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ForecastingOutline:I. IntroductionII. Demand ManagementIII. Demand PatternsIV. Principles of ForecastingV. Qualitative TechniquesVI. Quantitative TechniquesVII. Forecast ErrorVIII. Monitoring the Forecast
Kuliah ke-9Rabu, 19 Nov 2008
Assignment: Kerjakan semua soal Forecasting (Chapter 8) - Tidak dikumpul
I. Introduction
Many factors influence the demand for firm’s products and services:
General business and economic conditionsCompetitive factorsMarket trendsThe firm’s own plans, such as promotions, advertising, pricing and product changes.
Characteristics of Demand
II. Demand Management
Demand management is the function of recognizing and managing all demands for productsDemand management includes:- Forecasting- Order processing- Making delivery promises- Interfacing between production planning
& control and marketplaces.
Demand management
Marketplace Demand Management
ProductionPlanning
Master ProductionSchedule
• Forecasting• Order processing
• Making delivery promises
III. Demand Patterns
Stable versus dynamicStable demand retains same general shape over timeDynamic demand tends to be erratic
Demand Patterns
Hypothetical historical demand pattern:• Trend• Seasonality• Random variation• Cycle.
Time Series Forecasting
AverageAverage
TrendTrend
Seasonal Seasonal
Cyclical Cyclical
Random Random
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5
0 1 2 3 4 5
Dependent versus independent– Only independent demand needs to be
forecasted– Dependent demand should never be forecasted
Seat
Wheels
Handlebars
IV. Principles of Forecasting
ForecastsAre rarely 100% accurate over timeShould include an estimate of errorAre more accurate for product lines and familiesAre more accurate for nearer periods of time
Data Preparation and Collection
– Record data in terms needed for the forecast– Record circumstances relating to the data– Record demand separately for different
customer groups
Forecasting Techniques
• Extrinsic Techniques:– Based on external indicators– Useful in forecasting total company demand
or demand for families of products
• Intrinsic Techniques: use historical data to forecast.
V. Qualitative Techniques
Are based on intuition and informed opinionTend to be subjectiveAre used for business planning and forecasting for new products Are used for medium-term to long-term forecastingEx. Market Research, Delphi method, etc.
Example: Qualitative Methods
Grass Roots
Market Research
Panel Consensus
Executive Judgment
Historical analogy
Delphi Method
Qualitative
Methods
Strengths & Weaknesses Qualitative Methods
Type Characteristics Strengths WeaknessesExecutive opinion / Sales force composite
A group of managers meet & come up with a forecast
Good for strategic or new-product forecasting
One person's opinion can dominate the forecast
Market research
Uses surveys & interviews to identify customer preferences
Good determinant of customer preferences
It can be difficult to develop a good questionnaire
Delphi method
Seeks to develop a consensus among a group of experts
Excellent for forecasting long-term product demand, technological h d
Time consuming to develop
VI. Quantitative Forecasting
Based on historical data usually available in the companyAssume future will repeat past
Example: Historical data
Month SalesJanuary 92February 83March 66April 74May 75June 84July 84August 81September 75October 63November 91December 84January ?
Moving Average Forecasting
– Can be used to filter out random variation.– Longer periods smooth out random variation.– If a trend exists, it is hard to detect.– Manual calculations can be cumbersome
when dealing with more periods.
Simple Moving Average Problem (1)
Week Demand1 6502 6783 7204 7855 8596 9207 8508 7589 892
10 92011 78912 844
F = A + A + A +...+Ant
t-1 t-2 t-3 t-n
• Question: What are the 3-week and 6-week moving average forecasts for demand?
• Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts
500
600
700
800
900
1000
1 2 3 4 5 6 7 8 9 10 11 12
Week
Dem
and Demand
3-Week
6-Week
Plotting The Moving Averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example.
Example: Weighted Moving Average
F = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t- n
w = 1ii=1
n
∑
While The Moving Average Formula implies an equal weight being placed on each value that is being averaged, The “Weighted Moving Average”permits an unequal weighting on prior time periods.
wt = weight given to time period “t”occurrence. (Weights must add to one.)
The formula for the moving average is:
Weighted Moving Average Problem (1) Data
Weights: t-1 .5t-2 .3t-3 .2
Week Demand1 6502 6783 7204
Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?
Note that the weights place more emphasis on the most recent data, that is time period “t-1”.
Weighted Moving Average Problem (1) Solution
Week Demand Forecast1 6502 6783 7204 693.4
F4 = 0.5(720)+0.3(678)+0.2(650)=693.4
Weights: t-1 .5t-2 .3t-3 .2
Weighted Moving Average Problem (2) Data
Weights: t-1 .7t-2 .2t-3 .1
Week Demand1 8202 7753 6804 655
Question: Given the weekly demand information and weights, what is the weighted moving average forecast of the 5th period or week?
Weighted Moving Average Problem (2) Solution
Week Demand Forecast1 8202 7753 6804 6555 672
F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672
Weights: t-1 .7t-2 .2t-3 .1
2. Exponential Smoothing
– Provides a routine method of updating item forecasts
– Works well for stable items– Is satisfactory for short-range forecasts– Detects trends, but lags them– Note: Exponential smoothing gives the same
results as a moving average but without the need to retain as much data and with easier calculations. It works well when dealing with stable items.
Exponential Smoothing Model
• Premise: The most recent observations might have the highest predictive value.
• Therefore, we should give more weight to the more recent time periods when forecasting.
Ft = Ft-1 + α(At-1 - Ft-1)α = smoothing constant
Exponential Smoothing Problem (1) Data
Week Demand1 8202 7753 6804 6555 7506 8027 7988 6899 775
10
• Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using α=0.10 and α=0.60?
• Assume F1=D1
Week Demand 0.1 0.61 820 820.00 820.002 775 820.00 820.003 680 815.50 820.004 655 801.95 817.305 750 787.26 808.096 802 783.53 795.597 798 785.38 788.358 689 786.64 786.579 775 776.88 786.61
10 776.69 780.77
Answer: The Respective Alphas columns denote the forecast values. Note that you can only forecast one time period into the future.
Exponential Smoothing Problem (1) Plotting
500600700
800900
1 2 3 4 5 6 7 8 9 10
Week
Dem
and Demand
0.1
0.6
Note how that the smaller alpha the smoother the line in this example.
Exponential Smoothing Problem (2) Data
Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5?
Assume F1=D1
Week Demand1 8202 7753 6804 6555
Exponential Smoothing Problem (2) Solution
Week Demand 0.51 820 820.002 775 820.003 680 797.504 655 738.755 696.88
F1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75
periods all for sales Averagesales average Period =index Seasonal
3. Seasonality
– Measures the amount of seasonal variation of demand for a product
– Relates the average demand in a particular period to the average demand for all periods
Quarter Average Quarterly Sales/100 Seasonal Index1 128/100 = 1.28 (Q1)2 102/100 = 1.02 (Q2)3 75/100 = 0.75 (Q3)4 95/100 = 0.95 (Q4)
Total = 4.00
Sales History
Year Quarter
Total
1 2 3 4 1 122 108 81 90 401 2 130 100 73 96 399 3 132 98 71 99 400
Average 128 102 75 95 400
Example: Developing Seasonal Sales Indexes
units 1004
400quarters all for sales Average ==
Seasonal Sales
Average Salesfor All Periods
VII. Forecast Error
BiasRandom variationMean Absolute Deviation (MAD)
1. Forecast Error: Bias
Month Forecast Actual
Monthly Cumulative Monthly Cumulative
100 110
235
355
480
610
720
-
200
300
400
500
600
-
1 100 110
2 100 125
3 100 120
4 100 125
5 100 130
6 100 110
Total 600 720
Forecast and actual sales with bias:
2. Forecast Error: Random Variation
Month Forecast Actual Variation (error)
1 100 105 5
2 100 94 -6
3 100 98 -2
4 100 104 4
5 100 103 3
6 100 96 -4
Total 600 600 0
Forecast and actual sales without bias:
Forecasts Can Be Inaccurate in Two WaysBias Random Variation
Cumulative sales may not be the same as forecast
Sales will vary plus and minus about the average
Month Forecast Actual Variation Forecast Actual Variation
1 100 90 -10 100 105 +5
2 100 125 +25 100 94 -6
3 100 120 +20 100 98 -2
4 100 125 +25 100 104 +4
5 100 120 +20 100 103 +3
6 100 110 +10 100 96 -4
Cumulative Total 600 690 +90 600 600 0
Bias exists since cumulative variation is not zero.
There is no bias since cumulative variation is zero.
MAD = A - F
n
t tt=1
n
∑ 1 MAD 0.8 standard deviation1 standard deviation 1.25 MAD
≈≈
• The ideal MAD is zero. That would mean there is no forecasting error.
• The larger the MAD, the less the desirable the resulting model.
MAD Problem Data
Month Sales Forecast1 220 n/a2 250 2553 210 2054 300 3205 325 315
Question: What is the MAD value given the forecast values in the table below?
MAD Problem Solution
MAD = A - F
n=
404
= 10t t
t=1
n
∑
Month Sales Forecast Abs Error1 220 n/a2 250 255 53 210 205 54 300 320 205 325 315 10
40
Note that by itself, the MAD only lets us know the mean error in a set of forecasts.
Normal Distribution with Mean=0 and MAD=1
Error berada pada +-1 MAD 60%,; +- 2 MAD 90%; +-3 MAD 98%
60%
90%
98%
VIII. Evaluasi Forecast
Seberapa besar simpangan forecast?
Bagaimana kita mengevaluasi ini?
Forecasts
Demands
Evaluasi Forecast
Forecast error:
Mean Absolute Deviation
Mean Squared Error
Mean Absolute Percent Error 100/)/1(
)/1(
)/1(
1
1
2
1
×⎥⎦
⎤⎢⎣
⎡=
=
=
−=
∑
∑
∑
=
=
=
n
iii
n
ii
n
ii
ttt
DenMAPE
enMSE
enMAD
FAe
Tracking Signal Formula
• The TS is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand.
• Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts.
• The TS formula is:
TS =RSFEMAD
=Running sum of forecast errors
Mean absolute deviation
Tracking Signal
• Monitor, hitung tracking signal
• Range yang baik jika –4 ≤ TS ≤ +4
( )MAD
ForecastActual∑ −=Signal Tracking
Contoh TS: Diketahui MAD item adalah 2. Triggerkurang lebih 4. Pada periode ke berapa review dilakukan?
Period Forecast Actual Deviation Cumulative Deviation TS
5 2.5
1 100 96
2 100 98
3 100 104
4 100 110
Contoh TS: Diketahui MAD item adalah 2. Triggerkurang lebih 4. Pada periode ke berapa review dilakukan?
Period Forecast Actual Deviation Cumulative Deviation TS
5 2.5
1 100 96 -4 1 0.5
2 100 98 -2 -1 -0.5
3 100 104 4 3 1.5
4 100 110 10 13 6.5
A Plot of The Tracking Signals
Monitoring Forecast Akurasi
Monitor simpangan forecast setiap periode untukmelihat apakah masih dalam batas kendali
0
-10
10
Fore
cast
Err
or
Forecast PeriodLower Limit
Upper Limit
MAD25.1=σ
MAD25.1=σ
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