• Written by Sonali Subhadarsini, Product Manager

When a shift supervisor checks the maintenance log on the production floor, they often need to know if a critical spare part is available from a preferred supplier, but this information is typically buried in a report that the procurement team only generates on a weekly basis, leaving the supervisor to wait for an answer, which can delay production and impact overall plant efficiency. This is just one example of how procurement questions can bottleneck on reports, highlighting the need for more immediate access to data.

The procurement questions that currently wait on a report

The procurement team in a manufacturing organisation typically deals with a wide range of questions on a daily basis, from checking the status of purchase orders to verifying supplier contracts and terms. These questions often require access to up-to-date data, but the team may have to wait for a report to be generated or for someone to manually look up the information, which can cause delays and impact productivity. For instance, a category manager may need to know the current spend with a particular supplier in order to negotiate better prices, but this information may only be available in a monthly report.

In many cases, the procurement team may have to rely on manual processes or workarounds to get the information they need, which can be time-consuming and prone to errors. By having direct access to the data, the team can quickly answer questions and make informed decisions, without having to wait for reports or rely on manual processes.

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

The data that underlies procurement questions is typically stored in various systems, such as enterprise resource planning (ERP) systems, procurement software, and spreadsheets. This data includes purchase orders, supplier contracts and terms, invoice history, and category structure, among other things. By having access to this data, the procurement team can answer questions and make decisions based on accurate and up-to-date information.

For example, a procurement analyst can use the data to analyze spend patterns and identify opportunities for cost savings, while a supplier manager can use the data to monitor supplier performance and ensure compliance with contracts and terms. By leveraging this data, the procurement team can make more informed decisions and drive better outcomes for the organisation.

Sector constraints: changeover losses and multi-plant comparability

In the manufacturing sector, there are unique constraints that can impact procurement, such as changeover losses and multi-plant comparability. Changeover losses refer to the costs associated with switching production from one product to another, which can be significant in industries with high-volume production. Multi-plant comparability refers to the challenge of comparing data across multiple plants, which can be difficult due to differences in production processes, systems, and data formats.

These constraints can make it difficult for the procurement team to get a clear view of the data, which can impact their ability to make informed decisions. For instance, a procurement manager may need to compare spend across multiple plants in order to identify opportunities for cost savings, but the data may not be comparable due to differences in systems and processes. By using AI to analyze the data, the procurement team can overcome these constraints and get a more accurate view of the data.

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

When the procurement team has direct access to the data, they can drive significant improvements in procurement outcomes, such as increasing spend under management, reducing price variance to contract, and optimizing supplier concentration. Spend under management refers to the percentage of spend that is managed through formal procurement processes, which can help to reduce costs and improve compliance. Price variance to contract refers to the difference between the contracted price and the actual price paid, which can be a significant source of cost savings.

By analyzing the data, the procurement team can identify opportunities to improve these outcomes, such as renegotiating contracts with suppliers or identifying new sources of supply. For example, a category manager can use the data to analyze price variance and identify opportunities to reduce costs, while a procurement analyst can use the data to optimize supplier concentration and improve supply chain resilience. By leveraging AI to analyze the data, the procurement team can drive more effective procurement strategies and improve overall business performance.

For more information on how to implement AI for procurement in manufacturing, visit our operations page to learn more about how AsscherAi can help.

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

While AI for procurement in manufacturing can drive significant improvements in procurement outcomes, it is not a solution for every problem. For instance, it may not be the best solution for organisations with very simple procurement processes or those that do not have a large amount of data to analyze. In these cases, the benefits of AI may not outweigh the costs, and other solutions may be more effective.

In other cases, the procurement team may need to have more direct control over the procurement process, such as in situations where there are complex contractual requirements or highly customized products. In these cases, AI may not be the best solution, and more traditional procurement methods may be more effective. If you are unsure about whether AI for procurement in manufacturing is the right solution for your organisation, contact us to learn more and discuss your specific needs.