintenance data, fleet maintenance schedules, real-time fleet status indicators, and personnel schedules can all be integrated into a system that looks at historical trends and gives advice accordingly.
UPS is a real-world example of big data logistics leading to major savings. After examining their data, UPS found that trucks turning left were costing them a lot of money. In other words, UPS found that turning into oncoming traffic was causing a lot of delays, wasted fuel, and increased safety risk.
As a post from The Conversation titled “Why UPS drivers don’t turn left and you probably shouldn’t either” states, UPS “claims it uses 10m gallons less fuel, emits 20,000 tonnes less carbon dioxide and delivers 350,000 more packages every year” (after making the change). 10 million gallons of gas is a lot of money - that’s some serious benefit and a big data example in the supply chain.
UPS drivers now only turn left about 10% of the time, opting to go straight or turn right instead. Due to this “left turns only when absolutely necessary” strategy, UPS has also reduced the number of trucks it uses by 1,110 and reduced the company fleet’s total distance traveled by 28.5 million miles.
4) Sensitive goods are shipped with higher quality
Keeping perishables fresh is a constant challenge for logistics companies. However, big data and the Internet of Things could give delivery drivers and managers a much better idea of how they can reduce costs due to perished goods.
For example, let’s say a truck is transporting a shipment of ice cream and desserts. You could install a temperature sensor inside the truck to monitor the state of the goods inside and give this data, along with traffic and road work data, to a central routing computer.
This computer could then alert the driver if the originally chosen route would result in the ice cream melting, and suggest alternate routes instead.
5) Warehouses and the supply chain are automated
Soon enough, big data combined with automation technology and the Internet of Things may make logistics an entirely automated operation.
Big data enables automated systems by intelligently routing many data sets and data streams. In a recent move towards a more autonomous logistical future, Amazon has launched an upgraded model of its highly-successful KIVA robots. The all-new Proteus AMRs can carry out daily warehousing tasks while physically moving inventory and working among human personnel.
Additionally, Amazon has automated drones that can deliver items to customers within 30 minutes of certain Amazon centers.
Seeing as how Uber and droves of automotive companies are already on the cusp of launching self-driving vehicles for public consumption, it’s not hard to imagine that a whole supply chain could be automated, from loading and unloading, to driving, to final delivery.
Perhaps humans would still be involved in the last mile of delivery in urban areas, using bikes or scooters to navigate busy city streets and give customers a reassuring human component and suburban areas would have self-driving trucks or drones for delivery.
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6. Vehicle and machine maintenance are predicted accurately
Big data in transportation is revolutionizing the way companies move their goods and handle their vehicle fleets.
With access to vivid logistical insights and deep dive analytics, it’s possible to detect driver habits such as braking, driving time, acceleration, and handling. It’s also possible to track and measure vehicle usage over specific timeframes to make informed decisions on when you will need to carry out routine maintenance.
This melting pot of real-time and predictive intelligence empowers logistics companies to make informed choices on the frequency as well as the type of vehicle or machinery maintenance they need to carry out. In turn, this will keep fleets running with maximum fluency while avoiding wasted costs on unnecessary maintenance.
7. Operational growth and demand can be planned
For many modern businesses, big data analytics for logistics and transportation is used to keep a firm grip on operational demand.
As logistics companies scale, many experiences significant operational bottlenecks that stunt further growth and damage their brand reputation. Without the processes, vehicles, storage, and personnel in place to manage increased demand, service levels dwindle and budgets become stretched.
But, by using big data applications to plan for increased or even fluctuating demand across the supply chain, it’s possible to tweak strategies in line with annual trends like market changes or seasonal needs.
Domino’s Pizza, for instance, uses operational demand forecasting to deliver on its ‘30 minutes or less’ policy - a USP that has cemented the brand’s success in a saturated marketplace. Using big data for its logistical activities, Domino’s can predict specific demand in particular locations, ensuring that ingredients are available and its pizza ovens are ready to accommodate the right quantities for any given day. Using dynamic real-time data, Domino’s also tweaks its website content to offer deals and offers in line with market demand.
8. Cohesion is created across the network
Logistics is an incredibly dynamic, fast-paced, and ever-shifting industry. For companies with a network that spans across geographic locations, being able to communicate with razor-sharp efficiency is essential.
Any logistics-centric business is only as good as its weakest branch. That said, working with the right applications and data dashboard tools will facilitate goods management planning as well as geographical coverage between different locations in the network.
By consolidating data from a wide range of locations within the network, many logistics brands can distribute their goods and resources in a way that drives down fulfillment times, facilitates geographical expansion, and accelerates commercial growth. Big data visualization tools create transparency across the board, breaking down silos and empowering brands to work as one cohesive network, rather than disjointed entities.
9. Customer experience (CX) and service levels are improved
Niche or sector aside, in today’s digital world, customer experience is one of the biggest factors in driving brand growth and customer loyalty.
As a logistics provider, delivering on promises and managing consumer expectations is paramount. In addition to driving operational efficiency and consistently meeting fulfillment targets, logistics providers use big data applications to provide real-time updates as well as a host of flexible pick-up, drop-off, or ordering options.
To enhance customer experience, many modern brands are making greater investments when it comes to big data in logistics and supply chain management. Influential brands including Apple, Nokia, and Johnson & Johnson are placing a strong focus on data-driven solutions to improve their customer experience strategy. Speaking on this key trend, the brand consultant and research director at Gartner, explained:
"The supply chain is uniquely placed to identify customers’ needs and drive better customer experiences. Customers are influenced by their experience of the supply chain — even in the simplest terms, it’s easy to see that a late delivery can disappoint, whereas an expedited delivery can delight.”
Using visual data dashboard tools also makes it possible to analyze behavioral data as well as customer reviews and engagement or satisfaction with ease. Companies can use these insights to improve the content and optimize the entire fulfillment journey to meet specific needs. This, in turn, boosts client as well as customer loyalty while having a significantly positive impact on overall satisfaction levels.
10. Customer data is standardized and verified
Rounding out our rundown of big data logistics use cases, we’re going to look at personal data. Like many modern sectors, logistics processes involve large amounts of data collection. When curating sensitive customer information like address details, for example, keeping everything organized and compliant is 100% vital.
Working with BI and big data tools, scaling logistics companies can consolidate address details spanning the entire globe with pinpoint precision while improving security and data compliance. Implementing standardization and verification processes also mitigates issues such as customer typos or spelling mistakes when inputting their data into the system. These automated standardization processes minimize potential fulfillment mishaps while helping logistics companies consistently meet their targets.
Leading logistics innovators like DHL use concepts like applied analytics to standardize data inputs while creating accurate demand forecasts and providing personalized fulfillment to various segments of its customers. This is a testament to the brand-boosting power of big data in logistics.
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Welcome To The Future Of Logistics
We’re on the cusp of big data transforming the nature of logistics. Big data in logistics can improve financial efficiency, provide transparency to the supply chain, and enable proactive strategic decision-making. The right data can help reduce inefficiencies in last mile delivery, optimize delivery routes and maintenance schedules, protect perishable goods, improve the customer experience, and even automate the entire supply chain.
Armed with data-driven processes that align with your company goals and operational needs, you will gain a panoramic snapshot of every key component of the supply chain. As a result, you can make strategic changes and powerful real-time decisions that will keep your entire operation flowing smoothly from end to end. On the contrary, without using the right tools, intelligence, and insights, you’ll likely find yourself forever on the back foot.
Using sensors and concepts such as AI and the Internet of Things, combined with innovative business intelligence dashboard software, forward-thinking companies like yours will optimize their businesses in ways that drive sustainable growth and boost profits. Now’s the time to strike.