• Written by Sonali Subhadarsini, Product Manager

In the metals and mining industry, a typical operations team spends a significant amount of time waiting for reports to be generated, only to find that the information is outdated or incomplete, leaving them to rely on manual data collection and analysis, as evidenced by the numerous spreadsheets and dashboards that nobody opens, a stark reminder of the inefficiencies that plague the industry.

The operations questions that currently wait on a report

The operations team in a metals and mining organisation often finds itself waiting for reports to answer critical questions, such as what is the current production rate, what are the maintenance schedules for the heavy equipment, and what are the inventory levels of raw materials, questions that are essential to the smooth operation of the facility, but are frequently delayed due to the time it takes to generate the reports, and even then, the information may not be up-to-date or accurate.

A shift supervisor, for instance, may need to check the maintenance log to determine if a particular piece of equipment is available for use, or a category manager may need to compare the performance of different suppliers to determine which one to use for a particular raw material, but these tasks are often hindered by the lack of timely and accurate information, highlighting the need for a more efficient system, such as AI-powered operations management, which can provide real-time answers to these questions.

The data behind them: process and exception records, project status, budget consumption, supplier performance

The data that underlies these operations questions is diverse and complex, encompassing process and exception records, project status, budget consumption, and supplier performance, among other things, and it is this data that needs to be analysed and interpreted in order to answer the questions that the operations team has, but the sheer volume and complexity of the data makes it difficult to do so manually, which is why an AI-powered system is necessary to help make sense of it all.

For example, a project manager may need to track the status of a particular project, including the budget consumption and supplier performance, in order to determine if it is on track to meet its deadlines and stay within budget, but this requires access to a wide range of data, including financial records, project schedules, and supplier contracts, which can be difficult to gather and analyse manually, but can be easily accessed and analysed using an AI-powered system.

Sector constraints: grade variability and maintenance backlog

The metals and mining industry is subject to a number of unique constraints, including grade variability and maintenance backlog, which can have a significant impact on operations, and it is these constraints that need to be taken into account when analysing the data and answering the operations questions, but they are often overlooked or underestimated, which can lead to inaccurate or incomplete answers.

Grade variability, for instance, can have a significant impact on production rates and maintenance schedules, as different grades of ore may require different processing techniques and equipment, and maintenance backlog can have a significant impact on equipment availability and downtime, which can in turn impact production rates and maintenance schedules, highlighting the need for a system that can take these constraints into account and provide accurate and timely answers to the operations questions.

What improvement looks like: on-time delivery, exception rate, expediting cost

Improvement in the metals and mining industry can take many forms, but some common metrics include on-time delivery, exception rate, and expediting cost, which can be used to measure the effectiveness of the operations team and the efficiency of the facility, and it is these metrics that need to be tracked and analysed in order to determine if the operations team is meeting its goals and objectives.

For example, a reduction in exception rate can indicate that the operations team is doing a better job of managing the facility and minimizing downtime, while an increase in on-time delivery can indicate that the team is doing a better job of managing the production schedule and meeting deadlines, and a reduction in expediting cost can indicate that the team is doing a better job of managing the supply chain and minimizing the need for expedited shipping, highlighting the need for a system that can track and analyse these metrics in real-time.

What this does not do, or when it is the wrong choice

While an AI-powered operations management system can be a powerful tool for the metals and mining industry, it is not a panacea, and there are certain situations in which it may not be the best choice, such as when the data is incomplete or inaccurate, or when the operations team is not familiar with the system and how to use it, in which case it may be better to use a more traditional approach, such as manual data collection and analysis, or to seek the help of a consultant or expert, such as those available through our contact page.

Most people get wrong the idea that AI-powered operations management systems are a replacement for human judgment and expertise, but they are wrong, as these systems are designed to augment and support human decision-making, not replace it, and they are most effective when used in conjunction with human expertise and judgment, rather than as a replacement for it, highlighting the need for a nuanced understanding of the role of AI in operations management.