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. 

  1. 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. 

  1. 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. 

  1. Supporting Employee Training and Development

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

  1. 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. 

  1. 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. 

  1. 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.