AI for finance in logistics
A logistics finance manager uses AsscherAi to investigate current financial performance, asking what costs are currently exceeding budget, and how they compare to the same period last year, then acting on the insights to reduce costs and improve profitability, without waiting for a weekly report.
The logistics finance manager sits at her desk, staring at a spreadsheet that is supposed to help her understand the current state of the company's finances, but she knows that the numbers are already out of date and do not reflect the complex trade-offs between cost and service that are being made every day across the network. She is waiting for a report to be generated, one that will supposedly give her the insights she needs to make informed decisions, but she knows that this report will be based on data that is several days old, and will not take into account the constant fluctuations in carrier performance, dwell and detention times, and other factors that can have a significant impact on the company's bottom line.
The finance questions that currently wait on a report
The finance manager's questions are straightforward: what is the current state of our finances, and how are we performing against budget? But the answers to these questions are not always easy to come by, and often require a significant amount of time and effort to generate. This is because the data required to answer these questions is scattered across multiple systems, and must be manually compiled and analyzed in order to generate the insights that the finance manager needs. For example, the finance manager may need to know the current balance of the company's general ledger, as well as the status of all open commitments and purchase orders, in order to get a complete picture of the company's financial situation.
Currently, the finance manager must wait for a report to be generated, which can take several days or even weeks, depending on the complexity of the data and the resources available to generate the report. This means that the finance manager is often making decisions based on outdated information, which can lead to suboptimal outcomes and a lack of visibility into the company's financial performance.
The data behind them: general ledger, budget, open commitments and purchase orders, receivables and terms
The data required to answer the finance manager's questions is varied and complex, and includes information from the general ledger, budget, open commitments and purchase orders, receivables and terms. The general ledger provides a complete picture of the company's financial transactions, including all income and expenses, while the budget provides a roadmap for the company's financial performance over a given period of time. Open commitments and purchase orders reflect the company's future financial obligations, while receivables and terms provide insight into the company's cash flow and liquidity.
By analyzing this data, the finance manager can gain a deeper understanding of the company's financial situation, and make more informed decisions about how to allocate resources and manage risk. For example, by analyzing the company's open commitments and purchase orders, the finance manager can identify areas where costs can be reduced or optimized, and make adjustments to the budget accordingly. Similarly, by analyzing the company's receivables and terms, the finance manager can identify opportunities to improve cash flow and liquidity, and make decisions about how to manage the company's working capital.
Sector constraints: carrier performance variance and dwell and detention
One of the key challenges facing logistics companies is the variability in carrier performance, which can have a significant impact on the company's financial performance. Carrier performance variance can result in delays, missed deliveries, and other issues that can increase costs and reduce revenue. Similarly, dwell and detention times can also have a significant impact on the company's financial performance, as they can result in additional costs and reduced productivity.
These sector constraints can make it difficult for the finance manager to get an accurate picture of the company's financial situation, as they can result in significant variations in cost and revenue from one period to another. For example, a delay in delivery can result in additional costs for storage and handling, while a missed delivery can result in lost revenue and damage to the company's reputation. By taking these sector constraints into account, the finance manager can get a more accurate picture of the company's financial situation, and make more informed decisions about how to manage risk and optimize performance.
What improvement looks like: variance to budget, commitment coverage, days sales outstanding
Improvement in the logistics finance function can take many forms, but some key metrics include variance to budget, commitment coverage, and days sales outstanding. Variance to budget reflects the difference between actual and budgeted financial performance, and can provide insight into areas where costs can be reduced or optimized. Commitment coverage reflects the company's ability to meet its financial obligations, and can provide insight into areas where cash flow and liquidity can be improved. Days sales outstanding reflects the company's ability to collect revenue from customers, and can provide insight into areas where the company's working capital can be optimized.
By analyzing these metrics, the finance manager can identify areas for improvement and make data-driven decisions about how to optimize the company's financial performance. For example, by analyzing variance to budget, the finance manager can identify areas where costs are exceeding budget, and make adjustments to reduce costs and improve profitability. Similarly, by analyzing commitment coverage, the finance manager can identify areas where the company's cash flow and liquidity can be improved, and make decisions about how to manage the company's working capital. For more information on how to improve your company's financial performance, visit our finance page.
What this does not do
While AI for finance in logistics can provide significant benefits, it is not a silver bullet, and there are certain limitations and constraints that must be taken into account. For example, AI for finance in logistics is not a replacement for human judgment and expertise, and must be used in conjunction with the finance manager's knowledge and experience. Additionally, AI for finance in logistics requires high-quality data, and can only provide insights that are as good as the data that is used to train it.
Most people get wrong the idea that AI for finance in logistics is a simple plug-and-play solution that can be implemented overnight, but this is not the case. Implementing AI for finance in logistics requires significant time and effort, as well as a deep understanding of the company's financial systems and processes. If you are interested in learning more about how AI for finance in logistics can be implemented in your organization, visit our contact us page to speak with one of our experts.
Frequently asked questions
How can AI help logistics finance managers make better decisions?
AI provides real-time insights into financial data, enabling managers to make informed decisions and optimize performance.
What data does AsscherAi use to answer finance questions?
AsscherAi uses data from the general ledger, budget, open commitments, and purchase orders to provide a complete picture of the company's financial situation.
How can AsscherAi help logistics companies manage sector constraints?
AsscherAi helps companies manage sector constraints, such as carrier performance variance and dwell and detention times, by providing real-time insights into their impact on financial performance.
What metrics can be used to measure improvement in logistics finance?
Metrics such as variance to budget, commitment coverage, and days sales outstanding can be used to measure improvement in logistics finance and identify areas for optimization.