jury of executive opinion
TRANSCRIPT
![Page 1: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/1.jpg)
2-508-97 Production and Operations Management
ForecastingForecasting
Rajesh Tyagi
![Page 2: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/2.jpg)
2 2-508-97 Production and Operations Management
ForecastingForecasting
Predicting the future demand
Qualitative forecast methods Subjective
Quantitative forecast methods based on mathematical formulas
Copyright 2006 John Wiley & Sons, Inc.
![Page 3: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/3.jpg)
3 2-508-97 Production and Operations Management
Forecasting’s ImportanceForecasting’s ImportanceForecasting is important for:
Finance uses the long term forecast to evaluate capital investment needs.
Human Resources uses forecasts to evaluate personnel needs.
IT designs and implements systems that generate forecasts.
Marketing develops sales forecasts used for mid-term to long term planning.
Operations develops and uses forecasts to make decisions such as: scheduling, inventory management and long term capacity planning.
![Page 4: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/4.jpg)
4 2-508-97 Production and Operations Management
Forecasts by Time HorizonForecasts by Time Horizon
Short-range forecast Up to 1 year; usually less than 3 months
Job scheduling, worker assignments
Medium-range forecast 3 months to 3 years
Sales & production planning, budgeting
Long-range forecast 3+ years
New product planning, facility location
![Page 5: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/5.jpg)
5 2-508-97 Production and Operations Management
Demand BehaviorsDemand Behaviors
Trend a gradual, long-term up or down movement of demand
Seasonal pattern an up-and-down repetitive movement in demand occurring periodically
(short term: often annually)
Cycle an up-and-down repetitive movement in demand (long term)
Special events promotion, stock outs
Random variations movements in demand that do not follow a pattern
Copyright 2006 John Wiley & Sons, Inc.
![Page 6: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/6.jpg)
6 2-508-97 Production and Operations Management
Trend ComponentTrend Component
Persistent, overall upward or downward pattern
Linear, exponential
Several years duration
Mo., Qtr., Yr.
Response
© 1984-1994 T/Maker Co.
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
![Page 7: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/7.jpg)
7 2-508-97 Production and Operations Management
Seasonal ComponentSeasonal Component
Regular pattern of up & down fluctuations
Due to weather, habits etc.
Occurs within a predefined period: year, month, week, day
Mo., Qtr.
Response
Summer
© 1984-1994 T/Maker Co.
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
![Page 8: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/8.jpg)
8 2-508-97 Production and Operations Management
Cyclical ComponentCyclical Component
Repeating up & down movements
Due to interactions of factors influencing economy
Usually 2-10 years duration
Mo., Qtr., Yr.Mo., Qtr., Yr.
ResponseResponseCycle
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
![Page 9: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/9.jpg)
9 2-508-97 Production and Operations Management
Forecasting MethodsForecasting Methods
Judgmental (Qualitative) use management judgment, expertise, and opinion to predict future
demand
Time series statistical techniques that use historical demand data to predict future
demand
Associative models (Regression methods) attempt to develop a mathematical relationship between demand and
factors that cause its behavior
Copyright 2006 John Wiley & Sons, Inc.
![Page 10: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/10.jpg)
10 2-508-97 Production and Operations Management
Overview of Qualitative MethodsOverview of Qualitative Methods
Jury of executive opinion Pool opinions of high-level executives, sometimes augment by statistical
models
Delphi method Panel of experts, queried iteratively
Sales force composite Estimates from individual salespersons are reviewed for reasonableness,
then aggregated
Consumer Market Survey Ask the customer
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
![Page 11: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/11.jpg)
11 2-508-97 Production and Operations Management
Jury of Executive OpinionJury of Executive Opinion
Involves small group of high-level managersGroup estimates demand by working together
Combines managerial experience with statistical models
Relatively quick
“Group-think” disadvantage
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
![Page 12: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/12.jpg)
12 2-508-97 Production and Operations Management
Sales Force CompositeSales Force Composite
Each salesperson projects his or her sales
Combined at district & national levels
Sales reps know customers’ needs
Tends to be overly optimistic
SalesSales
© 1995 Corel Corp.
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
![Page 13: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/13.jpg)
13 2-508-97 Production and Operations Management
Delphi MethodDelphi Method
Iterative group process
Reduces “group-think”
Answer Answer
Qu
esti
on
Qu
esti
on
Feedback
Feedback
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
![Page 14: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/14.jpg)
14 2-508-97 Production and Operations Management
Consumer Market SurveyConsumer Market Survey
Ask customers about purchasing plans
What consumers say, and what they actually do are often different
Sometimes difficult to answer How many hours will you use the Internet
next week?
How many hours will you use the Internet
next week?
© 1995 Corel Corp.
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 07458
![Page 15: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/15.jpg)
15 2-508-97 Production and Operations Management
Qualitative Methods : Advantages & DisadvantagesQualitative Methods : Advantages & Disadvantages
Advantages :• Take intangible factors
into consideration.
• Useful when there are little data available (new product, new market, new business unit).
Disadvantages :
• Long consultation process
• High risk of getting a biased forecast
• Expensive
• Usually not precise
![Page 16: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/16.jpg)
16 2-508-97 Production and Operations Management
Quantitative Methods : Advantages & DisadvantagesQuantitative Methods : Advantages & Disadvantages
Advantages :• Easy to use once the right
model has been developed.
• Data collection is quick and easy since most of the required information is already in the business’ systems (ex. previous sales) or readily available (ex. consumer price index).
Disadvantages :
• Do not take « new information » into consideration :
« It’s like driving a car by looking in the rear-view mirror. »
![Page 17: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/17.jpg)
17 2-508-97 Production and Operations Management
Forecasting ApproachesForecasting Approaches
Used when situation is ‘stable’ & historical data exist Existing products Current technology
Involves mathematical techniques
Quantitative Methods Used when situation is vague &
little data exist New products New technology
Involves intuition, experience
Qualitative Methods
Considerations:•Planning horizon
•Availability and value of historical data
•Needs (precision and reliability)
•Time and budget constraints
![Page 18: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/18.jpg)
18 2-508-97 Production and Operations Management
Realities of ForecastingRealities of Forecasting
1. Forecasts are seldom perfect: almost always wrong by some amount
2. Aggregated forecasts are more accurate than individual forecasts
3. More accurate for shorter time periods
4. Most forecasting methods assume that there is some underlying stability in the system: watch out for special events!
![Page 19: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/19.jpg)
19 2-508-97 Production and Operations Management
Quantitative Forecasting Methods(Non-Naive)Quantitative Forecasting Methods(Non-Naive)
QuantitativeForecasting
MultipleRegression
AssociativeModels
ExponentialSmoothing
MovingAverage
Time SeriesModels
TrendProjection
![Page 20: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/20.jpg)
20 2-508-97 Production and Operations Management
Time SeriesTime Series
Assume that what has occurred in the past will continue to occur in the future
Relate the forecast to only one factor TIMETIME
Include naive forecast
simple average
moving average
exponential smoothing
linear trend analysis
![Page 21: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/21.jpg)
21 2-508-97 Production and Operations Management
Moving AveragesMoving Averages
Naive forecast Demand of the current period is used as next period’s forecast
Simple moving average stable demand with no pronounced behavioral patterns
Weighted moving average weights are assigned to most recent data
![Page 22: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/22.jpg)
22 2-508-97 Production and Operations Management
Naive forecastNaive forecast
JanJan 120120FebFeb 9090MarMar 100100AprApr 7575MayMay 110110JuneJune 5050JulyJuly 7575AugAug 130130SeptSept 110110OctOct 9090
ORDERSORDERSMONTHMONTH PER MONTHPER MONTH
--120120
9090100100
7575110110
50507575
130130110110
9090Nov -Nov -
FORECASTFORECAST
![Page 23: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/23.jpg)
23 2-508-97 Production and Operations Management
Simple Moving Average Simple Moving Average
MAMAnn = =
nn
ii = 1= 1 DDii
nn
wherewhere
nn ==number of periods in the number of periods in the moving averagemoving average
DDii ==demand in period demand in period ii
![Page 24: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/24.jpg)
24 2-508-97 Production and Operations Management
3-month Simple Moving Average3-month Simple Moving Average
JanJan 120120
FebFeb 9090
MarMar 100100
AprApr 7575
MayMay 110110
JuneJune 5050
JulyJuly 7575
AugAug 130130
SeptSept 110110
OctOct 9090NovNov --
ORDERSORDERS
MONTHMONTH PER PER MONTHMONTH
MAMA33 = =
33
ii = 1= 1 DDii
33
==90 + 110 + 13090 + 110 + 130
33
= 110 orders= 110 ordersfor Novfor Nov
––––––
103.3103.388.388.395.095.078.378.378.378.385.085.0
105.0105.0110.0110.0
MOVING MOVING AVERAGEAVERAGE
![Page 25: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/25.jpg)
25 2-508-97 Production and Operations Management
Weighted Moving AverageWeighted Moving Average
WMAWMAnn = = ii = 1 = 1 WWii D Dii
wherewhere
WWii = the weight for period = the weight for period ii, ,
between 0 and 100 between 0 and 100 percentpercent
WWii = 1.00= 1.00
Adjusts Adjusts moving moving average average method to method to more closely more closely reflect data reflect data fluctuationsfluctuations
Copyright 2006 John Wiley & Sons, Inc.
![Page 26: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/26.jpg)
26 2-508-97 Production and Operations Management
Weighted Moving Average ExampleWeighted Moving Average Example
MONTH MONTH WEIGHT WEIGHT DATADATA
AugustAugust 17%17% 130130SeptemberSeptember 33%33% 110110OctoberOctober 50%50% 9090
WMAWMA33 = = 33
ii = 1 = 1 WWii D Dii
= (0.50)(90) + (0.33)(110) + (0.17)(130)= (0.50)(90) + (0.33)(110) + (0.17)(130)
= 103.4 orders= 103.4 orders
November ForecastNovember Forecast
Copyright 2006 John Wiley & Sons, Inc.
![Page 27: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/27.jpg)
27 2-508-97 Production and Operations Management
Increasing n smooths the forecast but makes it less sensitive to changes
Do not forecast trends well
Require extensive historical data
Potential Problems WithPotential Problems With Moving Average Moving AveragePotential Problems WithPotential Problems With Moving Average Moving Average
![Page 28: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/28.jpg)
28 2-508-97 Production and Operations Management
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 SmoothingExponential SmoothingExponential SmoothingExponential Smoothing
![Page 29: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/29.jpg)
29 2-508-97 Production and Operations Management
Exponential SmoothingExponential SmoothingExponential SmoothingExponential Smoothing
New forecast =New forecast = last period’s forecastlast period’s forecast+ + (last period’s actual demand (last period’s actual demand
– – last period’s forecast)last period’s forecast)
FFtt = F = Ft – 1t – 1 + + (A(At – 1t – 1 - F - Ft – 1t – 1))
wherewhere FFtt == new forecastnew forecast
FFt – 1t – 1 == previous forecastprevious forecast
== smoothing (or weighting) smoothing (or weighting) constant (0 constant (0 1) 1)
![Page 30: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/30.jpg)
30 2-508-97 Production and Operations Management
Exponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing Example
Predicted demand = 142 Ford MustangsPredicted demand = 142 Ford MustangsActual demand = 153Actual demand = 153
Smoothing constant Smoothing constant = .20 = .20
![Page 31: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/31.jpg)
31 2-508-97 Production and Operations Management
Exponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing Example
Predicted demand = 142 Ford MustangsPredicted demand = 142 Ford MustangsActual demand = 153Actual demand = 153
Smoothing constant Smoothing constant = .20 = .20
New forecastNew forecast = 142 + .2(153 – 142)= 142 + .2(153 – 142)
![Page 32: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/32.jpg)
32 2-508-97 Production and Operations Management
Exponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing Example
Predicted demand = 142 Ford MustangsPredicted demand = 142 Ford MustangsActual demand = 153Actual demand = 153
Smoothing constant Smoothing constant = .20 = .20
New forecastNew forecast = 142 + .2(153 – 142)= 142 + .2(153 – 142)
= 142 + 2.2= 142 + 2.2
= 144.2 ≈ 144 cars= 144.2 ≈ 144 cars
![Page 33: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/33.jpg)
33 2-508-97 Production and Operations Management
Common Measures of ErrorCommon Measures of ErrorCommon Measures of ErrorCommon Measures of Error
Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)
MAD =MAD =∑∑ |actual - forecast||actual - forecast|
nn
Mean Squared Error (MSE)Mean Squared Error (MSE)
MSE =MSE =∑∑ (forecast errors)(forecast errors)22
nn
![Page 34: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/34.jpg)
34 2-508-97 Production and Operations Management
Trend analysisTrend analysis
Many trends are possible: Linear
Exponential
Logarithmic
S-growth curve
![Page 35: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/35.jpg)
35 2-508-97 Production and Operations Management
Linear Trend AnalysisLinear Trend Analysis
Demand
0
1000
2000
3000
4000
5000
6000
0 5 10 15 20 25 30
![Page 36: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/36.jpg)
36 2-508-97 Production and Operations Management
Discussion pointsDiscussion points
Ideally, forecast should not only include point estimates, but also a range of outcomes.
Think about the cost of a wrong decision based on the forecast: You have too much production
You have not enough production
Forecasts based on previous demand versus previous sales.
Choosing a forecast horizon: Goals
Flexibility
Lead times
![Page 37: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/37.jpg)
37 2-508-97 Production and Operations Management
Elements of a good forecastElements of a good forecast
1. Appropriate forecast horizon
2. Degree of accuracy should be taken into account
3. Reliable
4. Choose meaningful units (dollars versus units)
5. Use same forecast throughout organization
![Page 38: Jury of Executive Opinion](https://reader031.vdocuments.net/reader031/viewer/2022020715/55259d0b4a7959c2488b4cfd/html5/thumbnails/38.jpg)
38 2-508-97 Production and Operations Management
Reading ListReading List
Chapter 3 Stevenson and Hojati Page 59 - 69 (including exponential smoothing)
Page 81 (explanation of associate methods)
Page 90 - 94
Learning goals-
Define forecasting behaviors
Difference between qualitative and quantitative forecasting techniques
Learn forecasting methods such as moving averages, exponential smoothing.
Learn to calculate and evaluate forecasting errors using MAD and MSE (and importance of bias)