Artificial Intelligence: The Future of Searching for Spare Parts in the Maintenance of Large Industrial Enterprises
Artificial Intelligence: The Future of Searching for Spare Parts in the Maintenance of Large Industrial Enterprises
Patrick Schneider, VP Enterprise Solutions at Partium
Posted 3/21/2024
In large industrial enterprises, maintenance plays a critical role in guaranteeing continuous, efficient operations. A key aspect of this area is the search for spare parts.
This process is traditionally associated with challenges including time intensity, high error rates, and inventory management. Introducing artificial intelligence (AI) into this environment brings with it a transformative wave that has the potential to revolutionize the search for spare parts.
Not only are the processes for identifying spare parts accelerated by implementing AI technologies such as machine learning and image recognition, they are also made much more precise. This development greatly increases the efficiency of the search for spare parts while reducing inventory costs at the same time. Find out exactly how it works in this article.
Challenges in the Traditional Search for Spare Parts
Time-consuming manual search and identification processes
One of the biggest bottlenecks in the search for spare parts in industrial enterprises is the high amount of time required for manual search and identification processes. This section highlights the various aspects and implications of this challenge:
Manual data entry and query: In many cases, the search for spare parts is based on manual systems that require data to be entered and queried in databases. Not only is this process time-consuming, it’s also susceptible to human error.
Complexity of identifying parts: Correctly identifying spare parts is difficult, especially with specific, uncommon, or obsolete components. Manually going through catalogs and databases to find the right part is often a lengthy and laborious process.
Reliance on expert knowledge: In many cases, quickly identifying spare parts depends on employees with special knowledge or experience. This leads to relying on individuals, which can then lead to delays if they are absent.
Loss of time due to physical inventory verification: If inventory data is not up-to-date or accurate, employees may need to physically search the warehouse to find the part they need. This is an especially time-consuming process that makes other optimizations practically impossible.
Storage costs, especially for rarely used or obsolete parts
An essential aspect that influences the cost-effectiveness of maintenance in large industrial enterprises is storage costs, especially for rarely used or obsolete spare parts. These costs not only include the physical storage space, but the associated administration and maintenance costs too. So far, though, there has been no way around it!
AI in the Search for Spare Parts: Examples of Use, Advantages, and Solutions
Using artificial intelligence (AI) now (2023/2024) offers an array of possibilities to improve the search for spare parts in industrial enterprises:
Improved day-to-day searching for spare parts
Use of image recognition: Employees can take photos of the spare parts required and upload these images to the search system directly. AI-supported image recognition identifies the part more quickly, significantly reducing time spent searching.
Integration with text search: Large language models (LLM), image searches, and descriptions are combined in one search, resulting in faster, more accurate identification.
Re-digitization of unlabeled spare parts inventories:
Recording and cataloging: AI systems can help to record and catalog unlabeled or poorly documented spare parts inventories. These inventories can be efficiently digitized and systematically arranged by analyzing images and text data.
Reading PDFs and other document formats:
Data extraction: LLMs (large language models) are able to browse complex documents, such as PDFs, and extract relevant information about spare parts. This includes technical specifications, manufacturer information, and instructions for use, which are important for maintenance.
Experts need to check the results but no longer need to search for them themselves.
Optimizing warehouses:
Efficient warehouse organization: Warehouses can be optimized by identifying spare parts using image recognition. Difficult-to-describe spare parts can be completely packed, stacked, and organized to “wait” until they are needed. They can even be outsourced, making room for fast-moving products.
Implementing AI in the search for spare parts offers significant advantages for large industrial enterprises. The search process for spare parts is significantly accelerated and simplified by using technologies such as image recognition and large language models, while reducing errors and complexity at the same time. This increase in efficiency not only leads to faster, more precise identification of spare parts, but also contributes to reducing overall costs (operation and storage).
Conclusion
But what specific AI solutions are currently available for searching for spare parts?
One established AI solution to searching for spare parts already exists: Partium. The web-based application can be used on smartphones, tablets, or a desktop.
Partium is used in lots of industrial enterprises around the world and helps users accelerate and optimize processes involved in searching for and sourcing spare parts. Some of the best-known clients in the maintenance sector include Deutsche Bahn (German Railways), ÖBB, and Vienna Energy.
Patrick Schneider
Patrick Schneider is VP Enterprise Solutions at Partium. Patrick has managed many projects related to spare parts identification and spare parts search in the maintenance, after-sales, and service field.
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