How AIoT is helping Manufacturing Businesses Cut on Unplanned Downtime
Published on : Friday 03-06-2022
A combination of AI service and IoT solution customised to your manufacturing facility's needs could help cut downtime significantly.
Unplanned equipment downtime is a million-dollar pain for manufacturing businesses. If a machine or equipment crashes unexpectedly, a manufacturer incurs several costs related to its containment, discovery and recovery. In addition, there are costs related to disruptions in the supply chain and production.
An average manufacturer experiences 800 hours of downtime each year1 amounting to millions of dollars in lost revenue. To address this annoying problem, manufacturing organisations are implementing digital technologies. AI (Artificial Intelligence) and IoT (Internet of Things) are two such new-age technologies that are helping manufacturers address this problem.
What causes unplanned downtime?
A recent study2 by Vanson Bourne found that unplanned downtime affected more than 4/5 of organisations, i.e., 80% over the past few years.
Hardware and software malfunctions or failures are the most common causes of unplanned downtime followed by user error, security breaches, overload, etc. The key to addressing most downtime problems lies in predicting and preventing these malfunctions or failures. It also depends on how fast the maintenance crew of a manufacturing company reacts to these occurrences.
Now in a traditional factory setting, a team of maintenance experts would manually combine a mix of both quantitative as well as qualitative techniques to identify the causes of equipment failure or breakdown. This is where predictive analytics powered by AI and IoT is proving to be a boon to foresee equipment breakdown or failure before it actually occurs thus, helping to monitor equipment/asset health and life while keeping disruptions at bay. Predictive analytics/data not only helps a manufacturer to maximise the equipment life, but also helps to foresee and avoid unplanned downtime besides bringing down maintenance and repair costs drastically.
AI & IoT: A match made in heaven for the manufacturing industry
IoT has been a key driver in fuelling up the Fourth Industrial Revolution aka Industry 4.0. From driverless cars to intelligent manufacturing robots and remote equipment/asset monitoring to creating smart cities, IoT is turning out to be a game-changer in creating a connected world.
A report by Statista3 suggests that there will be 75 billion connected devices worldwide by 2025. These devices and equipment would create a gigantic pool of big data. The only way to make the most of this big data is to leverage AI and Machine Learning (ML) technology. While the Internet of Things deals with connected devices that communicate with each other, Artificial Intelligence on the other hand enables these devices to learn, reason as well as process information like humans, but in a much faster, intelligent and efficient manner. This in turn helps to predict outcomes, achieve real-time insights and drive efficiency across operations.
The nexus of AI and IoT transforms the way industries function. Yes! AIoT creates intelligent and smarter machines and equipment that simulate intelligent behaviour helping with sound decision-making with zero or little human interference.
AIoT in the manufacturing industry uses real-time and intelligent data insights and connected sensors for optimising the supply chain, logistics, production, remote asset monitoring, and more. The data generated from connected equipment helps manufacturing businesses identify and predict challenges and prevent costly system failures and errors well before time.
For instance, BMW, the world’s prominent automotive manufacturer has already begun utilising AI and IoT in their manufacturing operations to foresee system breakdowns and optimise supply chain as well as production operations.
AIoT & Predictive analytics in manufacturing industry
Maintenance personnel ought to ensure optimum availability of manufacturing equipment and systems and at the same time, they need to focus on cutting costs pertaining to spare parts and maintenance. AIoT helps maintenance experts monitor and receive real-time and actionable insights into production operations, equipment life, supply chain, etc.
For instance, a condition-based predictive maintenance (PdM) system or solution for a moulding machine would help to monitor the machine’s critical parts or components for wear and tear, helping to foresee a potential breakdown and avoid disruption.
Example of how PdM works:
Consider a moulding machine equipped with IoT sensors helping the maintenance experts notify when certain spare parts would fail or wear over time or would need replacement. By utilising IoT sensors integrated with a condition-based predictive maintenance system powered by AI, maintenance technicians can receive alerts when a heat sensor, mould, pressure gauge, calibrator or a motherboard breaks down or is about to fail. This is lifesaving for any manufacturing company, as a single piece of equipment failure can hamper the entire production and supply chain cycle affecting the company’s bottom line.
Next, predictive maintenance not only helps to prevent such disruption but also helps to pre-schedule maintenance activities before the equipment actually breaks down completely shutting the production. The prime benefit of PdM is that it helps to initiate corrective maintenance before the equipment reaches a point where it needs to be replaced or repaired. Predictive analytics powered by AI and IoT provides access to actionable insights pertaining to the equipment’s maintenance, health, service history, performance, etc., to help eliminate unplanned downtime, production disruption and improve uptime.
Unplanned downtime is devastating and expensive for a manufacturing business. This is where AI and IoT come to the rescue. A combination of AI service4 and IoT solution5 customised to your manufacturing facility’s needs could help you cut downtime significantly while optimising asset health, and drive production and supply chain operations.
In fact, PdM is turning out to be the stepping-stone for a successful digital transformation journey for manufacturing businesses. A digital transformation initiative directed at PdM would not only help to drive the facility’s asset utilisation and efficiency, but it would also help to achieve faster RoI while eradicating the problem of unplanned downtime.
Futurism Technologies6 offers full-scale predictive maintenance services and capabilities under its smart factory7 offering including solution development, smart factory planning and designing, consultation, integration and comprehensive IoT services8 including asset condition management and monitoring, smart warehouse management and more.
Need help to achieve your smart factory dream, Futurism can help.
1 & 2. https://dispel.io/blog/your-guide-to-reducing-unplanned-downtime-in-the-manufacturing-industry/