I have a couple of suggestions for the organization, and I've told them as well. Nowadays, time-series analysis for manufacturing units is a primary need. What they're looking for is the incorporation of artificial intelligence and machine learning into these tools and then getting some insights out of it, which is closed-loop. Till now, we have been facing open-loop solutions. But how to apply these insights or inferences into my manufacturing unit, which is running 24/7? Being an engineer and a person who comes from the industry, I would rather believe in first principles than a data science model. To break the ice, they need to come up with more of the predictive part of it using machine learning techniques, which can be closed-loop solutions, which can help operators and automation engineers to apply these insights into the units rather than keeping it open-loop.
Regarding AI, I wish Seeq had AI capabilities. For instance, if Seeq could analyze graphs and offer recommendations, it would be advantageous. Currently, there is no AI feature in Seeq. In terms of improvements, it would be great if Seeq could enhance its data export features and possibly integrate more functionalities to reduce the need for multiple software tools. I would like a specific feature related to predictive analysis, which Seeq currently lacks.
As for improvements, I felt there should be a more efficient way to address repetitive customer questions, perhaps using chatbot technology to streamline responses. In terms of functionality, I noticed that certain machine learning methodologies, like Principal Component Analysis (PCA), were missing from the Workbench. While it was possible to perform these analyses in DataLab, adding such features to Workbench would enhance user experience and allow for more complex calculations without needing to switch environments.
Seeq is evolving. We only included time series analysis in the first version. Going forward, we introduced many complex Seeq tables. You give the temperature and pressure, and the solution will give you the enthalpy of steam. It would be wonderful if a time series model could be built with the thermodynamic package. We have Aspen Hysys for thermodynamic package dissimulation. When we interpret both, it will be magical.
They have many features addressing various issues. For example, something I developed plots the gains in a prediction model, which could be a built-in capability in Seeq. Artificial intelligence support exists for the Datalab environment. The advanced functionality requires some training.
Seeq Organizer, which is used for dashboarding, has some limitations. In Seeq Organizer, we've realized that process engineers want dashboards with more drag-and-drop features, like Power BI. Seeq has limitations in terms of the variety of widgets and visualizations you can use. You're limited to a few types like line charts, bar charts, and pie charts (which was introduced recently). Power BI offers a wider range of dashboarding options. It's not that Seeq is solely a dashboarding tool; it can connect to Power BI and Tableau. But customers who purchase Seeq and expect its dashboarding feature to be competitive with Power BI and Grafana might be disappointed. So, dashboarding is where I see a lot of room for improvement in Seeq.
Supply Chain Analyst at a manufacturing company with 10,001+ employees
Real User
Top 20
2024-05-21T14:18:16Z
May 21, 2024
The analytical part is fine. We can do our analysis. But the dashboard, where we approve the dashboard, is not ideal for someone who uses Seeq only as a tool to see data in a report format. Seeq Workbench, Seeq Organizer, and Seeq Data Lab are great platforms where we do our analysis, but if they could add features to Seeq Organizer for publishing reports, that would be helpful. Currently, there are many tools on the market that provide aesthetic dashboards, like Power BI or Tableau. These tools make it easy to create reports that are understandable even for those who don't know anything about the data. So, Seeq could work on the reporting format, but the analytical tool is excellent.
I have noticed that Seeq sometimes struggles with connectivity issues, leading to data gaps. This is an area that needs improvement. While connecting to data, sometimes, due to connection loss or issues with the tool itself, the tags may not display any trends, resulting in missing information.
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I have a couple of suggestions for the organization, and I've told them as well. Nowadays, time-series analysis for manufacturing units is a primary need. What they're looking for is the incorporation of artificial intelligence and machine learning into these tools and then getting some insights out of it, which is closed-loop. Till now, we have been facing open-loop solutions. But how to apply these insights or inferences into my manufacturing unit, which is running 24/7? Being an engineer and a person who comes from the industry, I would rather believe in first principles than a data science model. To break the ice, they need to come up with more of the predictive part of it using machine learning techniques, which can be closed-loop solutions, which can help operators and automation engineers to apply these insights into the units rather than keeping it open-loop.
We face a bit of an issue if there are any server issues or upgrades from Seeq.
The technical support services need improvement.
Regarding AI, I wish Seeq had AI capabilities. For instance, if Seeq could analyze graphs and offer recommendations, it would be advantageous. Currently, there is no AI feature in Seeq. In terms of improvements, it would be great if Seeq could enhance its data export features and possibly integrate more functionalities to reduce the need for multiple software tools. I would like a specific feature related to predictive analysis, which Seeq currently lacks.
As for improvements, I felt there should be a more efficient way to address repetitive customer questions, perhaps using chatbot technology to streamline responses. In terms of functionality, I noticed that certain machine learning methodologies, like Principal Component Analysis (PCA), were missing from the Workbench. While it was possible to perform these analyses in DataLab, adding such features to Workbench would enhance user experience and allow for more complex calculations without needing to switch environments.
Seeq is evolving. We only included time series analysis in the first version. Going forward, we introduced many complex Seeq tables. You give the temperature and pressure, and the solution will give you the enthalpy of steam. It would be wonderful if a time series model could be built with the thermodynamic package. We have Aspen Hysys for thermodynamic package dissimulation. When we interpret both, it will be magical.
They have many features addressing various issues. For example, something I developed plots the gains in a prediction model, which could be a built-in capability in Seeq. Artificial intelligence support exists for the Datalab environment. The advanced functionality requires some training.
Seeq Organizer, which is used for dashboarding, has some limitations. In Seeq Organizer, we've realized that process engineers want dashboards with more drag-and-drop features, like Power BI. Seeq has limitations in terms of the variety of widgets and visualizations you can use. You're limited to a few types like line charts, bar charts, and pie charts (which was introduced recently). Power BI offers a wider range of dashboarding options. It's not that Seeq is solely a dashboarding tool; it can connect to Power BI and Tableau. But customers who purchase Seeq and expect its dashboarding feature to be competitive with Power BI and Grafana might be disappointed. So, dashboarding is where I see a lot of room for improvement in Seeq.
The analytical part is fine. We can do our analysis. But the dashboard, where we approve the dashboard, is not ideal for someone who uses Seeq only as a tool to see data in a report format. Seeq Workbench, Seeq Organizer, and Seeq Data Lab are great platforms where we do our analysis, but if they could add features to Seeq Organizer for publishing reports, that would be helpful. Currently, there are many tools on the market that provide aesthetic dashboards, like Power BI or Tableau. These tools make it easy to create reports that are understandable even for those who don't know anything about the data. So, Seeq could work on the reporting format, but the analytical tool is excellent.
I have noticed that Seeq sometimes struggles with connectivity issues, leading to data gaps. This is an area that needs improvement. While connecting to data, sometimes, due to connection loss or issues with the tool itself, the tags may not display any trends, resulting in missing information.