1. IES 371 Engineering Management Chapter 13: Forecasting
Week 12
August 24, 2005
Objectives
Understand and practice on various forecasting method

2. Strategic Role of Forecasting
Focus on supply chain management
Short term role of product demand
Long term role of new products, processes, and technologies
Focus on Total Quality Management
Satisfy customer demand
Uninterrupted product flow with no defective items
Necessary for strategic planning

3. Demand Forecast Applications
Time Horizon
Medium Term Long Term
Short Term (3 months– (more than
Application (0–3 months) 2 years) 2 years)
Total sales
Groups or families
of products or
services
Staff planning
Production
planning
Master production
scheduling
Purchasing
Distribution
Causal
Judgment
Forecast quantity Individual
products or
Decision area Inventory
management
Final assembly
Workforce
Forecasting Time series
technique Causal
Facility location
Capacity
Process

4. Components for Forecasting Demand
Time frame
Short to mid-range
Long-range
Depend on each firm
and industry type
Demand behavior
Trend
Cycle
Seasonal
Forecasting method
Forecasting process
Time
(a) Trend
Demand
Random
movement
Time
(b) Cycle
Demand
Time
(d) Trend with seasonal pattern
Demand
Demand
Time
Time
Time
Time
(c) Seasonal pattern
(c) Seasonal pattern
(c) Seasonal pattern
(c) Seasonal pattern

5. Forecasting Techniques
Judgment method
Qualitative method that translates the opinions of managers, expert opinions, customer surveys, and sale-force estimate into quantitative estimates
Causal method
Use of past data on independent variables to derive mathematical relationship Demand = f(relevant factors)
Linear Regression
Time Series analysis
statistical technique using past data for short-term forecasting applications
Naïve forecast
Moving average
Weighted moving average
Exponential smoothing
Adjusted exponential smoothing
Linear trend line

6. Causal Method – Linear Regression Method
Mathematical relationship between two or more variables
What causes to behave in a certain way?
Linear regression: y = a + bx
Correlation
Strength of the relationship between dependent and independent variables
Range of [-1.00, 1.00]

7. Causal Method – Linear Regression Method
Dependent variable
Independent variable X Y
Estimate of
Y from
regression
equation
Regression
equation:
Y = a + bX
Actual
value
of Y
Value of X used
to estimate Y
Deviation,
or error {

8. Ex 1: Causal Method – Linear Regression Method
Carpet City wants to develop a means to forecast its carpet sales. The carpet store manager believes that the store’s sales are directly related to the number of new housing starts in town. The manager has gathered data from county records of monthly house construction permits and from store records on monthly sales. These data are as follows:
Required
Develop a linear regression model for this data and forecast sales if 30 construction permits for new home are filed
Determine the strength of the casual relationship between monthly sales and new home construction using correlation

9. Time Series Method- Simple Moving Average
Use several demand values in the recent past
Smooth out randomness
Suitable for stable demand
Number of periods in the moving average   smoother forecast
n = Total number of periods in the average
Dt = Demand in period t
Ft+1 = Forecast for period t +1

10. Ex 2: Time Series Method- Simple Moving Average
The manager forecast weekly demand for his specialty pizza so that he can order pizza shells weekly. Forecast the demand for pizza for June 23 to July 14 by using the simple moving average method with n = 3
Recently demand has been as follows:
Week of
Pizzas
June 2
June 9
June 16 50 65 52
June 23
June 30
July 7 56 55 60

11. Time Series Method- Weighted Moving Average
More closely reflect data fluctuations
Recent data, more weight
Weight: trial-and-error experiment
Wi = weight for period i, [0%, 100%]
 Wi = 1.00
Ex 3:
With the demand data in Example 2, forecast the demand for pizza by using WMA method with n = 3 and weights of 0.50, 0.30, and 0.20, with 0.50 applying to the most recent demand

12. Time Series Method- Exponential Smoothing
Weights the most recent data more strongly
React to recent changes in demand
Seasonal pattern of demand
Widely used in many businesses
Requires minimal data
Ft+1 = the forecast for the next period
Dt = actual demand in the current period
Ft = the previously determined forecast for the current period
 = smoothing constant (weighted factor)

13. Ex 4: Time Series Method- Exponential Smoothing
Use the exponential smoothing method to forecast the number of units for June to January. The initial forecast for May was 105 units;  = 0.2
The monthly demand for units manufactured of a company has been as follows:
Month
Units
May
June
July
August
100 80
110
115
September
October
November
December
105
125
120

14. Time Series Method- Linear Trend Line
For demand with obvious trend over time
A least squares regression line
y = a + bx
Dependent variable (y) = Forecasted demand
Independent variable (x) = Time
a = intercept at period 0
b = Slope of the line

15. Choosing a Time-Series Method Forecast Error
Cumulative sum of forecast errors (CFE)
Mean squared error (MSE)
Standard deviation
Mean absolute deviation (MAD)
Mean absolute percent error (MAPE)

16. Tracking Signal CFE MAD Tracking signal = +2.0 — +1.5 — +1.0 — +0.5 —
–0.5 —
–1.0 —
–1.5 —
| | | | |
Observation number
Tracking signal
Control limit
Out of control