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Co-Creating the Future Series: Part 3 – Accelerating Performance
In part three of our Co-Creating the Future series with SIG, we’ll look at how manufacturers can turn data into discovery through optimized and automated decision making. This post was originally published on SIG’s SIGnals blog.
In part two of our Co-Creating the Future series, we discussed the benefits of moving from paper to data. But, simply collecting data alone isn’t enough. It’s only through intelligent data analysis, insights, and action that manufacturers can fully optimize efficiency and make production more transparent.
Enterprise-wide insights for better decisions
Data analysis in manufacturing has traditionally been the preserve of senior management. Over weeks or months, insights and decisions are typically filtered down to each department, right back to the factory floor where the data originated. So, without a feedback loop, management often don’t know if these changes have been successful or if they’ve even been implemented.
The digitalization of manufacturing changes this. For employees, having access to real-time data means they can be held accountable for the work they do and more easily raise issues or suggest ideas. For management, being able to see aggregate data from many factories in one dashboard – perhaps even merged with financial and marketing data – gives them the power to manage both trends and outliers.
Josh Bloom - VP of Data & Analytics at GE Digital
The next step, according to Josh Bloom, VP of Data & Analytics at GE Digital, is to use artificial intelligence (AI) and machine learning to help automate this decision-making process. As long as the software can be taught which conditions are favorable, it can recommend certain responses or predict consequences to the shop floor before they need to be raised to senior management.
It’s this kind of data-driven performance that SIG and GE Digital will enable together—using GE Digital’s industrial applications, Predix Asset Performance Management (Predix APM) and Predix ServiceMax, to turn data into discovery. This means providing operators with the information they need to maintain industrial equipment while balancing maintenance costs, risks, asset life, and external factors. And it’s being supported by field service management, dispatching the right engineers to the right job at the right time.
Optimizing manufacturing equipment performance
This way of handling data will be unique to SIG’s customers since Predix APM and Predix ServiceMax have never before been integrated together. Predix APM is designed to capture and analyse data – both real-time and historically – to optimize operational assets. Predix ServiceMax, meanwhile, is designed to optimize the teams that maintain these assets, using cloud-based data and scheduling to improve workflows, first-time fix rates, field service productivity and customer satisfaction.
By bringing these two industrial applications together for the first time, we’re going to be able to optimize both the machines and the teams that maintain them. This will allow SIG to open up new business models – such as servitization – and enable SIG’s customers to boost their productivity and competitiveness as well.
Scott Berg - CMO of GE Digital
Service is a core part of SIG’s business and helps explain why its market share has been growing year-on-year for the past two decades. But improving service quality also means optimising inefficiencies, which is why SIG considers digital service a key priority.
We wanted to move away from preventative to predictive maintenance. That’s why we looked for a partner that doesn’t just provide Asset Performance Management or Field Service Management, but one integrated solution – which we found with GE Digital’s predictive analytics and machine learning expertise.
Christian Alt - Director of Corporate Development and Digital Transformation, SIG
To find out where manufacturers can go beyond performance optimization, don’t miss part four of the Co-Creating the Future series where we’ll discuss the final stage in the digital transformation— utilizing intelligent prediction.
Miss the previous blog in the series? Read Part 2: Making Your Data Digital
Want to continue reading? Read Part 4: The Power of Prediction
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