Predictive maintenance with IoT: The road to real returns

The main refrigeration unit of a premium restaurant in South Mumbai started to malfunction. By the time, the kitchen staff realised that there was a  problem and informed the executive chef, it was too late in the day. A partial shutdown was required. The restaurant lost a huge amount of revenue and credibility with its guests. This could have been completely avoided had the restaurant management invested in predictive maintenance processes and technologies.  Sensors for vibration and temperature would have detected any potential failures that could lead to loss of food items, material and revenue. More importantly, guest safety and satisfaction would not have been challenged.

Predictive maintenance (PdM) involves identifying the likelihood of equipment failure before it occurs. While scheduled preventive maintenance is useful, it can often prove to be time-consuming and expensive, resulting in higher downtimes. Both forms of maintenance are proactive, but predictive maintenance is anticipatory in nature. With PdM, intervention is done only when necessary, which in turn saves costs, and minimises equipment downtime. PdM tracks real-time and historical performance data of equipment, by leveraging asset information and condition monitoring, to make accurate predictions of failure. While technology is a core element of PdM,  a successful predictive maintenance program has several key elements that need to work in cohesion. The critical success factors for PdM include people, processes, technology, data, tools, equipment and processes. Predictive maintenance can prove to be a game changer for businesses.

How IoT powers predictive maintenance

Internet of Things (IoT)  devices have greatly transformed the maintenance conundrum. IoT systems can be customised for specific equipment and scenarios for predictive maintenance. IoT devices or equipment powered by smart sensors record data and transmit it at real-time or regular intervals to data lakes. AI-powered analytics analyse the data to identify potential issues that may emerge.

Predictive maintenance reduces losses due to downtime, improves efficiency and maintenance planning, and delivers deep insights to operations managers and business leaders. For instance, in the aviation industry where safety is pivotal, predictive maintenance can help detect any aberrations in sensor vibrations, and schedule a maintenance activity ahead of time. Building management systems can help improve energy efficiency with predictive maintenance. With temperature and moisture sensors, PdM systems detect values that matter and send them to energy systems that regulate the parameters accordingly. And these are just a few instances. Predictive maintenance can be used across industries which have high-value assets that are critical to business operations, and have parts that have high repair and replacement costs, such as the oil and gas industry, transportation, pharmaceuticals, and manufacturing industries. Businesses in these industries need to be able to leverage data from smart assets to optimise their maintenance schedules and costs. Sensitive sensors, accurate tracking of data and the ability to transmit information in real time are necessary elements. Assuming that the other factors are in place, installing smart equipment for monitoring business assets, and leveraging artificial intelligence (AI)/ machine learning (ML technology) can pave the path for successful predictive maintenance.

Delivering PdM value at scale

A large-scale predictive maintenance program requires a strategic implementation to ensure you get a return on investment. PdM programs typically face several challenges – insufficient or low-quality data, poor technology infrastructure, a lack of capability, poor prioritisation, and the lack of skilled resources to build systems and change management to name a few. Low return on investment (ROI) is another common problem, due to poor planning on which assets to include as part of predictive maintenance. Companies need to do a cost-benefit analysis so that they can optimise the ROI of the PdM program. Assets must be chosen based on critical parameters such as those that can cause loss of production, the feasibility of extracting data from such assets, and those that are prone to frequent failure. Choosing technology partners who can implement PdM effectively is critical to the success of the program. The PdM program is one of continuous improvement, and change management is a crucial aspect. As the program scales up, the predictive maintenance program also needs to integrate seamlessly with the rest of the company’s digital infrastructure. This would enable instantaneous responses in real time. By adopting a people-first approach, hiring skilled technical resources, prioritising IoT assets wisely,  as well as choosing the right technology platform*companies can derive maximum business value with predictive maintenance.

*For organizations on the digital transformation journey, agility is key in responding to a rapidly changing technology and business landscape. Now more than ever, it is crucial to deliver and exceed on organizational expectations with a robust digital mindset backed by innovation. Enabling businesses to sense, learn, respond, and evolve like a living organism, will be imperative for business excellence going forward. A comprehensive, yet modular suite of services is doing exactly that. Equipping organizations with intuitive decision-making automatically at scale, actionable insights based on real-time solutions, anytime/anywhere experience, and in-depth data visibility across functions leading to hyper-productivity, Live Enterprise is building connected organizations that are innovating collaboratively for the future

Recent Posts