• Written by Sonali Subhadarsini, Product Manager

On a typical Monday morning, the finance team in a pharmaceutical company is likely to be waiting for a report to be generated, so they can start analyzing the latest financial data and making informed decisions. The team may be waiting to see the current budget vs actuals, or to understand the status of open commitments and purchase orders. This wait can be frustrating, especially when the team knows that the data is available, but it's locked away in various systems and needs to be manually extracted and compiled.

The finance questions that currently wait on a report

The finance team in a pharmaceutical company typically has a range of questions that they need to answer on a regular basis, such as what is the current cash position, what are the outstanding invoices, and what is the status of the budget vs actuals. These questions are critical to the smooth operation of the business, but often the team has to wait for a report to be generated before they can get the answers they need. This can lead to delays and inefficiencies, as the team is unable to make timely decisions.

A shift supervisor in the manufacturing team may also have questions about the financial impact of their operations, such as what is the cost of goods sold, or what is the revenue generated by a particular product. These questions may not be directly related to the finance team's responsibilities, but they are still important to the overall performance of the business. By having access to real-time financial data, the shift supervisor can make more informed decisions about their operations, and the finance team can provide more effective support.

The data behind them: general ledger, budget, open commitments and purchase orders, receivables and terms

The data that the finance team needs to answer their questions is typically stored in a range of systems, including the general ledger, budgeting systems, and accounts payable and receivable systems. The general ledger provides a complete and accurate record of all financial transactions, while the budgeting system provides a detailed breakdown of planned expenditure. Open commitments and purchase orders are also critical, as they provide a clear picture of the company's future financial obligations.

The category manager may also need to access data on receivables and terms, in order to understand the company's cash flow position and make informed decisions about payment terms and credit limits. By having access to this data in real-time, the category manager can optimize the company's cash flow and reduce the risk of bad debt.

Sector constraints: change control overhead and batch record review

The pharmaceutical industry is subject to strict documentation and validation rules, which can make it difficult to implement new systems or processes. Change control overhead can be significant, as any changes to systems or processes need to be thoroughly documented and validated. Batch record review is also a critical process, as it ensures that all products are manufactured and released in accordance with strict quality standards.

These constraints can make it challenging to implement AI for finance in pharmaceuticals, as the systems and processes need to be carefully validated and documented. However, by working with a trusted partner and following established protocols, it is possible to implement AI solutions that meet the strict requirements of the pharmaceutical industry. For more information on how to get started, visit our finance page.

What improvement looks like: variance to budget, commitment coverage, days sales outstanding

By implementing AI for finance in pharmaceuticals, companies can gain real-time insights into their financial performance and make more informed decisions. One key area of improvement is variance to budget, where AI can help identify areas where actual expenditure is deviating from planned expenditure. Commitment coverage is another critical area, where AI can help ensure that the company has sufficient funds to meet its future financial obligations.

Days sales outstanding (DSO) is also an important metric, as it provides a clear picture of the company's cash flow position. By using AI to analyze DSO, companies can identify areas where they can improve their cash flow and reduce the risk of bad debt. For example, a category manager may use AI to analyze DSO and identify opportunities to optimize payment terms and credit limits.

In order to achieve these improvements, companies need to have a clear understanding of their financial data and how it can be used to drive business decisions. This may involve working with a range of stakeholders, including the finance team, category managers, and shift supervisors. By working together and using AI to analyze financial data, companies can gain a deeper understanding of their business and make more informed decisions.

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

While AI for finance in pharmaceuticals can provide significant benefits, it is not a solution that is suitable for all companies or all situations. For example, companies that do not have a strong foundation of financial data and processes may struggle to implement AI solutions effectively. In these cases, it may be more effective to focus on building a strong foundation of financial data and processes before implementing AI.

In other cases, companies may find that AI is not the right solution for their specific needs. For example, companies that have very simple financial systems and processes may not need the advanced analytics and insights that AI can provide. In these cases, it may be more effective to use simpler solutions, such as spreadsheet-based analysis. If you are unsure about whether AI for finance in pharmaceuticals is the right solution for your company, contact us to discuss your options in more detail.