Considering using IoT in manufacturing? Read this first

In a recent PwC survey of more than 500 C-suite leaders, 93% said the manufacturing sector stands at the edge of a new industrial revolution, and 73% believe companies that fail to embrace digitalization will become irrelevant within a decade.

The catalyst behind this digital shift is the Internet of Things (IoT). By embedding sensors, connectivity, and intelligence across factories, warehouses, and supply chains, manufacturers are transforming operations that once reacted to problems into systems that anticipate them—seeing failure before it happens, adapting to disruption before it cascades, and improving continuously rather than incrementally.

For executives evaluating IoT in manufacturing, the question is understanding where connected systems create the most impact across operations, safety, sustainability, and supply chains—and how the right IoT consulting partner can help navigate this transformation. In this article, we explore the most important ways IoT is reshaping modern manufacturing.

Digital transformation has become the engine of the new industrial economy. Smart manufacturing is no longer a niche initiative. It’s a rapidly expanding market that was valued at $394.35 in 2025, is expected to reach $446.45 billion in 2026, and is projected to exceed $1,339.17 billion by 2034, growing at a CAGR of 14.7%.

At the center of this shift sits industrial IoT. As manufacturers deploy 5G-enabled sensors and edge computing across production lines, the IIoT market alone is expected to reach $654.84 billion by 2032, powering real-time, autonomous decisions on the factory floor. The economic stakes are enormous. Industrial IoT could generate $14.2 trillion in global economic value by 2030 and cut manufacturing costs by 4–6% through predictive maintenance and automated optimization.

With 15–17 billion IoT devices active worldwide in 2026, including around 6 billion in industrial and commercial environments, the smart factory has become a software-defined operation where data is the most valuable raw material.

Here are five key examples of IoT use cases in smart manufacturing​:

Let’s dive deeper into each use case along with real-world examples of IoT in manufacturing.

Unplanned downtime remains one of manufacturing’s most expensive problems. In high-output facilities, a single hour of disruption can cost up to $500,000 when lost revenue, idle labor, and supply chain delays compound.

When adopting IoT for manufacturing, businesses deploy dense networks of sensors across production lines, capturing vibration, temperature, electrical signals, and more in real time.

These data streams reveal potential failure long before machines break. The results are clear: IoT-driven predictive maintenance can reduce maintenance costs by up to 30% and decrease unexpected breakdowns by nearly 70%.

But detection is only the first step. The real shift underway is from systems that flag risk to systems that act on it.

How far that autonomy extends depends heavily on context.

In remote or high-criticality environments—offshore oil platforms, for example—self-managed operation isn’t optional; equipment must stay running until the next scheduled maintenance window, with no opportunity for unplanned intervention.

In batch production facilities, the priority shifts: systems need to detect and correct deviations within the cycle to protect on-time delivery. IoT platforms are increasingly designed to reflect these realities, calibrating the level of autonomous response to the operational environment.

Real-life example:

Siemens’ smart factory in Amberg, Germany, is a benchmark case for IoT-enabled autonomous manufacturing. By combining IIoT and AI, the plant achieves real-time, independent decision-making across its production lines.

The results include a 30% reduction in unplanned equipment failures, 99.9% quality levels, and annual operational savings exceeding $35 million. Without expanding its 10,000 square meter footprint or significantly increasing headcount, the factory has scaled production volume 13-fold since 1990—a figure that makes the business case for autonomous IoT operations hard to argue with.

Regulators and insurers now push organizations toward a “total worker health” model—one that prevents incidents rather than reacting to them.

IoT in manufacturing makes that shift possible.

The next frontier goes even further: monitoring the human operator, not just the environment.

IoT solutions for real-time monitoring in factories use motion capture technology to track movement patterns, body temperature, and subtle indicators of fatigue. When catching on to abnormal behavior, the system can trigger simple but effective interventions: adjusting workstation lighting, recommending a break, or alerting supervisors to rotate shifts.

Real-life example:

One example of IoT in manufacturing comes from Nation Waste, Inc., a waste recycling company. The firm is acutely aware of the dangers toxic substances and heavy machinery pose to human health. Nation Waste equipped its employees with IBM Watson IoT wearable solutions and fitted sensors around the workplace. These measures enabled managers to spot any signs of fatigue, dehydration, and exhaustion among employees and act upon this information. This initiative led to a 60% reduction in injury-related costs.

Environmental, social, and governance (ESG) reporting is moving from a voluntary exercise to a core operational requirement. Regulations, such as the EU’s Digital Product Passport (DPP) mandate, require manufacturers to provide verifiable, granular data about emissions, materials, and product lifecycles across all supply chains.

While Europe is enforcing comprehensive ESG frameworks, the US remains a patchwork of state-level requirements with no equivalent federal mandate. Even so, many US manufacturers are voluntarily adopting EU standards to avoid having two separate manufacturing and data architectures.

As an added benefit, with this level of visibility, manufacturers can cut energy consumption by around 20% and shift usage to off-peak hours, reducing costs while lowering their carbon footprint.

Real-life example:

One example of IoT in manufacturing is the Siemens Fürth facility, which was named a World Economic Forum Sustainability Lighthouse for 2025. The company installed an IoT system to measure its carbon footprint and analyze energy consumption. As a result, the plant achieved a staggering 64% energy savings per throughput and a 74% reduction in greenhouse gas emissions.

IoT for manufacturing can be combined with AI and other technologies to create digital twins of new products and factories.

A digital twin is a computer model of specific machinery, a process, or an entire factory floor, including all of its equipment and assets. It mimics real-world components’ status and interactions. Managers and engineers can use this technology to conduct experiments, test new processes, and observe the consequences before applying the changes in real life.

For instance, this technology allows manufacturers to validate hypotheses about equipment performance and resilience, such as acceptable stress levels.

Digital twins can help simulate new production lines and their operating configurations, detect areas that need optimization, plan warehouse architecture, validate product designs, and optimize existing processes to eliminate inefficiencies.

Real-life example:

Unilever, a British consumer goods manufacturer, operates eight digital twins of factories located in the US, Europe, and Asia. Coupled with business intelligence, the technology allowed the company to reduce the number of alerts requiring physical presence at the factory by 90% per day.

Manufacturing teams can also use digital twins to experiment with a product. For example, Ford typically develops seven virtual models of every one of its vehicles. Each model covers a different production aspect.

In today’s volatile global markets, executives need systems that adapt in real time, not dashboards that explain yesterday’s disruption.

Instead of simply alerting managers to delays or damaged goods, modern IoT platforms trigger immediate corrective actions. If sensors detect a temperature breach in a pharmaceutical shipment, the system can automatically file an insurance claim, place a replacement order with a secondary supplier, and notify the customer before the compromised shipment arrives.

Real-life example:

Henkel, a global consumer goods leader, demonstrates the power of a living supply chain. With the help of IoT, the company monitors temperature, humidity, and dew point across global sea routes. With this visibility, Henkel can optimize logistics so precisely that it plans to eliminate refrigerated containers on some routes—cutting costs while maintaining full product integrity.

IoT in manufacturing doesn’t deliver a single benefit—it shifts the economics of the entire operation. Here’s where manufacturers see the most measurable impact:

Cost optimization is the key driver behind deploying IoT in manufacturing—and it works across three distinct variables: equipment availability, production performance, and output quality. IoT addresses all three.

Equipment availability takes the biggest hit from unexpected, prolonged downtime. One study shows that such disruptions can cost large factories up to $500,000 per hour and can happen every week. IoT sensors can spot malfunctioning or overloaded equipment and alert the manager before a breakdown occurs. In advanced, self-optimizing architectures, sensors can even trigger an autonomous response—reducing motor torque or rerouting a workflow—to prevent the failure entirely. Studies show this approach can reduce overall maintenance expenditures by 30%.

Production performance is the second lever. By identifying underutilized or slower-than-expected machines, IoT can enhance equipment output and bridge the gap between actual and rated throughput.

Output quality is the third. Scrap and rework are cost drains that often go underreported—and digital twins give managers a way to experiment with process flows, spot the inefficiencies driving them, and test fixes before implementing changes on the floor.

Work-related injuries are alarmingly common.

US-based studies show that one employee was injured on the job every seven seconds in 2021—540 injuries per hour. Manufacturing ranks among the top five occupations with the highest number of disability-causing injuries, and despite decades of technological progress, 5,070 workers still died on the job in 2024.

IoT can move the needle on all of these figures.

Wearables and environmental sensors can monitor workers for signs of fatigue, dehydration, or other conditions that elevate the risk of error, alerting supervisors before a situation escalates. On the equipment side, sensors detect unusual machine behavior before it becomes a hazard—particularly important in hybrid environments where humans and machines share the same space. In these settings, proximity sensing enables machines to actively respond to workers around them: slowing down, stopping, or rerouting motion when a person enters a danger zone.

Connected devices can also monitor the broader environment for gas leaks, fire, or other facility-level hazards. In the event of an accident, IoT can assist rescue operations by accurately locating workers.

Beyond real-time monitoring, IoT data can feed back into how facilities, processes, and equipment were designed in the first place—shifting safety from a reactive discipline to one that’s built in from the start.

Delays rarely start on the production line — they start upstream. Connected devices help workers locate parts instantly across large warehouses, while sensors monitor goods in transit to flag temperature deviations, humidity shifts, or handling conditions that risk spoilage before materials even reach the floor. Catching these issues early prevents the kind of input-side disruptions that stall production mid-run.

Once materials are on the floor, IoT keeps production moving by giving managers real-time visibility into line performance, machine utilization, and job sequencing. When a bottleneck emerges—a slower-than-expected changeover, an underutilized cell, or a queue building at one station while another sits idle—the system surfaces it immediately rather than letting it compound into a missed delivery. The result is a production floor that doesn’t just run; it continuously rebalances itself against the schedule.

In conventional manufacturing, the relationship between vendor and customer effectively ends at the point of sale. Without visibility into how equipment performs in the field, manufacturers rely on complaints to surface problems—by which point the damage to the customer relationship is often already done.

IoT changes that dynamic fundamentally.

Connected products transmit performance data back to the manufacturer continuously, making it possible to identify degradation patterns, usage anomalies, or failure precursors before the customer notices anything is wrong. That intelligence feeds two directions at once: forward into product development, where real-world performance data informs design corrections in future releases, and back to the customer, where proactive, data-backed support replaces the reactive break-fix model.

For B2B manufacturers in particular, this shift has meaningful commercial implications. Being able to tell a client “we saw this coming and here’s what we’re doing about it” is a fundamentally different conversation than responding to a complaint.

Regulatory pressure around emissions, materials traceability, and product lifecycles is intensifying—particularly for manufacturers operating in or supplying into European markets. The EU’s Digital Product Passport mandate and similar frameworks require granular, verifiable data that most organizations simply can’t produce from manual processes or disconnected systems.

IoT closes that gap by continuously and automatically capturing compliance-relevant data—from energy consumption on individual machines to emissions across production lines—and feeding it into centralized platforms that generate audit-ready reports without additional overhead.

The business case extends beyond compliance: manufacturers deploying IoT for energy management could reduce consumption by 20%, turning a regulatory requirement into a cost optimization.

When implementing IoT in manufacturing processes, pay attention to the following challenges:

Securing an IoT deployment in manufacturing is fundamentally harder than securing a conventional IT environment.

The attack surface is vast—a single facility may run thousands of endpoints, from $1 sensors to $1 million CNC machines, many of which run legacy firmware that can’t be patched without taking equipment offline. With the full enforcement of the EU Cyber Resilience Act (CRA) and similar evolving standards in the US, this is no longer just an operational risk—it’s a compliance one.

The convergence of IT and OT networks compounds the problem. Industrial control systems weren’t designed with connectivity in mind, and integrating them into a broader IoT architecture introduces vulnerabilities that traditional cybersecurity tools aren’t built to address. A single compromised device can serve as an entry point that propagates laterally across the entire network.

Companies are increasingly adopting Zero Trust architecture—where no device is trusted by default, regardless of its position in the network—as the baseline response to this challenge. But implementing Zero Trust across a heterogeneous mix of legacy and modern equipment is itself a significant undertaking. For a deeper look at the five most common IIoT security challenges and how to address them, see our dedicated guide.

Cost is typically the first question executives ask when evaluating IoT manufacturing initiatives—but it’s rarely the only one.

Deploying connected infrastructure involves both capital expenditure (CapEx)—hardware procurement, sensor installation, connectivity infrastructure, and data platform builds—and ongoing operational expenditure (OpEx) for staffing, system maintenance, and capability development.

What makes the process particularly challenging is the difficulty of projecting returns with confidence before deployment.

Unlike replacing a machine with a newer model, IoT initiatives involve behavior change, data infrastructure, and process redesign—variables that are hard to model in advance. Executives need not just a cost estimate but a credible ROI framework: which metrics will improve, by how much, and over what timeframe.

The most effective way to build that case is to start small.

Targeted pilots in high-impact areas—predictive maintenance, energy monitoring, quality control—generate measurable data that supports broader investment decisions. Subscription-based IoT platforms and equipment-as-a-service models further reduce upfront CapEx, allowing organizations to prove operational value before committing to full-scale deployment.

For a detailed breakdown of what IoT deployments typically cost at different scales, see our IoT costs guide.

Most manufacturing plants aren’t built from scratch — they accumulate equipment over decades, sourcing machines from different vendors, running different protocols, and operating on different data models. Getting these systems to communicate with each other, let alone with modern IoT platforms, is one of the most underestimated challenges of any IIoT deployment.

The problem compounds when you factor in the broader ecosystem.

Manufacturers increasingly need data to flow not just across internal production lines but also between suppliers, logistics partners, and enterprise systems—each with their own standards and formats. Without a common data language, valuable operational insights stay locked inside isolated systems, unable to inform the decisions that depend on them.

There’s no single fix.

Manufacturers typically address interoperability through a combination of standardized industrial protocols like OPC-UA, middleware layers that translate between legacy and modern systems, and open IoT platforms that provide a unified data environment across vendors. Increasingly, the work also involves establishing shared data ontologies—agreed definitions of what data means across systems—without which integrations remain brittle, even when they technically function.

This is where implementation complexity tends to be most underestimated. The hardware is often the easy part; aligning data models across a heterogeneous installed base is where projects slow down.

Replacing legacy equipment isn’t always financially or operationally viable—but leaving it unconnected isn’t an option either. The real challenge is extracting modern data value from machinery that predates connectivity by decades, without disrupting production or introducing new security vulnerabilities.

Leading factories have moved past simple retrofitting to edge wrapping. Instead of forcing a 30-year-old lathe to speak directly to the cloud, manufacturers deploy edge gateways that wrap legacy assets in a modern digital layer. These gateways translate legacy protocols like Modbus or Profibus into secure, encrypted data streams in real time—extracting 2026-level intelligence from 1996-level machinery, extending the productive life of billions of dollars in existing capital assets while ensuring they don’t become the weakest link in the factory’s cybersecurity perimeter.

Manufacturing isn’t just an industry—it’s the foundation of economic stability, employment, and national prosperity. That’s precisely why getting IoT adoption right matters beyond any single company’s bottom line. IoT gives today’s leaders a lever that turns factories into living systems that learn, adapt, and improve continuously. The cost of falling behind isn’t just competitive—it compounds.

The real question is no longer whether to adopt IoT in manufacturing but how quickly you can put it to work across your operations—ideally with the right digital manufacturing solutions provider by your side.

If you’re interested in how other technologies are reshaping the industrial sector, explore our recent article on AI in manufacturing. And for a deeper foundation on connected systems, our guide to IoT architecture and design is a good next step.