Insights Hub for Enterprise Manufacturing

My main use case for Insights Hub is understanding the machine shop floor activities and monitoring, tracing, and providing accountable solutions to customers on a daily basis, although it is very limited. Based on my experience of 1.5 years, the integration between shop floor and the MES data layer represents a major role that Insights Hub plays. This makes it easier to integrate, configure, and analyze the data trends of shop floor activities.

A specific example of how I used Insights Hub in a real project involved monitoring a beer factory and a chocolate manufacturing line, which was a demo line. I needed to track and monitor how it would produce data, generate assets, monitor asset health, and predict new data. This gave me the realization that Insights Hub is not simply a basic tool; it is the largest cloud framework in which everything can be handled when deployed into the cloud environment, and users can utilize it according to their requirements. I feel this stands out compared to normal SCADA, MES, and even Power BI, since Power BI may be less effective than Insights Hub.

Insights Hub is a platform that is still under development because it is easier to handle, although many things need to be studied and updated. It can be extended for use over longer periods and for many projects. However, I have worked in a very limited period with limited projects, so I believe it will become a greater platform in the future.

The best features Insights Hub offers include predictive maintenance because there is a module called Predict which analyzes three months of data and provides insights for the next one hour. The integration with shop floor systems is also crucial because while we may face some delay in receiving data or may lose some data, I am currently receiving data continuously through Insights Hub unless there are major disruptions. I believe predictive maintenance and integration with the shop floor system are very easy and useful for users working on the shop floor with Insights Hub.

Insights Hub is a major tool for everything related to shop floor activities, monitoring, and providing traceable solutions. I consider this to be the best aspect of the features Insights Hub offers. It is easier and accessible to every user in a very accountable way, and we can provide access to those who require it. Insights Hub is an overall major good framework for everybody to monitor the shop floor.

In terms of positive changes in my projects after using Insights Hub, I would highlight OEE monitoring. We have Siemens Intelligence Cloud and many other tools, but in Insights Hub, OEE monitoring is very useful and attracts customers due to the user interface and the insights it provides on the shop floor.

As of now, Insights Hub is an under-developed platform, so there is no organization-level impact. We are trying to get some projects, but it has been in a dilemma state regarding whether we need to proceed with Insights Hub or use any other traditional ways of operating. However, this depends upon the customer. So far, whatever projects we have executed on Insights Hub have been completed smoothly and have been impactful for our organization.

I do not feel any challenges or frustrations with Insights Hub as of now since support has been provided by the product support team. However, I think I need some training and modular development to understand each concept and execute them effectively. I believe we need training and more integrations with Python or other interfaces to enhance Insights Hub's utility for developers compared to SCADA or similar automation tools.

Insights Hub needs to be useful for all users when interfaced with many programming languages and cloud systems. We have cloud databases as well as integration of other applications, and this is what I see as necessary improvements.

Regarding Insights Hub's AI capabilities, I think it currently lacks AI capabilities as it is not attached to AI. However, for security purposes, it provides better security to users with administrator access so that unwanted access to data can be avoided. Insights Hub needs to be integrated with AI to improve its functionality significantly.

I have been using Insights Hub for the past one and a half years.

In my experience, Insights Hub is stable, as I rated it an eight out of ten, but it needs a lot of improvement.

Insights Hub's scalability is promising as I believe it is scalable for all users, from those who have recently joined the organization to the experts. It does not limit access to just a specific group of people, making it quite resourceful for a wider audience in the organization.

Insights Hub's customer support receives a rating of four out of five because we have very limited support, considering Insights Hub offers many solutions and some of them do not have supporting ticketing tools. Overall, I find it good at four out of five.

Before Insights Hub, I primarily used Siemens Intelligence Cloud and Power BI, along with many other dashboarding tools. However, Insights Hub offers everything in a single framework and it is accessible on the cloud. It has features that are useful for future shop floor activities, which drove our decision to switch from our previous solutions to Insights Hub for its scalability and ease of integration with the shop floor.

My advice to others looking into using Insights Hub is that it is suitable for all users in an organization with the required prerequisite features and basic functionalities for shop floor integration with data, driving analytics, and enabling predictive maintenance. This would be the best feasible approach for initial shop floor activity maintenance. I would rate this solution an eight out of ten.

Insights Hub is used for monitoring equipment in the plant, mainly for OEE calculation. Within Insights Hub, Mendix has an application called OEE Hub, which allows me to connect the machines to the platform, meaning the machine has status, has speeds, and has production orders. I connect all that information to Insights Hub, and from OEE Hub, I can see the OEE metrics.

One of the best features that Insights Hub offers is that it is agnostic, meaning it has the ability, with its connectors, to link practically any type of machine that exists in the market to the platform and to monitor it.

Normally, companies have machines from various manufacturers, and the agnostic capability of Insights Hub has benefited my company by allowing communication with a variety of machines, including Mitsubishi, Toshiba, and those that speak the Allen-Bradley protocol or Schneider protocol, Modbus TCP. Given that great variety and diversity of machines, the main characteristic of Insights Hub is that it allows me to talk to that diversity of machines, and therefore I can extract data from them and monitor my plant without needing to use additional developments or additional configurations.

Insights Hub has the possibility of being able to integrate with Mendix, and given that Mendix is a versatile and powerful development platform, Insights Hub has positively impacted my organization by allowing the creation of infinite solutions within Insights Hub, combined with Mendix. The impact has been realized because of the OEE of the plant. Line seven of a bottling plant in Jalisco is the one I am referring to, and for confidentiality reasons, I could not be giving more details about the plants, but evidently, there has been an impact and a positive change.

Insights Hub has a connection with Grafana, but I would like it to be improved.

I have been using Insights Hub for five years.

I gave Insights Hub a rating of nine out of ten.

Insights Hub serves as a centralized monitoring and data-driven decision-making platform. It acts as a single place where data is collected, analyzed, and turned into actionable insights. The primary use case changes slightly depending on the platform. For application monitoring, the main use case is to monitor application performance and troubleshoot issues, tracking response time and failures, monitoring dependencies of databases and APIs, helping with log analysis, alerting on issues, and enabling performance optimizations. This is very common in DevOps and SRE environments.

Regarding troubleshooting, I will explain this using a real troubleshooting example from an application monitoring scenario in Microsoft Azure and AWS contexts. When users report that the application is very slow, Insights Hub helps troubleshoot step-by-step. First, I check the overview dashboard of Insights Hub where the request rate, average response time, failure rate, and server response trends are displayed. From this dashboard, I immediately noticed that response time suddenly increased from 200 milliseconds to 3 seconds and the failure rate also slightly increased, indicating that something has changed. In step two, I drill into performance by opening the performance section and checking requests. Insights Hub shows the slowest endpoints, percentile response times, and request breakdown by operations. I discovered a specific API command to check and narrowed the issue to one API. In step three, I check dependencies by opening the dependencies section in Insights Hub, which shows SQL calls, external APIs, and service-to-service calls. I noticed that the SQL dependency call to the order database is taking 3.5 seconds, revealing that the API itself is not slow but the database call is slow. In step four, I use logs with KQL queries, running a query like "request where duration is greater than 3000". From here I can correlate the slow request, database dependency duration, and any error patterns, potentially finding that a specific query is causing table scans, missing indexes, and lock contention. In the final step, I check live metrics to see if an ongoing issue exists. I can open live metrics streams and watch real-time CPU, memory, and request rate. The root cause identified in this example outcome was that a new deployment introduced a poorly optimized SQL query with a missing index that caused a full table scan, resulting in a slow database query that made the API slow and caused user complaints.

Insights Hub is a leading cloud-based industrial IoT platform designed to connect machines, analyze operational data, and drive digital transformation. The best features include advanced edge-to-cloud connectivity, AI-powered analytics, and low-code app development.

Advanced industrial connectivity is the first feature, which includes diverse data ingestion where machines, plants, and fleets are connected regardless of manufacturer, supporting protocols such as OPC UA, Modbus, S7, and Ethernet/IP. The second feature is powerful analytics and AI, specifically Insights Hub Predict. Regarding predictive maintenance, it uses machine learning with a GA model to forecast asset behavior, detect anomalies, and reduce unplanned downtime. It also analyzes quality prediction by detecting data to identify quality risks early, identify root causes, and minimize defects and rework. A comprehensive asset management and monitoring is also a good feature of Insights Hub. Insights Hub Monitor allows for real-time visualization of assets and creating rules-based alerts and tracking KPIs via dashboards. Additionally, there is low-code application development with Mendix, where custom apps are built on the Mendix low-code platform, allowing users to build and deploy personalized industry-specific apps quickly. Visual Flow Creator is a drag-and-drop tool to design workflows and analyze data without needing deep coding expertise.

The features my team uses most in daily operations are AI-powered analytics plus log querying with KQL. Production issues do not announce themselves politely; they show up as sudden latency spikes, random 500 errors, memory growth, and intermittent failures. AI-based detection and log querying help find the root cause quickly. In a real daily workflow example, when the error rate suddenly increases, the first step is AI smart detection, where Insights Hub automatically flags failure rate anomalies. Instead of manually checking the dashboard all day, the system tells me something is wrong, which already saves time. Secondly, I drill into logs and run queries such as "exceptions summarize count by type", which immediately shows me specific null reference exceptions or database timeouts. Now I am working with data rather than guessing.

This feature makes daily work easier with faster root cause analysis, reduced alert fatigue, and historical pattern recognition. Insights Hub has provided significant positive impact to my organization. The first positive impact is a significant reduction in MTTR, which is mean time to resolution. Before using Insights Hub, it was very difficult because manual log checks were tough, jumping between servers was challenging, looking at separate monitoring tools was difficult, and there were long bridge calls going on to identify the resolution. After implementing Insights Hub, end-to-end request tracing, correlated logs plus dependencies, and AI anomaly alerts resulted in issues being diagnosed in minutes instead of hours. The outcome has been faster incident resolution and improved service reliability. The second positive impact is proactive issue detection instead of reactive. AI-based anomaly detection helped catch memory growth before crashes, gradual performance degradation, and sudden traffic spikes. Instead of users reporting issues first, the monitoring system flagged them early. The outcome is reduced customer impact and fewer escalations. The third positive impact is improved deployment confidence. After new releases, I monitor live metrics, compare performance against baseline, and quickly roll back if anomalies appear. The outcome has been more stable deployments and fewer post-release incidents. The fourth positive impact is better cross-team collaboration. Before, there was blame between developers, infrastructure teams, and database teams. Now, full transaction visibility, clear dependency tracing, and shared dashboards mean everyone sees the same data. The outcome is less finger-pointing, faster RCA, and more accountability.

Measurable changes noticed include 30 to 50 percent faster incident resolution, fewer SEV one outages, reduced alert fatigue, and better SLA compliance. Alert fatigue was reduced by 20 to 30 percent and customer satisfaction has increased by some percentage.

Regarding improvements to Insights Hub, I have identified several areas. The first improvement would be smarter AI with clear root cause suggestions. Currently, AI detects anomalies but often only says "unusual increase in failures detected" without clearly stating what changed, which deployment caused it, or what likely component is responsible. Improvements could include automatic correlation with recent deployments, suggested probable root causes such as code changes, infrastructure scale events, or database latency, and a confidence score. This would reduce investigation time even more.

The second improvement would be better noise reduction in alerts. Sometimes anomaly detection generates too many notifications and minor fluctuations are treated as incidents. Improvements could include smarter alert grouping, better baseline tuning, and business impact aware alerting, where alerts are not triggered if latency increased but there is no user impact.

The third improvement is stronger cross-cloud and hybrid visibility. Many organizations use Azure, AWS, and on-premises infrastructure. Insights Hub could improve multi-cloud correlations and unified dashboards across environments. Currently, cross-platform visibility often requires custom integrations.

I'm using Insights Hub more than two and a half years.

I am giving Insights Hub a rating of eight out of ten. The reason I have given eight is because of all the features it has and the excellent observability capabilities it provides, such as end-to-end distributed tracing, dependency monitoring, real-time metrics, log correlation, and AI-based anomaly detections. Additionally, it has strong integration and is mature and enterprise-ready. However, I have not given nine or ten because there are some pain points. The learning curve is steep as it requires skill to fully utilize and is not very beginner-friendly, and querying can be complex. There is also significant noise and tuning required because AI detection is good but not perfect and can generate noisy alerts if not configured properly. Additionally, cost visibility and optimization is a concern as log ingestion cost can increase quickly and requires monitoring to avoid unexpected bills. Insights Hub earns eight out of ten because it dramatically improves troubleshooting and operational visibility and is powerful and enterprise grade, but still has room for improvement in usability, automation, and cost optimization.