Cost vs complexity trade-off in PCB Design - Business Scenario Comparison

Scenario Mode

👤 Your Role: You are a PCB design analyst at an electronics manufacturing company.

📋 Request: Your manager wants to understand how the complexity of PCB designs affects the manufacturing cost. They want a clear report showing the relationship between design complexity and cost to help decide on design standards.

📊 Data: You have data on several PCB projects including the number of layers, component count, design complexity score (1-10), and total manufacturing cost in USD.

🎯 Deliverable: Create a dashboard report that shows the cost vs complexity trade-off with charts and summary metrics.

Sample Data

Project ID	Layers	Component Count	Complexity Score	Manufacturing Cost (USD)
P001	2	50	3	1200
P002	4	120	7	3500
P003	6	200	9	5800
P004	2	40	2	1100
P005	8	300	10	7200
P006	4	100	6	3200
P007	6	180	8	5400
P008	2	60	4	1400

1

Step 1: Load the PCB project data into your BI tool.

Import the table with columns: Project ID, Layers, Component Count, Complexity Score, Manufacturing Cost (USD).

Expected Result

Data table with 8 projects loaded and visible.

2

Step 2: Create a scatter plot to visualize the relationship between Complexity Score and Manufacturing Cost.

X-axis: Complexity Score; Y-axis: Manufacturing Cost (USD); Add data points for each project.

Expected Result

Scatter plot showing points rising as complexity increases.

3

Step 3: Add a trendline (linear regression) to the scatter plot to show cost trend with complexity.

Enable trendline option on scatter plot with linear fit.

Expected Result

Trendline showing increasing cost with higher complexity.

4

Step 4: Calculate the average manufacturing cost for each complexity score.

Create a measure: Average Cost = AVERAGE('Table'[Manufacturing Cost (USD)]) grouped by Complexity Score.

Expected Result

Table or chart showing average cost per complexity score from 2 to 10.

5

Step 5: Create a bar chart to display average manufacturing cost by complexity score.

X-axis: Complexity Score; Y-axis: Average Manufacturing Cost (USD).

Expected Result

Bar chart showing rising average cost as complexity score increases.

6

Step 6: Add summary cards showing minimum, maximum, and average manufacturing cost across all projects.

Min Cost = MIN('Table'[Manufacturing Cost (USD)]), Max Cost = MAX('Table'[Manufacturing Cost (USD)]), Avg Cost = AVERAGE('Table'[Manufacturing Cost (USD)]).

Expected Result

Three cards showing Min: $1100, Max: $7200, Avg: $3600.

7

Step 7: Add a slicer to filter projects by number of layers to explore cost vs complexity for different layer counts.

Add slicer on Layers column with options 2, 4, 6, 8.

Expected Result

User can filter dashboard by layers to see how cost-complexity relationship changes.

8

Step 8: Review the dashboard to ensure it clearly shows that higher complexity scores generally lead to higher manufacturing costs.

Check charts and summary cards for consistency and clarity.

Expected Result

Dashboard ready to present with clear visual evidence of cost vs complexity trade-off.

Final Result

Cost vs Complexity Dashboard

+-----------------------------+
| Complexity Score vs Cost    |
| Scatter plot with trendline |
+-----------------------------+

+-----------------------------+
| Average Cost by Complexity   |
| Bar chart                   |
+-----------------------------+

+-----------------------------+
| Summary Cards               |
| Min Cost: $1100             |
| Max Cost: $7200             |
| Avg Cost: $3600             |
+-----------------------------+

+-----------------------------+
| Layers Filter Slicer        |
| [2] [4] [6] [8]             |
+-----------------------------+

Bonus Challenge

Add a calculated measure to estimate cost per component and analyze how it varies with complexity.

Show Hint

Create a measure: Cost per Component = DIVIDE('Table'[Manufacturing Cost (USD)], 'Table'[Component Count]), then visualize it against Complexity Score.