Chapter 10 Flashcards
Study Card 1: Linear Cost Functions
What it is:
Formula
What it is:
A mathematical formula showing how costs change with activity levels.
Y = a + bx
Formula: Y = a + bx
- Y = Total cost
- a = Fixed cost
- b = Variable cost
- X = Activity level (cost driver)
Importance of Causality in Cost Estimation
Causality ensures the activity (X) directly influences the cost (Y).
Without causality, cost estimation is inaccurate.
Methods of Cost Estimation
High Low Method
Regression Analysis
Visual Inspection
Engineering Estimates
High-Low Method:
Uses highest and lowest activity levels to estimate cost.
Regression Analysis
Uses statistical data to estimate cost.
Steps in Estimating a Cost Function
- Choose dependent (cost) and independent (activity) variables.
- Collect data on both.
- Plot data.
- Estimate cost function (using high-low or regression).
- Evaluate accuracy.
Nonlinear Cost Functions & Learning Curves
Nonlinear costs: Costs that don’t follow a straight line.
Learning Curves: As production increases, per-unit costs drop due to efficiency gains.
Quality Control as a Cost Driver
Quality control impacts costs:
- Prevention Costs: Measures to prevent defects.
- Appraisal Costs: Inspecting products.
- Failure Costs: Costs of defects found before or after shipment.
Implication: Strong quality control reduces overall costs.
Data Collection Issues
Accuracy of data
Consistency: Data should be collected uniformly.
Relevance: Data must relate directly to the cost.
Interpreting Regression Models
Intercept (a): Fixed cost.
Slope (b): Variable cost per activity unit.
R²: How well the model explains the cost.
P-Value: Shows if the relationship between cost and activity is statistically significant.
Learning Curve Model
Shows how costs decrease as production increases.
Formula: Y = aX^b
a = First unit cost
b = Learning curve slope (negative)
Relevant Range
The range of activity levels over which cost behavior is predictable and linear.
Outside the range: Cost behavior may change, making estimates unreliable.
High-Low Method
Identify the highest and lowest activity levels.
Calculate variable cost per unit
Find fixed cost
Regression Analysis
A statistical technique to estimate cost functions using all data points.
Benefits: More accurate than the high-low method, provides statistical significance (p-value).
Scatter Plot Analysis
A graphical tool to see the relationship between cost driver and total cost.
A strong correlation shows that the cost driver is explaining the changes in cost.
R² (Coefficient of Determination)
What it tells you:
Measures how well the cost driver explains the changes in total cost.
Higher R² means a stronger relationship (typically, a value of 0.3 or higher is good).
Learning Curve Effect
As production increases, the cost per unit decreases due to increased efficiency.
Economic Plausibility
Does the relationship between activity and cost make sense?
Example: More machine hours → Higher utility costs (reasonable
Time Drivers
What drives time-based costs:
Uncertainty: When customers place orders.
Bottlenecks: Limited capacity causing delays.
Balanced Scorecard & Time Measures
Financial: Profitability, cost control.
Customer: Response time, satisfaction.
Internal Processes: Efficiency, lead times.
Learning and Growth: Employee skills, innovation.
Costs of Quality (COQ)
Prevention Costs: Training, process improvements.
Appraisal Costs: Inspections, testing.
Internal Failure Costs: Rework, scrap.
External Failure Costs: Warranty, returns.
Control Charts
Monitor process variation over time.
Random variation is expected, but non-random variation requires investigation.
Control limits: Boundaries that help identify issues.
Pareto Diagram
A bar chart showing the frequency of defects, ordered from most to least frequent.
80/20 Rule: Often, 80% of issues come from 20% of the causes.
Cause-and-Effect (Fishbone) Diagram
A visual tool to identify root causes of defects or problems.
Resembles a fish skeleton, with the main “bone” being the problem and the “ribs” showing possible causes.
