Working Capital Cycle: A Key to Better Cash Flow

The working capital cycle (WCC) measures the time in days a company takes to convert its net working capital—current assets minus current liabilities—into cash, often termed the cash-to-cash cycle. A shorter cycle indicates faster cash turnover and better efficiency, while a long cycle ties up capital. 

The FormulaWorking Capital Cycle = Inventory Holding Period + Trade Receivables Collection Period − Trade Payables Payment Period

• Inventory Holding Period (Days Inventory Outstanding or DIO): How long stock sits before being sold.
• Trade Receivables Collection Period (Days Sales Outstanding or DSO): How long it takes customers to pay.
• Trade Payables Payment Period (Days Payable Outstanding or DPO): How long the business takes to pay suppliers.

The payables period is subtracted because it represents "free" financing from suppliers.

 ABC Co takes about 40 days on average to convert its operating investments back into cash. In other words, cash is tied up for 40 days between buying materials and receiving payment from customers (after accounting for supplier credit).

Why It MattersA 40-day cycle isn't unusually long, but businesses aim to shorten it by:

• Reducing excess inventory
• Speeding up customer payments (e.g., better credit terms or reminders)
• Negotiating longer payment terms with suppliers

Monitoring and optimizing the working capital cycle is one of the simplest ways to improve cash flow without needing new sales or funding.Understanding this metric helps owners and managers spot inefficiencies early and keep the business running smoothly.

Capital Structure: Share Capital vs Loan Capital

Real-World Scenario: "GreenEnergy Co" – Capital Structure in 2025

#By Grok
Company: GreenEnergy Co – a mid-sized solar + battery storage developer
Goal: Raise $150 million to build 3 large solar farms in the US Southwest
Date: December 2025
They decide on a mixed capital structure (very typical for renewable energy projects):

Source of Capital	Amount (USD)	Type	% of Total Funding
Equity from investors (Share Capital)	$45,000,000	Equity	30%
Bank term loan (20-year)	$75,000,000	Loan Capital (senior debt)	50%
Green bonds issued to public	$22,500,000	Loan Capital (subordinated)	15%
Tax equity investor	$7,500,000	Hybrid (counts as equity for this chart)	5%
Total	$150,000,000		100%

For simplicity in the pie chart, we group it into just two categories that matter most to the founders and lenders:

• Share Capital (Equity) = $45M + $7.5M = $52.5 million (35%)
• Loan Capital (Debt) = $75M + $22.5M = $97.5 million (65%)

This 35/65 equity-to-debt ratio is extremely common in project-financed renewable energy in 2025.

What the Pie Chart Looks Like (description if you run it)

• A clean, professional pie chart
• Equity slice (green) is noticeably smaller → 35%
• Debt slice (red) dominates → 65%
• Dollar amounts and percentages clearly labeled on each slice
• Title and legend make it perfect for investor presentations or a pitch deck

This exact 35/65 split is what banks and rating agencies love to see in renewable projects because:

• Enough equity “skin in the game” (at least 20–30%)
• High loan capital leverage keeps the return on equity very attractive (often 12–18% IRR)

Python Code by Grok

import matplotlib.pyplot as plt

# ============== GreenEnergy Co – Capital Structure 2025 ==============
labels = ['Share Capital\n(Equity)', 'Loan Capital\n(Debt)']
sizes = [52.5, 97.5]        # in millions
colors = ['#4CAF50', '#F44336']  # Green for equity, Red for debt
explode = (0.1, 0)  # slightly separate the equity slice

fig, ax = plt.subplots(figsize=(9, 7), dpi=120)

wedges, texts, autotexts = ax.pie(sizes, explode=explode, labels=labels, colors=colors,
                                  autopct=lambda pct: f"${pct*1.5:.1f}M\n({pct:.0f}%)",
                                  startangle=90, textprops={'fontsize': 14, 'weight': 'bold'},
                                  wedgeprops={'linewidth': 2, 'edgecolor': 'white'})

# Make the percentage text white and bolder
for autotext in autotexts:
    autotext.set_color('white')
    autotext.set_fontsize(13)

ax.set_title('GreenEnergy Co – $150M Project Funding Breakdown (2025)\n'
             'Share Capital vs Loan Capital', fontsize=16, fontweight='bold', pad=20)

# Legend with exact amounts
ax.legend([f'Equity (Share Capital)     – $52.5 million (35%)',
           f'Loan Capital (Debt)           – $97.5 million (65%)'],
          title="Breakdown", title_fontsize=13, fontsize=12, loc="lower left")

plt.tight_layout()
plt.show()