AI-driven tools bring practical dimension to IoT in asset maintenance

Lessons learned

Although best practices around IoT deployments have not been carved in stone, some general lessons can be derived from incidents gone wrong. For example, experienced implementation consultants have learned the essential value of cross-organizational engagement. In early adoption cases, managers often failed to consider which engineering disciplines should be invited to the planning process. This inevitably resulted in mountains of data and no clear end-value within reach. This oversight — and others like it — can be easily avoided by engaging with third-party consultants who have logged some practical experience.

Discerning what data to capture for analysis is an artform. Driving influences behind the issue in question can be subtle and hard to identify, perhaps occurring at an early stage in the workflow, far removed from the culminating evidence of a problem. For example, an organization might decide to address its high rate of quality complaints. Thousands of influences can impact quality, so focusing on the most relevant data will be essential. While examining operational processes might be the presumptive place to investigate first, in actuality, the cause can go back as far as the procurement stage, choice of suppliers and how raw resources are stored in inventory.

Finding where to capture influencing data is just part of the problem. Business analysts also must determine how often to capture data points, what context is needed, and what scale to use for acceptable and not-acceptable boundaries.

The state of the IoT industry

The early exaggerated hype that surrounded IoT projects is being replaced by more realistic, practical views of what the technology can and cannot do. An article by Forbes Councils Member Maciej Kranz recently addressed today’s reality versus yesterday’s predictions for IoT in an article. He discussed how the challenges — including security breaches and lack of skills or understanding — have limited widespread adoption of IoT and made it a greater risk.

Not all IoT deployments have lacked clarity and success. Investment in the technology continues, proving enterprises have faith in finding the right mix of goals, software tools and implementation strategies. When coupled with advanced analytics, the technology provides insights that can bring a reliable ROI. Rather than the broad-sweeping applications that become entangled in complications, organizations are striving for the practical, scalable and repeatable approach to IoT technology. This is where EAM professionals are leading the pack, providing best practices that others can emulate.

Market research firm IDC expects worldwide IoT spending will maintain a double-digit annual growth rate, surpassing the $1 trillion mark in 2022. The industries expected to focus on IoT technology are: Discrete manufacturing with $119 billion, process manufacturing with $78 billion, transportation with $71 billion and utilities with $61 billion.

Why IoT technology is a good fit for asset management

Organizations with capital-intensive assets and mission-critical machinery face intense pressures to keep assets running at peak performance without unexpected shutdowns. Lives might be at stake. National security, local commerce and global networks can be on the line. Maintenance technicians are responsible for the safe and reliable operation of many types of assets, from exterior lights and alarm systems to complex machinery with multiple high-tech components.

Forward-thinking maintenance teams turn to technology to help them optimize the use of resources and automate processes. IoT technology provides the added efficiency boost they need to keep pace with demands.

How it works

Smart sensors, which are tiny computers and communication devices, are embedded in equipment or machinery, sometimes in multiple places. Each sensor measures physical attributes, which provide a window into the operational health of the asset. Sensors can measure a wide variety of conditions such as vibration, moisture, temperature or density. The information is sent to the cloud, where it’s  aggregated and analyzed for anomalies or data points that fall outside of predefined guidelines. Over time, machine learning helps the system identify patterns and discern favorable data from issues that might be early warning signs of a failure or reduction in efficiency. Data that demands attention can trigger automatic responses such as stopping operations, supplementing resources or re-routing activities.

Final take-away

Paradigm-shifting technologies like IoT commonly weather growing pains. Despite a slower than expected phase one, organizations are now achieving traction with IoT projects. Some early adopters, particularly in asset maintenance, are experiencing dramatic improvements in their use of data to understand costs, the driving factors in asset performance and customer expectations. Companies are using IoT technology to create new value propositions, new revenue streams, new business models and glimpses into the future.

Industries and facilities are finding the predictive abilities of IoT technology to have great value in forming preventive strategies for asset maintenance. Combining IoT and advanced analytic solutions helps users identify early warning signs of equipment failure. This window into the future is proving valuable for preventive action. Asset management applications provide good examples of how IoT technology can be leveraged for success.

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