You’ve done everything by the book. Your bill of materials (BOM) is identical across the batch, the production line is running smoothly, and your team is following the standard operating procedure. Yet, when the modules come off the line, the flasher results are a frustrating puzzle—some modules hit the target power output, while others lag by 3, 5, or even 7 watts.

Sound familiar? This unpredictable variation, or inconsistent yield, is one of the most persistent challenges in solar module manufacturing. It’s not just a minor headache; it’s a direct threat to a project’s bankability and long-term reliability.

The 2024 PVEL PV Module Reliability Scorecard underscores just how critical manufacturing consistency is. Their research revealed that even modules with the exact same BOM from the same factory can have wildly different performance and degradation rates. With issues like Light-Induced Degradation (LID) still impacting a third of all tested BOMs, the pressure to produce consistently high-performing modules has never been higher.

So, where do you begin when the cause isn’t obvious? Instead of resorting to expensive, trial-and-error guesswork, a more structured approach can save you time, materials, and money. Let’s walk through a powerful tool designed for this very problem.

Beyond Guesswork: Introducing the Fishbone (Ishikawa) Diagram

The Fishbone Diagram, also known as an Ishikawa or Cause-and-Effect Diagram, is a visual tool used to systematically brainstorm and organize the potential causes of a specific problem. Think of it as a structured map for your investigation.

Instead of randomly listing ideas, the diagram prompts your team to think through different categories of influence, ensuring no stone is left unturned. The „head“ of the fish is the problem (e.g., „Inconsistent Module Yield“), and the „bones“ represent categories of potential causes.

The six main categories, often called the 6 Ms, are:

• Machine: The equipment and tools used in the process.
• Method: The standard operating procedures and instructions.
• Material: The raw components and consumables.
• Manpower (People): The human element involved in the process.
• Measurement: The data collection and inspection methods.
• Milieu (Environment): The conditions in which the process operates.

By mapping out possibilities within each category, you transform a complex problem into a series of specific, testable hypotheses.

Case Study: Diagnosing Inconsistent Module Yield

Let’s apply this to our real-world problem. A module developer is experiencing an unacceptable power output variance in their new half-cut PERC module prototypes. The problem statement, or the „fish head,“ is Inconsistent Power Output.

With the problem defined, let’s brainstorm potential causes for each bone.

Brainstorming the „Bones“

Materials

This bone explores everything that goes into the module. NREL research consistently points to encapsulants and backsheets as key factors in long-term degradation, and inconsistency often starts here.

• EVA/POE Batch Variance: Has the supplier changed? Could there be a difference in viscosity or curing properties between batches?
• Cell Mismatch: Are the cells properly sorted by efficiency class before stringing?
• Backsheet Quality: PVEL’s scorecard notes that backsheet material is a primary driver of degradation. Could variations in its composition or thickness be affecting performance?
• Ribbon/Busbar Coating: Is the solder coating consistent, ensuring a reliable electrical connection?

Machine

Here, we look at the hardware. Even the most advanced equipment can drift out of spec.

• Laminator Temperature Zones: Is there a „cold spot“ on the heating platen causing incomplete curing? Are all thermocouples calibrated and reading accurately?
• Stringer Alignment: Is the stringer placing cells with perfect consistency, or could subtle misalignments be creating stress and potential for microcracks?
• Flasher Calibration: Is the sun simulator itself the source of the variance? When was it last calibrated against a reference cell?

Method

This category covers the „how-to“ of the process. A procedure that seems robust on paper can have hidden variables.

• Layup Process: Are operators laying materials in the exact same sequence and orientation every time? Small deviations can impact lamination.
• Curing Recipe: Is the time, temperature, and pressure profile in the laminator optimized for the specific EVA/POE being used?
• Cooling Rate: How are modules cooled after lamination? An uncontrolled cooling rate can lock in internal stresses.

Manpower (People)

The human factor is always a consideration, especially across different shifts.

• Training Gaps: Do all operators on all shifts have the same level of training and understanding of the process sensitivities?
• Handling Differences: Does one operator handle cells more delicately than another, potentially reducing the risk of invisible microcracks?

Measurement

If you can’t trust your data, you can’t solve the problem.

• IV-Curve Parameters: Are the parameters in the flasher software set correctly for the type of module being tested?
• EL Testing Resolution: Is the Electroluminescence (EL) test sensitive enough to spot subtle cell-level defects that could be dragging down output?

Environment (Milieu)

The factory itself can introduce variables.

• Humidity & Temperature: Are raw materials like EVA stored in a climate-controlled area? High humidity can affect material properties before they even enter the laminator.
• Airborne Contaminants: Is there dust or other particulate matter in the layup area that could be creating contamination on the cell surface?

From Diagram to Diagnosis: What’s Next?

The Fishbone Diagram doesn’t give you the answer. It gives you a roadmap.

With this map, you can prioritize the most likely causes and design targeted experiments to prove or disprove them. Instead of randomly changing three things at once, you can now investigate systematically.

For instance, to test the „EVA Batch Variance“ hypothesis, you could run highly controlled lamination trials where the only variable is the EVA batch number. If you suspect the layup process is the culprit, you might prototype new solar module designs under strict observation to compare different methodologies.

This methodical approach is essential. Without it, you risk chasing ghosts, wasting expensive materials, and potentially making the problem worse.

Why a Structured Approach Matters

The solar industry is built on trust and data. When a project developer sees inconsistent performance, it erodes confidence. A structured, engineering-driven approach to problem-solving demonstrates a commitment to quality and builds a reputation for reliability.

This focus on process discipline is the foundation of high-quality manufacturing. It reflects a deep understanding of process stability and technical expertise, transforming production from an art into a science. By systematically identifying and eliminating sources of variation, you ensure that the module you produce on Monday performs identically to the one you produce on Friday.

FAQ: Getting Started with Root Cause Analysis

What’s the biggest mistake people make with Fishbone diagrams?

The most common mistake is creating one in isolation. The tool’s power comes from collaboration. A truly effective brainstorming session includes process engineers, machine operators, quality control staff, and even material handlers—each person brings a unique perspective on potential causes.

How is this different from just making a list?

A simple list is flat. The Fishbone Diagram provides structure, showing the relationships between causes and organizing them into logical groups. This helps you see patterns you might otherwise miss and ensures you’ve considered the full spectrum of possibilities, from the machine’s calibration to the humidity in the room.

Can I use this for problems other than power output?

Absolutely. The Fishbone Diagram is a universal problem-solving tool. It’s equally effective for diagnosing issues like encapsulant delamination, bubbles under the backsheet, high cell-to-module (CTM) losses, or inconsistent cycle times on the production line.

Your Path to Consistent Production

Inconsistent module yield isn’t something manufacturers have to accept. It’s a solvable problem, but it requires moving from reactive fixes to proactive, structured investigation.

By using simple but powerful tools like the Fishbone Diagram, you can stop guessing and start diagnosing. This methodical approach not only resolves today’s issues but also builds a more robust and reliable production process for the future—saving time, reducing waste, and ultimately protecting your reputation in a competitive market.