Srikanth
What if you’re using predictive analytics to predict events before they occur, use real-time actionable insights to boost efficiency, and assist your operatives in making the best decision at any given time? Industrial IoT is driving these capabilities, providing for unprecedented productivity, operational efficiency, and success. IIoT is expected to be a $225 billion market by 2021, according to industry analysts, with significant impacts on modern manufacturing, which includes increased performance, asset monitoring, streamlined servicing, and more, ushering in a new era of industrial competitiveness and growth.
What is Industrial IoT?
Stronger need for customization, and consumer demands, and the global supply chain’s complexity – these and other obstacles push manufacturers to come up with fresh, more creative ways to stay competitive. Businesses turn to digital transformation to increase efficiency and discover new ways to improve production and supply chain operations. The Industrial Internet of Things is a path to digital manufacturing transformation. Industrial IoT uses a network of sensors to capture vital production data, which is then transformed into useful information about the performance of manufacturing operations using cloud software.
Industrial IoT Adoption in manufacturing
Let us have a look at the impactful benefits that are driving manufacturers to incorporate IIoT.
- Cost reduction – Companies minimise operating costs and generate new revenue streams through improved asset and inventory management (resulting in search times and lower inventory carrying costs), decreased system downtime, more flexible operations, and more productive energy usage. Smart, connected products, for example, make it possible to transition from selling products to selling experiences – product use and after-sale services.
- Lower time-to-market – Reduced product cycle time is possible. All thanks to faster and more reliable production and supply chain operations. For example, Harley-Davidson used IoT to reconfigure its manufacturing facility in York, Pennsylvania, reducing the time it takes to manufacture a motorcycle from 21 days to 6 hours.
- Mass customization – Mass customization necessitates a significant increase in the variety of manufactured SKUs, causing inventory to rise and diversify. Manufacturing activities become more complicated as well – the production of 20 SKU X products can be followed by the production of 10 SKU Y items in a matter of seconds. Inventory and manufacturing processes tracking becomes time-consuming and, in some cases, impossible. By providing real-time data for thoughtful shop floor scheduling, routing and forecasting, the IIoT makes mass customization easier.
- Improved safety – The IIoT contributes to a healthy workplace. When combined with wearable devices, the IIoT allows for the tracking of workers’ wellbeing and potentially dangerous behaviours. IIoT tackles safety issues in potentially unsafe conditions in addition to ensuring workplace safety. In the oil and gas industry, for example, IIoT is used to monitor gas leaks as they pass through a network of pipe.
Industrial IoT Potential Implementations
According to a TATA survey, manufacturing industry leaders who invest in IIoT are reporting benefits such as improved efficiency and productivity. As a result, it’s critical to remember that IIoT use cases will continue to grow in the future. Have a look at the list of the top three industrial IoT use cases in the sector of manufacturing.
- Asset Monitoring – Manufacturing companies are connecting machines and devices with IoT properties, a paradigm shift that allows for real-time asset tracking. Coupled assets allow for real-time monitoring of equipment for reliability, enforcement, and protection. Asset monitoring is widely used in remote manufacturing, where sensors aid in the tracking of production processes and the transmission of status to the appropriate individuals. Asset tracking enables easy monitoring of key equipment and final goods, which helps with logistics, inventory management, and quality control.
- Operational Intelligence – Manufacturers may create intelligent networks by connecting machines/equipment that interact and coordinate with one another autonomously with little interference from operatives. Organizations may collect and contextualise data from remote manufacturing systems and assets that can create actionable applications for this use case. As a result, by having constructive views into key performance metrics, you would be able to gain a competitive advantage of faster problem identification. IoT-enabled machinery facilitates connected operational intelligence, which sends real-time feedback to manufacturing stakeholders, allowing them to monitor factory units remotely.
- Maintenance of Assets – Machine service and repair costs are in the millions of dollars. However, if equipment maintenance is performed on time, manufacturing processes would not be disrupted. Manufacturing firms will save a lot of money on maintenance expenses if downtime can be identified before it happens. In the IIoT, cameras, data analytics, and sensors, are used to anticipate failure before it happens. Such identification aids in the creation of strategic maintenance timelines that can be carried out only when necessary – before problems arise – such as fixing a dike crack. Manufacturers use the Internet of Things to integrate lively, qualified, and automated production processes, with maintenance schedules that are self-contained rather than reliant on unreliable maintenance staff. As a result, better-planned processes are triggered, promising significant cost savings as they decrease equipment failure and improve system lifetime.
The Industrial Internet of Things has the potential to completely change manufacturing. Smart manufacturing, enabled by IIoT-driven data analytics, is becoming increasingly important as global market and business trends force manufacturers to rethink their operations. However, given the scope and complexity of initiatives, effective IIoT adoption necessitates careful orchestration across all execution segments as well as the IIoT application design.
