Smart IoT Investments in Manufacturing: A Guide to Risk Mitigation and Decision-Making

In today's manufacturing landscape, the implementation of Internet of Things (IoT) networks offers the potential to transform operational efficiency, reduce downtime, and improve productivity. However, with the growing complexity of machinery and systems, there is a critical need to ensure that investments in IoT networks are directed in ways that truly mitigate risks and maximize value. At the same time, manufacturers must focus on upskilling their workforce to manage and leverage these technologies effectively. People play a key role in optimizing the value of IoT by interpreting data, diagnosing issues, and taking proactive actions. So, how do you determine where to invest? And how do you balance the high costs of instrumentation with the risk of failure, while ensuring your team is equipped to take full advantage of these new tools?

The Cost of Mechanical Failures

In the manufacturing industry, most operational failures and maintenance costs stem from mechanical issues. The U.S. Department of Energy reports that unplanned downtime costs industrial manufacturers up to $50 billion annually, with up to 80% of equipment failures resulting from mechanical breakdowns. This highlights a key challenge: while many failures are detectable, pinpointing where to deploy IoT monitoring and ensuring a return on investment (ROI) remains difficult for many manufacturers.

It's also important to note that mechanical failures don't always cause immediate downtime. In facilities with limited operating hours, equipment issues may not halt production right away. These failures might only be identified during scheduled maintenance or system checks, leading to unplanned work, extra labor costs, and downtime during off-hours.

Even when mechanical failures don't directly disrupt production, the indirect costs of unplanned maintenance can be significant. Time spent diagnosing and addressing issues, particularly those that don't immediately impact operations, can still result in high costs. The problem often becomes more severe as the failure worsens, causing unforeseen complications that could have been prevented with proactive monitoring and maintenance.

Manual Diagnostics vs. IoT Networks: The Case for Balance

Before jumping into IoT investments, it’s important to recognize that many diagnostic tasks designed to detect early-stage failures can still be effectively performed manually. Traditional methods, like routine equipment inspections and manual readings (e.g., vibration analysis, temperature checks, oil samples, and sound checks), have long been used to monitor equipment health and performance. In many facilities, operators, technicians, and maintenance teams are skilled at identifying early signs of wear and tear using these techniques, allowing them to take preventative action before problems escalate.

For instance, scheduled vibration analysis or manual temperature and pressure readings can help spot abnormal trends before they lead to major issues. In environments where these manual systems are already working well, there may be less immediate need for IoT—especially in simpler or lower-risk settings.

That said, IoT can provide significant value beyond just enabling 24/7 operations. These systems offer continuous, real-time data collection, enhancing predictive capabilities by identifying potential issues much earlier and with greater precision than occasional manual checks. Additionally, IoT allows for remote monitoring, enabling personnel to track equipment performance from anywhere and reducing the need for frequent on-site inspections. This empowers teams to make smarter maintenance decisions, optimize asset performance, and reduce unplanned downtime—no matter the operational schedule.

One of the key benefits of IoT is its ability to foster collaborative problem-solving and team-based approaches to maintenance. In environments with skill gaps, high turnover, or limited resources, IoT systems serve as a valuable tool for empowering teams. With real-time data, teams can work together more effectively to identify potential failures, prioritize tasks, and develop action plans. This is especially critical in high-pressure environments, where teams are stretched thin due to a lack of skilled workers or overwhelming operational demands.

In these situations, IoT acts as an extension of the workforce, providing visibility and data-driven guidance that helps teams focus their efforts on the most critical issues. It also facilitates knowledge-sharing across shifts and departments, ensuring valuable insights are not lost. Moreover, this collaborative approach supports workforce development—team members gain exposure to new technologies and processes, improving their ability to leverage IoT data for proactive troubleshooting.

Rather than seeing IoT as a one-size-fits-all solution, investments should be based on a careful assessment of the existing maintenance systems, operational complexity, and the organization’s specific needs. When implemented strategically, IoT can complement manual processes, enhance diagnostic capabilities, and create long-term value, while fostering collaboration that boosts team performance.

Risk Priority Numbers (RPNs) and FMEAs: A Strategic Approach

One effective approach to determining where IoT investments will provide the greatest value is to use Failure Modes and Effects Analysis (FMEA), a well-established risk assessment tool. FMEAs help organizations systematically evaluate the risks associated with various failure modes in their processes, equipment, or systems. It enables manufacturers to identify potential points of failure and prioritize them based on the likelihood of occurrence, the severity of their impact, and the ability to detect them.

FMEAs are an essential step in determining where IoT investments will provide the greatest return and risk mitigation. By assigning numerical values to each failure mode based on the three core criteria—Severity, Occurrence, and Detectability—manufacturers can quantify the risk of each potential failure and make more informed decisions.

How FMEAs Work:

• Failure Modes: Identifying all possible ways a component or system could fail.
• Effects Analysis: Determining the consequences or impacts of each failure mode on operations.
• Risk Priority Number (RPN): Calculating a score for each failure mode by evaluating three factors: Likelihood (Occurrence), Severity, and Detectability.

The RPN is calculated by multiplying these three values:

Each of these three factors plays a critical role in the risk assessment process:

1. Severity (S): This factor refers to the potential impact or consequence of a failure mode on the overall system. A higher severity value is assigned if the failure would cause major downtime, safety hazards, or costly repairs. Severity is typically rated on a scale from 1 to 10, where 1 represents a negligible impact and 10 represents catastrophic consequences.
2. Occurrence (O): This measures how likely a failure mode is to occur. The occurrence rating helps assess the probability of a failure happening based on historical data, the frequency of failure, or the inherent design weaknesses. Like severity, the occurrence is rated on a scale from 1 to 10, where 1 means a rare event and 10 represents an almost certain failure.
3. Detectability (D): Detectability refers to how easily a failure mode can be detected before it causes a significant impact. A failure mode with low detectability is harder to predict and prevent, making it more dangerous. If a failure is easy to detect early, the risk is lower. Detectability is rated on a scale from 1 to 10, where 1 represents a failure that is easy to detect, and 10 represents a failure that is almost impossible to detect until it causes a serious problem.

The Importance of a Bottom-Up Approach

When conducting FMEAs, the methodology employed can significantly impact the effectiveness of the analysis. Two primary approaches are typically used: the top-down approach and the bottom-up approach.

Top-Down Approach: In a top-down approach, senior leadership and engineering teams usually start the FMEA process, focusing on high-level system failures and broader risks that may affect overall production. The analysis begins with an overall perspective on the system or process and works downward to individual components. While this approach can be useful for understanding general trends or strategic risks, it often misses the nuanced, day-to-day operational issues that frontline workers experience.

Bottom-Up Approach: The bottom-up approach, on the other hand, involves starting the FMEA process from the ground level—with operators, maintenance staff, and other frontline workers who interact with the equipment and processes directly. This method focuses on understanding failure modes from the people who know the system inside and out. By engaging workers who operate the machinery daily, a bottom-up approach brings a detailed, practical perspective to the risk assessment, helping identify failure modes that may be overlooked in a high-level, top-down analysis.

Why the Bottom-Up Approach is Crucial for IoT Investment Decisions

When it comes to making smart investments in IoT systems for risk mitigation, a bottom-up approach offers several critical advantages:

1. Real-World Insights: Frontline employees are often the first to notice patterns, issues, or weak points in the machinery. Their insights into failure modes are typically more accurate and comprehensive than those of higher-level managers who may not be as intimately familiar with the day-to-day operations. This can lead to more targeted and effective risk identification.
2. Filling Knowledge Gaps: The bottom-up approach ensures that even the smallest failure modes, which may not be obvious in high-level analyses, are captured. These smaller issues can often lead to significant downtime or costly repairs if left unchecked, and they can be the ideal candidates for IoT sensor deployment.
3. Increased Employee Buy-in: Engaging operators in the FMEA process fosters a sense of ownership and responsibility over the equipment they manage. This involvement not only improves the accuracy of the FMEA but also upskills employees, making them more aware of how their day-to-day actions can affect the overall reliability of the system. When employees are involved in the decision-making process, they are more likely to support and effectively utilize IoT technologies that are deployed as a result of the analysis.
4. More Precise IoT Targeting: IoT systems can be expensive to implement, so focusing resources in the right areas is crucial. A bottom-up FMEA allows manufacturers to pinpoint the most critical failure points—those that are both high-risk and difficult to detect. This targeted approach ensures that IoT investments are made where they will have the most significant impact, improving uptime and system reliability.

Aligning IoT Investments with Risk Mitigation

The strategic application of IoT in manufacturing is not about blanket implementation; it’s about smart, targeted investments. Through the use of FMEAs and Risk Priority Numbers (RPNs), companies can:

1. Identify Critical Failures: By identifying and ranking failure modes, manufacturers can focus their IoT investments on areas that carry the highest risk, preventing costly downtime and maintenance issues before they occur.
2. Prioritize Resources: FMEAs help prioritize where resources should be allocated for monitoring and intervention, ensuring the highest return on investment.
3. Ensure Effective ROI: By making data-driven decisions on where to install IoT sensors, manufacturers can better justify their investments and ensure they are addressing the most pressing issues.
4. Upskill Employees: The FMEA process offers a valuable learning opportunity for employees, enhancing their understanding of risk management, system reliability, and IoT applications, which is crucial as the workforce becomes more tech-savvy and automated.

Conclusion

For manufacturers looking to invest in IoT networks, the key to success lies in targeted, risk-based investment. Through the use of FMEAs and Risk Priority Numbers (RPNs), manufacturers can identify the highest-risk areas of their operations, prioritize investments in IoT systems that will deliver the greatest value, and mitigate the costly risks associated with mechanical failures.

Importantly, a bottom-up approach to conducting FMEAs is invaluable in ensuring that risk assessments are rooted in the reality of daily operations. By empowering frontline workers to actively participate in the process, manufacturers can uncover hidden risks and gain insights that might otherwise be missed. This approach helps ensure that IoT investments are not just cost-effective but focused on areas where they will have the greatest impact on reliability, productivity, and cost savings.

Moreover, manual diagnostic techniques such as routine equipment inspections and sampling can often be sufficient to prevent emergent work and reduce the need for IoT systems. Leadership must engage directly with the workforce to assess the level of expertise, ownership, and accountability of existing teams to determine if IoT is truly necessary, or if manual processes can continue to ensure equipment reliability and reduce the cost of maintenance.

In an era of rapid technological advancement, investing wisely and strategically is more crucial than ever to maintain competitive advantage and profitability. By focusing on risk and using tools like FMEAs, manufacturers can ensure that their IoT investments deliver the most significant operational benefits, while also upskilling their workforce for a more resilient, future-ready operation.