Why does Predictive Maintenance top the priority list for CIOs and production directors?

Although the motivation varies by industry, three numbers appear most often on management slides when maintenance is discussed: 

• Fragile spare‑parts supply chains increase downtime risk. Deloitte’s 2025 Manufacturing Industry Outlook reports that, although average lead times for production materials have improved since the pandemic peak, they remain several percent longer than before 2020, and more than 35 % of manufacturers cite rising transport costs as a key operational challenge.

• Predictive analytics tangibly reduces failures. The Nucleus Research study Quantifying the Value of Predictive Maintenance shows that PdM programs can cut unplanned downtime by 35‑50 % and extend asset life by 20‑40 %. 
• Return on investment arrives faster than in classic IT projects. According to the US DOE’s Operations & Maintenance Best Practices Guide, companies achieve a 10× ROI while cutting maintenance costs by 25‑30 %.

Experience in Poland and Central Europe – among others, from projects delivered by ConnectPoint – confirms that combining IIoT, AI/ML and sound engineering practice allows companies to move from a pilot proof‑of‑concept to measurable cost reduction across the entire machine park within a few quarters. Below we examine the eight most expensive problems that Predictive Maintenance effectively addresses.

The most common maintenance challenges in manufacturing

1. Distributed and inconsistent sources of operational data

In a typical plant, machine‑condition data are generated simultaneously in SCADA systems, PLCs, vibration loggers, CMMS solutions and Excel spreadsheets. Each environment assigns its own tag names, stores history in a different format and releases data at a different cadence. This fragmentation blocks rapid operational decisions, because every production meeting requires manual report consolidation. 

Fragmentation is not merely a technical issue – it is also a business risk. Without a single “source of truth”, the production director may see a different failure count than the maintenance manager, while the IT team holds a third version of the story. Action plans can end up based on perception rather than fact. Predictive Maintenance eliminates this problem during its consolidation phase: raw signals are streamed into a shared real‑time data lake, tag names are normalized and failure histories unified. Only on such a foundation can reliable forecasts be built. 

2. Delayed access to critical operational data

Project data collected by ConnectPoint and client interviews show that, on average, engineers spend 80% of their time gathering data and only 20% analyzing it. In practice this means hours of CSV exports, files copied to USB sticks and late reactions to process deviations. In a world where an hour of downtime in automotive already costs USD 2.3 million, every minute of delay carries a hefty price tag. 

Predictive Maintenance systems invert this ratio. Machine data streams are ingested within seconds, and a ready‑made API makes them available simultaneously to maintenance, technology and IT. The business effect is a step‑change in work culture: engineers spend their time interpreting root causes, not hunting for numbers. 

3. Late anomaly detection and lack of automatic alerts

Without a predictive analytics layer, a plant finds out about a problem only after the line has already stopped. Most often, the absence of automatic alerts on deviations from the norm leads directly to costly breakdowns. Considering the downtime costs mentioned above, even a few minutes’ delay in response can mean tens of thousands of dollars lost. 

Machine‑learning models in Predictive Maintenance build a “health signature” of the asset—a mathematical description of vibration, temperature and energy consumption during stable operation. When any parameter drifts from the pattern, the system issues an alert before the deviation becomes visible to the operator. This early signal is not an abstract IT benefit but a real saving: it is far better to replace a bearing during a planned daytime window than halt an entire line at 3 a.m. on Sunday. 

4. Unstructured data and lack of standardization

Even if data land in a single database, they can remain useless when tag names reveal nothing and process attributes are incomplete. In this case, semantic chaos prevents analytics from scaling. Imagine two identical packaging lines where a sensor in the same station is called “T‑123_TEMP” on one line and “P3‑Temp_A” on the other. The algorithm will not recognize these as twin signals and will lose them in mapping. 

Standardization within a Predictive Maintenance strategy involves three steps: 

1. A tag dictionary describing exactly what each signal represents. 
2. A process ontology defining how elements connect into a whole. 
3. Data‑quality validation (ranges, units, precision). 

Only after these stages can one expect reliable failure‑prediction models. 

5.Lack of a digital twin of the production line

Linking signals to specific machine components is essential for predicting failures and benchmarking similar devices. A Digital Twin is a virtual replica of a physical object or system that is continuously updated with real‑world data. In Predictive Maintenance, a digital twin makes it possible to simulate scenarios, analyse machine condition and predict potential failures, enabling maintenance to be scheduled before downtime occurs. 

For a production line, the digital twin is an interactive replica: every gear motor, gearbox and actuator has its virtual “socket”, and associated sensors feed data in real time. Engineers can see not only a raw vibration of 14 mm/s² but the vibration of that specific bearing in the tension section—complete with service history, load and oil temperature. This allows a degradation model for a “family” of devices to be created, and solutions developed on line A to be transferred to line B without laborious remapping. 

6. Imprecise data‑access management

A common paradox in manufacturing: permissions that are too narrow block critical information from the line crew, while permissions that are too broad risk losing control over system changes. A mature Predictive Maintenance program builds a role matrix – operator, maintenance, planner, data scientist, management—and assigns each a precise scope of visibility and actions (view, comment, model edit). Every intervention is also logged, creating a digital audit trail. 

In the long term, such discipline translates into model stability: configuration changes are intentional and verifiable, and knowledge of who, when and why made a decision is not lost in email chaos. 

7. Difficult interpretation of operational data

Collecting data is only the beginning—the equal challenge is how to present them. Experience shows that the lack of intuitive visualizations lengthens reaction time to anomalies. Humans excel at trend graphs and colour cues but struggle with raw numbers scrolling like “Matrix rain”. 

Modern Predictive Maintenance dashboards, including the one we offer in the Smart RDM platform, combine dynamic KPIs (MTTR, MTBF, OEE) with a line map and the predicted time to failure. Process engineers see red risk spots, while managers see the financial impact of each alert. This is not mere UX cosmetics; shaving a few minutes off reaction time at the costs mentioned above translates into a six‑figure annual benefit. 

8. Operational data isolated from business systems (ERP/CMMS)

The final link in the chain of challenges is the separation of production from finance: a failure “lives” in SCADA, while its cost “lives” in ERP. This separation prevents full TCO (Total Cost of Ownership) calculation and hinders spare‑parts budgeting. Without integration, process and cost optimisation is obstructed. 

When a PdM system posts an event directly to CMMS, which then feeds the ERP purchasing module, a closed loop is created: prediction → work order → spare‑parts reservation → invoice → ROI report. Every dollar spent on maintenance thus has its context in production data, and decisions on modernisation or supplier changes can be backed by hard analytics. 

What do the numbers say? — benefits of implementing Predictive Maintenance

Before you look at the figures, consider the leverage involved: every hour of recovered availability or every percent shaved off maintenance costs flows straight to operating margin and delivery performance. The table below shows how quickly and tangibly PdM can impact a plant’s financial results.

*Averages from industry‑reported deployments; the actual effect depends on line criticality, data culture and maintenance‑process maturity. 

Results you can reliably estimate with Predictive Maintenance

The eight barriers described above form a coherent “hidden cost web” in a manufacturing organization. Implemented methodically—from data consolidation through standardization and digital twins to ERP integration—Predictive Maintenance cuts through this web. 

The benefits are threefold: 

• Financial – reducing unplanned downtime, measured in millions of dollars per hour in large plants. 

• Operational – faster, more accurate response to events thanks to automatic alerts, visualizations and a data‑driven culture. 

• Strategic – building a digital organizational memory that progressively strengthens the algorithms and eases knowledge transfer between shifts, lines and plants. 

If even one of the challenges described is an everyday reality in your factory, it is worth treating PdM not as an IT project but as a pathway to resilient, predictable and scalable production—which we will be happy to support.

See how to boost KPIs from OEE to ROI—learn how to move from the first measurement to a fully‑fledged Predictive Maintenance system in just three months and materially raise your plant’s efficiency.

The post 8 strategic challenges in manufacturing that you can eliminate by implementing Predictive Maintenance appeared first on ConnectPoint.

Predictive Maintenance – why does it matter?

Traditional maintenance approaches, based on reactive and preventive maintenance, generate excessive costs and lead to unplanned downtime. Predictive Maintenance allows you to predict failures based on an analysis of the actual operating conditions of the machines.

This enables more effective maintenance management. Implementing predictive maintenance means reducing the number of breakdowns, extending the service life of equipment and optimizing service schedules.

Predictive maintenance implementation methodology – a systematic approach

Implementing predictive maintenance requires not only technology, but above all a coherent strategy that includes both organizational preparation and integration with existing systems. Our predictive maintenance implementation methodology provides a complete implementation process, including:

• Analyzing the plant’s readiness for implementing Predictive Maintenance – identifying data sources, assessing the quality of information and organizational structure.

• Integrating IT systems – synchronizing data from SCADA, ERP, CMMS and IoT sensors,

• Advanced analytics – predictive modeling, trend analysis and anomaly detection.

• Optimization of maintenance schedules – reduction of unnecessary service interventions, minimization of the risk of downtime.

• Continuous monitoring and adjustment of the system – iterative improvement of analytical models and maintenance procedures.

The implementation methodology of Predictive Maintenance has been designed in such a way that it can be applied both in companies with an extensive digital infrastructure and in plants where access to data is limited.

What are the benefits of Predictive Maintenance?

When implemented correctly, Predictive Maintenance can help companies significantly improve their OEE (Overall Equipment Effectiveness). An OEE of 60-80% indicates stable processes, but still leaves room for optimization. Companies that achieve over 85% OEE are usually organizations with a high degree of automation and advanced digitization.

Implementing predictive maintenance can increase OEE by 5–15%, depending on the organization’s starting level. If a company generates 300 million PLN in annual turnover and the profit is 10%, each additional percentage point of OEE translates to approximately 0.5 million PLN in additional profit.

A minimum increase of 5% means an additional 2.5 million PLN in annual revenue. For plants that start at an OEE level of 50%, the potential for growth is even greater, although this often requires prior improvement of data quality and digitization of processes.

A new standard in maintenance

We believe that Predictive Maintenance will become the leading standard in maintenance, helping companies achieve better performance and operational reliability. We invite you to contact us and work with us to implement modern predictive maintenance solutions.

For more information on Predictive Maintenance, please visit: Predictive Maintenance | A strategy tailored to the needs of your company – Connectpoint

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AVEVA PI System for data context 

The AVEVA PI System is a widely used technology for collecting, managing, and analyzing real-time data in various industries. It allows organizations to build data context and gain insights that can help drive growth, optimize processes, and make informed decisions. In the subsequent discussion, we will delve deeper into the AVEVA PI System, exploring its features, benefits, and potential limitations.  

The AVEVA PI System is designed to collect data from different sources, such as sensors, machines, and devices, and organize it into a centralized repository. This allows organizations to monitor their operations in real-time, ensuring that they are aware of any issues as they arise. Additionally, the system can perform complex analytics on the data collected, generating insights that can help organizations optimize their operations, reduce downtime, and enhance productivity.  

The system allows organizations to collect and analyze data and provide context to the data. This means that organizations can understand the relationships between different data points and how they impact each other. This is particularly useful in industrial settings with many moving parts, and it can be challenging to identify the root cause of an issue.   

Another advantage of the AVEVA PI System is its scalability. Depending on the organization’s needs, the system can be easily scaled up or down. This means that organizations can start small and expand the system as their needs grow. Additionally, the AVEVA PI System has a wide range of connectors, allowing it to integrate with different technologies and systems. This means that organizations can easily integrate the system into their existing technology stack, reducing the need for significant changes to their infrastructure.  

However, like any technology, the AVEVA PI System has potential limitations that organizations should be aware of. One of the system’s potential limitations is its limited data modeling capabilities. Although the system excels at handling time-series data, which is prevalent in industrial settings, it may not be suitable for more complex data modeling tasks that involve non-linearly correlated data. This can limit the system’s ability to perform advanced analyses or modeling tasks, potentially reducing its usefulness in some industrial settings.  

Another potential limitation of the AVEVA PI System is its limited machine learning capabilities. The system may need to support advanced algorithms or deep learning models, making it difficult to perform more advanced analysis. This can be a significant disadvantage for organizations leveraging the system.  

Moreover, the system may have limitations regarding integration with other data analytics platforms. This can make it challenging to combine data from multiple sources or use different analytics tools in conjunction with the PI system. As a result, organizations may need help to get a complete view of all the data in an industrial setting, potentially limiting the system’s usefulness.  

Smart RDM for data science  

The Smart RDM platform is an excellent complement to the AVEVA PI system, delivering a suite of functionalities that broaden the scope of data analytics. The platform encompasses a comprehensive analytical module that enables authorized end-users to efficiently manage ETL processes and analyses. Its key features are the ability to manage algorithms available to users, run algorithms with customizable runtime parameters, and configure the launch context to related elements in the resource AF tree. Additionally, the platform offers a scaling mechanism implemented on Kubernetes to ensure each analysis operates in an isolated environment and does not interfere with other scripts’ functioning.  

Furthermore, the Smart RDM platform facilitates the parallelization of calculations, allowing multiple subtasks to be executed simultaneously on several PODs, thus reducing overall task execution time. For large-scale analyses, the platform offers scheduling for the complete set of analyses, ensuring that results from one analysis become input data for the next.  

The Smart RDM platform provides various data export and exchange approaches, depending on the analysis’s objectives and input data type. These include direct integration with PI AF or Parquet files via the Python scripting interface, direct integration with SQL or DataLake, and ETL processes using SSIS packages triggered via the Python scripting interface. Direct integration is primarily used to perform analyses using the PI AF or SQL structure or store results, while ETL processes are mostly used to prepare data for business reporting.  

Additionally, the Smart RDM platform can be integrated with Azure Cloud services such as Azure Synapse to enable the execution of complex data workflows and enable data transformation at scale. The results of the analyses can be saved back in the PI system and visualized on PI Vision dashboards or accessed through external reporting tools like MS Power BI, MS reporting services, Grafana, and Tableau. The Smart RDM platform’s flexibility makes it a versatile solution that provides a comprehensive view of all data in an industrial setting while leveraging the power of the Azure Cloud.

  Summary

The AVEVA PI System is the best technology used in various industries for collecting, managing, and analyzing real-time data. It allows organizations to gain insights and optimize processes by building data context and providing scalability. However, the system has potential limitations, such as limited data modeling and machine learning capabilities and challenges with integration with other data analytics platforms. The Smart RDM platform is a valuable addition to the AVEVA PI System, expanding its capabilities and providing a range of functionalities for efficient ETL processes, analyses, and parallelization of calculations. The platform also offers different data export and exchange approaches, making it a versatile tool for comprehensively viewing all data in an industrial setting. 

The post Beyond Data Context: Enhancing Analytics with Smart RDM Platform while Leveraging AVEVA PI System’s Strengths appeared first on ConnectPoint.

This article aims to provide a systematic guide that commences from data collection to its detailed workflow, analytics, and data visualization. By employing the Smart RDM platform, customers can expedite their data adoption strategy and derive valuable insights based on their data workflows to propel their business growth.

Step 1: Data collection

The initial step, i.e., data collection, holds utmost importance in current business operations as it offers organizations the necessary insights to make informed decisions. The PI System, a real-time data management platform, is extensively employed by industries to accumulate and retain data from diverse sources, including sensors, machines, and production lines.  Although not all the data collected via the PI System is used for analysis, chosen data is uploaded onto cloud storage, where it can be stored alongside data from other applications such as ERP, EAM, and CRM, among others. This storage integrates with the Smart RDM platform, consolidating all the required data.

Step 2: Data visualization and validation

The Smart RDM Platform allows users to visualize and validate raw data, with real-time data readily displayed through PI vision dashboards accessible via the platform. Additionally, users can leverage automated correction processes and a dedicated data edition, validation, and confirmation workflow to ensure accurate and reliable data.

Step 3: Data Analytics

Users can leverage the Cloud Dataflow feature to automate and execute parallel data processing tasks to enhance their data processing capabilities. Google Cloud Storage Bucket can source various file types, which can be uploaded into BigQuery for further processing. Releasing the Power of Data: A Step-by-Step Approach with Smart RDM Platform

Data is a valuable resource that can substantially influence the growth and innovation of businesses. Industries must leverage the power of data to maintain a competitive edge. The Smart RDM platform provides a comprehensive suite of features that can aid organizations in realizing the full potential of their data. By utilizing the AVEVA PI system (former OsiSoft PI System) and other applications as a data source, the platform employs analytical capabilities to convert it into key performance indicators (KPIs), reports, insights, and visualizations in diverse contexts.

This article aims to provide a systematic guide that commences from data collection to its complex workflow, analytics, and data visualization. By employing the Smart RDM platform, customers can expedite their data adoption strategy and derive valuable insights based on their data workflows to propel their business growth.

Step 4: Deeper Data Exploration

Users can perform more sophisticated data exploration based on their requirements using the advanced features of the big data and analysis management module available on the Smart RDM platform. This module allows users to manage algorithms, run algorithms, modify default runtime parameters, and configure the launch context for related elements in the resource tree. Additionally, the platform features a scaling mechanism implemented on Kubernetes to ensure that each analysis runs in an isolated environment without interfering with other script workflows.

Moreover, the Smart RDM platform facilitates the parallelisation of calculations, allowing for the execution of numerous subtasks simultaneously on multiple PODs to reduce the overall task execution time. For extensive analyses, the platform schedules the entire set of analyses, guaranteeing that the results from one analysis become input data for the next.

The outcomes of the analyses can be stored back in the PI system and visualized on PI Vision dashboards or accessed through external reporting tools like MS Power BI, MS reporting services, Grafana, and Tableau. This adaptability makes Smart RDM an adaptable platform that provides a comprehensive view of all data in an industrial setting.

Conclusion

To sum up, the Smart RDM Platform, in conjunction with the PI System and other data source systems or applications, offers customers an opportunity to unleash the potential of their collected data and gain comprehensive insights to enhance their performance. The platform integrates various tools to provide a holistic view of industrial data, including Google Cloud Storage, Big Query, Grafana, Tableau, MS Power BI, and other reporting tools. With its versatility and robustness, the Smart RDM Platform empowers organizations to leverage their data to fuel their business growth and success. To de-normalize and transform the data per specific requirements, with users creating detailed views and tables. Finally, Grafana can be a visualization tool for building robust and powerful dashboards.

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Industry 4.0 – what does it stand for?  

Industry 4.0 is all about using technology to automate and optimise manufacturing processes. This includes using sensors, data analytics, and artificial intelligence to monitor, analyse, and optimise the performance of machines and systems in real-time. The data generated from these machines and systems are then used to make data-driven decisions that improve efficiency, reduce downtime, and ultimately increase profitability.  

Industry 5.0 – what does it stand for?  

Industry 5.0 is the next step in the evolution of manufacturing, where the focus is on human-machine collaboration. This involves using technology to augment human capabilities, allowing for more flexibility and creativity in manufacturing processes. In this scenario, data analysis in real-time becomes even more critical, as it enables humans to make informed decisions that optimise the use of technology and improve productivity.  

Real-time data analysis and its impact on processes  

Real-time data analysis is essential in Industry 4.0 and 5.0 because it enables quick and informed decision-making. With the help of real-time data analytics, manufacturers can identify bottlenecks, predict potential issues before they occur, and optimise the performance of machines and systems to improve efficiency and productivity. This saves time and money and ensures that the manufacturing process is running at maximum capacity.  

Real-time data analysis also enables predictive maintenance, which is critical in Industry 4.0 and 5.0. By using predictive maintenance, manufacturers can identify potential issues before they occur and take proactive steps to prevent them. This reduces downtime and prolongs the life of machines and systems, leading to significant cost savings.  

Furthermore, real-time data analysis can be used to improve product quality. By monitoring the manufacturing process in real-time, manufacturers can identify any defects or issues with the product and take corrective action immediately. This ensures that the final product meets the required quality standards and reduces the risk of product recalls, which can be costly and damaging to a company’s reputation.  

The Smart RDM platform – analyse data, and generate insights 

By leveraging technology and digitalisation to optimise manufacturing processes, manufacturers can improve efficiency, productivity, and profitability. Real-time data analysis is the key to making this happen, enabling informed decision-making and predictive maintenance, improving product quality, and reducing downtime. Manufacturers must embrace this digital transformation and invest in the technology and infrastructure for real-time data analysis.   

Smart RDM is a platform designed to help companies achieve this goal by providing real-time data mining and analysis tools. This platform allows manufacturers to integrate data from multiple sources, analyse it in real-time, and generate insights that can be used to optimise production processes.

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The need   

The advent of technology and the widespread availability of digital devices have led to the generation of vast amounts of data. This data can be analyzed and utilized to improve business performance, identify new opportunities, and make informed decisions. Real-time data mining and analysis allow businesses to take advantage of this data as soon as it is generated, enabling them to respond quickly to changing market conditions and customer needs.   

Real-time data mining and analysis are essential in industries where rapid decision-making is necessary, such as production and manufacturing. In these industries, real-time data mining and analysis can help improve operational efficiency, reduce costs, and provide better customer service. In addition, by analyzing customer data in real-time, businesses can identify customer needs and preferences to respond quickly to customer inquiries and complaints.   

The solution   

Smart RDM stands for Real-time Data mining in the modern production company. It is a concept that involves the use of technology and data management practices to organize, validate, and utilize data effectively. Smart RDM aims to make information more accessible, trustworthy, and usable for business process optimization. To achieve this goal, Smart RDM is organized into six modules: Reports, Data Management and Validation, Process Monitoring and Visualizations, Alarms and Events, CMMS, and Big Data and Analysis Management.   

• The Reports module is designed to provide users access to reports built on the data managed within other Smart RDM modules or processed in external systems. The user can access different information the business administrator provides, including file reports, folder reports (e.g., network drive, Google Drive, OneDrive), Grafana, Tableau, Microsoft Power BI, and MS reporting services. Smart RDM functionality covers report viewing, printing, and exporting, as well as subscription service and information about changes in the report.   
• The Data Management and Validation module requires an AVEVA PI data structure. It helps ensure the quality and accuracy of the data collected or calculated automatically. Based on a predefined form, the user receives a report template supplied with data from source systems. The data can be verified, corrected, and accepted at this stage. All changes are tracked. Accepted data might be published as a pdf report.   
• The Process Monitoring and Visualizations module requires an AVEVA PI data structure. It allows users to display data in real-time in the form of interactive charts and diagrams, and it can be used to monitor various types of data, such as sensor readings, weather data, and financial data. This module is essential for businesses that want to optimize their processes and stay ahead of their competitors.   
• The Alarms and Events module requires an AVEVA PI data structure. It is a powerful combination of features that help users capture, store, find, compare, and analyze important events identified automatically based on the specific conditions related to data values collected in real-time. Smart RDM functionality covers Real-time notifications about system conditions that need particular attention, User actions related to the events handling (according to the predefined workflow), Event-related data editing, adding file attachments to the event, event division, etc., and editing history.   
• The CMMS module tracks and manages user-defined events related to maintenance operations in industries and organizations. CMMS aims to improve maintenance efficiency, reduce downtime and maintenance costs, and provide reliable data on equipment maintenance history. Smart RDM functionality covers notifications, user actions according to predefined workflow, event-related data editing, adding file attachments to the event, event division, and editing history.   
• The Big Data and Analysis Management module provides the management of analyses and ETL processes by authorised end-users.  

Key functionality includes:  

• Managing the algorithms available to users (including the definition of default runtime parameters),  
• Running algorithms with the ability to change default runtime parameters – on demand and scheduled,  
• The possibility of configuring the launch context to related elements in the assets tree (PI Asset Framework integration),  
• The possibility to associate the algorithm results with a specific report position or KPI, monitoring of the execution status.   

Data collected from user operations in Events, CMMS, or Big Data and analysis management modules might be used to create reports shared in the Reports module to provide insights into the business performance and support the decision-making process related to business process optimization. 

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Smart RDM – version 1.0 

Initially Smart RDM was created as a response to one of our clients’ needs who faced the challenge to manage many different reports created in different systems and applications. We found a way to streamline the process and bring all the reports together in one place. Where possible we’ve used APIs or built custom connectors or scripts to extract data from the different systems and feed it into  reporting S-RDM tool. Smart RDM is integrated with Microsoft BI, SSRS, Tableau, Grafana, Google Drive, Google Storage, Microsoft One Drive, and more. 

A natural consequence of these activities was to build a Big Data and analysis management module to support ETL processes in enterprises. At this stage we could already offer an advanced data management platform that collects and unifies data in one environment, makes it possible to schedule and run analyses and gives the access to reports at the same time.   

Smart RDM – version 2.0 

As more and more customers started using the Smart RDM platform, new challenges emerged. They needed access to all production data in one web application with the possibility of correcting and supplementing data for reporting purposes. Customers wanted something more than an Excel spreadsheet. And also have a good data exchange mechanism with the reporting system. 

To meet these needs, we added integrations with OT systems, that enables industrial companies to collect, manage, and analyze large amounts of real-time data from various sources, to centralize access to production data and reporting tools.  

As a result of integration with AVEVA PI (former OSISoft PI), Smart RDM gained three new modules: 

• Report data management and validation,  
• Process monitoring and visualizations, 
• Alarms and Events. 

Within these modules we have created accordingly: 

• A mechanism for managing data via web forms. This way, users can complete data, correct errors or enter comments. In addition, we have implemented a workflow related to the acceptance and publication of data, taking into account the authorization model. On this basis, the system automatically generates correct reports and sends them to the right recipients. You can read on our blog a series of articles about forms in Smart RDM.   
• A solution that provides comfortable navigation through PI Vision dashboards presenting data from multiple sources. This function allows users to monitor processes and operations in real-time in relation to data presented in web forms. 
• A combination of features that help user capture, store, find, compare and analyze important events: notifications in real-time about conditions in the system that need specific attention, user actions related to the events handling (according to the pre-defined workflow), event related data editing, adding file attachments to the event, event division, editing history.  

In addition we’ve introduced an operator context limiting the information (visualizations, AF forms, events) available to the user to the selected resource(s); at the same time, the user who logged in to a given resource is recorded in the resource’s attributes as an operator on a given shift, which can be the basis for later reporting of users in the shift and machine function. 

For the clients that want to keep all information about planned downtime periods in one place, we’ve implemented CMMS module that supports maintenance management by centralizing all maintenance information and helps optimize the utilization and availability of physical equipment like plant infrastructures and other assets. 

All data in one place! This data can be subject to advanced analyses, e.g. in order to calculate a digital twin, specify various KPIs and generate reports, accessed by the reporting module. 

Smart RDM – version 3.0 

Now while companies need data science to make sense of the vast amount of data they collect, and use the insights gained to improve their products, services, and overall business performance, we started to enhance Smart RMD Big Data and analysis management module to provide insights, that help companies make better decisions. By analyzing data from multiple sources, it is possible to identify patterns and trends that might not be immediately apparent to employees.  

In the nearest future we are planning to change the way in which analysis is  defined in Smart RDM and give more power over calculations performed. This includes: 

• break down the computation into smaller tasks that can be executed independently an parallel, 

• distribute the computation workload across multiple nodes in the Kubernetes cluster, 
• monitor and manage the computation, including scaling up or down the number of replicas based on resource utilization.  

Contact us for more information.  

The post Unlocking the Power of Smart RDM: A brief History and Functionalities Guide appeared first on ConnectPoint.

We already have a successful pilot of the solution, carried out in an office building. From December onwards, they will be tested by residents of a block of flats in Lodz. The apartments are equipped with thermostats, controlled remotely via an app (available for smartphones and computers). Users have several convenient options for ‘managing’ the temperature, e.g. setting a uniform temperature level throughout the flat or different values in each room. The app allows both ‘ad hoc’ adjustment of thermostats and planning of their settings (schedule for the day, week, etc.). Facilities also include. the possibility of creating a guest account in the app (so-called visitor account) – e.g. for a tenant who is renting a flat.

Traditional, manual adjustment of thermostats is also possible – the settings synchronise automatically with those stored in the app.

This is what the solution ‘looks like’ from the residents’ side. Its ‘back office’ is the infrastructure we have implemented in the building, i.e. the network that communicates with the thermostats. There is a control gate in each stairwell and devices on each floor to receive the signal from the flats.

Veolia hopes that the new, easy-to-use system will encourage residents to save heat – the effects can be assessed once the heating season is over. For the residents, it is – perhaps – an opportunity for lower bills, for the heat supplier to accurately analyse and plan energy consumption and, in the future, to implement similar systems on a larger scale.

The post Optimising energy consumption – a joint project between Veolia and ConnectPoint appeared first on ConnectPoint.

In ConnectPoint, we know how to deal with all these problems so that the process runs smoothly. We are a team of experts and developers dedicated to designing and delivering systems supporting the operation of smart heat and electricity transmission networks (Smart Grids), IIoT (Industrial Internet of Things) platforms, and critical business applications based on real-time data in energy and industry. Our teams specialize in solving a variety of data-related issues: 

• The Data Science team deals with acquiring, processing, and analysis of data, and above all, drawing conclusions from it and predicting the future.  
• The BI team analyses, designs, implements, and maintains database systems, Big Data/Data Lake platforms, and reports for the industry and energy sector. 
• The DevOps team develops and maintains IT infrastructure for customers.
• AVEVA PI team is responsible for standardizing and delivering all the OT data for further analysis. It specializes in implementing, maintaining, and developing the AVEVA PI System for Clients. The team’s scope of competence includes integrating distributed data sources, building data models and analytics based on process data, process visualization, building business and technology reporting, and integrating with other business systems. 

One of our solutions – The Smart RDM platform – collects and processes production data from AVEVA PI (or other equivalent solutions) to give quick and adequate information at every organizational level. Recently, due to legislative changes at the European Union level, most of our clients have asked us if we could support them in ESG reporting. Of course! Below we describe the main reporting challenges and how we can help address them. 

ESG reporting requirements 

ESG reporting aims to provide stakeholders, including investors, customers, employees, and the general public, with information about a company’s impact on the environment and society, as well as its governance practices (ESG). With the increasing focus on sustainability and corporate responsibility, there is a growing demand for ESG data that is accurate, consistent, and comparable across companies and industries. However, the lack of standardization and the variability in the quality and granularity of ESG data pose significant challenges for investors looking to integrate ESG factors into their investment decisions. This is why many organizations and initiatives are working to develop frameworks and standards to provide more consistent, comprehensive, and transparent ESG information. Some of the most widely used ESG frameworks include GRI, SASB, TCFD, SDGs, CDP, IIRC, and many others [1]. These frameworks guide what ESG information should be disclosed (e.g., specific metrics and KPIs) and how it should be reported to improve the comparability and transparency of ESG data across companies and sectors. 

Reporting Tools 

Sustainability Reporting Tools (SRTs) are software and platforms that provide companies with an instrument for reporting on their ESG performance and impact. SRTs can range from simple online reporting tools to more sophisticated reporting software and platforms. They typically include features such as data management and performance tracking based on KPIs defined in the framework (mentioned above). Each KPI has its internal guideline, including processes, tools, roles, and responsibilities.

By using SRTs, companies can demonstrate their commitment to sustainability and provide stakeholders with a more transparent view of their ESG performance. This can help establish the company’s reputation, legitimacy, and credibility and to create value and build trust with investors, customers, employees, and other stakeholders.   

The source of data 

Usually, all data to prepare ESG reports is extracted from companies’ systems or systems from third parties with their authorization when relevant. The data acquisition process (or its selected steps) is often done manually. Depending on the industry, not all information needed is readily accessible. For example, the best quality data related to environmental KPIs are available for the oil and gas industry, while data for other sectors, such as agriculture and forestry, still need to be included. The latter have not traditionally been heavily focused on monitoring CO2 emissions or waste produced, but they have to implement new solutions to meet ESG requirements.  

Our clients already use the Smart RDM platform integrated with AVEVA PI (formerly OSIsoft PI) to create a complete monitoring, analysis, and planning system. AVEVA PI is a source of all environmental data (e.g., greenhouse gas emissions, energy consumption, water consumption, and amount of waste produced) in real time. The Smart RDM is a technology solution that leverages data science techniques to collect, process, and analyze the data and provide improved data accessibility, real-time data analysis, enhanced data visualization, improved data management capabilities, and reporting. Smart RDM integrates with the most powerful reporting software, like Microsoft BI, to visualize the data.

The Smart RDM platform can bring many benefits to organizations that are obliged to prepare ESG reports, including increasing efficiency and accuracy in data collection and analysis, reducing manual data entry, and minimizing the risk of human input errors. Smart RDM enables the implementation of validation controls to ensure that the data collected is consistent and of high quality. These controls can range from simple data format validation to more complex data quality checks that flag potential errors or inconsistencies. Furthermore, Smart RDM makes it easier to monitor and track the collection and processing of data. This can help companies identify any issues or bottlenecks in the data collection process and take corrective action if necessary. Finally, based on the data collected and validated, Smart RDM can be used to prepare specific KPIs essential to ESG reporting.  

In summary, Smart RDM can be used as a data management platform integrated with specific SRT (e.g., Microsoft Sustainability Manager) to simplify the ESG reporting process greatly. 

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