• Written by Sonali Subhadarsini, Product Manager

A procurement team in a metals and mining organisation often finds itself waiting for a report to answer questions that seem straightforward, such as which suppliers are consistently delivering late, or how the current price of a key input is affecting the overall cost of production. The shift supervisor may need to know the maintenance history of a particular piece of equipment, while the category manager is trying to determine the best way to structure contracts with suppliers. These questions are critical to the smooth operation of the organisation, but the answers are often buried in complex data systems.

The procurement questions that currently wait on a report

The procurement team may spend a significant amount of time trying to track down the answers to these questions, which can delay decision-making and impact the organisation's ability to respond to changes in the market. For example, the category manager may need to know the current spend with a particular supplier in order to negotiate a new contract, but this information may be scattered across multiple systems and require a lengthy report to compile. The procurement team may also need to know the lead time for a particular item, or the level of inventory currently held, in order to make informed decisions about purchasing and stock management.

These questions are not just about procurement, but also about the overall operations of the organisation. The ability to answer them quickly and accurately can have a significant impact on the organisation's efficiency and effectiveness. By using AI to analyse the organisation's own data, the procurement team can get the answers they need in real time, without having to wait for a report.

The data behind them: purchase orders, supplier contracts and terms, invoice history, category structure

The data that underlies these questions is complex and multifaceted, and includes purchase orders, supplier contracts and terms, invoice history, and category structure. This data is often stored in different systems, and may require significant manual effort to compile and analyse. The category manager may need to review the terms of a supplier contract to determine the best way to negotiate a new agreement, while the shift supervisor may need to review the maintenance history of a piece of equipment to determine the best way to schedule maintenance.

The data is also subject to change, as new suppliers are added, contracts are renegotiated, and inventory levels fluctuate. This means that the procurement team needs to have access to up-to-date and accurate data in order to make informed decisions. By using AI to analyse the organisation's own data, the procurement team can ensure that they have the most current and accurate information available, and can make decisions based on real-time data.

Sector constraints: grade variability and maintenance backlog

The metals and mining sector is subject to a number of unique constraints, including grade variability and maintenance backlog. Grade variability refers to the fact that the quality of the raw materials used in production can vary significantly, which can impact the overall cost and efficiency of production. Maintenance backlog refers to the fact that equipment and machinery in the sector often require significant maintenance and repair, which can impact production schedules and costs.

These constraints can make it difficult for the procurement team to make informed decisions, as they need to take into account the potential impact of grade variability and maintenance backlog on production costs and schedules. By using AI to analyse the organisation's own data, the procurement team can better understand the impact of these constraints, and make decisions that take into account the unique challenges of the sector. For example, the procurement team may use operations data to determine the best way to schedule maintenance, or to identify opportunities to reduce costs and improve efficiency.

What improvement looks like: spend under management, price variance to contract, supplier concentration

Improvement in the procurement function can take many forms, but some key metrics include spend under management, price variance to contract, and supplier concentration. Spend under management refers to the percentage of the organisation's spend that is managed through formal contracts and agreements, while price variance to contract refers to the difference between the price paid for a particular item and the price specified in the contract. Supplier concentration refers to the level of reliance on a particular supplier, and can impact the organisation's ability to negotiate prices and terms.

By using AI to analyse the organisation's own data, the procurement team can better understand these metrics, and make decisions that improve the organisation's overall performance. For example, the category manager may use data on spend under management to identify opportunities to consolidate spend with a particular supplier, or to negotiate better prices and terms. The procurement team may also use data on price variance to contract to identify areas where the organisation is paying more than it should for a particular item, and take steps to address this.

By improving these metrics, the organisation can reduce costs, improve efficiency, and better manage risk. The procurement team can also use data to identify opportunities to improve the organisation's relationships with suppliers, and to build more strategic and collaborative partnerships. To learn more about how AsscherAi can help your organisation, please contact us.

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

While AI can be a powerful tool for procurement teams in the metals and mining sector, it is not a solution to every problem. For example, AI is not a substitute for human judgment and expertise, and should be used to support and augment the work of the procurement team, rather than replace it. Additionally, AI may not be the best solution for every organisation, particularly those with limited data or limited resources.

In these cases, the organisation may need to focus on building its data capabilities, or on developing more traditional procurement strategies. The procurement team should carefully consider its needs and goals, and determine whether AI is the right solution for its particular challenges. By taking a thoughtful and strategic approach to the use of AI, the procurement team can ensure that it is using the right tools to achieve its goals, and that it is getting the most value from its investment in AI.