AI Cash flow Management for Product Distribution

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AI Cash flow Management for Product Distribution

How a high-growth distribution company used AI to improve the cash flow management of discounts, payments, and line-of-credit.

AI cash flow management for a product distribution company

In the current volatile financial climate, ensuring liquidity is one of the most pressing challenges for finance executives. changing interest rates, supply chain disruptions, and changing customer behaviors, all have an impact on liquidity. Striking a balance between growth and liquidity constraints is critical. 

Liquidity constraints are often due to planning decisions made in sales and operations, but the impacts are felt most often in finance. Finance executives need to navigate the gap between customer payment dates and supplier payment terms.

One of our clients delivers omni-channel product distribution tailored to meet the needs of well-known manufacturers and retailers like Motorola, Costco, Best Buy and others. They turned to OmniData during their recent growth cycle to optimize cash flow management, mitigate impact on credit, and sustain their critical growth phase.

Artificial Intelligence (AI) solutions have been proven to help many organizations with automation, better forecasting and changing behaviors of users. With our client, we deployed the use of predictive analytics to improve decision-making for cashflow and improve credit risk management.

Our analysis model solutions ultimately enabled our client to visualize the impact of giving discounts on payment terms and what positive results could be achieved.

Business Problem

Healthy cash flow enables businesses to invest in expansion, product development, and innovation. Inefficient cashflow is often cited as one of the leading causes for a company’s inability to take advantage of growth opportunities as they present themselves. Fortunately, our client was in the position to navigate these stages and started the growth cycle in all earnest.

However, the challenge was to support the current growth trajectory while maintaining liquidity. Growth is on all companies’ agenda in some form, but growing too fast and losing liquidity can stunt progression. One of the biggest influences on positive cashflow in companies is the mismatch between payables and receivables. Customers and suppliers have different payment terms, causing an imbalance between the payments and sales proceeds.

How can we better estimate when they will pay? What can we learn about different customer and supplier payment behaviors? And what is the associated impact on credit?

The business problem for our client can be summarized as follows:

Can we use advanced analytics to better understand and predict payment behavior?
Can we simulate different payment scenarios?
What is the optimal time to give a discount on payment terms?
Can we inform credit decisions based on more accurate cashflow forecasting?

Solution Approach

OmniData’s approach to the business problem was to analyze the historical payment behaviors and build predictive AI models for payment behavior. We used our expertise in data analysis, machine learning, and Power BI to enable our client to increase the effective management of cashflow.

Here’s how we approached each step:

1. Payment Behavior Modeling

Organizations in all industries have different systems and processes in place to manage payments of debtors and creditors. These systems can be built in-house or paid-for solutions. They can vary between fully automated and fully integrated, to manual processes based off Excel and Outlook. Some organizations make payments in fixed intervals like only at the end of the month, while others pay a pre-determined period from receiving the invoice. Others still have some combination of the two.

Our approach in this step involved segmenting each customer and supplier so that their unique payment behaviors could be captured. Using predictive analytics in this manner we were able to provide accurate forecasts of the payments per customer and supplier. Using predictive analytics this way, we provided accurate forecasts of payments per customer and supplier.

Movements Overview

2. Payment Scenario Analysis

Having a predictive view of payables and receivables, and the resulting impact on cash flow was enlightening. But now the questions expanded to include:

What if a customer or supplier paid the client at different payment terms?
What if our biggest customers paid their invoices earlier?
How can we negotiate more favorable payment terms with our suppliers?

These are not unique questions for our client by any means, but our approach to solving them allowed us to unlock many new insights for the client.

OmniData built solutions by adding different payment terms for customers and suppliers. Our client could now simulate the impact of changing an individual customer or supplier’s payment terms and see the resulting impact on cash flow and credit. Their executive team could now sit in management meetings, simulate the resulting impact of different payment terms, and adjust their decisions as needed.

Net Cashflow What-if Scenarios

3. Recommendations for Discounts

Payment terms often come in the format of net payment in 30 days, or 60 days. Organizations in general will often try to incentivize their customers and suppliers by giving them discounts on the invoice amount if they pay before the payment term date.

Using this analysis, our client was able to visualize the payment behaviors of customers and suppliers and make informed decisions based on the forecasts on when to use discounts most effectively. Like retail, where discounts are given as part of promotions, B2B businesses must consider the timing and amount of these discounts.

Our AI-driven forecasting solutions gave our client the ability to assess the impact of giving discounts. The approach was used to reduce the cases where discounts were given with no change in behavior, or where discounts were used by the customers and suppliers with no reduction in payment term.