Using Industrial IoT Data to Make Better Business Decisions

Industrial organizations must be able to quickly identify ways to tighten production schedules and maximize revenue. Time is of the essence in industrial operations, so it’s critical that workers are able to consume data as close as possible to where it’s produced. Gaining insight into operations and production capabilities to make informed decisions has often involved time-intensive IT projects and a highly specialized skillset.

Reducing the complexity of the operations environment for manufacturers and producers and their employees who are driving operations is a constant challenge. To help customers make informed decisions quickly and confidently, Rockwell Automation has expanded their FactoryTalk® Analytics™ portfolio. This article reveals how using Industrial IoT data enables organizations to transform their business and gain a competitive edge.

Analytics for Industrial Productivity

Leveraging data and analytics is key to improving enterprise-wide productivity. Making decisions when and where they matter most requires access to information in real time to allow for ad-hoc analytics and perform advanced analysis by pulling structured and unstructured data from virtually any existing source in the enterprise.

At the heart of a data-driven approach is software that takes advantage of the smart devices and connected systems spreading across industrial enterprises. Software lets users explore their operations, using and fusing data from any existing source — be it controllers, historians, enterprise resource planning (ERP) systems and everything in between.

Previously, building a dashboard started with a data-integration plan that detailed how raw data would be transformed into production intelligence. It required manually mapping out current data sources, key performance indicators (KPIs) and other details.

A data-driven approach automatically discovers and indexes structured or unstructured data. This process saves time and reduces the risk of human error compared to the manual process. It also provides access to more details than you would get from manually mapping a device’s name, line location, facility location and other specifics.

Using data modelling, machine learning, predictive analysis and third-party analytics tools to massage and analyze data, software can create relationships among indexed data sets and calculate answers across billions of data points.

In other words, with minimal setup, you can access real-time, situation-relevant analytics that can address questions the moment they arise.

That flexibility is a tool for better understanding your operations. On a single screen, workers can access all their favourite ‘storyboards’. Storyboards present operational data in a preferred format and can include predefined dashboards plus any storyboard shared by a colleague. The storyboard helps team members begin to understand or investigate analytics.

Analytics On Demand

A data-driven approach shouldn’t limit teams to static storyboards. Beyond their preferred information, they can open the reporting environment to reveal the data behind whatever is being monitored.

In that open environment, they can sort the data however they want. A few clicks are all it can take to dig deeper into a specific data point, aggregate historical values against current performance, filter by different variables, apply different chart styles and more.

As changes are made, the software can process and remind them to create a dynamic report. For example, an employee who wants to understand a batch system’s performance based on which operator is managing the process can simply select the relevant variables such as shift or employee ID. The software will then mash the data together and build a report with the correlated data.

Managers can then act based on the findings they receive. They can also share the report with colleagues or save it as a default storyboard on their home screen.

Scalable Computing Delivers Industrial IoT Data to Where Decisions are Made

Industrial companies that want to use Industrial IoT data to make better business decisions must first make sure workers can access that data when and where they need it.

Data is in smart devices, on machines and across plant floors. The closer you get to the data when decisions are made, the more valuable the data becomes. You can then drive efficiency through the production process in real time to get more from your machines and equipment, on the machine, where the decisions can be made.

Decision-makers need access to real-time data to solve analytical challenges and adapt to changes at every level of an organization. This helps make better business decisions and improve productivity and efficiency.