The Cash-to-Cash Cycle:
when operations become finance

The Hackett Group's 2025 U.S. Working Capital Survey, covering the 1,000 largest public companies, reveals that $1.7 trillion remains trapped in excess working capital. That's 35% of gross working capital, sitting idle. And working capital optimisation has jumped to the #1 finance priority for 2025.

Yet the tools to unlock it are not in the CFO's office. They're in procurement, planning, logistics, and replenishment. The Cash Conversion Cycle (CCC), the number of days between paying your supplier and collecting cash from the sale, is the financial translation of every operational decision a supply chain team makes. It's composed of three levers: DIO (Days Inventory Outstanding, how long stock sits before it sells), DSO (Days Sales Outstanding, how fast you collect after a sale), and DPO (Days Payable Outstanding, how long you take to pay your suppliers). The formula is simple: CCC = DIO + DSO – DPO.

When safety stock is set emotionally, DIO rises. When order-to-cash is fragmented, DSO worsens. When DPO is extended without supplier alignment, you gain 15 days of cash but lose reliability on the next shipment. The gap between top-quartile and median performers is widening. In DPO alone, the delta reached 9% in 2024. And both DSO and DIO worsened that year, meaning that gains in one component are masking deterioration in others. The aggregate CCC number hides the dysfunction underneath.

CCC is not a finance KPI. It's the financial translation of every supply chain decision you make.

The CCC is the single metric that bridges supply chain operations and the P&L. Every day of DIO represents inventory carrying costs, typically 20–25% of inventory value annually when you include storage, insurance, obsolescence, and the cost of capital. For a company with €50M in inventory, each day of DIO reduction frees approximately €137K in cash. Multiply that across 5–10 days of improvement, and you're looking at €700K–1.4M returned to the business, without touching revenue, headcount, or operations.

But the real insight is in the decomposition. Top-quartile companies don't optimise CCC as a single number: they decompose it by business unit, channel, and product family. They link operational drivers to each lever: lead time variability drives DIO, invoicing accuracy drives DSO, supplier segmentation drives DPO. And critically, they include working capital targets in S&OP, not just service level and cost. When the planning team optimises for service and the finance team optimises for cash, nobody is optimising for both. CCC in S&OP changes that.

The moment I started presenting CCC in S&OP reviews, the quality of the conversation changed. Before, we talked about inventory levels, a number that means nothing without context. After, we talked about the operational drivers behind those levels. The CFO stopped asking "why is inventory high?" and started asking "which lever is most out of line, and what's the plan to fix it?" That's the shift. CCC turns a reporting conversation into a decision conversation. It took me years to understand that supply chain credibility in the boardroom doesn't come from service level achievements: it comes from speaking the language of capital efficiency. CCC is that language.

Of the three levers, DPO is the one I see touched most often. The logic is intuitive: extend payment terms, buy liquidity, breathe. And it is not wrong. The problem is that this logic was built for a world where Far East lead times were 30 days. Today, when a container takes 60 to 70 days at sea plus two weeks to reach the stores, the DPO is already eaten by the supply chain before you sell a single unit. Extending from 90 to 120 days does not fix the structural problem. It buys a few weeks. And it comes with a cost that does not appear anywhere in the CCC calculation: the supplier who gets paid last starts treating you accordingly. Priority allocation, flexibility, quality control. All of it quietly adjusts. The aggregate number will not show you that. Your next peak season will.

Most teams know their aggregate CCC. Few have decomposed it by product family, channel, or business unit. Pulling 12 months of DIO, DSO, and DPO data and breaking it apart manually is the kind of analysis that gets deprioritised every week. That is where the cash stays trapped.

First: build the decomposition you have been putting off. Upload 12 months of inventory snapshots, AR aging reports, and AP data, then ask the AI to calculate DIO, DSO, and DPO monthly and break DIO down by product family. The output tells you which categories are driving your cycle and which are subsidising the others. That is the conversation that changes planning decisions.

Second: build the driver tree for your specific situation. Given your DIO decomposition, ask the AI to map each driver (safety stock parameters, lead time variability, demand forecast accuracy) to its contribution to the total. This gives every lever an owner and makes the action plan concrete rather than directional.

Third: if you are bringing CCC to finance for the first time, ask the AI to model the cash release at 5, 10, and 15 days of DIO improvement against your actual inventory value, and to express it in terms your CFO cares about: cash freed, cost of capital released, and impact on operating cash flow. That is the one-page view that builds cross-functional credibility.

The specific failure mode: AI can calculate the metrics and flag the patterns, but it cannot explain why a specific product group's DIO spiked last quarter. That requires operational context: a delayed shipment, a promotion that did not sell through, a planning assumption that was never updated. Use AI for the decomposition and the math. Use your experience for the interpretation. The numbers without context are noise. Context without numbers is opinion.

AI with your numbers is leverage. AI without them is theater.