Edge Computing in the Manufacturing Sector: Use Cases for 2026

In the early days of industrial computing, the tech was mostly sealed off and purpose-built. Control logic lived inside cabinets and data stayed on the line, all to help keep machines running safely and predictably. 

Today, the manufacturing tech budget has flipped. IDC projects worldwide edge-computing spend will climb to nearly $350 billion by 2027. Across industries, discrete and process manufacturing will account for the largest portion of investments in edge solutions. 

Manufacturers have gotten here the hard way. Cloud-first pilots were valuable, but data volumes don’t scale cheaply. Add AI workloads that require instant feedback into the mix, and you’ve got plants pulling their industrial data back to the shop floor in an effort to save money and remove bottlenecks.

Ahead, you’ll explore what edge computing looks like in manufacturing, the top use cases, and how to decide on a hybrid approach to power your smart initiatives in 2026.

What is edge computing in manufacturing?

Edge computing in manufacturing means running computing or storage at or near machines on the factory floor, rather than sending everything to the cloud and back. It brings enterprise data closer to the base of operations through tools such as Internet of Things (IoT)-enabled devices and on-premises (on-prem) edge servers. 

By basing computing where the data is generated, enterprises save the time and effort of sending that data on a round trip to the cloud, allowing them to speed up response times and stretch out available bandwidth. 

Edge computing is growing in industrial manufacturing for a few reasons:

Reduced latency and jitter

The scale of work maintained by operational technology (OT) in manufacturing is too great to tolerate delay (also known as latency) in the communication of data; and jitter, or variability in the timing of data, is equally dangerous. Data must be on time and in sync in order to keep everything functioning properly, or the costs are significant—especially when it comes to safety systems.

Reliability

Many factories run 24/7. While IT systems can handle reboots or outages in the cloud, OT processes can’t. When computing equipment is accessible, it’s easier to schedule required updates during preplanned change windows to avoid any revenue loss from unplanned downtime.

Data management

Most modern manufacturing facilities use high-res cameras and sensors that generate huge amounts of raw data. Rather than ship all of it to the cloud for analysis, your edge-computing system can filter and aggregate that data, sending only relevant insights upstream to save on cost. 

Local autonomy

If the internet goes down, the plant doesn’t stop. If you keep critical logic local, the factory either continues operating or can shut down safely rather than crashing out. 

But what does edge computing look like on the floor? Overall, edge computing lives in a range of hardware at different layers of the factory:

• Embedded control layer: The closest layer to the programmable logic controllers (PLCs), drives, and robots, handling real-time safety and control
• Connectivity layer: Devices like protocol gateways and industrial routers that normalize and transport machine data
• On-prem edge computing layer: Local servers running SCADA/HMI systems and historians that record data locally
• Rugged AI edge layer: Specialized industrial PCs or micro data centers equipped with GPUs to handle heavy tasks like AI vision and real-time sensor analytics

To an extent, proximity of computer logic to the processes it controls is an obvious benefit, which is why manufacturers have used some of these tools for decades. PLCs, for example, have long been in place to perform important functions like detecting and responding to anomalies—say, a bottle falls and in response, a conveyor stops in milliseconds—long before a cloud-based system could process the signal. 

Edge computing in manufacturing today standardizes these systems using unified data pipelines and remote management, changes that allow for operational management at a larger, multi-site scale. But not everybody is on the same page: According to 2025 research by Orange Business, while 90% of manufacturers are integrating IT and OT, only 28% have a shared strategy. 

Although site-level resistance and governance are the biggest hurdles to unified operational strategies, Deloitte found that 92% of executives believe smart operations will be their main driver of competitiveness over the next three years.

Edge and cloud-based computing in manufacturing

A factory doesn’t have to choose between edge and cloud-based computing. Many use both. While speed and stability are benefits of edge computing, the added scale of cloud computing can handle tasks that are less “hands on” and time-sensitive. Here is an example of how tasks might be distributed in a hybrid system: 

Why edge is accelerating in 2026 

In 2026, the conversation around edge computing centers on its use as essential infrastructure for manufacturing. Driven by a mix of AI demand, escalating cyber risks, and ongoing workforce constraints, edge is what’s keeping the smart factory moving. 

What’s changed over the last 24 months to make edge such a priority today? The data shows that:

• Manufacturers are seeing gains in operational efficiency from smart operations. The industry is past the hype phase. According to Deloitte’s 2025 survey, manufacturers are seeing net impacts of 10%–20% higher production output and 10%–15% unlocked capacity.
• More manufacturers are investing in AI. While the world talks about LLMs, the factory floor is where AI value is truly captured. Rockwell’s 2025 State of Smart Manufacturing report highlights that 95% of manufacturers have invested in AI and machine learning (ML), with 50% specifically targeting quality control as their top use case.
• Cyber risk is a more prevalent operational problem. Rockwell’s report also found that cybersecurity is the second-most-cited external risk, and 49% plan to use AI to address it. 
• Industrial ecommerce is growing. As more manufacturers own their online sales channels, the need for increasing accuracy and speed to meet buyers demands is critical. Edge computing gives you more visibility into production lines, so you can keep things moving and keep your promises to customers. 

Overall, the acceleration is being fueled by both business and tech-driven pressures, and the interaction of the two. On the business side, manufacturers are always looking to increase output and reduce scrap/rework. With the onset of AI, the pressure is on to reduce waste as its the fastest path to return on investment (ROI). 

On the tech side, AI/ML stacks are mature enough for plug-and-play deployment at the line. Organizations are moving towards a hybrid IT environment, with the mindset shifting from streaming everything to processing locally. And tech pressures feed business imperatives as some companies gain efficiencies by adopting edge computing, and competitors must adapt in order to keep pace.

💡Remember: Edge is most valuable when the decision must happen in milliseconds. Examples include safety interlocks, stop-the-line quality events, preventing equipment damage, and routing for rework.

Despite the acceleration on the tech side, one factor is creating a bottleneck: people. Auburn’s ICAMS Smart Manufacturing Adoption Study reveals that 61% of manufacturers rank workforce operations as a top-three challenge. Edge computing is a solution here. 

Using standardized, remotely managed stacks, companies can bypass the lack of skill sets at individual sites. As Rockwell notes, 41% of manufacturers are now using AI and automation specifically to close these skill gaps.

Top edge computing use cases in manufacturing

1. Condition-based monitoring (CBM)

Condition-based monitoring (CBM) detects abnormalities in conditions like vibration, heat, and pressure to assess equipment health. 

This technique is used to involve threshold alerts. For example, if a motor’s vibration frequency exceeds 2.5 millimeters per second for more than three consecutive seconds, an “edge node” sensor could trigger a high-wear alert to the maintenance dashboard. 

In the present day, CBM can be set up as a unified architecture that includes the front end, back end, edge-computing tools, and factory equipment. Edge computing provides a flexible execution environment where algorithms are designed in a visual web interface, compiled into programs, and deployed to edge nodes in real time. The edge nodes are the first responders and act on real-time production data, so that only high-value events reach the central dashboard. 

Latency tolerance: A general rule of thumb for acceptable latency levels is less than 1 millisecond for safety and hard interlocks; less than 100 milliseconds for edge-based control adjustments; and 1 to 2 seconds to sync data to an operator dashboard. 

KPIs to track:

• Unplanned downtime: Total minutes/cost of unexpected halts
• Mean time between failures (MTBF): Intervals between incidents show the reliability of the asset over time.
• Alert-to-action time: The duration between an edge trigger and a documented maintenance response

Common pitfalls: 

• Noisy sensors triggering ghost alerts
• Alert fatigue from lack of root-cause grouping
• Missing asset hierarchies that make alerts impossible to triage

Minimum viable data checklist:

• Sensor/mounting: Tri-axial accelerometers on bearing housings or pressure transducers on hydraulic lines
• Sampling rate: Define Hz requirements and window sizes
• Asset/tag ID: Unified naming via OPC UA to ensure the node knows exactly which motor it’s monitoring
• Timestamp sync: PTP/NTP synchronization to align edge data with global logs

2. Predictive maintenance (PdM)

If CBB tells you what’s happening now, predictive maintenance (PdM) forecasts remaining useful life (RUL) to schedule maintenance before failure occurs. 

Manufacturers can now build end-to-end intelligence engines using AI. Integrating both structured and unstructured data, edge-computing systems can forecast fault types like belt loosening, shaft misalignment, or voltage fluctuations, before reaching functional failure. This way, you can choose a brief window of downtime to replace a part rather than a full collapse that can damage other systems and products.

Heavy deep learning models are trained in the cloud using historical and synthetic data, and the resulting API endpoints are deployed to edge nodes. It enables immediate fault diagnostics and RUL estimation, even in resource-constrained or offline plant environments. 

In a recent study using a framework called IntelliPdM, a large manufacturing unit in Singapore achieved 93%–95% fault-detection accuracy, a 70%–75% decrease in equipment failures, and a 20%–25% increase in total throughput through optimized uptime. 

Latency tolerance: Seconds to minutes, fast enough to intervene before damage propagates. Model training in the cloud can take weeks, depending on the complexity of the dataset. 

KPIs to track:

• Maintenance cost per asset: Total spend on labor and parts divided by the number of critical machines
• Downtime hours: Cumulative time a production line is nonoperational due to failure
• Spare parts forecast accuracy: The relationship between predicted component failures and actual inventory usage

Common pitfalls: 

• Lack of historical data to simulate fault states for model training 
• Structured sensor data and unstructured video/audio live in different systems. 
• Attempting to perform model training on the factory floor, where only lightweight inference should be deployed

The four-step PdM process:

1. Collect: Gather raw signals and operating context.
2. Engineer: Extract relevant data at the edge-computing level, discard raw data to save bandwidth.
3. Infer: Run the model at the edge to classify anomalies or estimate RUL.
4. Retrain: Send labels and outcomes to the cloud to refine the global model.

3. Manufacturing-as-a-service (MaaS)

Sellers traditionally turn to manufacturing firms to produce components and products. These firms have to regularly determine if a product can be manufactured according to agreed-upon standards based on the condition of machines across multiple sites. 

Edge-computing devices can monitor high-frequency data like torque and spindle position to compute the total position error. If wear has degraded precision below the required tolerance, the edge-computing system can be set up to notify the central marketplace to reroute the production step to a healthier machine, possibly in a different factory. 

Latency tolerance: In general, edge computing requires latency under 1 millisecond for mission-critical applications like robotic arms or closed-loop control systems.

KPIs to track:

• Time-to-ramp for a new line or site: The time required to deploy a new production workflow and validate machine skills
• Resource utilization: Measure the load on local servers, including CPU, memory, and storage usage
• Product quality (first pass yield): Reduction in defects due to real-time machine product inspection

Common pitfalls:

• Planning production based on as-built machine specs instead of as-in health 
• Different sites using different naming conventions 
• Rerouting parts across factories can kill margins. 

A scenario on resiliency 

During a 90-day contract manufacturing surge for an automotive company, Factory A’s high-precision milling machine reports a stiffness deterioration via its edge node. 

Rather than produce low-quality parts or halt the line, the MaaS company utilizes edge-computing data to automatically ascertain that Factory B has an idle machine with the exact capabilities, and in perfect working condition. The production plan is regenerated instantly, and the drive-side assembly is shipped to Factory B for the precision steps, maintaining the delivery deadline.

4. AR/VR for training, remote assist, and inspection

AR was once about cool training videos in the field, but today it’s a tool for real-time quality inspection and manual assembly guidance. Running computer vision models on edge nodes or high-performance handheld devices lets you identify assembly errors as they happen. 

The challenge here is that computer vision is computationally heavy. To achieve the sub-20-millisecond motion-to-photon latency required to prevent cybersickness in headsets, the edge node handles video decoding and AI inference. The setup guarantees that digital overlays stay locked to the physical part even when the user moves their head rapidly. 

Latency tolerance: A sub-20-millisecond tolerance is critical for usability and safety. 

KPIs to track:

• Error rate: Percentage of defects missed by human inspectors compared to AR-assisted checks
• Time-to-competency: How quickly a new hire reaches peak productivity on a complex assembly line
• Training throughput: Number of trainees certified per week without requiring a dedicated senior mentor for every hour of practice

Common pitfalls:

• AR performance is contingent on specific operating systems and software libraries.
• Running the latest version of computer vision models could exceed the processing power of a mobile device.
• AR/VR headsets (head-mounted displays, or HMDs) can suffer from thermal throttling and battery draining during long shifts. 

Checklist to get started:

• Choose a device tier. Decide on HMDs like HoloLens and/or a tablet like an iPad Pro based on task duration. 
• Determine your inference model. Deploy a quantized YOLO (You Only Look Once) model for object detection. Quantized means the AI runs on the edge hardware without an enormous GPU. 
• Decide on a tracking method. Use marker-based tracking for high-speed lines where the camera needs an instant anchor. Deploy markerless tracking for large, stationary assets like a jet engine, where the device needs to remember the room's 3D layout.
• Manage network stability. Set a hard cap of less than 5 milliseconds of jitter. High latency in a VR/AR environment makes workers sick; high jitter causes the virtual "tighten here" arrow to jump and vibrate on the screen, rendering precision work impossible.
• Choose the software stack. Choose one framework, such as Vuforia for CAD-to-AR sync or Unity for custom training, and lock it across all sites. Proliferation of too many software and hardware tools increases potential points of miscommunication and failure.

5. Precision monitoring and control

Precision monitoring and control is the pinnacle of edge-computing maturity. Powered by AI, edge computing is a high-speed co-pilot for your existing PLCs. It can process environmental variables, such as cleanroom particle concentration, chemical flow rates, or microfluctuations in voltage, and make live adjustments to recipes and process parameters to optimize yield. 

In high-precision environments such as semiconductor fabrication or chemical processing, the data volume required to maintain quality is too large for the cloud to handle in real time. The edge aggregates multimodal data and runs AI alongside the process for closed-loop optimization. 

Latency tolerance: As with other edge-computing use cases, sub-second timing is key for machine vision quality control, adjusting feed rates, and triggering accurate anomaly-based stops. Line balancing and energy optimization can span up to 60 minutes on a local server. 

Key KPIs:

• Scrap/rework rate: The percentage of materials discarded due to precision errors
• Overall equipment effectiveness (OEE): A composite of availability, performance, and quality
• Energy per unit: How much power is consumed to produce a single finished good 
• Quality escapes: The number of defective units that make it out of the plant and into the supply chain

Common pitfalls:

• Never use a general-purpose edge system for hard real-time safety interlocks.
• The AI needs to log and explain automated changes. If an operator doesn't understand why the AI just changed the spindle speed, they can override it—which is dangerous if the AI had a reason for making the adjustment.
• A change in a raw material supplier, which results in the use of a slightly different aluminum alloy, can confuse the AI. 

How to decide between edge, cloud, and hybrid

As noted above, manufacturers rarely choose between cloud or edge-based computing. The name of the game now is to move intelligence as close to the machine as possible—especially for highly time-sensitive functions— while leveraging the cloud for scale, and for less time-sensitive processes. 

According to Deloitte’s 2025 Smart Manufacturing Survey, 78% of respondents now allocate more than 20% of their improvement budgets to smart initiatives. As these investments scale, formalizing the decision between when to use edge or cloud computing is critical to sustaining operations. 

Start by running a workload litmus test. 

• Edge-first: Use the edge when a workload requires sub-second response times or must remain operational during a network outage. Think machine vision QC, high-frequency vibration filtering, and stability for AR applications. Keeping computing resources close to the data source reduces bandwidth and ensures reliability when there is no internet connection. 
• Cloud-first: Use cloud computing for workloads that require big datasets from multiple sources. Cross-site benchmarking, long-term forecasting, and training AI models for later deployment to the edge system are practical workloads for the cloud. 

Going hybrid, or using both edge and cloud, builds resilience. If the wide area network (WAN) fails, for example, the edge node continues to run local inference and control logic, buffering events until connectivity returns. That way a cloud outage won’t stop your physical production line.

Here’s a quick comparison to help choose your architecture: