Problem description

September 1, 2025 by ACADEMA

Working Capital Optimization (WCO) is the strategic management of short-term assets and liabilities—like receivables, payables, and inventory—to improve cash flow, reduce costs, and boost operational efficiency. The goal is to free up cash trapped in day-to-day operations so it can be reinvested or used to strengthen financial stability.

Example above: Cash flow optimization in this context is illustrated through several strategic financial decisions visible in the accompanying visuals. The company invests in equipment using its own funds, minimizing financing costs. For premises, a loan is used, with careful planning around the amount and timing to maintain liquidity. Deposits are employed to manage short-term cash surpluses efficiently. Material procurement is timed to take advantage of cash discounts (cassa-sconto), improving cost-effectiveness. VAT is paid quarterly, which helps smooth out cash outflows. The seasonal sales curve guides inventory and staffing decisions, while periods of increased workload are managed through overtime payments. During low-demand phases, fill-in contracts are used to keep operations running and ensure a steady inflow of cash.

A targeted financial strategy focused on minimizing the cash conversion cycle (CCC) by synchronizing receivables, payables, and inventory turnover. The objective is to maximize free cash flow (FCF) without impairing operational continuity or supplier/customer relationships. Through rigorous analysis of DSO (Days Sales Outstanding), DPO (Days Payable Outstanding), and DIO (Days Inventory Outstanding), companies can identify inefficiencies, reduce reliance on external financing, and improve key metrics such as ROIC, EBITDA margin, and net working capital ratio. Optimization initiatives often include dynamic discounting, supply chain financing, inventory rationalization, and predictive analytics to forecast liquidity needs and mitigate risk exposure.

What is Symbolic AI?

Symbolic AI (also known as rule-based AI or classical AI) is a branch of artificial intelligence that uses explicit rules, logic, and symbols to represent knowledge and reason about problems. Unlike machine learning, which learns patterns from data, symbolic AI relies on human-defined rules and ontologies.

• Uses logic and inference engines
• Knowledge is encoded in structured formats (e.g., decision trees, semantic networks)
• Ideal for tasks requiring transparency, traceability, and formal reasoning

Symbolic AI in Working Capital Optimization

Symbolic AI can play a strategic role in optimizing working capital by embedding financial rules, policies, and constraints directly into decision-making systems. Here’s how:

• Rule-Based Forecasting: Symbolic systems can model cash flow forecasts using predefined business rules (e.g., payment terms, seasonal cycles).
• Policy Enforcement: Automatically enforce credit policies, payment schedules, and inventory thresholds to maintain liquidity.
• Scenario Simulation: Run “what-if” analyses to evaluate the impact of changes in receivables, payables, or inventory on working capital.
• Invoice and Payment Logic: Symbolic AI can automate invoice matching and payment prioritization based on logical rules (e.g., early payment discounts, overdue penalties).
• Auditability: Since symbolic AI decisions are rule-based, they’re fully traceable—critical for compliance and financial audits.

Let’s dig a little deeper into how Academa Ltd’s symbolic AI system stacks up against more common machine learning (ML) approaches in financial modeling.

Symbolic AI vs. Machine Learning in Finance

Feature	Symbolic AI (Academa-style)	Machine Learning (ML)
Transparency	Fully transparent; rules are explicitly defined	Often opaque; decisions can be hard to explain
Auditability	Easy to audit and trace logic	Requires model interpretability tools
Adaptability to Regulation	Excellent; rules can be tailored to legal frameworks	Risk of non-compliance without retraining
Data Requirements	Low; works well with structured, known inputs	High; needs large datasets to train effectively
Scenario Planning	Precise and deterministic	Probabilistic; may miss edge cases
Learning Capability	Static unless manually updated	Dynamic; improves with more data

Why Academa’s Symbolic AI Is a Strategic Fit

Academa’s system is ideal for companies that:

• Operate in regulated environments (e.g., EU VAT rules, labor laws)
• Need precise control over financial flows and timing
• Want to simulate complex operational cycles without relying on historical data
• Prefer human-readable logic over black-box predictions

For example, our system can model:

• When to pay VAT quarterly to optimize liquidity
• How to time equipment purchases to avoid financing costs
• The impact of seasonal staffing on cash flow

Constraints

Many variations of the problem exist, including the following:

• ...

Example

The images above illustrate a 'what-if' analysis, comparing scenarios of regular payment versus delays of one, two, or three months. It’s important to note that VAT must still be paid—even if the invoice hasn’t yet been settled by the customer. (the result is symbolici, shown in spreadseet)

For more examples, continue.