Turning data into dividends takes planning

Turning data into dividends takes planning

In a digitalized world, manufacturers are surrounded by data that, properly harnessed, can deliver meaningful insights, insights, 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 collected, analyzed, and used in meaningful ways.

Being able to harness and exploit data helps businesses overcome challenges including supply chain disruptions, labor shortages and rapidly rising energy prices. It helps to gain 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 some organizations have tried that’s simplistic, expensive, and historically hasn’t demonstrated its real value: capturing all the 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 no one could have predicted. As appealing as it may be due to its simplicity, it’s generally a pointless exercise.

What can really 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 data sources

What is useful? Where is it situated? More data is not always better, and in many cases the actual data point of interest cannot be measured directly anyway, but rather must be derived from simulation models and digital twins. So, when looking at data sources, there is a 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 look first to OT (operational technology) data, drawn from sensors, devices and control systems close to production processes. These data points are typically captured at high frequencies suited to the speed of production processes. This basic data is often supplemented with secondary data such as environmental, power and utility, video, and point cloud data, among others.

Augment 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- compliances or approved deviations, adds a lot of value. Manufacturers also track and manage the configuration of their production systems: the complete set of information that defines the machines, production software and setup configurations in place at any given time.

Define the data model

Beyond data sources, the data strategy should include intended uses and further define the data model that makes sense for its specific production system. The model provides a structure to add context and meaning to the raw data, which is essential because without this contextual weight, the data is virtually meaningless.

There are several approaches companies can use to create value from reliable data. Some manufacturers, for example, could use data to help frontline workers make smarter, more informed decisions, improve productivity, and improve employee safety. Representation of data here can take many forms: for example, operator interfaces on machines, augmented reality experiences of workflows on mobile devices, or centralized dashboards providing a shared source of truth visible to the real-time manufacturing activity.

Other companies might focus on making data available and accessible to enterprise systems while eliminating 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 software programs that are unique to a specific company, plant, or even manufacturing cell without requiring the skills of rare and expensive professional software. developers. Some companies also benefit from applying advanced data analytics and ML to their data. The insights provided by these tools can be truly insightful when the underlying data is contextualized and trustworthy.

All of these uses of production system data are new and substantial opportunities to create value.

Plan how to handle data

The data strategy should also consider the optimal locations for data between the edge and the cloud considering cost, performance, scalability, and accessibility. The organization must decide how it will govern and manage this data to ensure its veracity and security. After all, data that cannot be trusted is worse than no data at all.

A solid, structured, and well-managed data strategy places data at the center of an organization’s productive 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 like AI and ML, manufacturers can optimize their entire operations. This can drive tangible benefits and business results for the organization, with actionable insights, throughout the entire manufacturing lifecycle, from designing new elements of the production system to shipping the finished product.

Brian Shepherd is Senior Vice President of Software and Control, Rockwell Automation.

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