1. OPERATIONS MANAGEMENT for MBAs Fourth Edition
Meredith and Shafer
I see that you will get an A this semester.
John Wiley and Sons, Inc.
Topic 2S: Forecasting
Chapter 8S : Forecasting

2. Outline Overview Purpose of forecasting
Choosing a forecasting technique
Time series methods
Causal / associative methods
Homework

3. To plan capacity, we need to estimate demand
Trends
Facilities
Price
Advertising
Staff
Competition
Purchasing
Quality
Location

4. Purpose of Forecasting
Market justification decision
Capacity and scheduling decisions
Long, Mid, & Short Term

5. Choosing a Forecasting Method
If historical data is available, use it.
Plot the data first.
If there is a trend, use a technique based on the trend.
If there is seasonality, use a technique that accounts for this.
If there are known associations, use a causal technique.
Choose a technique which gives the smallest mean absolute error.
Software and data mining may help.

6. Some Types of Forecasts
Qualitative
Time Series
Moving Averages
Exponential Smoothing
Seasonal
Causal / Associative
Simple Regression
Multiple Regression

7. Time Series Forecasting
Time series data is simply a set of values of some variable measured at regular intervals over time.
One data set (variable) over time.
Based on historical data. The more data the better.
Assumption: Past behavior helps us predict future behavior.
Time series data can have one or more of the following components / factors / variations.
Trend
Seasonal
Cyclical
Random

8. Time Series Forecasting
Quantity
| | | | | | | | | | | |
Months
Quantity
Time
Year 1
Year 2
Quantity
| | | | | |
Years

9. Time Series Forecasting

10. Time Series Forecasting Techniques
Moving Averages
Exponential Smoothing
Seasonal Methods

11. Moving Averages
Uses an average of past periods actual demands.

12. Exponential Smoothing

13. Exponential Smoothing
Date
Close
ES (alpha 0.1)
ES (alpha 0.9)
ES (alpha 0.2)
May-07
21.18
Jun-07
22.67
Jul-07
22.56
21.33
22.52
21.48
Aug-07
24.71
21.45
21.69
Sep-07
24.81
21.78
24.49
22.30
Oct-07
25.81
22.08
24.78
22.80
Nov-07
25.13
22.45
25.71
23.40
Dec-07
25.69
22.72
25.19
23.75
Jan-08
20.33
23.02
25.64
24.14
Feb-08
19.36
22.75
20.86
23.37
Mar-08
20.53
22.41
19.51
22.57
Apr-08
21.58
22.22
20.43
22.16
May-08
22.6
21.46
22.05
Jun-08
20.95
22.20
22.49
Jul-08
21.64
21.10
21.92
Aug-08
22.44
22.03
21.59
21.86
Sep-08
18.34
22.07
22.35
21.98
Oct-08
15.73
21.70
18.74
21.25
Nov-08
13.66
16.03
20.15
Dec-08
14.51
20.36
13.90
18.85
Jan-09
12.77
19.77
14.45
17.98
Feb-09
12.74
19.07
12.94
16.94
Mar-09
15.03
18.44
12.76
16.10
Apr-09
15.62
18.10
14.80
15.89
Forecast
17.85
15.54
15.83

14. Exponential Smoothing

15. Multiplicative Method

16. Multiplicative Method
Year1
Year2
Year3
Year4
Yr5Forecast Q1 45 70
100
132.82 Q2
335
370
585
725
843.62 Q3
520
590
830
1160 Q4
170
285
215
323.52
Totals
1000
1200
1800
2200
2600
Average
250
300
450
550
650
SFYr1
SFYr2
SFYr3
SFYr4
AvgSF
0.18
0.23
0.22
0.20
1.34
1.23
1.30
1.32
2.08
1.97
1.84
2.11
2.00
0.40
0.57
0.63
0.39
0.50

17. Simple Linear Regression Multiple Regression
causal / associative forecasting

18. For b, use exponential smoothing with smoothing constants of 0.1 and 0.9.
Part c. Compare the 3 methods using mean absolute deviation.
Part d. change to: for each method what is your sales forecast for next January (period 13)?
Omit e.

20. Additional Homework 1
D/FW Auto Imports (DAI) sells a variety of very high-end automobiles to customers in the Dallas-Fort Worth metroplex. Weekly demand for DAI’s 2011 BMW M3 has been increasing rapidly. High customer satisfaction is important to DAI and managers are wondering about relationships between customer satisfaction and a variety of service factors in the automobile customer preparation. Once a car is sold, auto preparation is quite extensive. Preparation involves 3 major steps; Performance Checking, Paperwork Preparation, and Detailing. If one resource is utilized (one person and a desk or a dock), the times for each of these processes is as follows:
Performance, 12 hours
Paperwork, 15 hours
Detailing, 8 hours
Weekly BMW M3 demand for the past 9 weeks has been; 9, 12, 15, 16, 20, 26, 39, 38, and 43.
Weekly customer satisfaction ratings were 7.9, 7.6, 8.3, 8.1, 8.5, 8.6, 9.1, 7.9, and 7.7.
Quality goals were met in every week except for weeks 4, 7, and week 9.
Develop a spreadsheet and multiple regression analysis that will address the following issues:
Assuming that each week 1 additional resource is added to handle demand, determine where resources should be added, determine the preparation operational weekly capacity, efficiency, and utilization.
For a preparation operation with an efficiency of 0.90, utilization of 0.80, and weekly quality goal met, predict that week’s customer satisfaction rating.
Which factor is most important in customer satisfaction (efficiency, utilization, or quality goal)?