Cloud & Edge Computing in 3PL Companies : The Data Infrastructure for Warehouse Automation
Smart Warehousing or comprehensive warehouse automation, defined by real-time solution-to-solution communication via network and the applications/services on the server, demands a robust cloud and edge infrastructure.
The vital tasks performed by cloud and edge computers, comprising data processing and storage foundation of warehouse automation, go far beyond consolidating data from solutions at different junctures of the 3PL operations workflow. The servers analyse the data received for various automated decision-making using their data repository and AI models. Additionally, edge and cloud computers, connected to strong network appliances, help route the right data packet to the intended solution.
Setting the Scene
We have already discussed the technologies powering key drivers of 3PL warehouse automation in detail in our blogs, Technology Trends for 3PL Companies for Better Productivity Part 1 and Part 2. These trends cover warehouse robots, AIDC and Machine Vision capabilities, AI insights, and cloud and edge-hosted software platforms such as warehouse management systems, amongst other factors.
However, the vital role cloud computing and edge computing play in 3PL warehouse automation, enabling solutions mentioned above to relay signals to and fro, was too vast to squeeze into the blog series. Hence, we will dedicate this blog to the functionality of the two server types at length, empowering companies in the sector to manage and store their data effectively while charting growth with data-backed automatic suggestions.
Let’s dive in!
Cloud and Edge computing for 3PL Warehouse Automation: Discover Adoption & Growth Rates, Stay Competitive!
Cloud Computing has become one of the fastest-adopted technologies in the supply chain market, helping companies cope with increasing market demand for 3PL services. “Cloud Computing and Storage” will reach a massive 86% adoption rate by 2027, informs the MHI-Deloitte report titled Evolution to Revolution: Building Supply Chains of Tomorrow, published in 2022. The study, covering a 5-year forecast period between 2022 and 2027, notes a 46 percentage point jump in (projected) adoption rates compared to the 40% adoption rate measured in 2022.
It is worth noting that the statistics mentioned above cover the scope of implementation in 3PL warehouse automation; these operations are an integral part of the supply chain sector.
On the other hand, Grand View Research predicted that the global Edge Computing market will grow at a massive 36.9% CAGR between 2024 and 2030. According to the market research company’s projections, the global market valuation of $ 16.45 bn in 2023 will reach $155.90 bn by 2030, should the growth rate be sustained.
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Only 6% of companies surveyed for an Accenture research report named Leading with edge: How to reinvent with data and AI were “Super Integrated”, i.e., they had harnessed the maximum capabilities of Edge Computing and auxiliary technologies. The 2023 report further revealed that 83% of the companies studied deemed Edge Computing vital for retaining competitiveness. At the same time, 81% suspected that failure to adapt quickly to Edge Computing would be a barrier to maximising the benefits of the technology. These figures shed more light on why the expected growth rate mentioned in the Grand View Research report is as high as 36.9% CAGR.
The Synergy of Cloud and Edge Computing for 3PL Warehouse Automation
An effective warehouse automation suite is as quick to respond as it is precise in making data-driven decisions. Since there can be no trade-off between speed and accuracy, neither Edge Computing nocloud computingng can be a standalone data processing and storage environment. In the following sections, we have explored the role of each technology in the warehouses.
Edge Computing for 3PL Warehouse Automation: Why Is It Crucial for Warehouse Management?
How Edge Computing Functions
In a warehouse setting, Edge Computing is vital for instantaneous actions. Here’s how Edge Computers work:
- An IoT product captures workflow data.
- The data is transferred to the server through LAN either via Ethernet as electrical signals or light pulses (wired LAN) or as radio waves via Wi-fi or Bluetooth (wireless LAN).
- The on-premises or local server receives the data captured by sensors, image-based and others, and AIDC products.
- The server stores the data as a cache.
- The data is also processed, if needed, through embedded AI models and applications before it is relayed.
- The data reaches the intended IoT device through routers or switches.
- The workflow continues.
Most high-performance environments, such as warehouses or manufacturing units, use hybrid LANs, which combine wired and wireless networks.
Key Advantages of Implementing Edge Computing for 3PL Warehouse Automation
The pointers below demonstrate the merits of Edge Computing in warehouses:
- Offline Computing: Data transfer in Edge Computing is not dependent on the Internet and, hence, is more reliable in mission-critical settings where productivity can’t be left to chance, which are influenced by Internet connectivity.
- Low Latency: Edge Computers, being close to the device, deal with lower latency than Cloud Computers, as the centralised servers are usually far from the device. In other words, data localisation is the key factor behind the quick data processing needs of high-throughput 3PL operations.
This degree of swiftness is non-negotiable when shipments worth millions are being processed to be delivered on time and in good condition. Therefore, if an error or oversight has crept into the workflow, the AI model in the local server can detect it faster than the one in the cloud, triggering remedial action sooner.
With several 3PL warehouse automation solutions communicating, to uphold seamless workflow, LAN bandwidth must be high enough to deal with frequent as well as large data packets. However, if the same data packet has to travel to the cloud, it would have higher latency, thus slowing the warehouse throughput.
Learn how to build a strong infrastructure serving the needs of Edge Computing for 3PL warehouse automation!
- Cybersecurity: An advantage of Edge Computing is data security. When we talk about data transfer or latency, in terms of the Internet, the network in question is not a single fibre optic cable carrying the data. While that may be true in a confined area, radio signals covering large distances cause the data packet to hop several networks before reaching the cloud.
Each data hop through the several public networks exposes the data to security threats. Localising the data minimises its risk exposure, making Edge computers more secure than their centralised counterpart. Therefore, if a particular data set in a given 3PL operation is confidential, it is best stored on local servers.
Cloud Computing for 3PL Warehouse Automation: Why Do We Need a Centralised Server?
In this blog, we have indirectly covered how Cloud Computing functions:
- Internet connectivity is crucial for data transfer
- Higher latency compared to Edge Computing, one-way as well as round-trip
- Data processed through cloud-hosted applications and AI models
With that said, it is equally important to understand the merits of Cloud Computing for a robust hybrid edge-cloud architecture. Below are the key result areas where Cloud Computing surpasses Edge Computing:
- Scalability and Cost-Effectiveness: Unlike Edge Computing, Cloud Computing doesn’t require significant CAPEX. Also, the OPEX is usually as per use, while the computing and storage capabilities are vastly more powerful.
- Remote Accessibility: As the data transfer is centralised and purely internet-enabled, the operational insights and workflow status can be checked from anywhere. Hence, the cloud helps realise a seamless communication loop for off-premises stakeholders and departments as they can access the required report or workflow status from afar.
- Data Recovery and Disaster-Proofness: Cloud data remains backed up across locations, often around the globe, ensuring that in the face of a calamity, a mirrored version exists.
- Centralised Data, Historical Data Mining, Real-Time Third-Party Data Integrations: On-premises servers must be cleaned periodically to ensure they function optimally for real-time decisions. However, for more detailed reports and advanced analytics, such as forecasting, crunching historical data is crucial.
While local servers have limited space, the cloud practically has no such constraints in making these long-term and complex decisions using Predictive and Prescriptive Analytics.
Also, being internet-dependent, the cloud can host computing models to reliably mine real-time data from third-party aggregators, which consolidate dynamic data sets such as weather conditions or traffic congestion status for delivery route optimisations, while local servers can’t.
This is why WMS and TMS systems are centralised platforms. The platforms offer decision-makers a snapshot of warehouses across locations—a key factor for enterprise-wide measures—and enable the use of dynamic data inputs from third-party sources.
- Improved Digital Twin Implementation: The virtual simulation of the warehouse and its capabilities on the cloud improves predictive modelling and optimisation, thus reducing downtime.
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Contextualising Edge Computing and Cloud Computing for 3PL Warehouse Automation Setups
Here are two real-world examples of how edge and cloud computing work in sync. In these use cases, we will look at specific junctures of the workflow to understand the collaborative computing and decision-making effort that Edge and Cloud Computers achieve.
Real-time Multi-Location Inventory Updates
Let’s imagine a workflow where an articulated robotic arm is tasked with picking shipments and piling them in a chute.
The sensor in the chute signals a Forklift AMR to carry the shipments to the packaging workstation. The signal from the chute sensor first goes to the edge computer before reaching the AMR. The AMR then uses its navigation sensor with the guidance of the local server to reach the chute, pick up and carry the shipments to the packaging station.
The number of items sent to the packaging system in a given time is sent to the cloud via the Internet to the cloud-hosted WMS, which is updated instantly. The same process is followed across warehouses. If an executive wanted to know the real-time status of the total number of shipments in packaging at a specific time throughout the location, the data would be visible on the WMS.
Accurate Smart Sortation System
Let’s take a high-throughput conveyor-based (dynamic) DWS system connected to a sorter. The DWS system consists of a high-speed conveyor scanner with Machine Vision capabilities. Having gone through the dimensioning, weighing, and scanning workflow, the shipment goes through the sorter, which has a certain number of chutes, depending on the PIN codes or the range of PIN codes the warehouse caters to.
Along with the data on the shipment’s dimensions and weight, the barcode data also reaches the edge computer. Based on the PIN code encrypted on the label, the edge computer directs the signal to the right sorter on the sorting belt, ensuring that the shipment reaches the correct bin.
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Building the Setup for Cloud and Edge Computing for 3PL Warehouse Automation
Benefits
In addition to improving operational efficiency of the warehouse, edge-cloud architecture creates a fine balance between speed and accuracy. Also, as edge computing deals with real-time data, sending only a filtered and relevant set of data to the cloud, the hybrid data processing and storage system slashes the bandwidth expenses of the company, which is charged based on its internet usage.
It is important to elaborate that when cloud services are added to edge computing, the operation’s resilience, scalability, and decision-making capability improve. Its superior computing power aside, Cloud Computing enables swift adjustment to increase
computing and storage needs, without a massive CAPEX. On the other hand, if there is downtime in either of the server types, the one working acts as a backup.
Challenges
CIOs must note that Cloud Computing becomes faster as the data is filtered or refined by Edge Computing. However, this increase in computing speed comes with a trade-off: loss of granularity in data insights as the core raw data is not crunched by the cloud.
Solutions
While less raw data may compromise computing outcomes, this problem can be easily overcome by filtering sufficient data on anomalies, successful actions, etc. When this expanded but selective data is used to train Predictive and Prescriptive Analytics models, the models can be sent to the edge computers to crunch the raw data better, thereby creating a balance between operational efficiency and quality of insights.
Also, larger local servers can store more data and train improved AI models to handle advanced analytics locally. Meanwhile, the raw data is sent to the cloud periodically. When a specific need for extensive data insights is required, cloud computing comes in handy. However, this approach requires a high setup cost.
Another method of ensuring optimum computing outcome is to decide the interval when the raw data at the edge will be synchronised to the cloud. Hence, the edge is not burdened with more complex analytics, while the internet bandwidth is also economically used.
Additionally, given that network distance is a key reason behind high latency, advanced networks such as 5G and 6G or private networks can be used to reduce latency. Similarly, warehouses can leverage Software-Defined Networking (SDN) and Network Function Virtualisation (NFV) for intelligent data routing.
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Key Considerations
3PL companies should be mindful of these pointers when selecting the edge-cloud architecture:
- Legacy modernisation strategies involving cloud and edge computing require extensive planning.
- While edge computing is less exposed to risk compared to cloud computing, adhering to cybersecurity best practices is still vital for compliance.
- In the long run, cloud-edge architecture often proves to be less expensive than being purely dependent on either of the servers, but the CAPEX and OPEX are still massive.
- Even as Edge Computing reduces reliance on the internet, a reliable network connection remains crucial for centralised tracking and management.
Enhance Your Cloud and Edge Computing in Your Warehouse Automation Plan with Quinta!
Over the years, Quinta has carved out a strong reputation as a comprehensive solutions integrator in the Intralogistics industry. We strive to do more than help 3PL players procure premium Industrial PCs, networking appliances, and servers for their Edge Computing and Cloud Computing needs. Our experts also assist in WMS integration and offer end-to-end deployment assistance and prolonged customer support.
However, the core differentiator of our service is the warehouse automation consulting we offer, which helps logistics players attack prevalent operational hurdles most efficiently and cost-effectively. A key focus area of the consulting service is identifying challenges and customising and designing a legacy modernisation plan which serves the needs and goals of the business with the least disruption in productivity.
Curious to know more? Contact us now, and our experts will reach out to you as soon as possible!
