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Forecasting-Exponential Smoothing

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Manajemen Operasi, Pasca Sarjana MM UNWIM oleh Prof.Ina Primiana Sagir

Manajemen Operasi, Pasca Sarjana MM UNWIM oleh Prof.Ina Primiana Sagir

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    Forecasting-Exponential Smoothing Forecasting-Exponential Smoothing Presentation Transcript

    • © 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
    • © 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
    • © 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 ??
    • © 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
    • © 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 CompanyStrategy/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
    • © 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 OMStrategy/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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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)
    • © 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
    • © 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
    • © 2008 Prentice Hall, Inc. 4 – 20 Trend Seasonal Cyclical Random Time Series Components
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 2008 Prentice Hall, Inc. 4 – 28 Graph of Moving Average | | | | | | | | | | | | J F M A M J J A S O N D ShedSales 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales Moving Average Forecast
    • © 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
    • © 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
    • © 2008 Prentice Hall, Inc. 4 – 31 Moving Average And Weighted Moving Average 30 – 25 – 20 – 15 – 10 – 5 – Salesdemand | | | | | | | | | | | | J F M A M J J A S O N D Actual sales Moving average Weighted moving average Figure 4.2
    • © 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
    • © 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)
    • © 2008 Prentice Hall, Inc. 4 – 34 Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant  = .20
    • © 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)
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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
    • © 2008 Prentice Hall, Inc. 4 – 42 Common Measures of Error Mean Absolute Percent Error (MAPE) MAPE = ∑100|Actuali - Forecasti|/Actuali n n i = 1
    • © 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
    • © 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
    • © 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
    • © 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
    • © 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%
    • © 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
    • © 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 Percentageofsales Figure 4.12
    • © 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