Impressive Predictive Maintenance dashboards look great in presentations, but in practice, every model is only as good as the data that feed it. According to ARC Advisory Group, up to 87% of industrial AI projects never move beyond the pilot phase, and the average annual losses caused by poor data quality reach USD 12.9 million.
No wonder that during Smart RDM workshops, half of the questions are no longer about algorithms but about… tags: “How do I know that P3_TEMP_A is the same signal as FILLER-TEMP-123?”. This article will show you how to create a “single source of truth” – a logically structured, secure, and real-time data repository that becomes the foundation not only for Predictive Maintenance analytics but for the entire digital factory ecosystem.

At ConnectPoint, since data quality is our top priority, our Smart RDM analytical platform goes beyond dashboards. We implement complete Predictive Maintenance use cases — from tag and data model standardization, through streaming ingestion and model training, to recommendations, alerts, and maintenance tasks with full status tracking — ensuring that predictions translate into real downtime reduction.

What does “Data Maturity” mean and where to start?

Data maturity simply reflects how well an organization manages its data — from collection to decision-making. At the lowest level, data are scattered, inconsistent, and unreliable. At the highest, they are integrated, standardized, up-to-date, and connected to business processes. This maturity determines whether a plant truly benefits from AI or just from “pretty charts.”

Before jumping into predictive models or AI, it’s worth understanding what constitutes a solid data architecture in industry. Each of the following stages represents a step toward organization, standardization, and securing information flowing from devices, sensors, and IT/OT systems.
They can be seen as layers of a “data maturity pyramid” – from diagnosing the current state to integration, codification, quality control, and cybersecurity. Each plays a specific role in building a reliable Predictive Maintenance ecosystem.

 1. OT/IT Data Audit – the Factory X-Ray

Data maturity doesn’t start with building a data warehouse but with understanding what you already have. During ConnectPoint’s audits, we first map all devices and parameters — from vibration sensors to process variables — often based on ISA-95 levels or other industry-specific data organization standards.

It often turns out that the biggest gaps are not in hardware but in processes: PLC transmissions are not redundant, SCADA stores history for only some tags, and CMMS includes failure codes unknown to IT.
The audit results in a “thermal pain map” showing which data gaps threaten production KPIs and where investment brings the fastest ROI.

2. IIoT Layer – the gateway that speaks every dialect

If the audit is an X-ray, the edge gateway is the heart of the new architecture. Its task is to communicate with diverse protocols — from legacy Modbus to modern OPC UA — and transform them into a lightweight, event-driven MQTT stream.

In practice, this means that vibration data from a hydraulic press and temperature from a filler PLC reach the same queue in under five seconds.
With dual buffering, no data are lost even during temporary network failures, while X.509 certificates ensure that no unauthorized packet is injected.

This stage — not the Machine Learning model — determines whether a clean river of data will flow further, or a muddy puddle.

3. Hot / Warm / Cold Data Repository – three temperatures, three purposes

Once the stream flows, it needs proper storage. The Hot / Warm / Cold architecture works like a boiler, thermos, and refrigerator:

• Hot stores “boiling” data from the last 48–72 hours – used for alarms and operator dashboards.

• Warm holds averaged minutes or hours, ideal for shift reports where trends matter more than micro-anomalies.

• Cold – the cheapest layer – archives months and years of history for ML models and audits.

An ARC study (“Industrial Data Infrastructure 2025”) showed that a three-tier retention model reduces total storage costs by about 30%, because:

• The freshest, most frequently used data stay on fast (but expensive) NVMe storage.

• Older data are compressed and automatically offloaded to cheaper object storage.

A side effect is faster queries: in the Hot layer, average response time drops from 220 ms to 140 ms, enabling near real-time alerts and smoother operator screens.

4. Signal Codification – the lingua franca of your factory

Even perfectly retained, millisecond-accessible data are useless if the data model doesn’t clearly specify what each signal measures and in which unit.
That’s why the ISA-5.1 standard (Instrumentation Symbols and Identification) promotes a clear Object–Section–Sensor–Unit scheme.

For example, LN3_Filler_Press_bar immediately tells you this is pressure (Press) measured in bar, at the filler station (Filler) on line 3.
Without this clarity, engineers may compare unrelated data, and algorithms lose context during model training.

The data model, part of the Central Process Data Repository, harmonizes data and provides business context. With a unified production database, operators, planners, and data scientists finally speak the same language, and every change — who, when, why — is logged and auditable.

As a result, Predictive Maintenance models learn not from mysterious “variables X1, X2,” but from clearly described signals tied to real machines and processes.

5. Process Map – the relationships algorithms need

Tag codification tells us what we measure; the process map tells us where and how everything is connected.
Imagine a plant layout showing not only each sensor’s location but also which line elements interact — both mechanically and energetically.

The Smart RDM data model follows this principle: it defines what belongs to what (line → module → element → sensor) and how components cooperate (e.g., pump → motor).

In Smart RDM, this network of dependencies is stored in the Central Data Repository: each record represents a physical asset, and each attribute — a real measurement.
When a PdM model detects increased pump vibration, it doesn’t raise a blind alert. It checks the map — how many motors drive the pump, the coupling order, and which component could cause resonance.
If dependencies point to the motor, the system assigns the event to the correct asset and creates a maintenance work order exactly where needed.

This “intelligent map” enables:

• Fewer false alarms — the system understands how the machine works.

• Faster root cause diagnosis — from hours to minutes.

• Better model learning — seeing how replacing one component affects others.

The result: Predictive Maintenance stops being a generator of anonymous alerts and becomes a technical advisor pointing to the most probable cause of failure.

6. Data Quality Validation – Real-Time Control

Where data streams boil at thousands of records per second, error × time = exponential cost.
Smart RDM enables real-time “auditing” of process data and assessing its analytical usefulness. Below are some examples of input data quality tests:

Each test can run as a microservice, ensuring high throughput without compromising analytical performance.

Results are well-documented: IBM Research found that automatic unit and range validation reduces erroneous records by up to 80%, while McKinsey & Co. reported that implementing dead-band and drift-time tests cuts false process alarms by 25–30%.

Thanks to these mechanisms, predictive models like those in Smart RDM learn from premium-grade data, allowing maintenance teams to focus on causes — not noise filtering.

7. Data Governance and Cybersecurity – who holds the vault key?

The NIS2 Directive (Network & Information Security) from 2023 requires critical service operators — including industrial plants — to maintain a complete OT asset catalog (machines, sensors, controllers, networks) and documented Change Management procedures.

This means every PLC configuration, tag name, or alarm threshold change must leave a trace in the system and be reproducible during security audits.

Smart RDM enables RACI-based role separation, for example:

• • Responsible – Line Manager

  • Accountable – Maintenance Manager

  • Consulted – CISO

  • Informed – Operators

The Cost of Inconsistent Tagging and Data Models – Profit & Loss Account

Before looking at numbers, understand that a single naming or unit error in one tag can “infect” the entire decision chain — from shop-floor alerts to board-level reports.
The following table shows how lack of standardization translates into real financial impact:

Order in Data Is the Fastest Path to ROI in Predictive Maintenance

When signals have clear names, models understand dependencies, and data acquisition runs flawlessly, Predictive Maintenance stops being a black box and starts delivering tangible value.
The company saves on downtime, shortens reaction times, and builds a digital organizational memory.

If your factory still loses data in a maze of protocols and spreadsheets — start with an audit. Within three months, you can have a solid foundation where Predictive Maintenance becomes not a gadget, but a competitive advantage.

Want to See How You Compare to the Best?

Download the Comprehensive Predictive Maintenance Implementation Guide and bring order to your data — from unique tag identification to OT segmentation — and turn chaos into actionable intelligence. Predictive Maintenance | A strategy tailored to your company’s needs – ConnectPoint

The post Data that tells the truth: How to prepare infrastructure and tag standardization for Predictive Maintenance appeared first on ConnectPoint.

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.

Knowledge must be constantly developed, questioned,and expanded; otherwise, it fades. – Peter Drucker,
management specialist, educator, author of books andnumerous academic publications that have shaped the
philosophical and practical foundations of moderncorporate organization. Drucker is often referred to as
the “father of modern management.”

The success of predictive maintenance (PDM) depends not only on selecting the right tool but also on using a tool that actively supports users in continuously improving processes at every level of the organization. The cornerstone of this approach is a dynamically developed knowledge base, user engagement, and support from artificial intelligence (AI). In the following sections, we will explore how these three pillars complement each other, their roles in the implementation of PDM, and how their synergy translates into measurable operational and strategic benefits. It is worth noting that the remainder of this article does not describe complex predictive maintenance algorithms based on machine learning models. Instead, it focuses on outlining the general requirements for modern maintenance systems and the tools that meet those requirements.

Knowledge Management

A knowledge base in production is a collection of organized information, data, and best practices that support operational processes. It is a key tool for manufacturing companies seeking to efficiently share essential information, enhance productivity, and drive innovation. 

A knowledge base serves as a centralized system for collecting, storing, and sharing information related to: 

• production processes, 
• resource management, 
• technologies, 
• operational and safety procedures, 
• and technical documentation. 

The Smart RDM platform is an advanced tool that integrates operational data management, knowledge management, and reporting functionalities into a unified environment. Through integrations with various data sources and flexible storage mechanisms, users can easily collect, organize, and analyze information critical to maintenance strategies.

One of the key features of Smart RDM is the ability to store files and documents in both local and cloud repositories, such as OneDrive, Google Drive, or local network drives. The platform supports a wide range of file formats, including text documents (PDF, DOC, TXT), spreadsheets (CSV, XLS), multimedia presentations (PPT), and image files (JPG, PNG). This enables users to store technical documentation, operational procedures, and process analysis reports in a single, centralized location.

A knowledge base, like every element of the productionprocess, must be continuously developed, and without full
user engagement, it will remain just another collection ofdata, failing to deliver the expected value. – Dawid Pilc, CEO of Connectpoint

Access to the collected information is managed through roles assigned by the System Business Administrator. Each user is granted specific permissions that define which data they can view, edit, or share. This structure not only ensures security but also provides transparency in knowledge management within the organization.

Smart RDM also serves as a central repository for production reports that include key performance indicators (KPIs). These reports can be generated and exported in various formats, such as PDF, TXT, or CSV, and subsequently archived as part of the knowledge base. Users can easily access machine performance analyses, downtime reports, or maintenance cost summaries, significantly supporting accurate operational and strategic decision-making. 

Smart RDM is not just a data management tool; it is a comprehensive platform for organizing knowledge that assists users in daily tasks and long-term maintenance strategy planning. With flexible storage methods and intuitive access to information, the platform forms the foundation for effective knowledge management in modern enterprises. 

The following sections will describe user roles in utilizing the knowledge base, the ways AI can support this process, and the positive impact effective knowledge management can have on reducing failures within an organization.

The Role of Users in Building a Knowledge Base 

Managers play a strategic and organizational role in creating, developing, and utilizing a knowledge base in manufacturing enterprises. Their actions influence organizational culture, process efficiency, and the use of knowledge resources in daily operations. Below are the key areas of their activities: 

• Creating Knowledge Management Strategies: Managers develop knowledge management policies, select appropriate tools for managing the knowledge base, and define business objectives related to the knowledge base. 
• Fostering a Culture of Knowledge Sharing: They promote openness and collaboration between departments and encourage employees to contribute to the development of the knowledge base. 
• Managing the Knowledge Collection Process: Managers ensure a systematic and effective knowledge collection process and monitor the quality of information added to the knowledge base. 
• Analyzing and Using Data: They utilize data from the knowledge base to make better business decisions and optimize operations. 
• Facilitating Cross-Department Collaboration: Managers organize interdisciplinary teams responsible for developing the knowledge base and create mechanisms for information exchange between departments. 
• Investing in Training and Development: They invest in training on knowledge management systems and the development of employee skills. 
• Monitoring and Evaluating Knowledge Base Effectiveness: Managers assess whether the knowledge base delivers expected benefits and regularly update the knowledge management strategy. 

Smart RDM serves as a comprehensive support tool for managers, enabling the effective implementation of knowledge management strategies, centralization of informational resources, and their efficient use in daily operational processes. Through Smart RDM, managers can define and oversee knowledge management policies, monitor the achievement of business goals related to its development and maintenance, and actively promote an organizational culture based on knowledge sharing. 

Intuitive mechanisms for sharing documents and reports in Smart RDM encourage employees to actively use the platform and co-create the knowledge base. A significant feature of Smart RDM is the automation of data collection from various sources, such as production reports and technical documentation. The platform ensures high quality and consistency of the information collected through validation and control mechanisms, giving managers confidence in making decisions based on accurate and reliable data. 

Smart RDM also offers advanced analytical tools that transform data into clear reports and visualizations of KPIs. This facilitates the identification of areas requiring optimization and monitoring the effectiveness of implemented actions.

Machine operators, directly involved in daily operations, maintenance, and resolving technical issues, continuously contribute to the knowledge base. Their experience, practical knowledge, and observations are a valuable source of information. Their roles include: 

• Providing Practical Knowledge: Operators possess unique insights into machine operations and optimal working parameters. 
• Identifying Potential Issues: They detect early signs of unusual sounds, vibrations, or changes in equipment performance. 
• Documenting Procedures and Best Practices: Operators create step-by-step instructions and recommendations for workplace safety. 
• Supporting Training Efforts: The knowledge base enriched by operators is valuable for new employees and training programs. 
• Testing and Verifying Information: They assess the effectiveness of procedures and provide feedback. 
• Creating Real-Time Data: Operators log events, parameters, and faults in real-time. 

Smart RDM enables operators to effectively document, analyze, and utilize collected information, resulting in improved work efficiency, minimized downtime, and more effective resolution of technical issues. 

One of the key strengths of Smart RDM is its intuitive user interface, which allows operators to quickly input data related to machine operating parameters, malfunctions, and any unusual events. This ensures that information is recorded in real-time and can be immediately analyzed by other teams or maintenance management systems. The platform automatically structures and categorizes this data, eliminating informational chaos and facilitating easier retrieval of specific entries later. 

Another advantage of Smart RDM is its capability to document procedures and best practices in the form of interactive instructions or attachments to the knowledge base. Operators can create detailed descriptions of maintenance procedures, step-by-step instructions, and recommendations for optimal machine operating parameters. With features for adding photos, videos, or other multimedia materials, the documentation becomes more transparent and accessible for new employees and other team members. 

Real-time parameter monitoring is another function that significantly supports operators. Smart RDM enables tracking of key performance indicators (KPIs) for machines and automatically generates alerts when anomalies are detected. This allows operators to address potential issues before they escalate into serious breakdowns, reducing downtime and repair costs. 

Mobile access is another aspect that enhances operator efficiency. Smart RDM offers a mobile application, enabling operators to log events and access documentation even when away from their workstations. This is particularly important in a production environment, where quick access to information can greatly impact response time and the effectiveness of actions taken. 

In the maintenance department, employees also have a significant impact on the knowledge base, especially in ensuring the continuity of operation for machines and production equipment. Their experience and actions form the foundation of effective knowledge management in a production facility. The primary tasks of users in this group include: 

• Documenting Incidents and Failures: Recording detailed information about breakdowns and creating post-incident reports. 

• Creating Maintenance Schedules: Inputting optimal timelines for technical inspections. 

• Monitoring Technical Conditions: Using sensor data for real-time analysis. 

• Standardizing Repair Procedures: Developing and updating standard operating procedures (SOPs). 

• Process Analysis and Improvement: Conducting analyses of machine performance and reliability. 

• Collaborating with Operators: Analyzing issues identified by operators and prioritizing repairs. 

• Training and Knowledge Transfer: Sharing expertise with team members and creating training materials. 

One of the main advantages of Smart RDM is its ability to precisely document incidents and failures. The platform enables recording detailed information about malfunctions, such as the time of occurrence, description of the issue, corrective actions taken, and the personnel responsible for the repair. Post-incident reports can be stored in various formats, including PDF, CSV, or DOC, allowing for later analysis and use in preventive actions. 

Smart RDM also supports the creation of maintenance schedules. With planning functions and automatic reminders, maintenance teams can efficiently manage technical inspections and avoid unexpected failures. The platform also analyzes historical data to identify optimal inspection schedules and predict potential malfunctions. 

In the area of machine condition monitoring, Smart RDM integrates data from sensors and OT systems, allowing maintenance teams to analyze machine performance parameters in real time. The platform automatically generates alerts when deviations from norms are detected, enabling quick responses and minimizing the impact of failures. 

Standardizing repair procedures is another area where Smart RDM provides tangible benefits. The platform facilitates the creation and updating of standard operating procedures and maintenance instructions. With a centralized document repository, teams have quick access to up-to-date guidelines, streamlining repair actions and reducing the risk of errors. 

The analysis of data collected in Smart RDM helps improve maintenance processes. Advanced analytical tools, supported by machine learning algorithms and predictive models, allow for identifying recurring issues, analyzing machine reliability, and implementing actions to enhance operational efficiency. KPI reports provide clear insights into the performance of individual devices and the effectiveness of repairs conducted. 

Without the involvement of users, there is no chance of creating a reliable knowledge base to
support organizational operations. However, for the entire process to function effectively, it is
necessary to consider leveraging the latest tools that support user efforts. Since the advent of AIbased solutions, the possibilities for utilizing
knowledge within an organization seem limitless and accessible to everyone. – Dawid Pilc, CEO of Connectpoint

Smart RDM also supports training processes and knowledge transfer. Maintenance teams can create training materials, document best practices, and share them with new team members. This makes the platform a central hub for accessing technical knowledge, facilitating faster adaptation of new employees and improving the skills of the existing team. Additionally, it provides access to critical information in the absence of key personnel with expertise in a specific area (e.g., due to illness or unforeseen circumstances).

A Knowledge Base on Steroids 

Artificial intelligence can significantly enhance the creation, development, and management of knowledge bases in manufacturing enterprises, ensuring efficiency, accuracy, and accessibility of information. Here are the key ways AI can help: 

1. Automating Knowledge Collection

• Real-Time Data Analysis: AI can automatically process production data, IT system logs, production reports, and other sources. 
• Pattern Recognition: AI algorithms identify patterns in data, such as recurring failures, and suggest optimal solutions. 
• Capturing Expert Knowledge: AI supports the digitization of knowledge from machine operators and maintenance specialists, for example, by analyzing their notes. 

2. Organizing and Classifying Knowledge

• Automatic Categorization: AI can organize and tag documents, reports, and other information, making them easy to locate. 
• Increased Efficiency: Automating knowledge management processes reduces the time and resources required to execute them. 
• Improved Information Accessibility: Quick and easy access to accurate data facilitates work across all company departments. 
• Cost Reduction: Rapid access to knowledge shortens problem-solving times. 
• Enhanced Innovation: Leveraging data supports the development of new ideas and strategies. 
• Increased Competitiveness: Companies using AI in knowledge management gain a market advantage. 
• Organizational Culture: AI enables swift dissemination of standards to employees and provides multilingual access to stored knowledge. 

3. Facilitating Access to Knowledge

• Contextual Search: With natural language processing (NLP), AI allows users to search for information intuitively, such as by asking questions in natural language. 
• Virtual Assistants: AI chatbots can answer employee questions, delivering the needed information in real time. 
• Mobile Access: AI-supported knowledge bases can also be accessed via mobile apps, providing operators and managers with immediate information, even outside the workplace. 

4. Supporting Employee Training and Development

• Personalized Training Programs: AI analyzes employee skills and suggests courses or materials tailored to their needs. 

5. Managing Knowledge Quality

• Information Verification: AI can identify inconsistencies, errors, or outdated data in the knowledge base and suggest updates. 
• Effectiveness Assessment: AI algorithms analyze which parts of the knowledge base are most frequently used and which need improvements. 
• Compliance Monitoring: AI can ensure documentation and processes meet industry standards and regulations. 

In Smart RDM, we can provide AI support for all users. A user-friendly chat interface enables “dialogues” with
artificial intelligence on topics stored in the knowledge base. One of its advantages is the division into thematic
rooms tailored to different areas and user permission levels.
However, implementing AI is a complex process that requires careful planning. An unconsidered approach
could, at best, elicit a smirk from operators, and at worst, lead to chaos. – Dawid Pilc, CEO of Connectpoint

Why AI Is Not an “Out-of-the-Box” Solution 

Implementing artificial intelligence in an enterprise is a complex process that requires close collaboration between the organization and specialists. Ready-made, universal solutions rarely meet the specific needs of businesses. Below are the key reasons why AI is not an “out-of-the-box” solution: 

• Customization to Specific Needs: Every company has unique processes, data, and requirements, so AI solutions must be tailored to specific contexts and business objectives. 
• High-Quality Data Requirements: The effectiveness of AI depends on access to large volumes of high-quality data. Before implementation, an advanced process of data cleaning, classification, and structuring is often necessary. 
• Employee Expertise: Implementing AI requires specialists who understand the organization’s specifics and can determine which data and scenarios the AI should learn from to operate effectively. 
• Quality of Prompts and System Configuration: AI’s success often depends on correctly phrased prompts and system parameter configurations. Without these, AI may produce inaccurate or unusable outputs. 
• Access Control and Permission Management: AI implementation demands clearly defined rules for data access and user permissions. Poor role allocation can result in errors or security breaches. 
• Continuous Monitoring and Optimization: AI is a dynamic tool that requires constant oversight, model updates, and adjustments to changing business conditions. 
• Understanding the Business Context: AI performs best when deployed in close collaboration with experts who understand both the technology and the specific nature of the company’s operations. 

Artificial intelligence is a powerful tool, but its full potential can only be realized through a combination of technology, the right human resources, and a strategic approach to knowledge and data management. 

How an AI-Driven Knowledge Base Supports Maintenance in Manufacturing 

Modern manufacturing facilities face increasing demands to optimize processes and minimize machine downtime. Maintenance plays a crucial role in ensuring production continuity, and an AI-driven knowledge base, particularly in the context of predictive maintenance, is a powerful tool in this effort. By analyzing real-time machine data, AI can predict potential failures before they occur. Learning from historical data, algorithms can identify which components are most prone to wear and when they need replacement. This enables companies to: 

• Reduce unplanned downtime. 
• Optimize maintenance schedules. 
• Lower repair costs by avoiding critical failures. 

The use of an AI-driven knowledge base results in: 

1. Faster Problem Solving

An AI-driven knowledge base can store detailed information about past failures, steps taken to address them, and the outcomes of those actions. This allows: 

• Technicians to quickly find information about similar issues. 
• The system to suggest the most effective corrective measures. 
• Reduced time required for diagnosis and repair. 

2. Collecting and Managing Expert Knowledge

AI supports the digitization of expert knowledge from machine operators and maintenance specialists. By analyzing documentation, notes, or voice recordings, the system can: 

• Capture and share informal knowledge often left undocumented. 
• Create an accessible knowledge base for new employees. 
• Reduce the risk of knowledge loss when key staff leave. 

3. Support for Operators and Technicians

With AI, operators and technicians can access resources that provide real-time assistance: 

• Answering questions about machine operation. 
• Suggesting next steps during troubleshooting. 
• Acting as interactive guides for maintenance procedures. 

Summary 

An AI-driven knowledge base is a powerful tool for supporting maintenance in manufacturing. By enabling failure prediction, speeding up problem resolution, collecting expert knowledge, and integrating with other systems, AI significantly improves the efficiency and reliability of production processes. 

Smart RDM, with its integration of AI technology, not only supports maintenance but also contributes to the optimization of the entire production management process. This comprehensive solution facilitates effective resource management, minimizes downtime, and maximizes the efficiency of manufacturing operations. 

Read also: Is it worth investing in predictive maintenance systems? An analysis of benefits, concerns, and ways to reduce risks – ConnectPoint

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The decision to purchase a Predictive Maintenance (PdM) system comes with unique challenges. The primary value of this technology lies in prevention—avoiding failures that the buyer ultimately does not experience. This specific benefit model, although confirmed in the literature, may raise uncertainty, especially in the face of difficulties in initially estimating the return on investment (ROI). This article analyzes these challenges from the perspective of a potential buyer, presents the possible benefits of implementing PdM, and identifies strategies to increase investment confidence and transparency in communicating value.

1. Introduction

PdM systems use advanced data analytics and machine learning algorithms based on production data to predict potential machine and equipment failures before they actually occur (Carvalho et al., 2019; Lee et al., 2015). Although scientific publications frequently emphasize the effectiveness and long-term benefits of PdM, from the perspective of a company considering a purchase, a specific problem arises: how to assess the value of a solution whose main advantage is preventing something that does not happen?

The perspective of a potential buyer differs from that of researchers or technology providers. Buyers expect tangible evidence, clear reasoning, and convincing data. This issue resembles the situation in the insurance or cybersecurity industries, where payments are made for risk prevention (Jimenez-Cortadi et al., 2019), and the effectiveness of the solution manifests in the absence of negative incidents. This article aims to help understand the nature of PdM benefits by drawing conclusions from scientific literature and implementation reports and highlighting practical strategies to minimize purchasing risk and uncertainty. 

1.1. From Reactivity to Prediction – The Evolution of Maintenance Strategies

Maintenance plays a crucial role in ensuring the reliability, efficiency, and safety of technical systems in industry. The evolution of maintenance strategies—from reactive through preventive and proactive to predictive maintenance—reflects the growing importance of data, analytics, and new technologies in managing industrial infrastructure (Bocewicz et al., 2024). 

Reactive Maintenance was the earliest form of failure management. Its goal is to restore functionality after a failure occurs. It is characterized by a lack of planning, high repair costs, and the risk of secondary damage and production downtime. 

Preventive Maintenance introduced regular maintenance schedules aimed at reducing the risk of failure. This approach is time-consuming and costly, with limited flexibility in adapting to the actual technical condition of equipment. 

Proactive Maintenance represents a step forward, focusing on identifying and eliminating root causes of faults. It utilizes wear indicators, environmental analysis, and weather forecasts to effectively plan repair activities (see Table 1). 

Tab. 1 Traditional Maintenance Strategies (based on Bocewicz, et al. 2024) 

As technology advances, proactive maintenance has begun to give way to predictive maintenance, which relies on real-time data analysis and machine learning algorithms. Unlike proactive maintenance, which focuses on analyzing historical failure causes, predictive maintenance enables dynamic failure forecasting based on current sensor data (see Table 2). 

Tab. 2 Differences Between Proactive Maintenance and Predictive Maintenance 

2. Why is it difficult to assess the value of PdM from the buyer’s perspective? 

Assessing the value of PdM from a buyer’s perspective is one of the main challenges associated with implementing this technology. Scientific literature emphasizes that these difficulties arise from both the nature of the value offered and the complexity of the implementation process, as well as cost-benefit analysis. 

The most significant source of uncertainty in assessing PdM is the intangible nature of its benefits. In traditional maintenance models, companies pay for specific repair services, spare parts, or corrective work, which are easy to quantify and evaluate (Klees & Evirgen, 2022). PdM, on the other hand, offers risk reduction for costly failures, and the system’s success is measured by the absence of negative events rather than the delivery of a tangible product or visible intervention. This situation is analogous to the insurance industry, where the value of a service is assessed through the absence of incidents rather than specific actions (Jimenez-Cortadi et al., 2019). 

Estimating the return on investment (ROI) for PdM is particularly challenging, especially in the initial stages of implementation. Lee et al. (2015) indicate that PdM benefits become apparent over time when sufficient data is available to compare the frequency, costs, and consequences of failures before and after system implementation. Initially, with limited data, it is difficult to calculate financial benefits conclusively. Companies may struggle to justify high initial costs without clear success indicators. 

Traditional maintenance methods have well-defined performance metrics, such as Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR). In contrast, PdM lacks standardized metrics and universally accepted indicators of effectiveness. This complicates comparisons of PdM efficiency with traditional maintenance strategies and makes financial justification of PdM implementation more challenging. 

The effectiveness of PdM is directly linked to the quality of collected data and the organization’s ability to analyze it effectively. Data gathered by IoT sensors and systems must not only be accurate but also properly analyzed using advanced algorithms. Organizations without experience in data analysis may face difficulties in fully utilizing PdM’s potential, which in turn leads to uncertainty about the actual benefits of implementing this strategy. 

Cost analysis associated with PdM implementation (see Table 3) highlights significant differences between this method and traditional maintenance strategies. Scientific literature emphasizes that PdM implementation costs can be significantly reduced by leveraging existing historical data and appropriate analytical methods, minimizing the need for additional sensor deployment. Traditionally, PdM implementation required installing advanced sensors and monitoring systems, generating high costs. However, research indicates that it is possible to apply analytical methods based on historical data from previous maintenance activities, significantly reducing the need for additional sensors. Analyzing data from past service interventions and failure histories allows building predictive models without investing in new measurement equipment. This approach not only reduces implementation costs but also shortens the time required to deploy a PdM system. Using advanced data analysis algorithms, such as machine learning, enables effective prediction of potential failures based on existing data. As a result, organizations can implement PdM without significant investments in new measurement technologies while achieving higher operational efficiency and infrastructure reliability. 

Tab. 3 Cost Analysis of PdM Implementation 

3. Potential Benefits of PdM Implementation Despite Initial Uncertainty

Traditional maintenance approaches focus either on preventive or reactive equipment repair or replacement only after failure. PdM represents a fundamental shift in maintenance strategies, relying on data analysis, sensor technology, and machine learning algorithms to predict failures before they occur, enabling proactive planning of maintenance activities (Carvalho et al., 2019; Poór et al., 2019; Wellsandt et al., 2016; Patel et al., 2023). The key benefits include: 

• Reduced Failure and Repair Costs: Early detection of potential problems prevents serious damage that would generate high repair costs. 

• Increased Reliability and Equipment Availability: Stable machine operation translates into predictable production, reduced downtimes, and improved resource utilization, ultimately increasing Overall Equipment Effectiveness (OEE). 

• Extended Infrastructure Lifecycle: Continuous monitoring and prevention result in less wear and tear on components and machines, delaying the need for modernization or replacement of key assets (Maktoubian & Ansari, 2019). 

• Improved Safety and Work Quality: Reducing the risk of unexpected failures not only lowers costs but also increases employee safety and process stability. 

Tab. 4 Benefits and Concerns of Predictive Maintenance Implementation 

4. How to Reduce Investment Uncertainty and Risk?

From the perspective of a buyer hesitant about investing in PdM, scientific literature identifies several key factors for minimizing uncertainty related to the implementation of this solution. Effective PdM implementation requires a comprehensive approach that integrates technological, organizational, and human aspects. Both proper management of the implementation process and the engagement of all stakeholders are crucial. 

The following table presents the main strategies identified in scientific literature that play a significant role in the successful implementation of PdM. Each strategy focuses on a different aspect of managing this process, highlighting key challenges and recommended actions that can significantly enhance the efficiency and effectiveness of implementation. 

Tab. 5 Tab. 5 PdM Implementation Strategies 

4.1. Integration of Technology, Organizational Culture, and Team Competencies as a Foundation for Effective Implementation 

The foundation of successful PdM implementation lies in comprehensive employee education. Workers must understand how the system operates, the benefits of its implementation, and their roles in the entire process. Both theoretical and practical training play a key role in improving staff competencies, enabling them to effectively recognize maintenance needs and respond quickly to potential threats. 

At the same time, from a management perspective, PdM success largely depends on a shift in organizational culture. It is necessary to create an environment that fosters collaboration between maintenance, production, and IT departments. Such an approach facilitates better coordination of activities and seamless integration of analytical models. Research indicates that effective team integration supports knowledge exchange and allows continuous adjustment of maintenance strategies to changing organizational needs. 

In the context of research on maintenance management models, the mutual relationship between machine performance and quality requirements in the production process is crucial. Studies show that using machine learning algorithms allows maintenance and production managers to plan maintenance activities based on real-time monitoring of machine parameters and quality indicators. This ensures not only production continuity but also minimizes the number of products that fail to meet specifications. 

From a management perspective, PdM research emphasizes the importance of evaluating the relationships between maintenance costs and quality costs. Integrating these two areas into a single coherent management model allows organizations to more precisely allocate resources and optimize operational processes. PdM implementation not only reduces failure risks but also supports long-term business goals related to production quality and efficiency. 

At the operational level, PdM requires continuous monitoring and optimization. Organizations must implement mechanisms that enable regular evaluation of predictive model performance and fine-tuning algorithms based on current production data. This is a dynamic process requiring close cooperation between all involved departments. Only through an integrated approach, encompassing both technology and organizational culture, can PdM’s potential be fully utilized and sustainable benefits achieved for the enterprise. 

4.2. Gradual Implementation Strategy: Minimizing Risk and Building Trust 

Implementing PdM on a limited scale, such as on a single production line, is an effective strategy for minimizing initial risk and facilitating performance evaluation. This approach allows organizations to collect necessary data, identify potential problems, and tailor the technology to specific operational conditions. Research indicates that gradual PdM implementation can be key to building trust in this technology, both among management and operational staff. Pilot PdM programs demonstrate benefits such as reducing unplanned downtimes, lowering maintenance costs, and improving operational efficiency. This enables organizations to better understand the technical and organizational requirements associated with full PdM deployment, leading to more informed investment and operational decisions. 

Literature also emphasizes the importance of integrating PdM tools with existing IT systems and training personnel in new technologies. Gradual implementation facilitates better management of these aspects, minimizing disruptions to daily operations and ensuring a smooth transition to a new maintenance model. As a result, organizations can gradually adapt to changes, increasing acceptance of new solutions and promoting their long-term efficiency. 

An example is the pilot implementation of PdM in a manufacturing company, where the technology was applied to a selected production line. This pilot resulted in significant reductions in unplanned downtimes and optimized maintenance schedules, convincing management to expand PdM across other production areas. 

This approach allows organizations to gradually build competencies in PdM and develop technical infrastructure tailored to their specific needs and capabilities. 

4.3. The Importance of Transparent Supplier Communication in Implementation 

Transparent and open communication from PdM technology suppliers plays a key role in the decision-making process for companies considering implementing such solutions. Suppliers who can provide clear analyses, simulations, and references to successful implementations in similar environments significantly increase the likelihood of their systems being accepted. Scientific literature emphasizes that documenting the added value of PdM not only builds trust but also simplifies the adaptation process, enabling organizations to make more confident investment decisions. 

An example highlighting the importance of such communication is a McKinsey & Company report, which indicates that implementing PdM strategies can yield savings of 10% to 40% in maintenance-related costs and reduce capital expenditures on equipment and machinery by up to 5%. These data, supported by specific analyses and case studies, provide a solid foundation for investment decisions and convince management of the profitability of PdM implementation.

The educational aspect is equally important. Suppliers who actively engage in educating their clients by offering training and technical support in integrating PdM systems with existing IT infrastructure play a crucial role in the efficiency of implementation. Research indicates that integrating PdM tools with production management systems and adequately preparing personnel are foundational for the long-term effectiveness of predictive maintenance systems. 

The value of cooperation with suppliers also depends on their ability to present successful implementations in production environments with similar parameters and challenges. Practical PdM application examples help potential clients better understand both the opportunities and limitations of this technology. Case studies serve as a roadmap, identifying potential implementation challenges while illustrating measurable benefits and tangible results. As a result, organizations can make more informed and data-driven decisions regarding PdM implementation. 

4.4. Standardization and Data Management as the Key to Effective Analytics and Precise ROI Determination 

Today’s world relies on data, which has become an indispensable element of organizational operations. However, for data analytics to be reliable and provide real benefits, it is essential to ensure high data quality. Without proper standardization of data collection, storage, and analysis processes, it is difficult to achieve reliable results that effectively support decision-making processes at various management levels. 

Data standardization involves unifying formats and structures of information from different sources, enabling effective comparison and analysis. Research shows that standardizing these processes significantly improves the quality of business decisions, allowing the consolidation of various sources of information into a cohesive and unified whole. As a result, management and analysts gain access to more precise and complete data, directly contributing to better strategic decisions and more efficient resource management. Equally important is data management, which encompasses the processes of collecting, storing, protecting, and analyzing data. 

Clear standards for collecting, storing, and analyzing data enable the creation of long-term comparative databases that support organizational performance monitoring over time. These databases facilitate identifying trends and precisely evaluating the effectiveness of implemented management strategies. In data analysis, best practices focus on defining clear analysis objectives, systematically collecting data from various sources, and continuously monitoring and controlling data quality. Ensuring the accuracy and reliability of this data becomes the foundation for making effective managerial decisions. 

The ultimate goal of these activities is to precisely determine the return on investment (ROI) in the context of data management. High-quality data, carefully analyzed, forms the basis for reliably assessing the profitability of investments in data management technologies and tools. Research indicates that data management processes, like any other business project, should have a defined ROI indicator. A reliable analysis of profitability requires moving from general assumptions to detailed performance metrics, allowing the precise identification of benefits resulting from improved data quality.

5. Summary

From the perspective of a potential PdM system buyer, a natural dilemma arises: how to pay for value that is not immediately visible, and how to trust technology that promises the absence of certain events instead of delivering tangible products or services? Scientific literature, previous implementations, and examples from other sectors suggest that although it is initially difficult to estimate ROI, over time, as data and experience accumulate, the benefits become clear and measurable. 

Through education, gradual implementation, transparent supplier communication, and data standardization, uncertainty and risk can be significantly reduced. As a result, investing in PdM, though based on prevention and invisible effects, can prove to be one of the most valuable and strategic moves for companies striving for long-term stability and competitiveness. 

Bibliography

Bocewicz, G., Frederiksen, R., Nielsen, P., Banaszak, Z. (2024). Integrated preventive–proactive–reactive offshore wind farms maintenance planning. Annals of Operations Research. 1-32. 10.1007/s10479-024-05951-4. 

Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. 

Jimenez-Cortadi, A., Irigoien, I., Boto, F., Sierra, B., & Rodriguez, G. (2019). Predictive maintenance on the machining process and machine tool. Applied Sciences, 9(21), 4506. 

Klees, M., Evirgen, S. (2022). Building a smart database for predictive maintenance in already implemented manufacturing systems. Procedia Computer Science, 204, 14-21. 

Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP, 72, 267–272. 

Maktoubian, J., & Ansari, K. (2019). An IoT architecture for preventive maintenance of medical devices in healthcare organizations. Health and Technology, 9, 233–143. 

Patel, M., Vasa, J., Patel, B. (2023) Predictive Maintenance: A Comprehensive Analysisand Future Outlook. 2023 2nd International Conference on Futuristic Technologies (INCOFT)Karnataka, India. Nov 24-26 

Poór, P., Ženíšek, D., & Basl, J. (2019). Historical overview of maintenance management strategies: Development from breakdown maintenance to predictive maintenance in accordance with four industrial revolutions. Proceedings of the International Conference on Industrial Engineering and Operations Management. 

Wellsandt, S., Nabati, E., Wuest, T., Hribernik, K. A., & Thoben, K. D. (2016). A survey of product lifecycle models: Towards complex products and service offers. International Journal of Product Lifecycle Management, 9(4), 353-390. 

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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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The Significance of Efficient Event Management in Business Operations  

The Smart RDM Events Module works with the PI System, allowing authorized users to define the analyses that generate events and set the content of event notifications. When an event occurs, the employee responsible for handling it receives vital information with the event message, enabling timely assessment and planning of appropriate reactions. This feature results in a consequent reduction in the costs of failure handling.  

While multiple companies use mechanisms provided by AVEVA PI System to handle events, they often find them to some extent incomplete. Smart RDM understands that comprehensive event handling requires persons with the appropriate rights to take action and overview what is happening in the environment. With this vision, Smart RDM has deeply integrated its Events Module with the PI System, providing all the processes for operations event handling in the organization. Smart RDM Events Module allows users to assign events to specific employees, enter comments, add attachments (including pictures), perform cause/effect analysis, change statuses, and even enter corrections of original data.  

Reasons for splitting events  

However, there are situations where it is necessary to split an original event frame created by the PI System. One reason for splitting such an event frame is when the event is ongoing and the duration is longer than expected. In such cases, splitting the event frame into multiple frames can provide a more accurate picture of the event’s timeline. For example, suppose an event frame is created to capture a maintenance activity expected to take four, but it took eight hours. In that case, an authorised person can split the event frame into two separate frames, one for the first four hours and another for the remaining four hours.  

Another reason to split an event frame is to capture different stages or phases of an event that are not definable based on PI data. During a manufacturing process, a product may go through several stages before completion. Each stage can be captured in separate event frames, allowing operators to monitor and analyse each stage’s performance and identify areas for improvement.  

Splitting an event frame can also be helpful when multiple events co-occur or when there are events within events. In such cases, creating separate event frames for each event can help to distinguish between them and provide a clearer picture of what is happening. For example, if there is a power outage at a manufacturing facility, multiple events may occur simultaneously, such as equipment shutdown and production data loss. Creating separate event frames for each event can help operators and engineers identify the root cause of the outage and take appropriate action.  

Splitting events in Smart RDM  

When a particular event has a defined start and end date and time, Smart RDM users with the appropriate permissions can manually split the event frame into multiple frames of any duration within the original time. This functionality allows the user to define new events based on pre-configured options. For example, if the event is machine downtime, the user can split it into different stages, such as diagnosis, repair, and machine start-up.  

Splitting events can help organizations to gain a better understanding of their production or manufacturing processes. By dividing a single event into multiple stages, users can identify the event’s root cause more accurately and analyse each stage’s performance. This analysis helps identify areas for improvement, resulting in increased efficiency and productivity.  

In addition, splitting events can also help to improve event analysis by replacing the initial event frame with multiple event frames. This feature allows organizations to view and analyse each stage separately, providing a more detailed and accurate picture of the event timeline.  

Summary  

The article discusses the importance of efficient event management in businesses. It highlights the benefits of using the Smart RDM Events Module, augmenting PI System Event Frames with comprehensive event handling. Splitting events can help organizations better understand their production or manufacturing processes and increase efficiency and productivity. 

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What exactly is predictive maintenance and why does it gain popularity at such a pace?

Goal of Maintenance

The goal of predictive maintenance is, first of all, to keep the cost of equipment maintenance low through the decreasing frequency of tasks involving maintenance; reduction of failures and downtime and elimination of unnecessary precautionary measures. The probability of failures is reduced by means of direct monitoring of condition and performance of equipment.

Types of Maintenance

There are three basic types of maintenance differing in the adopted model of the procedure.

Suppose that we own and use a passenger car. One day the car won’t start. We call in assistance, which transports the car to a repair shop. A mechanic identifies the problem, let’s say fuel supply pump failure. We have to replace the broken pump for a new one. We pay for the part and the service. We applied the model of Reactive Maintenance, which is fixing equipment when it breaks. It’s the most expensive possible option.

With the car running right we commute every day. Odometer ticks. It is time to change the engine oil. From the producer’s recommendations, we know we should do it as often as every 15 thousand kilometers. We go to the repair shop, where filter and oil are changed. Most likely we avoid a much graver failure of the engine. This is an example of Preventive Maintenance, which is repair and conservation based on risk analysis. This model lets us save some money. In the case of most cars, this is a method that gives satisfactory effects.

Now, imagine that our car could talk and tell us exactly when we should change the engine oil. It turns out that it’s not such a surrealistic vision! There are systems on the market that allow to analyze the car’s vibrations and based on this data plan repairs and conservation. This kind of solution is implemented in car rentals and transportation fleets. Why? The answer is simple – it saves money.

By interpreting ongoingly supplied data you can point to an exact moment the precautionary measures will be required. Let’s say the above-mentioned engine oil we can change not every 15 thousand kilometers, but according to actual consumption every 18, 21, 14, 23 thousand and so on. These are not only less frequent changes but also minimalization of malfunction occurrence. This model is the most efficient and the most economical – we call it Predictive Maintenance.

The best solution most often turns out to be an adequate balance between preventive and predictive maintenance. How to strike this balance?

In case of production plants and other greater industry enterprises, a good solution is adopting Reliability Centred Maintenance (RCM). That means creating a maintenance strategy for the whole plant or infrastructure. You develop plans for each and every department or facility considering equipment, functions, probable failures, their effects and causes. Then, you make choices of specific tasks and precautionary measures both preventive and predictive. In order to achieve that you need to designate a person responsible for the entire process.

A trend that is also gaining more and more popularity is also Risk-Based Maintenance (RBM), which is a prioritization of threats and directing resources toward those that effect in the riskiest consequences. To achieve that a risk matrix is used:

Consulting approach

RCM and RBM are a part of what we call consulting approach. It is a maintenance option in which you have business workshops where you set priorities. The advantages of this approach are that it is easy to introduce to employees and the fact that you analyse your resource. Among the disadvantages, there are required efforts, need of frequent updates and designation of a person managing the implemented system.

Technological approach

An alternative to this is a technological approach. Its strong points are shorter time involvement on the side of the customer and a simpler process of implementation, though this process requires well-prepared data and historical data for algorithm training. It also may be necessary to install additional sensors.

Benefits of Predictive Maintenance

Why is maintenance organization so important after all? For a number of reasons like:

• maximization of accessibility,
• optimization of maintenance conditions,
• full utilization of maintenance resources,
• optimization of equipment life-cycle,
• minimization of spare storage,
• ability to react quickly and
• minimization of downtimes.

All of the above reasons can be summed up to a primary purpose of optimization of production cost, an increase of competitiveness and of course, maximization of profit.

ConnectPoint – we help find the right maintenance

Write to us to learn more about what approach will be right for your organization. With our partners such as GE Digital and Microsoft, we can build a solution tailored to your needs. We handle the entire process, from business workshops to building tools and integration with other systems in the company.

Read also: 8 strategic challenges in manufacturing that you can eliminate by implementing Predictive Maintenance – ConnectPoint

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