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Reap the Benefits of Operational Insights in a Data-Driven Factory
Using advanced technology to explore the information contained in operational data opens the door for many improvements and future advancements.
An abundance of data generated from manufacturing processes can provide valuable insights into operations but using it all to become a Data-Driven Factory does not happen overnight. Companies have many decisions to make about how they will explore and use operational data before they can begin to reap its benefits. They also might find that some people resist new technology that helps them explore time-series data.
What is a Data-Driven Factory?
When companies move from an Automated to a Data-Driven factory, they also go from a state of simply collecting and storing operational data to using it as the basis for making daily decisions. The Data-Driven Factory is the first step forward in a manufacturer’s Digitalization Journey.
The rewards of making data-driven decisions are many, but they are not automatic. Leveraging operational data to gain insights on production requires the use of advanced technology. To be successful, operational experts must trust these new systems and use the insights they gain from them.
Many engineers use trending clients, such as AVEVA PI Vision, and spreadsheets, such as Excel, for analysis. These solutions have some benefits. Trending clients offer data exploration, opportunities for manual monitoring, and visual pattern recognition. They are also good for simple calculations. Meanwhile, spreadsheets offer many benefits for organizing data and creating custom visualizations.
However, both trending clients and spreadsheets have limitations. Spreadsheets can become very large and difficult to manage, and they are not designed to analyze time-series data. They also have limitations when it comes to handling large datasets. But the biggest drawback of both is the lack of ability to contextualize the information.
A more robust platform for analysis is necessary. TrendMiner provides an advanced industrial analytics platform to fill the voids left by other solutions. But people still have to accept change and use TrendMiner. One challenge that prevents adoption is the Mindset Gap, as discussed in a previous story on the Digitalization Journey. But after the initial rejection, another adoption issue can happen long after the initial deployment: the Double-S Curve of Innovation.
Overcoming the Double-S Curve
The Double-S Curve of innovation, which is a conceptual model used to illustrate the lifecycle of technological innovations and their adoption patterns. It is characterized by two successive S-shaped curves. Each represents different stages of technology adoption.
The first S follows the adoption and use of traditional analytics tools. There is an initial learning curve, as with any new method, that results in slow adoption as users learn the method. As these methods prove their value by increasing operational efficiency, the adoption rate accelerates. However, they eventually reach their limitations. Usage eventually slows again and then begins to drop.
The first S follows the adoption and use of traditional analytics tools. There is an initial learning curve, as with any new method, that results in slow adoption as users learn the method. As these methods prove their value by increasing operational efficiency, the adoption rate accelerates. However, they eventually reach their limitations. Usage eventually slows again and then begins to drop.
A second S develops when users need to adopt a newer tool or solution. For example, they might begin using an advanced industrial analytics platform, such as TrendMiner, to replace the currently favored method. Even though the adoption rate climbs as the platform demonstrates its value, there is a gap between the knowledge of the old methods and the new software until users learn how to use it. Operational experts might return to traditional methods because they know how to use them even though they have much more benefit to gain by using the new and innovative solution.
The Double-S Curve model emphasizes the cyclical nature of technological innovation and the importance of continuous improvement and adaptation. It highlights the need for manufacturers, or even managers, to anticipate an efficiency drop before the true value of a newer solution can bring users and the organization to the next level of efficiency.
Moving Toward a Connected Factory
The journey doesn’t stop there. By contextualizing operational events in a Connected Factory, engineers can get even deeper insights and a more holistic view of operations. In the next story, we will learn how manufacturers break down data silos and democratize data throughout the organization. We will also discuss the next challenge that lies ahead: The IT/OT Gap.
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