Deeptech Yield Improvement Forecasting: Modeling Financial Impact on COGS and Runway

How to Forecast Cost Savings from Manufacturing Yield Improvements

For many scaling deeptech companies, converting variable production line data into a dependable cost-of-goods forecast is a constant struggle. The numbers in your QuickBooks or Xero account often feel disconnected from the daily reality of scrap, rework, and production hiccups. This gap makes it difficult to forecast cost savings from manufacturing yield improvements or to confidently prioritize projects. When yield gains slip, the resulting working-capital crunch can threaten investor confidence and shorten your runway.

This article provides a three-step framework for building financial models that connect operational performance to financial outcomes. It is designed for operations leads and founders at pre-seed to Series B startups, using the tools you already have. The goal is to move from reactive problem-solving to proactive financial planning, turning production efficiency metrics into a strategic lever for growth. For broader context, see the manufacturing scale-up cost forecasting hub.

Step 1: Calculate True COGS with Yield-Adjusted Data

If your Cost of Goods Sold (COGS) feels unpredictable, the likely culprit is a failure to account for production yield. Many early-stage teams calculate COGS using a 'best-case scenario' model, dividing total input costs by the total number of units started. This approach ignores the costly reality of manufacturing waste reduction and creates a fundamental flaw in your financial forecasting.

The first step in any credible manufacturing yield analysis is to calculate your Yield-Adjusted COGS. This metric provides a true picture of what each sellable unit costs to produce. The formula is straightforward:

Yield-Adjusted COGS = Total Input Costs / Number of Good Units Produced.

Consider a simple example. If you spend $1,000 on materials and labor to produce 100 units, the best-case cost is $10.00 per unit. But if only 90 of those units are sellable, representing a 90% yield, your actual cost for each good unit is different. The example calculation: spending $1000 for 100 units with 90 sellable (90% yield) results in a cost per good unit of $11.11, not $10.00. That 11% increase in unit cost directly erodes your gross margin and impacts every financial projection you build. In practice, we see that building this simple calculation into your spreadsheet model is the foundational link between the factory floor and your profit and loss statement.

Step 2: Use Manufacturing Yield Analysis to Pinpoint Financial Leaks

Once you have a reliable Yield-Adjusted COGS, the next question is where to focus your improvement efforts. With limited resources, you cannot fix every issue at once. The key is to prioritize projects that will deliver the most cash savings, which requires moving beyond simply counting defects to quantifying their financial impact.

First, it is important to make a critical distinction between scrap and rework. Scrap represents the total cost of a discarded unit, a complete loss of all materials and labor invested up to the point of failure. Rework is the additional cost in labor and materials required to fix a defective unit. Both hurt your margins, but they represent different types of financial loss and often demand different solutions. This is part of the broader cost of poor quality. For more on assigning dollars to failures and prevention, see our guide on quality cost modeling: prevention vs failure.

To guide your focus, you can use First Pass Yield (FPY), a core production efficiency metric that measures the percentage of units that pass a process step without any rework. The gap between top and bottom performers is significant.

Research shows that in mature electronics manufacturing, top performers achieve First Pass Yields over 95%, while others are stuck below 70% (McKinsey, 2021).

The most effective way to prioritize is by applying Pareto analysis to your yield loss. The consistent pattern is that 80% of yield loss cost often comes from one or two process steps (the Pareto principle). By tracking the cost of scrap and rework at each stage of your production line, you can identify the one or two areas causing the most financial damage. This transforms a long list of operational problems into a clear, data-driven investment case. For example, an example investment case could be: a $20,000 machine fixes a rework issue costing $10,000 per month, resulting in a two-month payback period.

Step 3: Use Financial Modeling for Yield to Forecast Runway Impact

Your board and investors are primarily focused on one strategic metric: cash runway. They need to know how operational improvements will extend the company’s life. Connecting a 5% improvement in FPY to a concrete number of weeks of runway is a powerful way to demonstrate control over the business and build confidence.

The connection is direct: higher yield means a lower Yield-Adjusted COGS. This reduces the amount of cash tied up in each good unit of inventory, freeing up working capital and extending your runway. This is where financial modeling for yield becomes a strategic tool.

To show the numbers, you can use scenario modeling. Create a 'Yield Slip' and a 'Yield Gain' version of your cash forecast. A 'Yield Slip' scenario models the impact of a small drop in yield, showing how it pulls forward your cash-out date. A 'Yield Gain' scenario demonstrates how specific, targeted improvements translate into more time to operate. For instance, an example runway impact could be: a $50,000 project that increases overall yield by 5% can add two full weeks to the cash runway. This makes the return on investment tangible and strategically relevant. Use scenario planning to structure these cases.

This dynamic also helps explain the Bullwhip Effect within your operations. Small, seemingly minor variances in yield on the production line can cause disproportionately large swings in inventory purchasing and cash requirements, creating surprises that undermine financial stability. Modeling these scenarios makes your cash flow forecast more resilient.

Common Pitfalls When Building Your First Yield Model

As you begin to build these models, several common challenges emerge for early-stage teams. What founders find actually works is anticipating these issues from the start.

First, teams often confuse internal operational models with external accounting compliance. Your financial model for prioritizing yield projects is a decision-making tool. It does not need to be perfectly aligned with the accounting standards used for your formal financial statements. For US companies, that is US GAAP; in the UK, it is typically FRS 102. The goal of the model is directional accuracy for internal strategy, not perfect compliance for an audit.

Second, it is easy to overlook the tax implications of your improvement projects. These initiatives often qualify as R&D. In the UK, this may fall under the HMRC R&D Scheme. For US companies, recent changes to Section 174 (R&D Capitalization) have altered how these expenses must be treated for tax purposes, requiring them to be capitalized and amortized rather than immediately expensed. Understanding these rules is crucial for accurate cash flow forecasting.

Third, there is a strong temptation to over-engineer the model. The reality for most startups is more pragmatic: a simple, shared spreadsheet that the operations lead can own and update is far more valuable than a perfect, complex model that goes unused. The goal is a usable tool that drives better decisions, not an academic exercise.

Putting Your Manufacturing Yield Analysis into Action

Forecasting cost savings from process improvements requires translating operational data into financial impact. By building a three-tiered model, you can create a clear line of sight from the production floor to your cash runway. The objective is to make smarter, data-driven decisions about where to invest your limited time and capital for scaling manufacturing operations.

To begin, focus on three immediate actions:

1. Establish Your Foundational Model: This week, calculate your true Yield-Adjusted COGS for your primary product. Stop using the best-case number in your forecasts and start using the figure that reflects reality.
2. Build Your Operational Model: Identify and quantify the financial cost of your top three sources of yield loss. This is not about counting defects; it is about attaching a dollar or pound value to each one to enable clear prioritization.
3. Create Your Strategic Model: Run a simple 'Yield Slip' scenario in your cash forecast. Model a 5% decrease in your primary product’s yield and calculate the specific impact on your cash-out date. This exercise will immediately highlight the strategic importance of operational efficiency.

By taking these steps, you can turn manufacturing yield analysis from a technical exercise into a powerful lever for operational cost reduction and strategic financial management. For broader models and tools, visit the manufacturing scale-up cost forecasting hub.