Manufacturers in a digitized world are surrounded by data that, leveraged correctly, can provide significant intelligence, insight and value to a business. However, if data from smart devices, automation systems and software applications is siloed, it is largely worthless, unable to be fully gathered, analyzed and utilized meaningfully.
Being able to leverage and harness data helps companies overcome challenges including supply chain disruptions, labor shortages and rapidly rising energy prices. It contributes to achieving competitive advantage while continuing to deliver value to customers, despite any adversity in the markets.
So how can manufacturers achieve this?
Well, what really doesn’t work is an approach that some organizations have tried that is simplistic, expensive and historically has not been shown to have any real value: capturing all data and dumping it into a huge data lake. Artificial intelligence (AI) and machine learning (ML) algorithms are then applied to seek out insights that nobody could have predicted. As appealing as this may be due to its simplicity, it’s generally a pointless exercise.
What really can pay dividends for manufacturers is a structured, well-thought-out and clearly defined data strategy that lays out the answers to some key questions.
Define the Data Sources
What is useful? Where is it located? More data is not always better, and in many cases, the real data point of interest cannot be directly measured anyway, but instead must be derived from simulation models and digital twins. So when considering sources of data, there is real benefit in providing many different types of data that, together, provide a more complete picture of a production system for the connected enterprise.
When capturing data, most manufacturers first look to OT (operational technology) data, drawn from sensors, devices and control systems close to the production processes. These data points are usually captured at high frequencies that are matched to the speed of the production processes. Often augmenting this core data is secondary data such as environmental, power and utility, and video and point cloud data, among others.
Augmenting this real-time data with operations management and manufacturing execution data—such as supplier and material tracking, worker engagement and qualifications and quality system data such as non-conformances or approved deviations—adds a lot of value. Manufacturers are also tracking and managing the configuration of their production systems: the complete set of information that defines the machines, production software and setting configurations in place at moment in time.
Define the Data Model
Beyond data sources, the data strategy needs to include planned uses and further define the data model that makes sense for its specific production system. The model provides a structure for adding context and meaning to the raw data, which is essential because without that contextual weight, data is essentially meaningless.
There are several approaches that companies can use to create value from trusted data. Some manufacturers, for example, might use data to help front-line workers make better and more informed decisions, enhance productivity and improve employee safety. The representation of data here may take many forms: for example, operator interfaces on machines, workflow augmented reality experiences on mobile devices or centralized dashboards providing a shared source of visible truth of real-time manufacturing activity.
Other companies might focus on making the data available and accessible to enterprise systems while abstracting out all the complexity and domain expertise normally required to effectively use manufacturing data across the enterprise. This can be achieved by using low-code or no-code application development environments that facilitate the rapid creation of special-purpose software programs that are unique to a specific company, plant or even manufacturing cell without requiring the skills of scarce and expensive professional software developers. Some companies are also taking advantage of applying advanced data analytics and ML to their data. The insights provided by such tools can be truly illuminating when the underlying data is contextualized and trustworthy.
All of these uses of production system data are new and substantial value-creation opportunities.
Plan How to Manage the Data
The data strategy must also consider the optimum locations for the data between the edge and the cloud considering costs, performance, scalability and accessibility. The organization must decide how it will govern and manage that data to ensure its veracity and security. After all, data that can’t be trusted is worse than no data at all.
A solid, structured and well-managed data strategy puts data at the center of an organization’s production system and creates a powerful reservoir that can be tapped for many new sources of value. By holistically harnessing the combined power of data, expertise and advanced technologies such as AI and ML, manufacturers can optimize their entire operations. This can power tangible benefits and business outcomes for the organization, with actionable insights, across the manufacturing lifecycle—from the design of new production system elements to shipping the finished product.
Brian Shepherd is senior vice president of software and control, Rockwell Automation.
