ThermOS (Thermal Optimal System) is a system designed for space heating, space cooling, and domestic hot water (DHW) preparation in buildings. In January 2026, our innovative approach to thermal energy storage – implemented as an integrated, low-temperature outdoor thermal storage unit that operates as part of the complete system – was granted patent protection (No. P.447775).

ThermOS was designed as a complete solution that can be applied across different types of facilities, including retail and commercial buildings, service facilities, office buildings, public buildings, hotels, and residential buildings. The concept is that the system helps better utilize on-site generated energy and reduce the consumption of energy purchased from external sources, while maintaining the required comfort levels and ensuring safe, reliable operation of the installation.

In practice, ThermOS can integrate, among other things, solar PV and a heat pump, thermal storage, as well as an analytics layer and a control application. The key point is that these elements are designed to operate together within one integrated system, rather than functioning as separate, independent devices.

The key feature of ThermOS is this two-stage storage approach – an internal buffer and an external storage unit. This allows the system to better match the operation of energy sources to variable renewable generation and to when the building actually needs energy. In practice, the solution is intended to help increase on-site consumption of PV energy and reduce grid electricity draw during higher-price hours.

Energy is stored in the form of thermal energy in water (for example, in the 45–55°C temperature range). This approach is widely used in hydronic heating systems and is valued for safety and predictable operation.

ThermOS aligns with the development of hybrid energy systems in buildings, meaning the combination of different energy sources and storage with intelligent control. The system can operate in both heating and cooling modes, and the control strategy aims to optimize when energy is produced, stored, and used. A very important aspect is the ability to work with a district heating substation / heat interface, which – by using network-supplied heat – can serve as a peak-load or backup heat source for the building, improving reliability and security of supply in uncertain times.

The patent protection (No. P.447775) defines the principles of how the solution can be used and may provide a foundation for further development and implementation partnerships, including under a licensing model.

The post ThermOS – ConnectPoint’s building energy storage & control system now protected by patent (No. P.447775) appeared first on ConnectPoint.

There are many factors that influence motivation, and the Gallup Institute has developed a Job Satisfaction Questionnaire that highlights six areas researchers consider crucial for building motivation and engagement.

Knowing what I’m supposed to do

A fundamental element of engagement is knowing what needs to be done and having access to the tools required to do it. If we are meant to complete a task, we need a clearly defined goal and a clear path to achieve it.

If you are a leader, you must be aware that the way you communicate tasks directly affects your team’s level of engagement. Employees need to know what they are supposed to do and why they are doing it.

What threatens motivation in this area are not only poorly defined goals and tasks or misunderstandings caused by differences in context.

If you frequently change decisions and ask employees to interrupt tasks they’ve already started, it can lead to a drop in motivation and a belief that it’s not worth putting in effort – because everything will change again soon anyway. Of course, sometimes a shifting reality requires sudden decisions, but in such cases it is essential to clearly

explain the change and show the broader context. Understanding helps maintain a sense of purpose.

Competence

Each of us has a fairly clear sense of our own skills and a need for those skills to be used appropriately. Sometimes we all need to do something simple and basic, but if we spend most of our time performing tasks far below our level of competence, our engagement weakens. We begin to feel undervalued and unnecessary. We develop a sense that our manager does not recognize our value or potential. We feel deprived of growth opportunities.

A leader must therefore be able to recognize employees’ competence levels and their developmental ambitions. Assigning tasks that are below someone’s capabilities becomes a threat to their motivation, engagement, and self-worth.

Sense of purpose

Closely tied to proper use of competence is the sense of meaning in one’s work. This is one of the most important factors influencing motivation and engagement. Practically everyone I speak with, when asked what matters most in their work, points to the sense of purpose and the certainty that their tasks serve a meaningful goal.

If a leader does not take the time to show employees the bigger picture and the direction the team is heading, they cannot expect real engagement or lasting motivation.

The sense of purpose is especially threatened when tasks are frequently changed due to constantly shifting priorities. This creates uncertainty about whether a task will even be completed. That uncertainty leads people to invest less energy, anticipating yet another sudden change.

Collaboration

Another area that can weaken motivation and engagement is collaboration – or more precisely, the lack of it. Some years ago, it was believed that competition, paired with the promise of reward, drives people to work harder. However, both experience and research show something different. People prefer to work when they have intrinsic motivation rather than extrinsic rewards, and in the long term, teams that prioritize collaboration perform better. Of course, there are specific scenarios where competition works and shouldn’t be completely abandoned.

In most cases, however, collaboration based on mutual trust and support – without judgment – is what matters. This environment encourages people to share knowledge and insights, creating synergy and producing better results than the sum of individual efforts.

If a leader fails to foster an atmosphere of trust and does not create space for safe communication within the team, engagement will slowly leak out, motivation will drop, and the situation will be difficult to repair. Not to mention pathological behaviors like mobbing, which make engagement impossible and destroy any motivation to work at all.

Feedback

When I talk about collaboration without judgment, many people push back. “How can you work without judging? Employees need to know what they’re doing right or wrong and what is expected of them.” And that’s true – but it has very little in common with the

traditional kind of evaluation we’re used to from school. And that’s where the biggest challenge with constructive feedback lies.

The school-style evaluation is neither helpful information nor support, nor does it have motivating value. It mainly enables comparison, which – as we know – rarely inspires more effort. In most cases, it is discouraging. Being judged makes people less willing to ask questions or reveal their limitations. Instead, they tend to pretend everything is fine. This emotional effort consumes energy and reduces engagement in actual tasks.

So how do you give feedback without demotivating? I’ve written about this many times, and I surely will again, because it remains a key question.

Growth

Gallup experts argue that growth is equally important for engagement and motivation.

Today, business owners and executives understand that people are the company’s most valuable asset – and this is no longer an empty slogan. Younger generations in particular emphasize that opportunities for growth matter to them, and they prefer “training in the job,” which produces the greatest progress in developing new skills.

If an employee feels stuck, bored, and lacking challenges, their engagement will inevitably decline, and they will not feel motivated to exert extra effort.

This has been a broadly outlined list of pitfalls. You can now work with your coach to analyze your team’s situation and identify individual, specific ways to avoid these pitfalls and create an environment where engagement and motivation have room to grow.

The post What drains motivation and engagement? appeared first on ConnectPoint.

This is a key milestone for ConnectPoint as well as for our partners and customers. It confirms the company’s operational maturity and alignment with internationally recognized security standards. These aspects are particularly important in the energy, utilities, and modern IT services sectors, where trust and data protection are fundamental to long-term cooperation.

What is ISO/IEC 27001?

ISO/IEC 27001 is a globally recognized standard that defines the requirements for an Information Security Management System (ISMS). It covers, among other things:

• identifying, assessing, and controlling risks related to information processing,
• ensuring the confidentiality, integrity, and availability of data,
• implementing a clearly defined set of organizational and technical security controls,
• continuously improving processes related to information security.

Achieving this certification means that an organization meets stringent data protection requirements and is committed to continuously enhancing its approach to risk management.

What does this mean for our customers and partners?

Obtaining ISO/IEC 27001 certification not only confirms the maturity of our internal processes, but more importantly delivers tangible value for our customers:

• Security of entrusted information – we apply proven, independently audited data protection practices.
• Alignment with international standards – our operations are consistent with global requirements for information security.
• Continuous improvement – we regularly enhance our procedures and systems to maintain the highest level of security.

The ISO/IEC 27001 certificate confirms that the processes and practices used at ConnectPoint meet international requirements for information security management. This is especially important in the energy, utilities, and IT services sectors, where we work with sensitive data and systems that are critical to our customers’ operations.

Earning this certification is the result of a company-wide effort at ConnectPoint. Together, we have implemented and embedded standards that directly translate into the quality and reliability of our services. This has included: harmonizing security policies and procedures, structuring and strengthening our risk management processes, enhancing access control and identity management, conducting regular tests and technical reviews and introducing formalized security standards across the entire organization.

The post ConnectPoint achieves ISO/IEC 27001 certification appeared first on ConnectPoint.

In this article, we focus on the practical aspect of digitalization – showing step by step how the decision-making process unfolds: from the initial audit, through defining strategic goals and selecting technologies, to implementation, performance monitoring, and KPI analysis – and how to execute the planned roadmap.

What benefits does network digitalization bring to heating companies?

Modern measurement technologies and telemetry devices with remote communication – digital sensors, controllers, heat meters, thermometers, and flow meters, as well as hybrid substations enabling integration of renewable energy sources and real-time data transmission – give companies full insight into the condition of their networks. This enables remote reading, control, and optimization of the system in near real time, which translates into fewer breakdowns (predictive maintenance), lower operational costs, and greater reliability of supply.

Reading temperatures and controlling thermal comfort in consumer premises (with their participation) also leads to lower heat consumption and reduced bills.
Digitalization supports EU climate objectives – including compliance with Fit for 55 – and enables a higher share of renewables in the energy mix.

In short: lower costs, higher reliability, compliance with EU regulations, and – importantly – the ability to offer added-value services to heat customers who want to actively manage their comfort and reduce heating expenses.

The decision-making process for launching a heating network digitalization project

Below are the key stages of the decision-making process which – if conducted properly – lead to the launch of a district heating network digitalization project.
A Sankey diagram illustrates the dependencies between each stage and their impact on the overall implementation outcome.

1. Preliminary audit – analysis of operating costs

The preliminary audit is the first and critical step toward digitalization and achieving the intended goals. It involves a comprehensive assessment of the technical condition of the heating infrastructure – pipelines, substations, heat sources, and control systems.

The audit also includes financial data analysis – failures, maintenance costs, and energy expenses. Its goal is to identify business and technical areas with the highest optimization potential achievable only through a digitalized network.

The audit should result in a report containing recommendations, a risk map, and an opportunity matrix.

Audit scope:

• Assessment of infrastructure condition.
• Analysis of operating and failure costs.
• Benchmarking against market standards.
• Review of available telemetry and GIS data.
• Inventory of IT tools and functionalities.

The audit results provide decision-makers with insights into current operational efficiency and indicate whether digitalization can reduce costs and improve performance.

It should cover several years of data to capture seasonality and trends and include industry comparisons.

Audit conclusions may point to modernization needs or management strategy changes.
Cost data serve as a foundation for ROI evaluation of future investments – without this, rational digitalization decisions are impossible.

2. Defining strategic digitalization goals

After analyzing the audit results, strategic goals must be clearly defined.
These may include:

• Reducing heat losses.
• Increasing energy efficiency.
• Improving customer service quality.
• Integrating renewable energy sources.
• Reducing network failure rates.
• Engaging consumers in reducing heating costs.

Goals should be measurable, realistic, and aligned with the company’s long-term strategy and stakeholder expectations – both customers and regulators.

Clear goal definitions allow for selecting suitable technologies and implementation methods.

Key questions for the project team to ask stakeholders:

• Are operational savings or network expansion the priority – or both?
• What are the expectations of customers and regulators?
• What are the company’s financial and technological capabilities?

The answers are essential for determining the project’s scope, approach, and schedule.

3. Technical infrastructure assessment

This stage determines the network’s current condition, covering pipelines, substations, heat sources, and control systems, with the goal of identifying elements needing modernization or replacement.

The assessment should be supported by telemetry data and field inspections, considering the age of infrastructure and compliance with standards.

The results of this assessment will indicate which areas are most prone to failures and will allow investment actions to be prioritized.

The resulting report should include:

• A risk map.
• Compliance evaluation.
• Modernization recommendations.

The technical data prepared in this way are essential for selecting the digitalization technologies and inform decisions about the scope of work and the implementation timeline.

Without such an analysis, it is difficult to determine the actual modernization needs, estimate the target project budget, and secure the funds for its execution.

This is a crucial step to ensure the project’s success and, ultimately, to achieve a high level of network safety and reliability.

4. Setting priorities: savings or expansion

The next important step is to set priorities – this is the point where stakeholders must decide whether to focus on modernizing the existing district heating infrastructure or expanding the network.
This decision depends on the results of previous cost analyses, technical evaluations, and compliance reviews.

If the network is in good condition but operating costs are high, the focus should be on cost savings – such as reducing energy purchase and/or production expenses, integrating renewable energy sources (which provide cheaper energy), and diversifying energy supply based on current prices and weather forecasts.

If, on the other hand, there is demand for new connections in areas where the network is not ready to supply new buildings (such as houses, housing estates, or shopping centers), network expansion will be necessary.

Priorities should align with the company’s strategy, stakeholder expectations, urban development plans, and financial capabilities.
This choice directly influences the selection of technologies, the implementation schedule, and defines the key performance indicators (KPIs) that will measure project success.

Hybrid approach – the best of both worlds

The hybrid approach model combines the benefits of operational optimization with infrastructure expansion. It is especially recommended for companies that want to simultaneously improve the efficiency of the existing network and prepare for future market challenges.

Stage 1: implement telemetry, analytics, and control solutions that reduce heat losses, optimize energy use, and enable failure prediction – delivering quick savings.

Stage 2: expand the network – both geographically and technologically – by installing hybrid substations, intelligent controllers, integrating renewable energy sources (RES), and deploying real-time, AI-supported data analytics systems.

Thanks to this, dispatchers gain online visibility into the state of the network – on GIS maps and through Digital Twins of the most critical components.

The hybrid approach enables flexible adaptation to market and regulatory changes, as well as gradual innovation without the risk of destabilizing the company’s operations.

5. Stakeholder engagement – a condition for project success

Stakeholder engagement is an essential condition for the success of any digitalization project. All groups that influence the project or will benefit from it should be identified:

• management,
• the technical department,
• network operators,
• end customers,
• technology partners.

It is worthwhile to conduct informational workshops and consultations to understand the needs and concerns of each group. Transparent communication and jointly developed goals increase project acceptance and minimize the risk of resistance. Stakeholders should be actively involved in successive implementation stages.

6. Selecting technologies: IoT, SCADA, Digital Twin, Big Data & AI

Technology selection is the stage where the tools necessary to achieve the digitalization goals are defined.
The most commonly used solutions include:

• Modern SCADA systems,
• IoT platforms for communicating with network infrastructure (data collection and sending control commands),
• IoT sensors for measuring key network operating parameters (temperature, flows, pressure, heat consumption),
• Digital Twins of key network devices to monitor their operation and anticipate potential failures and/or optimize their control,
• Big Data and AI-class solutions to analyze network data for implementing predictive maintenance, anticipating issues/failures, and supporting dispatchers in optimal network control.

The choice of technologies should depend on the specifics of the network and the project’s priorities.
Solutions must be compatible with the existing infrastructure (unless it is obsolete and needs replacement) and adapted to modern communication methods (IoT) – a fundamental requirement that enables integration, among other things, with billing and Customer Service systems, as well as solutions for Heat Comfort Management on the consumer side.

Because technology selection directly affects implementation costs and effectiveness, it should be preceded by market analysis and consultations with vendors, as well as a sensible approach to adjusting your own requirements – even if some vendors are unable to meet them – and these requirements are critical to your project’s success – do not abandon them unless the target KPIs can be achieved in another way.

Selecting the right solutions increases the chances of project success and ultimately helps meet regulatory requirements such as Fit for 55.

7. Pilot implementation – a practical test of the concept

A pilot implementation makes it possible to test the adopted assumptions in controlled yet real conditions.

Pilot stages:

• Selection of a test area – e.g., a particular district or neighborhood representing the most complex parts of the network,
• installation of devices and system integration,
• KPI monitoring and results analysis.

The purpose of the pilot is to verify how the technologies work in practice and to gather experience that will enable smooth, full-scale implementation.
If, at this stage, it turns out that the concept does not work, you should return to the previous step and change it.
If the pilot implementation is carried out within the public procurement (PZP) process and the Pilot fails – proceed in accordance with the signed Agreement, which should govern such a situation.

8. Project implementation – executing the strategy

The implementation stage is the moment of transition from planning to action. It includes:

• replacement/installation of devices,
• system integration,
• delivery of data models and their analytics layer,
• ensuring data presentation,
• functional and integration testing,
• personnel training.

Real implementation requires close cooperation between technical, IT, and operations teams – both on the maintenance and operations sides.
At this stage, stakeholder support, a clear schedule, a budget, and coordination of activities are crucial.
Implementation must be documented and monitored with particular emphasis on data security and continuity of network operations.

Example implementation schedule:

9. Monitoring results and KPI analysis

The final stage is monitoring the effects of the implementation and analyzing key performance indicators (KPIs).
The purpose of these activities is to assess whether the project delivers the expected results. Monitoring should be continuous and carried out using telemetry systems.
KPI analysis makes it possible to identify areas requiring further optimization and to report results to management and stakeholders.
Monitoring data can be used for continuous system improvement.

The analysis should cover both technical and financial aspects. KPIs should be aligned with the project’s strategic goals – their monitoring increases transparency, control, and the project’s adaptability over time.

This is the stage that closes the digitalization cycle, while at the same time opening the way for its further development.

The role of historical documentation and data in heating network digitalization

Heating companies often have vast amounts of historical documentation related to network operations – operational reports, service orders, defect lists, or repair protocols. Digitizing this data using OCR (Optical Character Recognition) makes it possible to create a central analytical database that combines historical data with current telemetry measurements.

Thanks to this approach, it becomes possible to analyze these data in the same way as network data – using Big Data solutions with AI support:

• searching the content of digitized documents by keywords,
• analyzing the frequency of failures,
• assessing the effectiveness of maintenance actions,
• correlating historical data with current telemetry data,
• building predictive models based on a long-term failure history.

Such a knowledge base significantly increases the possibilities for data analysis (including historical data from before the company’s digitalization project), enabling a better understanding of processes occurring in the network and more effective planning of preventive and investment activities.

Checklist for digitizing historical documents

The process of digitizing historical documents should follow clearly defined steps. The checklist below helps maintain order, data quality, and consistency of the entire repository.

1. Inventory of available paper and digital documents.
2. Assessment of document quality and selection of an appropriate digitization method (scanning, OCR).
3. Text recognition (OCR) and conversion to an editable format (e.g., DOCX, TXT).
4. Validation of the recognized text’s accuracy.
5. Standardization of formats and metadata to unify the data structure.
6. Import of data into the target analytical system database.
7. Integration with analytical and AI systems that will enable subsequent data processing.
8. Maintenance and updates of the repository – ensuring its continuous currency and quality.

Documents worth digitizing and their analytical significance

It is advisable to first digitize documents that contain important operational and strategic information. Converting and storing them in a database enables later analyses, reporting, and building predictive models.

The most important documents to digitize:

• operational reports – data on network operation, technical parameters, heating seasons,
• service and repair orders – types of failures, response times, repair costs,
• reported defect lists – location and frequency of issues,
• acceptance and technical inspection protocols – the technical condition of infrastructure,
• invoices and cost documents – costs of energy, fuels, materials,
• modernization and investment documentation – project descriptions, schedules, ROI.

Analyzing these documents enables:

• identification of areas requiring modernization,
• optimization of operating costs,
• investment planning and evaluation of their effectiveness,
• building predictive models and supporting strategic decisions.

Data integration and the role of artificial intelligence in using documents

Digitized documents should be stored in a central analytical system database, enabling quick access to content, metadata, and change history.

How to use such digitized documents? Especially valuable are AI-based solutions that allow the user – e.g., a dispatcher, operator, or engineer – to communicate with the system in natural language, both textually and by voice. The user can ask questions about the documentation content, and the system searches for answers in its assigned datasets and technical documents.

We have implemented this type of solution on our Smart RDM analytical platform, which enables:

• assigning documents to specific user groups who, within a secure space, can search them – without the risk of data leakage or uncontrolled Internet access (so-called hallucinations are minimized),
• searching content based on questions asked in natural language – both text and voice,
• integration with process data – the user can not only search documents but also ask questions about data from process databases, for example about statistics, summaries, or rankings.

As a result, Smart RDM transforms traditional technological documentation into an active source of knowledge, supporting operational decisions and significantly accelerating daily work.

Data should be indexed by document types, dates, locations, types of failures, and maintenance actions, or in any other way agreed upon at the analysis stage. Integration with telemetry systems makes it possible to combine historical data with current measurements, which forms the basis for predictive models and comparative analyses.

Goals of heating network digitalization and key performance indicators (KPIs)

The effectiveness of heating network digitalization can be measured using clearly defined KPIs (Key Performance Indicators). They make it possible to assess both the technical and financial aspects of project execution.

Examples of successful implementations in Poland and Europe

Polish district heating companies increasingly show that digital transformation is not theory but real results. In Warsaw, Veolia, together with ConnectPoint, implemented the Smart District Heating Network 2.0 project – one of the most advanced in Europe. The system already includes more than 6,000 controllers, and its implementation has reduced CO₂ emissions by 14,500 tons per year – you can read more about the detailed assumptions and results of this project in our case study. Parallel programs to improve efficiency and manage customers’ thermal comfort demonstrate that digitalization can go hand in hand with care for the end user.

In Puławy, modernization of substations and network telemetry made it possible to integrate RES installations, increasing the city’s energy independence. In Bełchatów, a remote control system and IT infrastructure modernization were implemented, significantly improving response times to network events. Kwidzyn, in turn, focused on automating heat meter readings – enabling real-time monitoring of heat consumption and more accurate billing for consumers.

At the European level, the RELaTED project is noteworthy; it promotes low-temperature district heating networks and the use of heat pumps, waste heat recovery, and bidirectional substations. This direction sets a new standard for modern, sustainable district heating systems.

Summary: from data to decision-making – how digitalization changes district heating

Digitalization of district heating networks is no longer a project of the future but a necessity and an opportunity for lasting competitive advantage. Thanks to the integration of telemetry data, real-time analytics, Digital Twins, and artificial intelligence, heating companies gain full visibility into the state of the infrastructure – from the source to the consumer.

A properly planned digitalization process – from the audit, through defining strategic goals and choosing technologies, to implementation and KPI analysis – makes it possible to genuinely reduce costs, increase operational efficiency, and improve service quality.
At the same time, it enables better use of historical data, which – once digitized – become the foundation for predictive models, ESG reporting, and intelligent modernization planning.
Today, digitalization is not only a tool for process optimization – it is a strategic element of the energy transition that supports the implementation of EU climate goals and the development of a modern heat economy.

Do you want to digitalize your heating network but don’t know where to start?

Start with an audit of infrastructure and data, move on to designing the telemetry and analytics architecture, and then implement SCADA, IoT, Digital Twin, and AI systems.
ConnectPoint has supported heating companies for years in end-to-end digitalization – from data analysis and modeling, through system integration, to building modern analytics platforms in the cloud and on-premises.

With the experience of the ConnectPoint team and implementations in Poland and abroad, we help companies move from the vision of digitalization to measurable business results.

Contact us if you want to learn how to carry out the digitalization of your network step by step – from the audit to KPI analysis. Together, we will build an intelligent heating network or deliver your other projects!

The post How should a district heating network digitalization plan look in practice? appeared first on ConnectPoint.

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.

Recently, the Polish government announced new financial support packages for infrastructure companies. These funds are primarily allocated for technical modernization, allowing heating providers to reduce maintenance costs and optimize energy production. 

Why digitalize district heating networks?

Industry has evolved through successive revolutions – from the steam age, through electrification and computerization, to the Internet. The next phase is digitalization – shifting from analog to digital processes, based on real-time data and modern communication technologies. 

In recent years, the term “digitalization” has become common across infrastructure sectors, including heating. Unfortunately, many definitions diverge from real-world practice. Below is ConnectPoint’s perspective — as a company actively implementing smart district heating networks — on the essential stages and benefits of this transformation. 

What digitalization district heating networks are – and what they are not?

Digitalizing a district heating network means a comprehensive deployment of IT, IoT, and advanced analytics solutions that enable continuous, near real-time data collection, transmission, and processing from the network infrastructure. With this data, operators can: 

• optimize temperature, pressure, and flow, 

• make decisions based on actual operational data, 

• predict failures and plan maintenance in advance, 

• consistently lower heat production and distribution costs. 

The core shift lies in moving from isolated components to a single intelligent ecosystem. Sensors and actuators, powered by Big Data and AI tools, allow automated and precise control of the network — leading to better efficiency, reliability, and lower energy bills for customers. 

Proper digitalization goes far beyond office IT. Deploying an ERP system or updating individual heat substations without data integration won’t change how the network functions. 

Also, digitalization is not the same as computerization. Computerization refers to digitalizing administrative processes (ERP, CRM, Office tools), whereas network digitalization focuses on continuous measurement, remote control, data analytics, prediction, and automation of physical infrastructure operations. 

What’s often mistaken for digitalization, but isn’t.

In practice, many technical improvements — while necessary and valuable — don’t qualify as true digitalization. Common examples include: 

• Replacing pipelines with newer or better-insulated ones, unless they’re equipped with smart sensors or monitoring systems. 

• Installing heat meters in substations, without automated data collection and analysis. 

• Building new heat sources without integrating them into digital control and analytics systems. 

• Purchasing ERP or CRM software that helps manage the company, but doesn’t directly enhance digital control of the district heating network. 

Digitalization is defined by the continuous, automated flow of operational data — used to support real-time decision-making and intelligent network management. 

Stages of district heating digitalization – goals and KPIs.

Digitalization is not a one-time effort, but a multi-stage project usually broken down into key subprojects. Below are the most important stages, with their objectives and sample KPIs.

1. IoT infrastructure and real-time monitoring.

Building a sensor-based monitoring system enables collection of data on temperature, pressure, flow, and anomalies throughout the network. These sensors communicate with data collection and analytics platforms (via NB-IoT, LoRaWAN, etc.), giving operators real-time visibility into network operations. 

Example KPIs: 

• Number of installed IoT sensors vs. target.

• Real-time data quality (>95%).

• Number of anomalies detected per month.

2. SCADA/DMS systems and remote control.

SCADA and DMS systems allow remote control of the network — managing flow, temperature, and valves. Integrated with actuators and pump stations, they enable quick responses to changes and help reduce losses. 

Example KPIs: 

• Average failure response time (<5 min).

• % of network length under remote control.

• Data continuity to IT systems.

3. Integration with GIS, EAM, and CRM systems.

Combining spatial data (GIS), asset data (EAM/CMMS), and customer data (CRM/billing) allows full oversight of the network and services — from asset management to handling customer requests. 

Example KPIs: 

• Number of systems integrated on a single platform.

• Time to generate a full operational report (<15 min).

4. Digital Twin of the district heating network.

A virtual model of the network that mirrors infrastructure and operational data allows for scenario testing, investment planning, and risk forecasting — without affecting real-world operations. 

Example KPIs: 

• % improvement in data quality.

• Number of decisions made using the Digital Twin.

5. Predictive Maintenance.

By analyzing data using AI/ML, the system can predict equipment failures and schedule preventive maintenance. This enables a shift from reactive to proactive operations, reducing risks and costs. 

Example KPIs: 

• Number of failures detected in advance.

• Emergency downtime cost reduction (%).

• Decrease in routine maintenance interventions.

6. Field operations and dispatcher support.

Mobile dashboards, GIS-based apps, and task scheduling tools help dispatchers and crews respond faster and more effectively. Field staff document activities directly from incident sites. 

Example KPIs: 

• Average crew response time.

• Number of tasks assigned via mobile system.

7. Smart Customer Service and connection processes.

Online forms, customer portals, comfort monitoring, and mobile integration let customers manage services independently and contact operators quickly and easily. 

Example KPIs: 

• % of customers using digital services (eBOK/app).

• Number of fully digital connection requests.

• Customer satisfaction (NPS).

8. Energy storage, hybrid substations, and renewables.

Integrating energy storage, heat pumps, and hybrid nodes improves peak load management and increases renewable energy use. 

Example KPIs: 

• Storage capacity (MWh) / % of demand.

• Number of hybrid operation days per season.

• CO₂ reduction.

How can district heating networks be successfully digitalized? An example is the Smart Heating Network at Veolia Poland in Warsaw.

Veolia is implementing the ISC (Smart Heating Network) 2.0 project in Warsaw, with ConnectPoint as its technology partner. The project’s goal is to equip and digitally control 6,000 heat substations. The system enables: 

• real-time monitoring of temperature and pressure, 

• automated heat supply adjustments, 

• rapid fault detection and response. 

The project aims to reduce heat losses by over 64,000 GJ annually and cut CO₂ emissions by more than 4,500 tons. It is co-financed by the Modernization Fund and the National Fund for Environmental Protection (NFOŚiGW), with completion planned by 2027. 

Want to learn how we helped Veolia digitize its district heating network?
Check out our case study “Monitoring of the Warsaw District Heating Network” and reach out to our experts!

Building team capabilities while digitalizing district heating networks.

Any infrastructure modernization requires upskilling. Digitalizing an entire network is a strategic initiative and cannot succeed without expanding internal competencies. 

Equipping teams to effectively use new tools is often the key success factor – leading to reduced costs and improved reliability. In organizations rich in IoT and SCADA data, an advanced analytics team can: 

• Detect unusual network behavior and predict failures 

• Recommend preventive actions using Big Data analysis 

• Suggest the most efficient infrastructure expansion strategies 

Smaller companies can rely on external services or cloud-based AI modules – the key is using data for smart network management. 

A digital heating network means real savings – proven together with Veolia Poland.

The ISC 2.0 project experience in Warsaw shows that full-scale digitization — from IoT sensors and SCADA to Digital Twin and predictive maintenance — can reduce heat losses by tens of thousands of GJ and cut CO₂ emissions by several thousand tons per year within just the first few years. The earlier a utility company adopts a unified data architecture and automation, the sooner it will see measurable benefits: lower fuel costs, fewer heat supply interruptions, and improved customer comfort. 

ConnectPoint supports heating network operators at every step of this journey — from concept and financing to deployment and long-term support. If your company is ready to follow in Veolia’s footsteps and shift from technical upgrades to a truly intelligent heating network, reach out to our team of experts. 

The post Digitalizing district heating networks – how to do it right? 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. 

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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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