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Forecasting-Exponential Smoothing
1.
© 2008 Prentice
Hall, Inc. 4 – 1 Operations Management Chapter 4 – Forecasting PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 7e Operations Management, 9e
2.
© 2008 Prentice
Hall, Inc. 4 – 2 Learning Objectives When you complete this chapter you should be able to : Understand the three time horizons and which models apply for each use Explain when to use each of the four qualitative models Apply the naive, moving average, exponential smoothing, and trend methods
3.
© 2008 Prentice
Hall, Inc. 4 – 3 What is Forecasting? Process of predicting a future event Underlying basis of all business decisions Production Inventory Personnel Facilities ??
4.
© 2008 Prentice
Hall, Inc. 4 – 4 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 Forecasting Time Horizons
5.
© 2008 Prentice
Hall, Inc. 4 – 5 Product Life Cycle 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 Introduction Growth Maturity Decline Company Strategy/Issues Figure 2.5 Internet search engines Sales Xbox 360 Drive-through restaurants CD-ROMs 3 1/2” Floppy disks LCD & plasma TVs Analog TVs iPods
6.
© 2008 Prentice
Hall, Inc. 4 – 6 Product Life Cycle Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Introduction Growth Maturity Decline OM Strategy/Issues Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focus Enhance distribution Standardization Less rapid 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
7.
© 2008 Prentice
Hall, Inc. 4 – 7 Types of Forecasts Economic 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
8.
© 2008 Prentice
Hall, Inc. 4 – 8 Strategic Importance of Forecasting Human Resources – Hiring, training, laying off workers Capacity – Capacity shortages can result in undependable delivery, loss of customers, loss of market share Supply Chain Management – Good supplier relations and price advantages
9.
© 2008 Prentice
Hall, Inc. 4 – 9 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
10.
© 2008 Prentice
Hall, Inc. 4 – 10 The Realities! Forecasts are seldom perfect Most techniques assume an underlying stability in the system Product family and aggregated forecasts are more accurate than individual product forecasts
11.
© 2008 Prentice
Hall, Inc. 4 – 11 Forecasting Approaches Used when situation is vague and little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet Qualitative Methods
12.
© 2008 Prentice
Hall, Inc. 4 – 12 Forecasting Approaches Used when situation is ‘stable’ and historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions Quantitative Methods
13.
© 2008 Prentice
Hall, Inc. 4 – 13 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
14.
© 2008 Prentice
Hall, Inc. 4 – 14 Overview of Qualitative Methods Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated Consumer Market Survey Ask the customer
15.
© 2008 Prentice
Hall, Inc. 4 – 15 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 Jury of Executive Opinion
16.
© 2008 Prentice
Hall, Inc. 4 – 16 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
17.
© 2008 Prentice
Hall, Inc. 4 – 17 Delphi Method Iterative group process, continues until consensus is reached 3 types of participants Decision makers Staff Respondents Staff (Administering survey) Decision Makers (Evaluate responses and make decisions) Respondents (People who can make valuable judgments)
18.
© 2008 Prentice
Hall, Inc. 4 – 18 Consumer Market Survey Ask customers about purchasing plans What consumers say, and what they actually do are often different Sometimes difficult to answer
19.
© 2008 Prentice
Hall, Inc. 4 – 19 Overview of Quantitative Approaches 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 5. Linear regression Time-Series Models Associative Model
20.
© 2008 Prentice
Hall, Inc. 4 – 20 Trend Seasonal Cyclical Random Time Series Components
21.
© 2008 Prentice
Hall, Inc. 4 – 21 Persistent, overall upward or downward pattern Changes due to population, technology, age, culture, etc. Typically several years duration Trend Component
22.
© 2008 Prentice
Hall, Inc. 4 – 22 Regular pattern of up and down fluctuations Due to weather, customs, etc. Occurs within a single year Seasonal Component 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
23.
© 2008 Prentice
Hall, Inc. 4 – 23 Repeating up and down movements Affected by business cycle, political, and economic factors Multiple years duration Often causal or associative relationships Cyclical Component 0 5 10 15 20
24.
© 2008 Prentice
Hall, Inc. 4 – 24 Erratic, unsystematic, ‘residual’ fluctuations Due to random variation or unforeseen events Short duration and nonrepeating Random Component M T W T F
25.
© 2008 Prentice
Hall, Inc. 4 – 25 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
26.
© 2008 Prentice
Hall, Inc. 4 – 26 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 Method Moving average = ∑ demand in previous n periods n
27.
© 2008 Prentice
Hall, Inc. 4 – 27 January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Month Shed Sales Moving Average (12 + 13 + 16)/3 = 13 2/3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1/3 Moving Average Example 10 12 13 (10 + 12 + 13)/3 = 11 2/3
28.
© 2008 Prentice
Hall, Inc. 4 – 28 Graph of Moving Average | | | | | | | | | | | | J F M A M J J A S O N D Shed Sales 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales Moving Average Forecast
29.
© 2008 Prentice
Hall, Inc. 4 – 29 Used when trend is present Older data usually less important Weights based on experience and intuition Weighted Moving Average Weighted moving average = ∑ (weight for period n) x (demand in period n) ∑ weights
30.
© 2008 Prentice
Hall, Inc. 4 – 30 January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Weighted Month Shed Sales Moving Average [(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 Weighted Moving Average 10 12 13 [(3 x 13) + (2 x 12) + (10)]/6 = 121/6 Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago 6 Sum of weights
31.
© 2008 Prentice
Hall, Inc. 4 – 31 Moving Average And Weighted Moving Average 30 – 25 – 20 – 15 – 10 – 5 – Sales demand | | | | | | | | | | | | J F M A M J J A S O N D Actual sales Moving average Weighted moving average Figure 4.2
32.
© 2008 Prentice
Hall, Inc. 4 – 32 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 Exponential Smoothing
33.
© 2008 Prentice
Hall, Inc. 4 – 33 Exponential Smoothing New forecast = Last period’s forecast + (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + (At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous forecast = smoothing (or weighting) constant (0 ≤ ≤ 1)
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© 2008 Prentice
Hall, Inc. 4 – 34 Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant = .20
35.
© 2008 Prentice
Hall, Inc. 4 – 35 Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant = .20 New forecast = 142 + .2(153 – 142)
36.
© 2008 Prentice
Hall, Inc. 4 – 36 Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant = .20 New forecast = 142 + .2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars
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© 2008 Prentice
Hall, Inc. 4 – 37 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 () (1 - ) (1 - )2 (1 - )3 (1 - )4 = .1 .1 .09 .081 .073 .066 = .5 .5 .25 .125 .063 .031
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© 2008 Prentice
Hall, Inc. 4 – 38 Impact of Different 225 – 200 – 175 – 150 – | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Demand = .1 Actual demand = .5
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© 2008 Prentice
Hall, Inc. 4 – 39 Impact of Different 225 – 200 – 175 – 150 – | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Demand = .1 Actual demand = .5 Chose high values of when underlying average is likely to change Choose low values of when underlying average is stable
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© 2008 Prentice
Hall, Inc. 4 – 40 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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© 2008 Prentice
Hall, Inc. 4 – 41 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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© 2008 Prentice
Hall, Inc. 4 – 42 Common Measures of Error Mean Absolute Percent Error (MAPE) MAPE = ∑100|Actuali - Forecasti|/Actuali n n i = 1
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© 2008 Prentice
Hall, Inc. 4 – 43 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded = .10 = .10 = .50 = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62
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© 2008 Prentice
Hall, Inc. 4 – 44 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded = .10 = .10 = .50 = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD = ∑ |deviations| n = 82.45/8 = 10.31 For = .10 = 98.62/8 = 12.33 For = .50
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© 2008 Prentice
Hall, Inc. 4 – 45 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded = .10 = .10 = .50 = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD 10.31 12.33 = 1,526.54/8 = 190.82 For = .10 = 1,561.91/8 = 195.24 For = .50 MSE = ∑ (forecast errors)2 n
46.
© 2008 Prentice
Hall, Inc. 4 – 46 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded = .10 = .10 = .50 = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD 10.31 12.33 MSE 190.82 195.24 = 44.75/8 = 5.59% For = .10 = 54.05/8 = 6.76% For = .50 MAPE = ∑100|deviationi|/actuali n n i = 1
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© 2008 Prentice
Hall, Inc. 4 – 47 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded = .10 = .10 = .50 = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD 10.31 12.33 MSE 190.82 195.24 MAPE 5.59% 6.76%
48.
© 2008 Prentice
Hall, Inc. 4 – 48 Forecasting in the Service Sector Presents unusual challenges Special need for short term records Needs differ greatly as function of industry and product Holidays and other calendar events Unusual events
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© 2008 Prentice
Hall, Inc. 4 – 49 Fast Food Restaurant Forecast 20% – 15% – 10% – 5% – 11-12 1-2 3-4 5-6 7-8 9-10 12-1 2-3 4-5 6-7 8-9 10-11 (Lunchtime) (Dinnertime) Hour of day Percentage of sales Figure 4.12
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© 2008 Prentice
Hall, Inc. 4 – 50 FedEx Call Center Forecast Figure 4.12 12% – 10% – 8% – 6% – 4% – 2% – 0% – Hour of day A.M. P.M. 2 4 6 8 10 12 2 4 6 8 10 12
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