It’s still unclear when data collection for industrial operations began, but someone determined it was a smart thing to do and it’s a good thing they did. At first, it was probably hard to imagine technology advancing to the point where robots in manufacturing plants became common, but here we are.
Before the digital age, operators collected data about their industrial processes hoping for the kind of insights that would improve operations’ performance. But the collection process was manual, leaving room for errors and that aside, it was chock full of limitations that humans couldn’t penetrate. Even when the data was robust, it still wasn’t helping industrial operators answer the tough questions like, “How can I increase my throughput?” or “how can I reduce my downtime?” And as data sets grew, so did the cumbersomeness that came with them. Now, technological advances like artificial intelligence (AI), use that data to show how an operation can increase productivity.
Before advanced technology, maintaining a productive operation meant little more than dodging unplanned downtime. Now, AI-enabled technologies that mine clunky data sets help operators pinpoint solutions to reduce downtime, recommend ways to improve overall performance and achieve goals.
According to this IndustryWeek article, process analysts at industrial service providers are responsible for the data for up to 15 plants. This data is often spread among different software systems. One can imagine how difficult it would be to manually monitor this information. Data and endless number crunching may have been daunting or seemingly impossible in the past. Now, the integration of technology makes robust data much more manageable and helpful in uncovering resolutions.
AI connects historical data from various systems with real-time information to develop insights and fixes linked to key business metrics. For instance, if an AI data analysis locates a performance inefficiency during the industrial process, it adjusts it in real time. Eliminating that inefficiency means less waste and money saved. Even the most diligent process analyst can’t locate such opportunities once the data reaches a certain level. In contrast, more data is like fuel for AI technologies. The added information provides more learning “juice” to continuously adjust performance indicators.
Industrial manufacturers crave simple, flexible operations. The integration of data doesn’t have to change that. Advanced technologies like AI use data to keep processes simple. What’s simpler than having an error adjusted in real time? As manufacturers delve deeper into IIoT, data will continue to be an important factor in ensuring industrial performance. Luckily technological advances give manufacturers the flexibility to “outsource” the complicated number crunching while still maintaining control of operations.