• Written by Sonali Subhadarsini, Product Manager

A category manager in a pharmaceutical company typically spends a significant amount of time waiting for reports to be generated, only to find that the data is outdated or incomplete, which can hinder their ability to make informed decisions about procurement strategies. This is often the case when trying to determine the best course of action for managing spend, assessing supplier performance, or identifying areas for cost savings.

The procurement questions that currently wait on a report

The procurement team in a pharmaceutical company often has to wait for reports to be generated in order to answer questions such as what is the current spend with a particular supplier, or what is the price variance to contract for a specific category of products. These questions are critical to the procurement team's ability to manage spend effectively and ensure that the company is getting the best value for its money. However, the wait for reports can be frustrating and can delay decision-making.

Most people get wrong the idea that these questions can be easily answered by simply looking at the data, but the reality is that the data is often scattered across different systems and departments, making it difficult to get a complete picture. The shift supervisor, for example, may have access to the maintenance log, but may not have the ability to see the bigger picture of how the maintenance costs fit into the overall procurement strategy.

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

The data that underlies the procurement questions is typically stored in a variety of systems, including purchase orders, supplier contracts and terms, invoice history, and category structure. The category manager, for example, may use this data to compare the prices of different suppliers and determine which one is offering the best value. The data is often complex and nuanced, and requires careful analysis in order to extract meaningful insights.

The data is also often subject to strict documentation rules, which can make it difficult to access and analyze. For example, the purchase orders may be stored in a separate system from the supplier contracts, and the invoice history may be stored in yet another system. This can make it challenging for the procurement team to get a complete picture of the data and make informed decisions.

Sector constraints: change control overhead and batch record review

The pharmaceutical industry is subject to strict regulations and guidelines, which can create constraints for the procurement team. For example, the change control overhead and batch record review processes can be time-consuming and labor-intensive, and may require significant documentation and approval. These processes can also create delays and bottlenecks in the procurement process, which can impact the ability of the team to respond quickly to changing market conditions.

The category manager, for example, may need to navigate these constraints in order to implement changes to the procurement strategy. This can be a complex and challenging process, and may require significant coordination and communication with other departments and stakeholders. By using AI to analyze the data, the procurement team can gain insights that can help them navigate these constraints and make more informed decisions.

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

Improvement in procurement can take many forms, but some common metrics include spend under management, price variance to contract, and supplier concentration. The procurement team may use these metrics to evaluate the effectiveness of their procurement strategy and identify areas for improvement. For example, the category manager may use spend under management to determine which categories of products are being effectively managed, and which ones require more attention.

By using AI to analyze the data, the procurement team can gain insights that can help them improve their procurement strategy and achieve better outcomes. For example, they may be able to identify areas where they can reduce costs, or improve the efficiency of their procurement processes. They can also use AI to streamline their operations and make more informed decisions about procurement.

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

While AI can be a powerful tool for procurement teams, it is not a silver bullet. There are certain situations where AI may not be the best choice, such as when the data is incomplete or inaccurate. In these cases, the procurement team may need to rely on other methods, such as manual analysis or consultation with experts. They can also reach out to us to discuss their specific needs and determine whether AI is the right choice for their procurement team.