The Cloud’s Edge: How Edge Computing is Reshaping the Future of Logistics

How is edge computing helping logistics firms overcome the challenge of handling massive amounts of real-time data from the proliferation of IoT sensors? Read on to learn how edge computing is streamlining the future of logistics!

Organizations in the logistics industry are facing unprecedented changes as digitization takes hold and customer expectations evolve. The future of logistics looks promising with the integration of data-driven technologies such as RPA and AI. With the rise of IoT sensors, a vast amount of data is available to logistics firms for analysis to make well-informed decisions.

How can logistics firms effectively analyze this tremendous volume of data? Imagine a driverless truck from which its IoT sensor data is gathered. It may only be feasible to send some of its data to the cloud for processing due to latency and bandwidth limitations. In such a scenario, edge computing can play a significant role in analyzing the data for real-time insights to improve the performance of the driverless truck.

Similarly, logistics firms can utilize edge computing to improve their data analytics capabilities and stay competitive in an ever-changing market.

How is Edge Computing Different from Cloud Computing?

In a way, edge computing and cloud computing are related, yet they are different. Below, we compare how edge computing and cloud computing differ.

Overall, edge computing and cloud computing can have individual and collaborative applications, depending on the scenario. Cloud computing remains a viable solution for computing challenges. However, in some instances, organizations use it in tandem with edge computing to achieve a more comprehensive solution.

Let’s see how edge computing plays a significant role in making the supply-chain journey more robust for third-party logistics companies.

Edge computing has the potential to revolutionize the way logistics firms process and analyze data and provide real-time insights that can enhance predictability and improve decision-making.

Edge computing refers to the use of computing devices and systems closer to the point of data generation or use, such as in warehouses, distribution centers, and transportation vehicles. By bringing computing resources closer to data, edge computing can enable faster processing and data analysis.

Indeed, the rise of IoT devices in the logistics sector is unparalleled. However, organizations still struggle to unlock the value of its raw data located within thousands of sensors located at distant locations. Some organizations may be using edge computing to a certain degree but are not fully aware of its vast potential uses. Let’s have a look at how edge computing works to deliver insights from logistics data:

Types of Edge Computing

Edge computing is based on a simple idea – if you can’t move your data closer to your data center, you move your data center closer to the data. One needs to create a distributed environment where the applications reside closer to the end users as well as the devices. This environment has three locations: the premises edge, edge cloud, and public cloud.

Let’s take a closer look at the three types of edge computing:

1. Premises Edge

   The premises edge consists of specialized compute and storage located on-premises, such as, a corporate office or a sports arena. On-premises computing devices function as edge gateways delivering network routing, security services, data filtering, and even application hosting. When ultra-low latency is required for mission-critical applications, many businesses opt for this strategy.

   Example: UPS and its ORION System

   UPS is using on-premises edge computing in its ORION (On-Road Integrated Optimization and Navigation) system. It is used to optimize the routing and scheduling of its delivery trucks. ORION uses data from various sources, including GPS, weather forecasts, and customer information, to create optimized routes and schedules for UPS drivers.

   The on-premises edge computing technology used in ORION is installed in a small computer in each UPS truck. This computer is responsible for processing data collected from sensors and devices within the truck, such as fuel sensors and GPS receivers, as well as external data sources such as weather reports and traffic updates. By processing this data on the truck, rather than sending it to a central data center for processing, ORION can provide:

   • real-time routing
   • scheduling information to UPS drivers, even in areas with poor network connectivity.

   According to UPS, ORION system has helped the company save approximately 10 million gallons of fuel per year and reduce CO2 emissions by around 100,000 metric tons per year. These savings are attributed to the system’s ability to optimize delivery routes, and reduce the number of miles driven by UPS drivers.

   This on-premises edge computing technology has allowed UPS to significantly improve the efficiency of its delivery operations.

2. Edge Cloud

   The edge cloud combines cloud and edge resources to establish a dispersed network for edge application data storage and transmission. The edge cloud allows direct connectivity to public cloud service providers via nodes at the edge with low latency (usually 5ms or less).

   Example: Maersk and its Remote Container Management system

   Maersk, the world’s largest container shipping company, is using edge cloud computing in its Remote Container Management (RCM) system, which is used to monitor and optimize the conditions inside shipping containers during transport. The RCM system uses sensors and IoT devices to collect data on factors such as temperature, humidity, and CO2 levels inside shipping containers.

   The edge cloud computing technology used in the RCM system allows Maersk to process this data in real-time, even when the containers are in remote locations with limited network connectivity. Data from the sensors is collected and processed on edge servers located near the containers, and then transmitted to the cloud for further analysis and storage.

   By using edge cloud computing, Maersk can provide real-time updates on the status of shipments to its customers, allowing them to track the location and condition of their goods during transit. The RCM system also allows Maersk to:

   • optimize shipping routes
   • reduce the risk of spoilage or damage to goods during transport
   • improve the overall efficiency and reliability of its logistics operations.
3. Public Cloud

   It is a model of information technology in which on-demand computing resources and infrastructure are controlled by a third party and shared with various enterprises via the public internet. AWS, Microsoft Azure, and Google Cloud are examples of public cloud providers where clients share the same storage, hardware, and network devices with other enterprises (or tenants) and access services and manage resources using a web portal.

   Example: DHL and its Smart Warehouses

   DHL, one of the world’s largest logistics companies, is using edge public cloud computing in its smart warehouses to optimize inventory management and improve operational efficiency. The smart warehouses use IoT sensors and devices to collect data on factors such as product location, weather conditions, as well as employee and equipment movements.

   The edge public cloud computing technology used in DHL’s smart warehouses allows for the real-time processing and analysis of this data. By leveraging the processing power of edge cloud servers located near the warehouses, DHL can analyze the data and generate insights quickly and efficiently, without the need for expensive on-site computing infrastructure.

   By using edge public cloud computing, DHL can optimize its warehouse operations by:

   • predicting inventory needs
   • improving supply chain visibility
   • increasing efficiency through better coordination of resources.

   DHL can use data on product demand and location to optimize the placement of products within the warehouse, reducing the time and distance required for employees and equipment to retrieve them.

Solving Logistics Challenges with Edge Computing

Edge computing can solve various technology-related challenges in the logistics industry. Here are some of the challenges that edge computing can solve:

1. Latency

   Latency can appear in various ways, and results in delays between sending and receiving data.

   • Fortunately, edge computing can solve this challenge by processing and analyzing data closer to where it is generated, i.e., at the edge of the network. This reduces the need for data to travel to a central data center.

   Example: FedEx’s implementation in edge computing

   FedEx is implementing edge computing in its logistics operations. The approach behind processing data at the edge, rather than sending it to a central data center for analysis, is adopted to gain quicker insights to improve the speed and reliability of its delivery services. Additionally, edge computing provides low latency to a highly resilient computing environment for storing and retrieving the data required to operate its logistics network. This can enable FedEx to transform the digital network that connects thousands of FedEx locations for greater efficiency and lower costs.

2. Bandwidth Limitations

   Bandwidth limitations can also be a challenge in the T&L industry, especially in remote areas where network connectivity is limited.

   • Edge computing can help reduce the amount of data transmitted over the network. Logistics organizations can filter data at the edge of the network, transmitting only the essential data to the central data center, thereby reducing the amount of bandwidth required.
3. Data Privacy & Security

   Data privacy and security are major concerns in the T&L industry, especially when dealing with sensitive data such as customer information, shipment details, and inventory data.

   • With edge computing, data can be processed and analyzed locally, reducing the need for transmission over the network to a central data center, where it could be more vulnerable to cyberattacks.
4. Unreliable Connectivity

   Unreliable network connectivity can be a significant challenge for T&L companies, especially when operating in remote or rural areas.

   • Edge computing can process and analyze data locally, even when network connectivity is limited or unavailable.

   For example, a logistics company can use edge computing to process and analyze data from sensors on their containers, even when they are in areas with poor network connectivity, ensuring that critical data and insights are not lost.

5. Transportation Cost Management

   Transportation cost optimization is another challenge for T&L companies, as transportation costs can account for a significant portion of their overall expenses.

   • Edge computing can help by enabling real-time data processing and analysis, which can help logistics companies optimize their transportation routes and schedules.

   For example, DHL, a major player in the logistics industry, has also implemented an edge computing solution that uses IoT devices to monitor the goods’ condition during transportation. The solution uses sensors to track the temperature, humidity, and other environmental factors that can affect the quality of goods during transport.

6. Real-time Tracking of Inventory

   The consequences of poor real-time tracking of inventory in logistics can be significant. Inaccurate inventory data can lead to stockouts or overstocking. It can further result in lost sales, increased costs, and reduced customer satisfaction.

   • Edge computing can provide real-time tracking of inventory by processing data at the edge of the network instead of sending it to the cloud for analysis.

   For example, in a warehouse, edge devices such as sensors and RFID readers can be deployed to monitor the movement of inventory. The data collected by these devices can be analyzed in real-time to ensure that inventory is being moved efficiently and the right products are delivered at the correct address on time.

   Organizations across various industries can expect a 10% to 30% saving in operational costs by using edge computing solutions.

   The Enterprise Project

7. Advanced Predictive Analysis

   The absence of predictive analysis in logistics can result in inefficient use of resources, such as excess inventory or underutilized transportation capacity.

   • Edge computing can enable advanced predictive analysis by using machine learning (ML) algorithms to analyze data collected at the edge of the network. ML algorithms can be used to predict when a shipment will arrive at a certain location based on historical data and real-time sensor data.

   This helps logistics companies optimize their supply chain and reduce delays.

8. Pallet/Package Matching

   Inaccurate pallet/package matching can result in incorrect inventory data, which can impact forecasting and planning for future shipments.

   • Edge computing can also help with pallet/package matching by using computer vision algorithms to match packages with their corresponding pallets.

   For example, cameras at a loading dock can capture images of packages and compare them to a database of expected packages. If a package is identified in the wrong location, an alert can be sent to the appropriate personnel to correct the mistake in real-time.

9. Robotics

   Without implementing robotics techniques, logistics companies can lose a competitive advantage, as companies may struggle to keep up with their competitors. Also, they may face challenges related to real-time updates, accuracy, and safety.

   • Edge computing can help with robotics in logistics by providing real-time data processing for robots operating in warehouses or on factory floors. Robots can use sensors to navigate through a warehouse and collect data on their surroundings. This data can be processed at the edge of the network to help the robot make decisions in real-time, such as avoiding obstacles or choosing the most efficient route.

Which Edge is Right for Your Logistics Business?

If you’re wondering which edge is right for your business, the answer depends on various factors. We have included a quick side-by-side comparison of different kinds of edges to help assess which is the most befitting for your needs.

An edge computing architecture is not a one-size-fits-all approach because each option has its own advantages and disadvantages. Logistics companies should, therefore, carefully evaluate each option before selecting the edge computing architecture that is most appropriate for their businesses.

Benefits of Edge Computing in Logistics

Edge computing comes with an array of benefits especially focused on logistics firms:

• Enabled Connectivity in Regions Lacking Legacy Infrastructure

  Edge computing can help IoT devices connect at places that are hard to reach or don’t have the infrastructure, called ‘dark zones.’ Edge computing analyzes data at the source instead of sending it to remote data centers or network infrastructure. Once data has been processed at the edge, it can still be sent to apps in the cloud.

• Supply Chain Resilience

  Logistics firms need to have accurate visibility into the inventory levels of the supply chain. To address supply chain resilience, logistics companies employ IoT devices enabled by edge computing to track temperature, real-time location, and stock levels so that they can make business decisions based on data.

• Operational Efficiency & Security

  Edge computing makes it possible to keep a close eye on manufacturing and warehouse activities. For example, monitoring how well equipment and production lines are working can help find and resolve problems before they occur, avoiding costly delays from downtime.

• Increasing Overall Equipment Efficiency (OEE)

  OEE is one of the most critical items for any logistics organization. When no-reads occur, the logistics managers can review to take note of anything strange that occurred prior. The ability to see early indicators of a problem and take steps to stop it is key to maintaining efficiency. No-read scenarios can also be saved for regulatory, or audit needs while also being utilized to measure performance over time. Predicting device failures and swiftly finding and correcting problems have a huge impact on OEE.

Find the Right Edge with Emtec Digital

Are you ready to move to the edge? Emtec Digital can help!

As your trusted technology partner, we can help by integrating your low-latency edge compute, cloud, storage, networking, security, and orchestrating/collaborating with your preferred IoT partner to seamlessly take over the data ingestion process and more. Our team of experts can efficiently handle data processing and analysis to extract valuable insights from your IoT devices.

Our edge-first architecture services are designed to give you the best latency, performance, and reliability, whether your system applications are located on premises edge, edge cloud, or public cloud.

Looking to efficiently manage data processing from your IoT devices and streamline your logistics operations? Our expert team can help you leverage the power of edge computing for your IoT devices. Contact us now!

References

Author

Emtec Digital Think Tank

We are an enthusiastic group of technologists, market and trend analysts, digital evangelists, and subject matter experts. We discuss and share our thoughts on digital enablement, business strategies, customer/market insights, and advanced technologies that help organizations improve operational efficiency and boost revenue.