Integration in metals and mining
A maintenance planner uses AsscherAi to investigate equipment failures by querying the computerised maintenance management system and production plan in the enterprise resource planning system, identifying discrepancies and potential causes, and adjusting the maintenance schedule accordingly to reduce downtime.
A maintenance planner in a metals and mining operation checks the production schedule to determine which equipment needs maintenance, but the information in the computerised maintenance management system does not match the production plan in the enterprise resource planning system, causing delays and inefficiencies. This is a common problem in the industry, where integrating AI systems with existing systems of record can be challenging.
Industry-specific challenges
The metals and mining industry presents unique challenges for AI systems integration, primarily due to grade variability, maintenance backlog, and energy intensity. Grade variability refers to the differences in ore quality, which can affect production planning and equipment maintenance. Maintenance backlog occurs when equipment maintenance is delayed, leading to increased downtime and reduced productivity. Energy intensity is a significant factor, as metals and mining operations require large amounts of energy to power equipment and processes.
These challenges make it harder to integrate AI systems with existing systems, as the AI system needs to account for the variability in ore quality, the backlog of maintenance tasks, and the energy requirements of the operation. For example, a shift supervisor may need to adjust the production plan based on the quality of the ore being mined, which can affect the maintenance schedule and energy usage.
Read paths, write paths, and data consistency
When integrating AI systems with existing systems of record, it is essential to consider both read paths and write paths. Read paths refer to the flow of data from the system of record to the AI system, while write paths refer to the flow of data from the AI system back to the system of record. The write path requires more care, as it can create inconsistencies in the data if not managed properly.
A category manager comparing two suppliers may use the AI system to analyse data from the enterprise resource planning system, but if the AI system writes back to the system of record without proper validation, it can create errors and inconsistencies. This can lead to incorrect decisions being made, based on inaccurate data.
Reconciling definitions and data formats
Another challenge in integrating AI systems with existing systems of record is reconciling definitions and data formats that differ between systems. For example, the definition of "production volume" may vary between the production planning system and the accounting system. The AI system needs to be able to reconcile these differences and ensure that the data is consistent and accurate.
This can be a complex task, especially when dealing with systems that have been in place for many years and have evolved independently. A data analyst may need to work with the AI system to ensure that the data is properly mapped and transformed to ensure consistency and accuracy. For more information on how AsscherAi can help with this process, visit our website.
Latency budgets and precomputation
Latency budgets refer to the maximum allowable delay between the time data is generated and the time it is available for analysis. In the metals and mining industry, latency budgets can be critical, especially when dealing with real-time data from sensors and equipment. The AI system needs to be able to process data quickly and efficiently to ensure that decisions can be made in a timely manner.
Precomputation can help reduce latency by processing data in advance and storing it in a cache or database. This can be especially useful for data that is used frequently, such as production schedules and maintenance plans. A maintenance planner can use precomputed data to quickly identify equipment that needs maintenance, without having to wait for the data to be processed in real-time.
Limitations and alternative approaches
While AI systems integration can be beneficial for metals and mining operations, there are limitations and alternative approaches that should be considered. For example, if the operation has a relatively simple production process and limited data requirements, a manual system may be sufficient. In other cases, a more traditional data analytics approach may be more suitable, especially if the data is relatively static and does not require real-time analysis.
In some cases, the cost and complexity of implementing an AI system may outweigh the benefits, especially for smaller operations or those with limited resources. In these cases, it may be more practical to focus on improving existing systems and processes, rather than investing in new technology. For more information on how to determine the best approach for your operation, contact us at AsscherAi.
Frequently asked questions
How can AI systems integration improve production planning in metals and mining?
AI systems integration can improve production planning by reconciling discrepancies between systems and providing real-time data analysis.
What are the challenges of integrating AI systems with existing systems of record in metals and mining?
Challenges include grade variability, maintenance backlog, and energy intensity, which require careful consideration of read and write paths and data consistency.
How can latency budgets and precomputation improve AI systems integration in metals and mining?
Latency budgets and precomputation can reduce delays and improve real-time data analysis, enabling maintenance planners to quickly identify equipment that needs maintenance.
What are the limitations of AI systems integration in metals and mining, and when might alternative approaches be more suitable?
Limitations include cost and complexity, and alternative approaches may be more suitable for operations with simple production processes or limited data requirements.