Well, as you may have guessed, we have given a tech twist to the famous proverb – a stitch in time saves nine. Knowing what could go wrong in real time and acting on it before it even becomes a risk – especially in manufacturing, transport and process industries – has a huge impact on the top line as well as bottom line. Thanks to Industrial IoT and Machine Learning, we are now entering into ‘Predictive Maintenance’ from Reactive Maintenance and Routine Maintenance. In fact, IoT has been changing the way we do predictive maintenance for the last few years. Real-time sensors installed on equipment provide real-time data that you can feed into predictive models to help determine when something is about to fail or what the remaining useful life is for that equipment. We can take data from a wide range of data sources, and may even combine weather information with equipment data, and then determine when to send a crew for Predictive Maintenance.

Ever visited a power plant in recent times? The number of personnel on the floor is hardly 5. A couple of people, operating a little away from the plant, watching a number of displays and light bulbs would be ‘quietly’ monitoring the entire production process. In fact, you would almost be greeted with an eerie silence, except for the hum of the turbines. Now, contrast that with a traditional manufacturing plant – the number of moving parts (literally and figuratively) make the shop floor a beehive of activity. What if the traditional shop floor can also be managed and monitored with quiet confidence and outstanding efficiency – by connecting machines, systems and people seamlessly.