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41 Pages
009-chap11Forecasting
Course: OM 302, Fall 2008School: CSU San Marcos
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management Forecasting Forecasting operations operations management Predicting future events Qualitative methods Based on subjective methods Quantitative methods Based on mathematical formulas 2 Forecasting not often think of it as such. Examples: How long will it take me to complete the homework? How good a job will my teammates do on their portions of our group project? How long will it take me to...
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management
Forecasting
Forecasting
operations operations management
Predicting future events Qualitative methods
Based on subjective methods
Quantitative methods
Based on mathematical formulas
2
Forecasting
not often think of it as such. Examples: How long will it take me to complete the homework? How good a job will my teammates do on their portions of our group project? How long will it take me to drive to work? How long will it take me to drive to Mammoth? Will this class end early or on time?
3
operations management
You "forecast" all the time, though you may
Forecasting
"cause" or effect the underlying pattern, monitor those, and then construct your forecast as best you can Example: What are the causal underlying effects that influence whether or not it will rain here Saturday? Casual effects: air movement patterns, air pressure changes, weather patterns in surrounding regions
4
operations management
You need to find the underlying variables that
Strategic Role of Forecasting
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
5
operations management
Focus on supply chain management
Components of Forecasting Demand
operations management Time Frames
Short-range medium-range long-range
Demand Behavior
Trends, cycles, seasonal patterns, random
6
Time Frame
operations management Short-range to medium-range
Daily, weekly, monthly, quarterly forecasts of sales data Up to 2 years into the future
Long-range
Strategic planning of goals, products, markets Planning beyond 2 years
7
Demand Behavior
operations management
Trend
gradual, long-term up or down movement
Cycle
up & down movement repeating over long time frame
Seasonal pattern
periodic oscillation in demand which repeats
Random movements follow no pattern
8
Forms of Forecast Movement: Trend
Idealized
Actual
Random fluctuations Time Time
9
operations management
Demand
Demand
Forms of Forecast Movement: Cyclic
Ideal
Actual
Time
Time
10
operations management
Demand
Demand
Forms of Forecast Movement: Seasonal
Idealized
Actual
Time
Time
11
operations management
Demand
Demand
Trend with Seasonal Pattern
Time
12
operations management
Demand
Forecasting Methods
operations management Qualitative methods
Management judgment, expertise, opinion Use management, marketing, purchasing, engineering Quantitative methods Time series analysis, regression, or causal modeling
Delphi method
Solicit forecasts from experts
13
Forecasting Process
1. Identify the purpose of forecast 2. Collect historical data 3. Plot data and identify patterns
6. Check forecast accuracy with one or more measures
5. Develop/compute forecast for period of historical data
4. Select a forecast model that seems appropriate for data
7. Is accuracy of forecast acceptable?
8b. Select new forecast model or adjust parameters of existing model
8a. Forecast over planning horizon
9. Adjust forecast based on additional qualitative information and insight
10. Monitor results and measure forecast accuracy
14
operations management
Time Series Methods
operations management Assume patterns will repeat Naive forecasts
Forecast = data from last period
Statistical methods using historical data
Moving average Exponential smoothing Linear trend line
Demand?
15
Moving Average
operations management
Average several periods of data Dampen, smooth out changes Use when demand is stable with no trend or seasonal pattern
16
Moving Average
operations management
Average several periods of data Dampen, smooth out changes Use when demand is stable with no trend or seasonal pattern
17
i = 1 Di
MAn =
where n Di
n
n
= number of periods in the moving average = demand in period i
Simple Moving Average
ORDERS PER MONTH 120 90 100 75 110 50 75 130 110 90 ???
MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov
It's October 31st, how much Product should we order for Sale in November?
18
operations management
Smoothing Effects
150 125 100 Orders 75 50 25 0 | Jan | Feb | Mar Actual | | | | Apr May June July Month | | Aug Sept | Oct
| Nov
19
operations management
Simple Moving Average
ORDERS PER MONTH 120 90 100 75 110 50 75 130 110 90 ???
MONTH Feb Jan Mar Apr May June July Aug Sept Oct Nov
i = 1 Di
MA3 =
3
3
90 + 110 + 130 = 3 = 110 orders for Nov
20
operations management
Previous 3 periods
Simple Moving Average
ORDERS THREE-MONTH PER MONTH MOVING AVERAGE 120 90 100 75 110 50 75 130 110 90 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0
MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov
21
operations management
Simple Moving Average
ORDERS THREE-MONTH PER MONTH MOVING AVERAGE 120 90 100 75 110 50 75 130 110 90 ??? 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0
MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov
Previous 5 periods
i = 1 Di
MA5 =
5
5
90 + 110 + 130 + 75 + 50 = 5
= 91 orders for Nov
22
operations management
Simple Moving Average
ORDERS THREE-MONTH PER MONTH MOVING AVERAGE 120 90 100 75 110 50 75 130 110 90 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0 FIVE-MONTH MOVING AVERAGE 99.0 85.0 82.0 88.0 95.0 91.0
MONTH Jan Feb Mar Apr May June July Aug Sept Oct Nov
23
operations management
Smoothing Effects
150 125 100 Orders 75 50 25 0 | Jan | Feb | Mar Actual | | | | Apr May June July Month | | Aug Sept | Oct
| Nov
24
operations management
Smoothing Effects
150 125 100 Orders 75 50 25 0 | Jan | Feb | Mar Actual | | | | Apr May June July Month | | Aug Sept | Oct 3-month
| Nov
25
operations management
Smoothing Effects operations management
150 125 100 Orders 75 50 25 0 | Jan | Feb | Mar Actual | | | | Apr May June July Month | | Aug Sept | Oct 3-month 5-month
| Nov
26
Weighted Moving Average operations management
Adjusts moving average method to more closely reflect data fluctuations, often those most recent in time
27
Weighted Moving Average
WMAn = Wi Di Adjusts i=1 moving where average Wi = the weight for period i, method to between 0 and 100 more closely percent reflect data fluctuations Wi = 1.00
28
operations management
Weighted Moving Average Example
MONTH August September October
WEIGHT 17% 33% 50%
DATA 130 110 90
29
operations management
Weighted Moving Average Example
MONTH August September October
WEIGHT 17% 33% 50%
DATA 130 110 90
3
November forecast WMA3 = i1 Wi Di = = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders
30
operations management
Exponential Smoothing
Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method
31
operations management
Linear Trend Line
y = a + bx
where a b x y = = = = intercept (at period 0) slope of the line the time period forecast for demand for period x
32
operations management
Least Squares Example: Raw Data
x(PERIOD) 1 2 3 4 5 ...
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