How manufacturers make the most of machine data

Digitizing the manufacturing process via ERP systems can greatly improve ROI. However, it doesn’t come without challenges.

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Take a custom engineering-to-order project, for example. Working out the specifications would involve engineers, procurement, and manufacturing. Quality assurance personnel might be needed later on. Customer service and sales need insight into the process to make sure product and delivery expectations are met. “These are all normally quite disparate points with disparate systems outside ERP,” says Sinfield.

When something fails during the process, alerts might go to the project engineer, the sales engineer, and maybe an outside consultant. They are all able to dialog within Epicor ERP, pulling in things like business objectives around the project or the original engineering breakdown when needed. If a fault in a material is found, the supplier might also be brought into the loop within the system.

At some point, finance might get an alert that a project is delayed. They can see through the ERP system exactly what is causing the delay and make decisions about planning or change forecasts based on what they see. “That’s where it really starts to make sense to bring [data] together in ways we were never able to do before,” says Sinfield.

Implementation considerations and challenges

Wilkerson outlined four key challenges for companies looking to digitize their manufacturing processes.

1.     The integration of digitization into the operational management and improvement systems. “The technology needs to support the production systems of the companies in their continuous improvement and not build additional layers of management systems,” he says.

2.     The management and transformation of legacy systems. “In digitizing a ‘brown field’ manufacturing site, you struggle with numerous systems,” says Wilkerson. “Investments are often made in specific situations. It is necessary to use these windows of opportunity in a conscious development towards a smart factory vision.

3.     Ensure robustness in the systems and do not build in sensitive technologies that endanger the delivery or quality of manufacturing.

4.     Setting up pre-engineering platforms, test rigs, and demonstration capabilities within production development. “This has been a natural element within product development for decades but not seen as necessary in production development,” says Wikerson. “With the emergence of new technologies while maintaining absolute delivery precision in existing processes, pre-engineering platforms for testing and validating new technologies is central to speed up adoption of technology and best practices.”

Sinfield advises companies considering implementing a system to integrate manufacturing intelligence with their ERP systems to consider two metrics: efficiency and quality. “Companies need to take a step back and ask what they want to see,” he says. “They may not know where they need to be focused.”

In manufacturing, new technology can sometimes be a tough sell to the people on the shop floor, but that might not be the case with digital transformation. Lackey told of one high-value SAP deployment. “We thought we would get push-back from the shop floor,” he says. “But the shop floor supported it. It made the job easier, and the project was successful because of that buy-in.” He attributed that support to frustration using existing antiquated systems.

The prospect of having more data available might seem overwhelming to some stakeholders in the manufacturing process. However, the digital transformation of industry isn’t simply about making more data available. It’s about making the right data available when it will do the most good. “We’re living in the age of information overload,” says Sinfield. “Having the concept of in-context information is incredibly important to derive value for the business.”

Michael Nadeau is an analyst and writer in New Hampshire.

Copyright © 2016 IDG Communications, Inc.

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