A case study in operational complexity, capital planning, and decision support
1. Context
A UK-based advisory firm approached us to support one of their clients in designing a robust financial framework to underpin an ambitious expansion plan.
The client operates in the paper goods industry, commercializing multiple variants of toilet paper and kitchen rolls across different formats and specifications. While the business had strong commercial traction, it lacked a structured financial model capable of supporting:
- Medium-term planning
- Operational decision-making
- Capital allocation
- Financing strategy
The objective was clear: develop a fully integrated, monthly financial model over a 5-year horizon that could capture the operational complexity of the business and translate it into actionable insights.
2. The Challenge
At first glance, this might resemble a standard three-statement model. It wasn’t.
The main challenges were:
Operational complexity
- Potentially up to 50 SKUs (stock keeping unit)
- Each product with different cost structures
- Multi-layered bill of materials
Supply chain
- Up to 50 suppliers
- Unit prices and lead times variability
Inventory dynamics
- Dual inventory structure (raw materials and finished goods)
- Forward-looking stock policies based on expected demand
Capital intensity
- Ongoing CAPEX requirements
- Asset-level depreciation modeling
Financing structure
- Debt modeling using French amortization method
- Integration of short-term financing tools
Workforce planning
- Multiple employee categories
- Monthly evolution of headcount and salaries
This required more than a spreadsheet — it required a structured, scalable financial system.
3. Solution Design
We developed a fully integrated financial model structured around clarity, modularity, and usability.
A core feature of the model was the ability to seamlessly operate across three distinct scenarios:
- Base case (expected trajectory)
- Best case (upside scenario)
- Worst case (downside / stress scenario)
Scenario selection was fully centralized:
a single input cell in the Control sheet allowed users to switch scenarios instantly, dynamically updating the entire model — from operational drivers to financial statements.
This enabled real-time comparison and significantly improved decision-making under uncertainty.
4. Model Structure
4.1 Control Layer
A centralized Control sheet allowed users to manage key global assumptions:
- Start date of operations
- Inventory policy (% of next month demand)
- Inflation assumptions
- Capital structure
It also hosted the scenario selector, acting as the control point for the entire model logic.
4.2 Revenue & Product Engine
A dedicated module enabled the creation and management of up to 50 products, each defined by:
- Monthly volume projections
- Pricing assumptions
- Product-specific cost structures
Each product incorporated up to 10 distinct components, representing raw materials with:
- Different unit prices
- Different measurement units (e.g., grams, tonnes, liters)
This allowed for a highly granular and realistic build-up of cost structures.
4.3 Supplier, Components & Cost Structure
A separate layer modeled up to 50 suppliers, including:
- Unit pricing
- Lead times
These inputs fed directly into product components, creating a fully linked structure between:
Suppliers → Raw materials → Products → Financial outputs
This ensured that operational changes (e.g., supplier pricing or delays) flowed consistently through the entire model.
4.4 Inventory Modeling
Inventory was modeled with a dual structure:
- Raw materials inventory (driven by production needs and supplier dynamics)
- Finished goods inventory (driven by expected demand and stock policy)
All raw material requirements derived from product composition fed directly into inventory calculations.
Ultimately, inventory levels were anchored to forward demand expectations, ensuring alignment between:
- Operations
- Supply chain
- Financial planning
This had a direct and dynamic impact on working capital and cash flow.
4.5 CAPEX & Depreciation Module
A flexible CAPEX module allowed users to input:
- Asset value
- Residual value
- Useful life (in months)
The model automatically generated:
- Monthly depreciation
- Accumulated depreciation
- Net book value
Crucially, depreciation calculations were fully integrated across the three financial statements, ensuring full consistency and auditability:
- Feeding directly into the Profit & Loss (as depreciation expense)
- Impacting the Balance Sheet (through accumulated depreciation and net book value of assets)
- Flowing into the Cash Flow Statement (as a non-cash adjustment)
Scenario-driven CAPEX assumptions allowed the client to test different investment paths and understand their financial impact holistically.
4.6 Debt & Financing Module
A comprehensive financing module was implemented, including:
Term debt (French amortization method)
- Initial principal
- Start and end dates
- Interest rate
Revolving credit facility (credit line)
- Dynamic drawdowns and repayments
- Interest calculated on utilized amounts
- Short-term liquidity management
Factoring facility
- Acceleration of receivables collection
- Impact on working capital
- Associated financing costs
Outputs included:
- Interest expense
- Principal repayment
- Outstanding balances
All elements were fully integrated into the financial statements and dynamically adjustable across scenarios.
4.7 Workforce Planning
A dedicated personnel module captured:
- Employee categories
- Salary levels
- Monthly headcount evolution
Workforce assumptions could be flexed by scenario, aligning cost structure with growth expectations.
4.8 Three-Statement Integration
All modules were fully integrated into:
- Profit & Loss
- Balance Sheet
- Cash Flow Statement
On a monthly basis over 5 years, ensuring full alignment between operational drivers and financial outputs across all scenarios.
4.9 Dashboard & Decision Support
A visual dashboard summarized key outputs:
- Revenue, costs, EBITDA and EBITDA margin
- Year-on-year comparisons (P&L and Balance Sheet)
- Key financial trends
Users could seamlessly switch between scenarios to compare performance, risk exposure, and liquidity needs.
5. Impact
The model became a central decision-making tool for the client.
It enabled:
- Structured and data-driven expansion planning
- Alignment between operational complexity and financial visibility
- Clear anticipation of cash needs and funding requirements
- Faster and more informed decision-making
Importantly, the integration of short-term financing tools — such as the credit line and factoring — allowed the client to actively manage liquidity under different growth and stress scenarios.
6. Key Takeaways
This case highlights a common pattern in scaling businesses:
Operational complexity often outpaces financial structure.
A well-designed financial model is not just a reporting tool — it is a strategic decision system.
When operational drivers (products, suppliers, inventory) are fully integrated with financial outputs, and enhanced with scenario flexibility, companies can:
- Quantify uncertainty
- Stress-test both profitability and liquidity
- Optimize capital allocation
- Scale with discipline
7. Closing Thought
Expansion is not just about growth — it is about controlled growth under uncertainty.
That requires more than visibility.
It requires a system where operations, financing, and strategy are fully connected — and instantly adaptable.
