om week-forecasting 2
TRANSCRIPT
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Operations ManagementWeek-5
Forecasting Collaborative Planning, Forecasting and Replenishment (CPRF) Demand Patterns and Forecasting Techniques
Design of Forecasting Systems
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Forecasting What is it all about ?
Process of developing the most probable view of whatfuture demand will be, given a set of assumptions about :
Technology
Competitors
Pricing
Marketing
Expenditures
Sales efforts
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Elements of a Good Forecast
Timely
AccurateReliable
Written
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Types of Forecasts
Qualitative Methods
Judgmental- uses subjective inputs
Quantitative Methods
Time series- uses historical data assuming thefuture will be like the past
Associative models / Causal Methods- usesexplanatory variables to predict the future (whenhistorical data are available and the relationshipbetween
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Demand Patterns
The repeated observations of demand in their order ofoccurrence form a pattern known as time series.
Five basic patterns: Horizontal
Trend
Seasonal
Cyclical Random
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Steps in Forecasting
Step 1 Determine purpose of forecast
Step 2 Establish a time horizon
Step 3 Select a forecasting techniqueStep 4 Gather and analyze data
Step 5 Prepare the forecast
Step 6 Monitor the forecast
The forecast
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Designing Forecast Methods
Deciding what to forecast
Level of Aggregation
Units of measurement Choosing the type of technique
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1. Judgment Methods
In some cases, these are the only way to make a forecast. Inothers, these can be used to modify forecasts generatedquantitatively. Four types are common:
1) Salesforce Estimates2) Executive Opinion
3) Market Research
4) Delphi Method
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Are Judgmental Methods always reliable?
"Man will never reach the moon regardless of all future scientific
advances." -- Dr. Lee DeForest, Inventor of TV
"The bomb will never go off. I speak as an expert in explosive." -- AdmiralWilliam Leahy, U.S. Atomic Bomb Project
"I think there is a world market for maybe five computers." -- Thomas Watson,
chairman of IBM, 1943
"640K ought to be enough for anybody." -- Bill Gates, 1981
"This 'telephone' has too many shortcomings to be seriously consideredas a means of communication. The device is inherently of no value to
us." -- Western Union internal memo, 1876
We've all heard predictions about the future.Sure, sometimes "experts" are right on target,but check out what they got wrong
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2. Causal Methods
Linear Regression Analysis
- Establishes relationship between a DEPENDANT variable and one ormore INDEPENDENT variables
-We use our knowledge of the relationship between the two and aboutthe future values of the independent variables to forecast the futurevalues of the dependant variable.
- If there is only one independent variable, it is called as SimpleRegression Analysis (Generally time period)
Y = a + bX a0 1 2 3 4 5 X (Time)
Y
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2. Causal Methods
Linear Regression Analysis
Problem:
The Renovators construction company repairs/reconstructs old roads inSacramento, U.S. Over time they have found that companys dollar volume ofreconstruction work (Sales) is dependant on the total amount of road constructioncontracts offered by City Council every quarter . Management wants to establish
a mathematical relationship to help predict sales.!!!!!!!!!!!!!!!!!!!!!!!!
Year Quarter Sales(Thousand $)
Contracts(Thousand $)
1 Q1Q2
Q3Q4
810
159
150170
190170
2 Q1Q2Q3
Q4
121312
16
180190200
220
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Linear Regression Analysis
x= independent variable
y= dependent variablen= number of observationsa= vertical axis interceptb= slope of regression liney = mean value of dep. Variable
Y = values of y that lie on the
trend lineX = values of x that lie on thetrend liner= coefficient of correlationr2 = coefficient of determination
a= (x2y - x xy) / (n x2(x)2)
b= ( n xy - x y) / (n x2(x)2 )
r= (nxy - x y) / [n x2(x)2][ny2 (y)2 ]
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Time Series Methods
Naive Forecast :Forecast for the next period equals thedemand (Dt) for current period
o Simple and low-cost
o If random variation is large, highly useless for planning
Estimating the Average: Simple Moving Average
Weighted Moving Average
Exponential Smoothing
Unlike causal methods, these methods use historical information regarding
ONLY the dependent variable
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Exponential Smoothing
A sophisticated weighted-moving average forecasting methodthat involves very little record keeping of past data.
New Forecast = Last periods forecast + ( Last periods actualdemandLast periods forecast)
Or
Ft + 1 =Ft + (Dt Ft)
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Mean Actual Deviation (MAD)
First measure of the overall forecast error
MAD = I Actual demand - Forecast I---------------------------------------------
n
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Trend Adjusted Exponential Smoothing
Simple exponential smoothing, like moving averagesmethods fail to respond to .??????
At = (Demand this period) + (1-) (Average + Trendestimate last period)
= ( Dt ) + (1-)(At-1 + Tt-1 )
Tt = (Avg. this period Avg. last period) + (1 ) (Trend
estimate last period)= (At - At-1 ) + (1 ) Tt-1
Ft+1 = At + Tt