Demand Forecasting
The Naïve Method of demand forecasting is the simplest forecasting
technique, where the forecast for the next period is simply the actual demand
from the previous period. It’s based on the assumption that future demand will
mirror the most recent actual demand.
Key Features of the Naïve Method:
• Simplicity: Very easy to understand and implement.
• No Complex Calculations: It requires no statistical tools or data
analysis.
• Reactive: It responds quickly to changes in demand, as it directly reflects
the last observed value.
Example 1: Monthly Sales Data
Let's say you have the following sales data for a product over five months:
Month Actual Sales
Jan 100
Feb 120
Mar 110
Apr 130
May 140
Using the Naïve Method:
• Forecast for June: The forecast would be equal to May's actual sales,
which is 140.
Example 2: Weekly Demand for a Coffee Shop
Assume a coffee shop tracks the number of cups sold each week:
Week Cups Sold
1 200
2 220
3 210
4 250
5 240
Using the Naïve Method:
• Forecast for Week 6: The forecast would be equal to Week 5's actual
sales, which is 240 cups.
Advantages of the Naïve Method:
1. Easy to Use: No need for statistical knowledge.
2. Quick Adaptation: Responds well to recent trends or changes.
3. Baseline for Comparison: Provides a simple benchmark to compare
with more complex methods.
Disadvantages of the Naïve Method:
1. No Trend Consideration: It doesn’t account for upward or downward
trends in the data.
2. No Seasonality Consideration: Ignores seasonal effects which can
significantly impact demand.
3. Potential Inaccuracy: In volatile markets, relying solely on the last
period can lead to inaccurate forecasts.
When to Use the Naïve Method:
• Short-Term Forecasting: When looking at very short time frames where
demand is relatively stable.
• Low Variability: When demand patterns are consistent and do not show
trends or seasonal effects.
• As a Starting Point: It can serve as a baseline to compare with more
sophisticated forecasting methods.
By using real-world examples and explaining the pros and cons, students can
grasp how the Naïve Method works and understand its practical applications in
demand forecasting.
The Least Squares Method is a statistical technique used to find the best-fitting line through
a set of data points in order to predict values. It's particularly useful in regression analysis,
where the goal is to model the relationship between a dependent variable and one (or more)
independent variables.
Key Concepts of the Least Squares Method:
• Line of Best Fit: The method minimizes the sum of the squares of the vertical distances
(residuals) between the actual data points and the line.
• Equation of the Line: The line is typically expressed as y=mx+b where:
✓ y is the dependent variable (what you're trying to predict).
✓ m is the slope of the line.
✓ x is the independent variable.
✓ b is the y-intercept.
Disadvantages:
1. Sensitive to Outliers: Extreme values can heavily influence the slope and intercept.
2. Linear Assumption: Assumes a linear relationship between variables, which may not
always be the case.
