Lesson 1: Data Collection for Problem Solving: Checksheets, Stratification, and Trend Charts

Setting the Scene: When Numbers Tell a Story

It’s Monday morning at a mid-sized automotive components plant. The shift supervisor reports yet another spike in defective parts from Line 3 — the third time this month. The production manager asks the obvious question: “Why does this keep happening?” But without structured data, the team can only guess. They know something is wrong, but they cannot say what, when, where, or how often. This is precisely where the problem solver’s journey must begin — not with solutions, but with data. In Kobetsu Kaizen, Step 2 of the structured problem-solving process is dedicated to understanding the current situation. And that understanding is only as good as the data you collect and how you display it.

Learning Objectives

• Understand the purpose of structured data collection within the Kobetsu Kaizen problem-solving framework
• Design and apply a checksheet (tally chart) to capture defect or loss occurrence data at the source
• Use stratification to segment data and reveal hidden patterns by shift, machine, operator, or material
• Construct and interpret trend charts to identify whether a problem is worsening, improving, or cyclical
• Select the appropriate data collection tool based on the type of problem being investigated

The Foundation: Why Data Collection Comes First

In Kobetsu Kaizen, structured problem solving follows a defined sequence. Before setting goals (Step 3) or analyzing root causes (Step 4), the team must objectively represent the problem (Step 2). As the principle states: speak with data. Opinions, gut feelings, and individual recollections are not sufficient. Without reliable data, countermeasures are based on assumptions — and assumptions lead to wasted effort and recurring problems.

The three core tools for this phase are the checksheet (tally chart), stratification, and trend charts. These are part of the classic 7 Quality Tools (7QT) referenced throughout the Kobetsu Kaizen toolkit and form the bedrock of Step 2 problem representation. Each tool serves a distinct purpose, and together they give the problem solver a complete, fact-based picture of what is actually happening on the shop floor.

Checksheets (Tally Charts): Capturing Data at the Source

A checksheet — also called a tally chart — is a simple, structured form used to collect data in real time at the point of occurrence. Its power lies in its simplicity: anyone on the line can use it, no software is required, and it produces consistent, comparable data. In the Kobetsu Kaizen Board methodology, the tally chart is explicitly listed as a tool applicable to both problem concern (understanding what is happening) and problem cause (understanding why).

An effective checksheet is designed before data collection begins, not after. It should specify:

• What is being counted — defect types, machine stoppages, safety incidents, etc.
• Where the data is collected — which machine, line, or workstation
• When data is recorded — shift, time interval, production run
• Who is responsible for recording — operator, quality inspector, team leader

The output of a checksheet is a frequency table — a clear, numerical summary of how often each event occurs. This data then feeds directly into Pareto diagrams and trend analysis in subsequent steps. Poorly designed checksheets, or checksheets filled in retrospectively, introduce bias and undermine the entire analysis. Discipline at this stage protects the integrity of everything that follows.

Stratification: Slicing Data to Reveal the Truth

Stratification is not a separate tool in the same way as a checksheet or chart — it is a technique applied to data to break it into meaningful subgroups. Aggregated data often hides the real picture. When you separate data by shift, by operator, by machine, by raw material batch, or by time of day, patterns that were invisible suddenly become obvious.

For example, a total defect count of 120 per week may look uniform at first. But when stratified by shift, you might find that 95 of those defects occur on the night shift. This immediately refocuses the investigation. Is it an operator training issue? A maintenance schedule gap? A temperature variation? Stratification does not answer these questions, but it eliminates irrelevant variables and points the team toward the right area to investigate.

In the context of the 5W1H and N5W analyses used in Kobetsu Kaizen, stratification is directly aligned with the questions Where?, When?, and Who? — the spatial and temporal dimensions of the problem. It is a prerequisite for meaningful Pareto analysis and root cause investigation.

Trend Charts: Reading the Problem Over Time

A trend chart (also called a run chart or time-series chart) plots data points over time — hours, shifts, days, or weeks. Its primary value is revealing directionality: is the problem getting worse? Is it cyclical? Did it start after a specific event, such as a machine overhaul, a new supplier batch, or a personnel change?

In Kobetsu Kaizen, trend charts support the principle of orientation on zero — the long-term goal of eliminating losses entirely. To move toward zero, the team must first understand the current baseline and the trajectory. A three-month trend window, as referenced in the Kobetsu Kaizen Board methodology, is typically sufficient to identify patterns and confirm whether early countermeasures are producing measurable results.

Trend charts also serve a motivational purpose. When improvements are made, a well-maintained trend chart makes the progress visible to the entire team. This reinforces the SMART goal-setting framework used in Kobetsu Kaizen — specifically the Measurable and Time-limited dimensions of any improvement target.

Practical Case Study: Fenix Plastics

Fenix Plastics is a fictional injection molding company supplying components to household appliance manufacturers. Their quality team noticed a rising trend in surface defects on a key product line. Rather than jumping to solutions, the Kobetsu Kaizen team initiated a structured data collection phase.

First, they designed a checksheet deployed at the end-of-line inspection station. Operators recorded each defect type — sink marks, flash, discoloration, short shots — along with the mold cavity number, shift, and time of occurrence. After two weeks, the frequency table clearly showed that flash defects accounted for 61% of all rejects.

Next, they stratified the data by mold cavity and shift. The analysis revealed that 80% of flash defects originated from Cavity 4, and almost exclusively during the afternoon shift. The night and morning shifts showed negligible flash from the same cavity.

Finally, the team plotted a trend chart covering the past three months. The chart revealed that flash defects had been gradually increasing since a mold maintenance intervention six weeks earlier — a detail no one had consciously connected before.

With this data in hand, the team entered Step 3 (Set Targets) and Step 4 (Root Cause Analysis) with a precise, fact-based problem statement: “Flash defects in Cavity 4, predominantly in the afternoon shift, have increased progressively over the past six weeks.” This is incomparably more actionable than: “We have too many defects.”

Key Takeaways

• Data collection precedes all analysis. In Kobetsu Kaizen, Step 2 — problem representation — must be grounded in real, structured data collected at the Gemba. Assumptions are not evidence.
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