Automation Today Issue 84 | Feature Story

Unlocking Untapped Value with Data-ready Smart Machines

A female operator wearing safety glasses looking at a OptixPanel display in front of an assembly machine.

Manufacturers across the industrial spectrum are facing challenges to become more efficient and dynamic than ever before. In response, approximately 93% of manufacturers in Asia Pacific are using or evaluating smart manufacturing technology. There’s no argument that data is key to meeting these demands, however, staying competitive as future dynamics change will require more than just gathering the data but also in transforming a vast quantity of available data into actionable insights that drive results. Manufacturers need machines that can organize, contextualize and share the data they generate in order to unlock untapped value in their facilities and deliver new levels of intelligence throughout their operations.

End User Success Starts with OEMs that Design with Data in Mind

End users know they need more data but exactly what data they need now, and what they might need to succeed tomorrow, is often difficult to define. This is coupled with changing dynamics between OEMs and end users where OEMs are expected to be integral to the success of the equipment for extended periods of time and often must take a more active role in areas such as training, advising and integration to other processes. These factors drive the need for OEMs to develop a new type of machine that not only provides more data, but data that can be easily accessed by other systems. Enter the data-ready smart machine.

What Makes a Smart Machine Data-ready?

Designing machines using data-ready technology offers a significant advancement in leveraging operational data. These machines go beyond traditional smart machines by organizing, contextualizing and making information available for egress, allowing customers to unlock new insights across production lines, facilities and fleets of equipment.

This enables OEMs and end users to define the data that is needed and move it beyond the equipment level, thus breaking the digital transformation standoff where both parties struggle to align due to unclear data requirements and high upfront investment costs.

AI-generated image of partially open black cardboard box with blue 3D pie charts and an orange bar chart with light shining from the bottom.

Using a data-ready approach, manufacturers can lay the foundation that allows them to react quickly to changes in data requirements and meet future demands in a much faster, more efficient and lower-cost way.

Top 3 Key Components of a Data-ready Smart Machine

Data-ready solutions enable users the flexibility to introduce new components, revisit design and adjust or modify applications to fit their unique needs. Most importantly, no one gets locked into decisions that can’t be changed down the road. With the ability to organize and egress data and offer compatible sharing with nearly any external application, data-ready smart machines help unlock value through:

Data organization and contextualization
Decoupling and optimizing the flow of data
Combining functions like visualization, data analysis, remote access and edge IoT

1. Organize and Contextualize the Data

Firstly, OEMs need to evaluate not only how to use data for additional value at the equipment level, but how to stage that data for egress to end user’s digital environments. Data from OEM equipment often appears as a large, and relatively flat, list of data points that provides little understanding as to what each data point can provide.

By organizing the data into a model, individual data points can be grouped together to help define their relationship to one another. This includes additional data that helps define relationships between different parts of the model, providing contextualization to what is happening across the overarching process. It is this approach to organization and contextualization that begins the transformation of data into information.

2. Push vs. Pull; Decoupling and Optimizing the Data Flow

Secondly, there needs to be an intention behind how the data will be consumed. One can determine whether there is going to be a steady stream of data (pulled by external end user applications such as SCADAs and historians), or whether the data must be in a more transactional form (pushed at the right time from the control level to an application that is event driven). And then, users need to make sure that they have a modern platform that serves up the organized and contextualized data through a variety of IT-friendly protocols. With data organized into a well-thought-out and easily understood format, the process of identifying the data needed by a specific end user application is eased as only the data that has meaning is served up. Solutions that offer this optimization cut down on the data that is sent out significantly, saving time and increasing productivity.

3. A Modern Machine Level Solution

Lastly, it’s important to recognize the need for a modern, machine-level solution that combines visualization, data analysis, remote access and edge IoT. The ability to move data leads to a differentiated solution in the market. There are a lot of customers that don’t know exactly the information they want. By taking this data-ready approach, it allows them to prepare equipment to be data flexible.

It is all in the name of efficiency. By unlocking the value in data-ready smart machines, users see an increase in efficiency and productivity right out of the gate. Organizing information in the right way and taking the modern approach to how it is created allows users to move the data that brings value versus pulling everything all the time and then trying to contextualize that data.

Real-World Application: Performance Visibility

Traditionally, machine performance visibility was achieved by sending non-contextualized data to a local SCADA system and rarely further. Differences in reporting of data often led to an unclear picture of equipment and line performance.

With data-ready smart machines, performance data is being organized into information models and, with a modern platform, giving greater insights at the machine level. The same information models can then provide a consistent picture of machine performance at the line level or beyond. This information is even being made available to the OEM for them to monitor performance, offer support and gain powerful insights across their entire fleet of equipment. All these insights are being gained from one source of information.

Horia Saulean, Director of Robotic Solutions at DCC Automation, is an early adopter of a data-ready approach. “Our [end user] customers are really feeling the pain of labor shortages. They need to make informed decisions that help them achieve the highest OEE given their constraints”, he explains.

“Through implementation of data-ready solution sets in our equipment, we can give them information that supports predictive maintenance efforts and even power usage of the machines. This helps them make informed decisions about the use of their resources”

Transformative Efficiency

Data-ready smart machines offer a transformative approach to leveraging data, enabling OEMs and end users to unlock new levels of operational efficiency and business value. By decoupling data availability from consumption, users become intentional with how they organize information allowing for more efficiency with less data being moved.

Additionally, end users must make sure that they have a modern platform to get the most value that combines visualization and the ability to egress that data from the equipment.

As the manufacturing landscape continues to evolve, embracing data-ready smart machines will be crucial for staying competitive and driving innovation.