How to Improve Logistics Operational Efficiency With Edge Intelligence
Anyone in logistics knows the challenges. Consumers demand faster and more accurate delivery as well as granular tracking that shows en-route packages in near real-time. The number of SKUs and single-unit sales continue to increase. New models of retail such as buy-online-pick-up-in-store (BOPIS) put increasing demands on supply chains.
While automation is growing quickly as a response to these challenges, logistics managers know that their biggest hurdle in increasing efficiency is the lack of accurate data. They are aware that their inventory numbers are inaccurate, their control over delivery expectation accuracy inadequate, and their ability to ensure vendor compliance is limited.
They also have trouble identifying the cause of no-reads in their barcode reading operations. This results in extra manual work to get packages finally moving to their destinations. And they know that if they could trace down those causes, they could make effective systematic changes that would consistently increase throughput.
Logistics managers want data and analytics that will let them identify, highlight, and respond quickly to inefficiencies and bottlenecks anywhere in the supply chain.
The solution is linking image-based barcode readers to edge computing processors in the distribution center floor to provide real-time operational visibility, a solution Cognex calls Edge Intelligence.
No-reads are the killer. Enormously capable image-based barcode readers in high-speed scan tunnels still throw no-reads, which require segregation of packages on hospital lines for manual rework. Identifying the causes of no-reads such as - no label present, torn label, or product inducted improperly - would allow for tracking down the cause of the problem and fixing it before it causes further delays.
The solution lies in edge computing. By continuously picking up every one of the many kinds of data from every reader, Cognex Edge Intelligence provides continuous and detailed monitoring of performance and combines and normalizes it all to form a detailed historical record of conditions on the line. Quick adjustments at just the right point keep the line moving, later analysis increases uptime in the long term.
Every time no-reads start showing up, the manager can look back to see if any anomalies preceded that event. Eventually, it becomes possible to spot the first signs of a possible problem and take action to prevent it. And all no-read images are collected and can be analyzed for performance metrics and to provide a record if there are regulatory or audit concerns.
Overall equipment efficiency (OEE) is the biggest driver for any e-commerce, retail, or parcel post operation, and this ability to anticipate device failures and to pinpoint problems quickly and fix them promptly has a strong effect on OEE.
Alerts and reporting
Edge Intelligence can be set to automatically generate and push reports at regular intervals, rather than requiring someone to request data and use spreadsheets to analyze it. Managers can set their most useful key performance indicators (KPIs), and define alerts for particularly important performance problems.
When a facility has hundreds of readers in it, along with many other sensors and devices, set up, updates, and monitoring take an increasing amount of time and effort. Software versions diverge between cameras, increasing the difficulty of tracking down the source of a problem.
Edge Intelligence enables the setting of IP addresses, the loading of firmware, and the installation of configuration files for multiple cameras simultaneously, ensuring that throughout their deployment there is no variation in software from one to the other. It also provides a log of all configuration changes in case a software change negatively affects performance and needs to be rolled back.
Interoperability with existing management systems
Edge Intelligence data serves as an input to WMS, ERP, MES, SCADA, and SCM, and other analytics software such as Ignition. Edge Intelligence can generate data in a wide range of formats usable by other systems, typically via the robust MQTT protocol, meaning that systems acquired at other times for other purposes can incorporate EI data for making decisions.
Solving the key problems of logistics
Logistics operations are constantly seeking ways to reduce inefficiencies and improve throughput. Edge Intelligence provides operations a way to know if the equipment is running optimally, easily update networks of image-based barcode readers to the latest firmware available and identify the root cause of no-reads for corrective action.
The supply chain transparency that comes with widely deployed image-based barcode readers and Edge Intelligence reduces the number of lost packages and misidentified products, ensures that items move through the DC at maximum rates, and minimizes the number of packages that miss getting on a truck.
For vendor compliance, Edge Intelligence makes it possible for retailers to store images, barcode result data, and code quality of incoming shipments, assisting in efforts to remove compliance issues in the network that are resulting in supply chain inefficiencies. In case of dispute as to where damage or inadequacy occurred, both benefit from acquiring and storing barcode and product images.
A logistics-specific edge computing solutionThe Cognex Edge Intelligence Platform brings smart data collection and real-time performance metrics to the distribution center. It is fully integrated with Cognex fixed-mount devices and logistics tunnels and uses the full range of data from these devices to provide a logistics-specific visual dashboard of real-time system performance. It will support expansion and integration across the supply chain and existing software systems.
Global Content Marketing Manager, Cognex
A technology marketer for over two decades, Mike enjoys communicating how technology can improve business outcomes. Over his career, he’s helped businesses in many industries understand the value of technology through work at Dell/EMC, Sun Microsystems, a few technology startups, and, since January 2020, at Cognex. When not extolling the virtues of using machine vision across various industries, he can be found exploring local Massachusetts and Cape Cod waterways with his kayak, taking in local craft breweries, or working on home improvement projects.