We’ve been talking about the Fourth Industrial Revolution (4IR) for some time now. In this era, the way we approach and think about manufacturing is being completely upended.
- Supply chain operations will be streamlined and provide real-time data for businesses of all industries and sizes.
- The Industrial Internet of Things (IIoT) will collect more shop floor data than ever.
- Artificial Intelligence (AI) will mimic human behavior and activities, decreasing workplace hazards and overcoming talent shortages.
- Increasingly sophisticated analytics will tell us what it all means in the blink of an eye.
However, as much as the IIoT, big data analytics, and AI are fueling the next industrial revolution, edge computing plays an important role and is often overlooked.
What is edge computing?
Edge computing is designed to put IT resources closer to your end-users and Internet of Things (IoT) devices, making new services and applications possible. Processing and data collection are done at edge nodes, as opposed to a central data center or cloud. Using the edge of the network can reduce latency and improve the user experience (UX).
While it used to be limited to Tier 1 (largest population cities), it has expanded over time to Tier 2 cities and beyond. With expansion into new areas, devices and services have been able to move closer to the end-user. By 2028, edge computing is predicted to become a $700 billion market.
Depending on the user and device needs, edges can look different. They may consist of hundreds of micro data centers around the world, a series of colocation data centers, or private cloud hosting providers located anywhere between 1-50 miles from your customers.
With computing at the edge, devices can lighten their processing load, making the end products physically lighter. A closer processing center can also improve storage costs of data from things like sensors, monitors, production line equipment, and shipment trackers.
One challenge with edge computing will be finding staff that has enough understanding and skill to implement and manage it, all while being mindful of security concerns. You can also deploy an edge computing model with the help of a cloud computing provider that has a rich data network.
How does edge computing work for manufacturers?
For manufacturing, edge computing may mean using a data center closer to your facilities to reduce latencies.
Let’s say, for example, that you have a distribution center (DC) in St. Louis. You have several route carriers operating out of this DC, serving cities and towns throughout Illinois and Missouri. Your product is one that is highly variable based on weather patterns, e.g., camping supplies.
A retailer may modify their order several times a day, depending on what’s moving. The faster you can replenish their supply, the more of your products they’ll sell.
By placing the supply chain applications that optimize your on-truck inventories and distribution routes closer to St. Louis instead of in a far-away city like New York, you’ll enable faster response times. New orders won’t have to travel from your Midwestern retailers to the East Coast to process data, and then back to the route drivers.
While this may not seem like a big deal, a delay in routing a new order from one of your best customers could mean that your drivers restock the shelves of one of their lower velocity stores instead.
How else can edge computing be used in manufacturing?
Manufacturing is one of the top five industries currently innovating with edge computing. For manufacturers, the benefits of edge computing are both internal and external.
You may choose to use edge computing to get closer to a distributed workforce. Say you manage multiple locations and you want to make sure everyone receives the same experience, both onsite and in the field.
For customers, edge computing allows smart devices to operate with greater efficiency and speed. This includes environmental sensors, machine controls, assembly-line robots, and asset tracking. Real-time responses and tight feedback loops are especially important for quality control monitors and environment sensors.
Manufacturers can also produce more streamlined products with the help of edge computing. If computing power can be offloaded to a local data processing center, wearables and devices can weigh less.
Examples of edge applications for manufacturers include:
- Machine control
- Equipment monitoring
- Environmental monitoring
- Vision-based analytics
- Remote facility monitoring
- Asset tracking
- Fleet vehicle diagnostics
Let’s look at a couple more specific examples where edge computing resources could prove beneficial to manufacturing.
Quality control and scheduling
When a vital piece of equipment fails unexpectedly, it increases downtime and can create tremendous scheduling headaches as well as lost dollars in scrap and rework. Predictive analytics have been used for years to predict when a machine will need maintenance. This allows schedule managers to work around that planned downtime and avoid the scrap/rework expense.
The rapid communication provided by edge computing allows manufacturers to accurately track their equipment’s condition, location, and current usage at any time. Monitoring equipment can save a company from incurring expensive downtime. More advanced sensors can detect even minute fluctuations in performance that can indicate problems with product quality or a pending malfunction.
If a part is wearing out faster than expected, real-time edge computing can alert you to it before it becomes a bigger problem. When that data is analyzed by intelligence built into the equipment itself, machines can respond automatically to these minute fluctuations, adjusting as needed or taking themselves offline until a manual recalibration can be performed.
When this intelligence is connected via an intranet to the rest of the factory or the supply chain via the internet, it can even facilitate scheduling changes to improve production flow – without the need for human intervention. This can help prevent recalls or delays in production.
Picking proficiency
Let’s look at a second example where edge computing allows machines to mimic human behaviors even more closely.
Moving components and products around is one of the most time-consuming aspects of manufacturing. All kinds of strategies have been tried to make it more efficient (e.g., Kanban, point of use storage, redesigning the factory layout, etc.). Now, some companies are looking at using robots to eventually replace human warehouse workers altogether.
Amazon is probably the most cited example of an organization leveraging robots in its warehouses. Even so, they still have plenty of human workers doing the actual packing of your orders. That’s because the movement through the warehouse (and the packing of an order) is an incredibly complex task that involves more variables than most people realize. The human brain can take in these variables (often without even realizing it), analyze them, and respond faster and more accurately than even the most sophisticated robots.
Scientists are working on identifying those variables, building models, and imbuing warehouse robots with the AI they need to make snap “decisions.” Eventually, they will be able to perform the same work as their human counterparts, making the warehouse a far less chaotic place. This, in turn, will allow manufacturers to reduce the motion and movement of parts down to the bare minimum.
Not only will this require an incredible amount of data and computational power, it will also require speed. That means putting the processing power on the warehouse robots themselves, not in some centrally located processing unit that analyzes the data and then gives each robot its marching orders.
What does your IT Infrastructure look Like?
To maintain competitiveness, manufacturers will need to leverage the productivity benefits of multiple technologies. This includes IIoT, AI, and analytics, all of which can be improved with edge computing. Do you manage a manufacturing operation? Have you explored how your IT department can contribute to your business’s bottom line with edge computing? If not, let’s talk.
Want to learn more? Read our Strategic Guide to Edge Computing.
