1. 4 Forecasting PowerPoint presentation to accompany Heizer and Render
Operations Management, 10e
Principles of Operations Management, 8e
PowerPoint slides by Jeff Heyl
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2. Forecasting at Disney World
Global portfolio includes parks in Hong Kong, Paris, Tokyo, Orlando, and Anaheim
Revenues are derived from people – how many visitors and how they spend their money
Daily management report contains only the forecast and actual attendance at each park
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3. Forecasting at Disney World
Disney generates daily, weekly, monthly, annual, and 5-year forecasts
Forecast used by labor management, maintenance, operations, finance, and park scheduling
Forecast used to adjust opening times, rides, shows, staffing levels, and guests admitted
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4. Forecasting at Disney World
20% of customers come from outside the USA
Economic model includes gross domestic product, cross-exchange rates, arrivals into the USA
A staff of 35 analysts and 70 field people survey 1 million park guests, employees, and travel professionals each year
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5. Forecasting at Disney World
Inputs to the forecasting model include airline specials, Federal Reserve policies, Wall Street trends, vacation/holiday schedules for 3,000 school districts around the world
Average forecast error for the 5-year forecast is 5%
Average forecast error for annual forecasts is between 0% and 3%
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6. ?? What is Forecasting? Process of predicting a future event
Underlying basis of all business decisions
Production
Inventory
Personnel
Facilities ??
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7. Forecasting Time Horizons
Short-range forecast
Up to 1 year, generally less than 3 months
Purchasing, job scheduling, workforce levels, job assignments, production levels
Medium-range forecast
3 months to 3 years
Sales and production planning, budgeting
Long-range forecast
3+ years
New product planning, facility location, research and development
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8. Distinguishing Differences
Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes
Short-term forecasting usually employs different methodologies than longer-term forecasting
Short-term forecasts tend to be more accurate than longer-term forecasts
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9. Influence of Product Life Cycle
Introduction – Growth – Maturity – Decline
Introduction and growth require longer forecasts than maturity and decline
As product passes through life cycle, forecasts are useful in projecting
Staffing levels
Inventory levels
Factory capacity
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10. Product Life Cycle Introduction Growth Maturity Decline
Company Strategy/Issues
Best period to increase market share
R&D engineering is critical
Practical to change price or quality image
Strengthen niche
Poor time to change image, price, or quality
Competitive costs become critical
Defend market position
Cost control critical
Internet search engines
Sales
Drive-through restaurants
CD-ROMs
Analog TVs
iPods
Boeing 787
LCD & plasma TVs
Twitter
Avatars
Xbox 360
Figure 2.5
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11. Product Life Cycle Introduction Growth Maturity Decline
OM Strategy/Issues
Product design and development critical
Frequent product and process design changes
Short production runs
High production costs
Limited models
Attention to quality
Forecasting critical
Product and process reliability
Competitive product improvements and options
Increase capacity
Shift toward product focus
Enhance distribution
Standardization
Fewer product changes, more minor changes
Optimum capacity
Increasing stability of process
Long production runs
Product improvement and cost cutting
Little product differentiation
Cost minimization
Overcapacity in the industry
Prune line to eliminate items not returning good margin
Reduce capacity
Figure 2.5
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12. Types of Forecasts Economic forecasts Technological forecasts
Address business cycle – inflation rate, money supply, housing starts, etc.
Technological forecasts
Predict rate of technological progress
Impacts development of new products
Demand forecasts
Predict sales of existing products and services
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13. Seven Steps in Forecasting
Determine the use of the forecast
Select the items to be forecasted
Determine the time horizon of the forecast
Select the forecasting model(s)
Gather the data
Make the forecast
Validate and implement results
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14. Forecasting Approaches
Qualitative Methods
Used when situation is vague and little data exist
New products
New technology
Involves intuition, experience
e.g., forecasting sales on Internet
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15. Forecasting Approaches
Quantitative Methods
Used when situation is ‘stable’ and historical data exist
Existing products
Current technology
Involves mathematical techniques
e.g., forecasting sales of color televisions
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16. Overview of Qualitative Methods
Jury of executive opinion
Pool opinions of high-level experts, sometimes augment by statistical models
Delphi method
Panel of experts, queried iteratively
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17. Overview of Qualitative Methods
Sales force composite
Estimates from individual salespersons are reviewed for reasonableness, then aggregated
Consumer Market Survey
Ask the customer
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18. Jury of Executive Opinion
Involves small group of high-level experts and managers
Group estimates demand by working together
Combines managerial experience with statistical models
Relatively quick
‘Group-think’ disadvantage
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19. Sales Force Composite Each salesperson projects his or her sales
Combined at district and national levels
Sales reps know customers’ wants
Tends to be overly optimistic
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20. Delphi Method
Iterative group process, continues until consensus is reached
3 types of participants
Decision makers
Staff
Respondents
Decision Makers
(Evaluate responses and make decisions)
Staff
(Administering survey)
Respondents
(People who can make valuable judgments)
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21. Consumer Market Survey
Ask customers about purchasing plans
What consumers say, and what they actually do are often different
Sometimes difficult to answer
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22. Overview of Quantitative Approaches
Naive approach
Moving averages
Exponential smoothing
Trend projection
Linear regression
time-series models
associative model
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23. Time Series Forecasting
Set of evenly spaced numerical data
Obtained by observing response variable at regular time periods
Forecast based only on past values, no other variables important
Assumes that factors influencing past and present will continue influence in future
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24. Time Series Components
Trend
Cyclical
Seasonal
Random
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25. Average demand over 4 years
Components of Demand
Trend component
Demand for product or service
| | | |
Time (years)
Seasonal peaks
Actual demand line
Average demand over 4 years
Random variation
Figure 4.1
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26. Trend Component Persistent, overall upward or downward pattern
Changes due to population, technology, age, culture, etc.
Typically several years duration
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27. Seasonal Component Regular pattern of up and down fluctuations
Due to weather, customs, etc.
Occurs within a single year
Number of
Period Length Seasons
Week Day 7
Month Week 4-4.5
Month Day 28-31
Year Quarter 4
Year Month 12
Year Week 52
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28. Cyclical Component Repeating up and down movements
Affected by business cycle, political, and economic factors
Multiple years duration
Often causal or associative relationships
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29. Random Component Erratic, unsystematic, ‘residual’ fluctuations
Due to random variation or unforeseen events
Short duration and nonrepeating
M T W T F
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30. Naive Approach
Assumes demand in next period is the same as demand in most recent period
e.g., If January sales were 68, then February sales will be 68
Sometimes cost effective and efficient
Can be good starting point
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31. ∑ demand in previous n periods
Moving Average Method
MA is a series of arithmetic means
Used if little or no trend
Used often for smoothing
Provides overall impression of data over time
Moving average =
∑ demand in previous n periods n
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32. Moving Average Example
January 10
February 12
March 13
April 16
May 19
June 23
July 26
Actual 3-Month
Month Shed Sales Moving Average 10 12 13
( )/3 = 11 2/3
( )/3 = 13 2/3
( )/3 = 16
( )/3 = 19 1/3
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33. Weighted Moving Average
Used when some trend might be present
Older data usually less important
Weights based on experience and intuition
Weighted moving average =
∑ (weight for period n) x (demand in period n)
∑ weights
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34. Weighted Moving Average
Weights Applied Period
3 Last month
2 Two months ago
1 Three months ago
6 Sum of weights
Weighted Moving Average
January 10
February 12
March 13
April 16
May 19
June 23
July 26
Actual 3-Month Weighted
Month Shed Sales Moving Average 10 12 13
[(3 x 13) + (2 x 12) + (10)]/6 = 121/6
[(3 x 16) + (2 x 13) + (12)]/6 = 141/3
[(3 x 19) + (2 x 16) + (13)]/6 = 17
[(3 x 23) + (2 x 19) + (16)]/6 = 201/2
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35. Exponential Smoothing
Form of weighted moving average
Weights decline exponentially
Most recent data weighted most
Requires smoothing constant ()
Ranges from 0 to 1
Subjectively chosen
Involves little record keeping of past data
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36. Exponential Smoothing
New forecast = Last period’s forecast
+ a (Last period’s actual demand
– Last period’s forecast)
Ft = Ft – 1 + a(At – 1 - Ft – 1)
where Ft = new forecast
Ft – 1 = previous forecast
a = smoothing (or weighting) constant (0 ≤ a ≤ 1)
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37. Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant a = .20
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38. Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant a = .20
New forecast = (153 – 142)
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39. Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant a = .20
New forecast = (153 – 142) =
= ≈ 144 cars
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40. Effect of Smoothing Constants
Weight Assigned to
Most 2nd Most 3rd Most 4th Most 5th Most
Recent Recent Recent Recent Recent
Smoothing Period Period Period Period Period
Constant (a) a(1 - a) a(1 - a)2 a(1 - a)3 a(1 - a)4
a =
a =
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41. Impact of Different  Actual demand a = .5 a = .1 225 – 200 – 175 –
225 –
200 –
175 –
150 –
| | | | | | | | |
Quarter
Demand
Actual demand
a = .5
a = .1
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42. Impact of Different 
225 –
200 –
175 –
150 –
| | | | | | | | |
Quarter
Demand
Chose high values of  when underlying average is likely to change
Choose low values of  when underlying average is stable
Actual demand
a = .5
a = .1
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43. Choosing 
The objective is to obtain the most accurate forecast no matter the technique
We generally do this by selecting the model that gives us the lowest forecast error
Forecast error = Actual demand - Forecast value
= At - Ft
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44. Common Measures of Error
Mean Absolute Deviation (MAD)
MAD =
∑ |Actual - Forecast| n
Mean Squared Error (MSE)
MSE =
∑ (Forecast Errors)2 n
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45. Common Measures of Error
Mean Absolute Percent Error (MAPE)
MAPE =
∑100|Actuali - Forecasti|/Actuali n
i = 1
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46. Comparison of Forecast Error
Rounded Absolute Rounded Absolute
Actual Forecast Deviation Forecast Deviation
Tonnage with for with for
Quarter Unloaded a = .10 a = .10 a = .50 a = .50
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47. Comparison of Forecast Error
MAD =
∑ |deviations| n
Rounded Absolute Rounded Absolute
Actual Forecast Deviation Forecast Deviation
Tonnage with for with for
Quarter Unloaded a = .10 a = .10 a = .50 a = .50
= 82.45/8 = 10.31
For a = .10
= 98.62/8 = 12.33
For a = .50
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48. Comparison of Forecast Error
MSE =
∑ (forecast errors)2 n
Rounded Absolute Rounded Absolute
Actual Forecast Deviation Forecast Deviation
Tonnage with for with for
Quarter Unloaded a = .10 a = .10 a = .50 a = .50
= 1,526.54/8 =
For a = .10
MAD
= 1,561.91/8 =
For a = .50
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49. Comparison of Forecast Error
MAPE =
∑100|deviationi|/actuali n
i = 1
Rounded Absolute Rounded Absolute
Actual Forecast Deviation Forecast Deviation
Tonnage with for with for
Quarter Unloaded a = .10 a = .10 a = .50 a = .50
= 44.75/8 = 5.59%
For a = .10
MAD
MSE
= 54.05/8 = 6.76%
For a = .50
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50. Comparison of Forecast Error
Rounded Absolute Rounded Absolute
Actual Forecast Deviation Forecast Deviation
Tonnage with for with for
Quarter Unloaded a = .10 a = .10 a = .50 a = .50
MAD
MSE
MAPE 5.59% 6.76%
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