Introduction: AI-enabled MRO
Industrial enterprises in Ukraine face a constant challenge — ensuring reliable supply of spare parts on time. The increase in costs of production downtime and the rise in requirements for spare part availability make the use of modern technologies for automating the process of finding equivalent spare parts critical. AI-based MRO solutions enable fast and accurate identification of equivalent components, significantly reducing the time from search to order.
How it works: AI tests for automation search
AI-driven MRO uses machine learning algorithms to analyze dates, component parameters and their physical properties. The system compares part characteristics to determine equivalent alternatives. This includes analysis of parameters such as nominal power, pressure, temperature, weight, material and certification requirements (for example, EN, ISO, DSTU).
Example: if spare parts are needed for a compressor type A, the system analyzes all available spare parts that meet the technical parameters, including the requirements for certification. This allows quickly finding an equivalent alternative, ensuring continuous production operation.
Data requirements: what data is needed?
For effective operation of the AI system, it is necessary to use high-quality and complete data on spare parts. This includes technical specifications, certificates, requirements for the operating environment (temperature, humidity, pressure) and usage parameters (for example, MTBF, maximum power, maximum speed).
The amount of data determines the accuracy of the system. For effective search of equivalents, more than 10 000 records of spare parts should be maintained, ensuring a wide range of options. The data must be in a format that supports automatic processing (for example, CSV, Excel, XML).
Architecture of implementation: from sensors to action
The AI-enabled system in MRO uses an architecture that includes three main stages: information collection, processing, and response.
- Sensors and sensors: Collect data on spare parts used in installations. This includes parameters such as mean time between failures (MTBF), operating temperature, pressure, weight, material.
- Обробка на межі: Даний етап виконується на проміжному сервері, де AI-алгоритми аналізують дані, порівнюють їх з базою запчастин та визначають еквівалентні альтернативи.
- Дія: Результат пошуку відображається у системі MRO, де можна вибрати запчастину та виконати замовлення через електронний каталог.
Real results: percentage reduction in stoppages and ROI
Implementation of AI-driven MRO solutions leads to significant reduction in time spent searching for spare parts and cost reduction. For example, in one Ukrainian compressor factory, implementation of the system reduced the time to find equivalent spare parts from 48 hours to 3 hours. This allowed to reduce the cost of downtime by 32%, achieving ROI within 12 months.
In addition, production reduced the cost of purchasing spare parts by 15%, due to the accuracy of equivalent selection. The average cost of implementing the system ranges from 5000 to 15 000 euros, depending on the volume of data and the number of spare parts.
Limitations and risks: what can go wrong?
Although AI-enabled MRO has a high cost, it is important to understand that this is not "magic". One of the main risks is incomplete or poor data on spare parts. Incorrect parameters may lead to the selection of the wrong alternative, causing equipment stoppages or incorrect operation.
Another limitation is dependency on infrastructure. For effective operation of the system, a stable connection to the internet and local servers for data processing are required. It is also important to understand that AI cannot replace humans — it is a tool that helps in decision-making.
Build or buy: when is it better to develop your own system?
The decision to build or purchase an AI system depends on the volume of data, technical base, and budget. If a company has a large volume of data on spare parts and technical tools, it is advantageous to develop its own system. For example, a company with 1000+ spare parts can advantageously develop its own system.
In the case where the company does not have sufficient technical resources, it is better to use commercial solutions that already meet the certification requirements. For example, systems complying with standards ISO 9001, EN 13482, DSTU 4153, ensure reliability and quality.
Start: Practical steps for production engineers
For the implementation of AI-based support in MRO, several steps need to be carried out:
- Demand assessment: Determine which spare parts are most frequently used, what requirements apply to their selection, and what risks arise in the absence of equivalents.
- Збір даних: Створіть базу даних про запчастини, включаючи технічні характеристики, сертифікати, вимоги до середовища роботи.
- System selection: Select an AI-ensurance system that meets the certification requirements (ISO, EN, DSTU) and ensures accurate search.
- Впровадження: Встановіть систему, проінтегруйте її з існуючими процесами MRO та виконайте тестові випробування.
- Оптимізація: Неперервно аналізуйте результати, вдосконалюйте систему та вдосконалюйте процес пошуку.
Conclusion: Using UNITEC-D E-Catalog for digital transformation
Implementation of AI-powered support in MRO is a step towards digital transformation of production. UNITEC-D E-Catalog provides a convenient tool for quick search of equivalent parts, meeting certification requirements (ISO, EN, DSTU) and ensuring reliability and quality. Using our catalog, manufacturing companies can reduce costs, increase efficiency and ensure continuous operation.
For more information about our electronic catalog and MRO services, visit UNITEC-D E-Catalog.
Sources
- ISO 9001:2015 — Quality Management System
- EN 13482:2001 — Standards for Industrial Spare Parts
- DSTU 4153:2006 — Requirements for Certification of Spare Parts
- UNITEC-D E-Catalog: https://www.unitecd.com/e-catalog/
- Case Study: «Reduction of Stoppage Costs through AI-Driven MRO»
