1. Fundamentals of Production Planning and Control
Chapter 2
Forecasting Fundamentals

2. 2.1 Fundamental Principles of Forecasting
Forecasting defined:
Forecasting is a technique for using past experiences to project expectations for the future.
Why forecast? Time consumer is willing to wait for product is less than the time it takes to make it.

3. 2.1 Fundamental Principles of Forecasting
It is not a prediction of the future
It is a structured projection of past knowledge
Some are for long-range planning
capacity needs
strategic plans
strategic purchasing decisions
Some are for short-range planning
used for scheduling
used to launch production prior to an order
Why forecast? Time consumer is willing to wait for product is less than the time it takes to make it.

4. 2.1 Characteristics of Forecast
Forecasts are almost always wrong
How wrong do we think it will be?
what will we do if it is wrong this much?
Buffer stock or capacity may be needed
Why forecast? Time consumer is willing to wait for product is less than the time it takes to make it.

5. 2.1 Characteristics of Forecast
Forecasts are more accurate for groups or families of items
It is easier to forecast a product line than an individual item
expected increase in auto repair families
Forecast errors cancel each other out when aggregated
more accurate to forecast total sedans rather than particular type
Why forecast? Time consumer is willing to wait for product is less than the time it takes to make it.

6. 2.1 Characteristics of Forecast
Forecasts are more accurate for shorter time periods
There are fewer potential disruptions in the near future that can impact production.
Demand for extended time periods is less reliable.
Demand for HUMVEE bearings in Iraq now v. year 2007
Why forecast? Time consumer is willing to wait for product is less than the time it takes to make it.

7. 2.1 Characteristics of Forecast
Every forecast should include an estimate of error.
First principle is “How wrong is the forecast?”
A good forecast has both an estimate of error and a forecast estimate
Why forecast? Time consumer is willing to wait for product is less than the time it takes to make it.

8. 2.1 Characteristics of Forecast
Forecasts are no substitute for calculated demand.
Use actual demand data if it is available.
Do not make calculations based on the forecast alone.
Why forecast? Time consumer is willing to wait for product is less than the time it takes to make it.

9. 2.2 Major Categories of Forecasts
There are two major types of forecasts
Qualitative
Quantitative (two subcategories)
time series
causal
The forecast is usually based on personal judgment or some external qualitative data;
The forecast tends to be subjective and, since they tend to be developed from experience of people involved, will often be biased based on the potentially optimistic or pessimistic position of those people;
An advantage is that this method often does allow for some fairly rapid results;
In some cases, qualitative forecasts are especially important as they may be the only method available;
These methods are usually used for individual products or product families, seldom for entire markets;

10. Qualitative Forecasting
Generated from information that is not well-defined or analytically structured
Useful when no past data is available
new products or no sales history
how many products to make and market when being introduced (auto sweeper)

11. Qualitative Forecasting
Usually based on judgement or external qualitative data
Tends to be subjective
Experience of others (optim. V. pessim.)
Allows rapid results
May be the only method available
Usually used for individual products, not entire markets

12. Qualitative Forecasting
Market Survey
Delphi Method/Panel Consensus
Life Cycle Analogy
Informed Judgment

13. Market Survey
Structured Questionnaire to potential customers of market
Solicits opinions on likelihood of purchase
Very accurate
Expensive and time consuming
Structured Questionnaire
Solicit opinions to determine likelihood of purchase

14. Delphi Method/Panel Consensus
Uses experts in the field to form a consensus
Very accurate
Expensive and Time Consuming
Uses defined experts in the field
Interact together to form a consensus
In Delphi the participants don’t interact

15. Life Cycle Approach
Based on the fact that that products have a well defined life cycle
Good starting point for a new product
Fairly simple and inexpensive
May not be particularly accurate
Questions to answer:
What is the time frame of the life cycle?
How rapid will the growth (decline) be?
How large will the overall demand be?

16. Informed Judgment Very common, but often inaccurate
Ask opinions often from Sales Managers
Among the most popular method
Least expensive
Worse results
Can be an optimistic goal
Can be a pessimistic goal
Can be affected by recent events

17. Anecdotal Example 2.1 Forecast is for 16,000
What type of forecast is this?
What should Joe have done?
What are the consequences of Joe’s actions?
…if he makes 10,000
…if he makes 16,000

18. Quantitative Techniques
Causal quantitative forecasting technique:
Based on relationship between variables…one causes the other to change in a predictable manner
Assumes this variable can be measured
housing starts as a leading indicator
Can gain market knowledge
Seldom used for products (markets)
Time-consuming and expensive
Causal – based on data exterior to the firm (extrinsic)
Based on the relationship between variables
Assumption of causality (ex new housing starts)
Good leading indicator bring excellent results
Usually used for an industry, not a product
Methods are time-consuming and expensive
Input/Output
Examine flow of goods and services throughout the entire economy
Substantial amounts of data
Econometric models
Statistical analysis of various sectors of the economy
Simulation
Time Series – past predicts the future – based on data internal to the firm (intrinsic)

19. Input-Output Models
Complex model that examines the flow of goods and services through the entire economy
Accurate but expensive
Time-consuming (shorter periods…)
Usually apply to market predictions

20. Econometric Models
Similar to Input-Output except use statistical analysis of sectors of the economy
Accurate but expensive
Usually apply to market predictions

21. Simulation Models Uses computer simulation
Expensive and time consuming to collect data
Achieve accurate results inexpensively once simulation is designed

22. Regression Uses statistical relationship between two or more variables
Assumes a causal relationship between factors..one causes the other to move
Independent variable is often called a leading indicator
Automotive after-market sales v. new auto production

23. Quantitative Forecasting -Time Series
Most commonly used for product demand forecasts
They assume past demand follows a pattern that can be used to predict the future
The only independent variable is time
Based in internal (intrinsic) data
Used by operations mgmt. for production plans

24. Quantitative -Time Series
Most time series models attempt to mathematically capture patterns of past demand
Random pattern
no predictable or uniform demand pattern
Trend
Seasonality
Cyclical

25. Random Demand Pattern
Time
Demand

26. Examples of Trend Patterns
Time
Demand
Linear increasing Trend
Time
Demand
Linear decreasing Trend
Time
Demand
Time
Demand
Nonlinear increasing Trend
Nonlinear Decreasing Trend

27. Seasonal or Cyclical Pattern
Time
Demand
Seasonal/Cyclical Pattern

28. Composite Demand Pattern
Time
Demand
Seasonal, Trend and Random Patterns

29. Time Series Forecasting
Random patterns
use simple smoothing method
if no pattern exists, use last period demand
because of the randomness of the pattern, the smoothing will remove the demand spikes
too much smoothing and the actual demand is missed

30. Methods for Random Patterns
Moving Average
Weighted Moving Average
Exponential Smoothing

31. 3-Period Moving Average
Demand
MA (n=3) FE
MA (n=5) 1 2 26 3 22 4 25
24.0 5 19 6 31 7 8 18 9 29 10 24 11 30 12 23
MFE
MAD
MSE

32. 5-Period Moving Average
Demand
MA (n=3) FE
MA (n=5) 1 52 2 48 3 36 4 49
45.3 5 65
44.3 6 54 50 7 60 56 8
59.7 9 51 10 62 53 11 66
53.7 12
MFE
MAD
MSE

33. 5-Period Moving Average
Demand
MA (n=3) FE
MA (n=5) 1 52 2 48 3 36 4 49
45.3 5 65
44.3 6 54 50 7 60 56
50.4 8
59.7
52.8 9 51
55.2 10 62 53
55.6 11 66
53.7 55 12
57.4
MFE
MAD
MSE

34. 3-period vs. 5-period
Period
Demand
MA (n=3) FE
MA (n=5) 1 52 2 48 3 36 4 49
45.3
-3.7 5 65
44.3
-20.7 6 54 50 -4 7 60 56
50.4
-9.6 8
59.7
11.7
52.8
4.8 9 51
55.2
4.2 10 62 53 -9
55.6
-6.4 11 66
53.7
-12.3 55
-11 12
-2.3
57.4
-4.6
MFE
-3.8
MAD
7.9
6.4
MSE
95.1
47.4

35. 3-period vs. 5-period Plots
Moving Average Forecasts 10 20 30 40 50 60 70 5 15
Period
Demand vs. Forecasts
Demand
MA (n=3)
MA (n=5)
Method lags behind a trend

36. Moving Average Points to Consider...
The forecast line is smoother than the demand line
the more periods, the smoother the line
The forecast will always lag behind the actual demand
Moving averages should not be used when the demand follows a trend or cyclical pattern

37. Weighted Moving Average
The same as simple moving averages except…
The weight assigned to each past demand point can vary
More influence can be given to specific demand points
the most recent is important

38. Weighted Moving Average
Period
Demand
WMA(.5,.3,.2) FE 1 52 2 48 3 36 4 49
47.6
-1.4 5 65
44.5
-20.4 6 54
45.7
-8.3 7 60
54.8
-5.2 8
60.7
12.7 9 51
54.6
3.6 10 62
-7.4 11 66
51.7
-14.3 12
57.3
-4.7
MFE
-5.04
MAD
8.45
MSE
107.74

39. Exponential Smoothing
Ft=αDt-1+(1-α)Ft-1

40. Exponential Smoothing
Period
Demand
ES (α=.1) FE
ES (α=.5)
ES (α=.8) 1 52 2 48 3 36 4 49 5 65 6 54 7 60 8 9 51 10 62 11 66 12
MFE
MAD
MSE
For the first period, let forecast equal demand

41. Exponential Smoothing
Period
Demand
ES (α=.1) FE
ES (α=.5)
ES (α=.8) 1 52 2 48 3 36 4 49 5 65 6 54 7 60 8 9 51 10 62 11 66 12
MFE
MAD
MSE

42. Exponential Smoothing
Period
Demand
ES (α=.1) FE
ES (α=.5)
ES (α=.8) 1 52 2 48 3 36
51.6 50
48.8 4 49 5 65 6 54 7 60 8 9 51 10 62 11 66 12
MFE
MAD
MSE

43. Exponential Smoothing
Period
Demand
ES (α=.1) FE
ES (α=.5)
ES (α=.8) 1 52 2 48 4 3 36
51.6
15.6 50 14
48.8
12.8 49 43 -6
38.6
-10.4 5 65
49.9
-15.1 46
-19
46.9
-18.1 6 54
51.4
-2.6
55.5
1.5
61.4
7.4 7 60
51.7
-8.3
54.8
-5.3
-4.5 8
52.5
4.5
57.4
9.4
59.1
11.1 9 51
52.1
1.1
52.7
1.7
50.2
-0.8 10 62
-10
51.8
-10.2
50.8
-11.2 11 66 53
-13
56.9
-9.1
59.8
-6.2 12
54.3
-7.7
61.5
-0.5
64.8
2.8
MFE
-2.8
-1.8
-1.2
MAD
7.5
7.3
8.1
MSE
83.1
83.2
89.6

44. Comparison of Techniques
Method Lags behind a trend

45. Review of Methods for Randomness
Moving Average
Must store n values
All N values have equal weight
Ignores all data in prior periods
Exponential Smoothing
Essentially a weighted average of all values
Less storage requirements

46. Comparison of Techniques
Similarities
Both methods assume that the underlying demand is constant plus some random fluctuation
Both depend on the specification of a single parameter
Both will lag behind a trend if one exists When α = 2/(n+1), forecast errors have the same distribution
Differences
Exponential smoothing is a weighted average of all past data points vs. n for moving average
To use moving average, last n demands must be saved vs. exponential smoothing only needs the last one.

47. Methods for Trend
Regression Analysis
Double Exponential Smoothing

48. Regression Analysis

49. Regression Analysis – modified equations

50. Regression Analysis Period Demand Period^2 Period* Forecast FE 1 52 248 4 96 3 36 9
108 49 16
196 5 65 25
325 6 54
324 7 60
420 8 64
384 51 81
459 10 62
100
620 11 66
121
726 12
144
744
SUM 78
653
650
4,454
MFE
MAD
MSE

51. Regression Analysis Period Demand Period^2 Period* Forecast FE 1 52 248 4 96 3 36 9
108 49 16
196 5 65 25
325 6 54
324 7 60
420 8 64
384 51 81
459 10 62
100
620 11 66
121
726 12
144
744
SUM 78
653
650
4,454
MFE
MAD
MSE

52. Regression Analysis

53. Regression Analysis Period Demand Period^2 Period * Forecast FE 1 52
46.4 2 48 4 96 3 36 9
108 49 16
196 5 65 25
325 6 54
324 7 60
420 8 64
384 51 81
459 10 62
100
620 11 66
121
726 12
144
744
SUM 78
653
650
4,454
MFE
MAD
MSE

54. Regression Analysis Period Demand Period^2 Period * Forecast FE 1 52
46.4
-5.6 2 48 4 96
47.8
-0.2 3 36 9
108
49.3
13.3 49 16
196
50.8
1.8 5 65 25
325
52.2
-12.8 6 54
324
53.7
-0.3 7 60
420
55.1
-4.9 8 64
384
56.6
8.6 51 81
459
58.1
7.1 10 62
100
620
59.5
-2.5 11 66
121
726 61 -5 12
144
744
62.5
0.5
SUM 78
653
650
4,454
MFE
MAD
5.2
MSE
46.2

55. Regression Analysis

56. Methods for Seasonality
Seasonal Index
Triple Exponential Smoothing

57. Seasonal Index
Calculate a seasonal index
Apply to forecasts

58. Seasonal Index/No Trend
How do I know that this is seasonal and not cyclical?

59. Seasonal Index/No Trend
Quarter
Year  1 2 3 4
Total
122
108 81 90
401
130
100 73 96
399
132 98 71 99
400
Average
128
102 75 95

60. Seasonal Index/No Trend
Quarter
Year  1 2 3 4
Total
122
108 81 90
401
130
100 73 96
399
132 98 71 99
400
Average
128
102 75 95

61. Seasonal Index/No Trend
Quarter
Year  1 2 3 4
Total
122
108 81 90
401
130
100 73 96
399
132 98 71 99
400
Average
128
102 75 95

62. Seasonal Index/No Trend
Quarter
Year  1 2 3 4
Total
122
108 81 90
401
130
100 73 96
399
132 98 71 99
400
Average
128
102 75 95

63. Seasonal Index/No Trend
Quarter
Year  1 2 3 4
Total
122
108 81 90
401
130
100 73 96
399
132 98 71 99
400
Average
128
102 75 95

64. Seasonal Index/No Trend
Seasonal Indices 1
1.28 2
1.02 3
0.75 4
0.95

65. Seasonal Index/No Trend
Assume that 420 units are forecast for next year
Qtr.
Seasonal Indices
Deseasonalized Forecast
Seasonalized Forecast 1
1.28
105
134.4 2
1.02
107.1 3
0.75
78.75 4
0.95
99.75

66. Evaluating Forecasts Forecast Error Mean Forecast Error
Mean Absolute Deviation
Mean Square Deviation
Tracking Signal
Forecast error
et = Ft - Dt
Mean Forecast Error – measure of bias
MFE= (1/n) Σ et
Mean Absolute Deviation
Approximate σ = 1.25 MAD
MAD = (1/n) Σ | et |
Based on the normal curve
±1 MAD of the mean about 60%
±2 MAD of the mean about 90%
±3 MAD of the mean about 98%
Mean Square Deviation
MSE = (1/n) Σ et2
Tracking Signal
Algebraic sum of forecast errors / MADEE

67. 2.3 Forecast Errors Simple Straightforward
Difficult to see what’s happening over time
Is a mathematical average of forecast error over time

68. Forecast Error
Period
Demand
MA (n=3) FE
MA (n=5) 1 52 2 48 3 36 4 49
45.3
-3.7 5 65
44.3 6 54 50 7 60 56
50.4 8
59.7
52.8 9 51
55.2 10 62 53
55.6 11 66
53.7 55 12
57.4
MFE
MAD
MSE

69. Forecast Error
Period
Demand
MA (n=3) FE
MA (n=5) 1 52 2 48 3 36 4 49
45.3
-3.7 5 65
44.3
-20.7 6 54 50 -4 7 60 56
50.4
-9.6 8
59.7
11.7
52.8
4.8 9 51
55.2
4.2 10 62 53 -9
55.6
-6.4 11 66
53.7
-12.3 55
-11 12
-2.3
57.4
-4.6
MFE
MAD
MSE

70. Mean Forecast Error (MFE)
Over’s and under’s cancel each other out
Measures bias
Positive numbers = act. demand > forecast
Negative numbers = act. Demand < forecast
Used as a meaus

71. Mean Forecast Error
Period
Demand
MA (n=3) FE
MA (n=5) 1 52 2 48 3 36 4 49
45.3
-3.7 5 65
44.3
-20.7 6 54 50 -4 7 60 56
50.4
-9.6 8
59.7
11.7
52.8
4.8 9 51
55.2
4.2 10 62 53 -9
55.6
-6.4 11 66
53.7
-12.3 55
-11 12
-2.3
57.4
-4.6
MFE
-3.8
MAD
MSE
Bias

72. Mean Absolution Deviation (MAD)
Approximate σ= 1.25 MAD
Based on the normal curve
60% of observations fall between ±1 MAD
90% of observations fall between ±2 MAD
98% of observations fall between ±3 MAD
Approximate σ = 1.25 MAD
MAD = (1/n) Σ | et |
Based on the normal curve
±1 MAD of the mean about 60%
±2 MAD of the mean about 90%
±3 MAD of the mean about 98%

73. Mean Square Error (MSE)
Similar to standard deviation calculation

74. Mean Absolute Deviation
Period
Demand
MA (n=3) FE
MA (n=5) 1 52 2 48 3 36 4 49
45.3
-3.7 5 65
44.3
-20.7 6 54 50 -4 7 60 56
50.4
-9.6 8
59.7
11.7
52.8
4.8 9 51
55.2
4.2 10 62 53 -9
55.6
-6.4 11 66
53.7
-12.3 55
-11 12
-2.3
57.4
-4.6
MFE
-3.8
MAD
7.9
6.4
MSE

75. Mean Square Error
Period
Demand
MA (n=3) FE
MA (n=5) 1 52 2 48 3 36 4 49
45.3
-3.7 5 65
44.3
-20.7 6 54 50 -4 7 60 56
50.4
-9.6 8
59.7
11.7
52.8
4.8 9 51
55.2
4.2 10 62 53 -9
55.6
-6.4 11 66
53.7
-12.3 55
-11 12
-2.3
57.4
-4.6
MFE
-3.8
MAD
7.9
6.4
MSE
95.1
47.4

76. Tracking Signal Similar to concept of control limits
Rule of Thumb +/- 4
Watch for trends in the Tracking Signal
RSFE – running sum of the forecast error

77. Tracking Signal Period Demand ES (α=.1) FE Cum FE Tracking Signal 1 52 2 48 4
0.53 3 36
51.6
15.6
19.6
2.6 49 50
20.64
2.74 5 65
49.9
-15.1
5.58
0.74 6 54
51.4
-2.6
3.02
0.4 7 60
51.7
-8.3
-5.28
-0.7 8
52.5
4.5
-0.76
-0.1 9 51
52.1
1.1
0.32
0.04 10 62
-10
-9.71
-1.29 11 66 53
-13
-22.74
-3.02 12
54.3
-7.7
-30.47
-4.04
MAD
7.5

78. Homework
Problems 4 and 6
Use Excel to ease your calculations