ch 15 demand manmagement & forecasting-hk

7/31/2019 Ch 15 Demand Manmagement & Forecasting-HK

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1

Chapter 15. Demand Management and Forecasting

Demand Management Independent vs. dependent demand

Qualitative Techniques Market research, focus groups, Delphi technique,

Quantitative Techniques 1. Time series based models

2. Associative (causal) Models

Accuracy and Control of Forecast (errors)

1. Measuring and comparing forecast errors usingMAD,MAPE, MSE, RMSE

2. Controlling Forecasting Process via Tracking Signal


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2

A

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand:

Raw Materials,Component parts,Sub-assemblies, etc.

Independent Demand:Finished Goods

Demand Management


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DemandManagement

Active role to influence demand. Examples from seasonal goods or services

Campaigns, discounts, etc.

Incentives to sales personnel

Passive role, limited or no action taken. Why?


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4

Qualitative Methods

Grass RootsMarket Research

Panel Consensus

Executive Judgment

Historical analogy

Delphi Method

Qualitative

Methods


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6

Quantitative forecasting methods

Time series models

Past predicts future

Uses time series data

Key variable: time (t)

Easier to apply

Less accurate Examples:

Moving averages

Exponential smoothing

Causal models

Examines potential cause=> effectrelationships

Requires cross sectional data

Key variables are usually denotedas X

1, X

2, X

3,

More difficult

Takes more time

But worth it since it providesinsight to the system (process)

under study Examples:

Various regression models


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Quantitative techniques

Basic time series approaches i. Moving averages, simple &weighted

ii. Exponential smoothing, simple & trend adjusted

iii. Linear regression (linear trend model)

iv. Techniques for seasonality and trend -

Decomposition of time series

Causal approach i. Simple Linear Regression

ii. Multiple Linear Regression


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Finding Components of a Time Series

1 2 3 4

x

x xx

xx

x xx

xx x x x

xxxxxx x x

xx

x x xx

x

xx

x

x

xx

xx

xx

x

xx

xx

x

x

x

Year

Sales

Seasonal variation

Linear

Trend

15-8


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What to look for in a time series

Trend - long-term movement in data

Seasonality - short-term regular and repetitive variations in data

Cyclical variations long(er) term, occasionally caused by

unusual circumstances, (war, economic downturn, etc.)

Autocorrelation denotes persistence of occurrence (momentumdriven)

Random variations - caused by chance


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11/2011

Exponential Smoothing Models

Simple exponential smoothingmodel

Alpha is the smoothing constant

Whenever appropriate more weightcan be given to the more recentdata (time periods)

Double exponential smoothing(Holts model)

Adds trend component,

Tand delta(gamma) as the smoothing constant

for trend

Forecast Including Trend (FIT)

Excel time!

Problem 20, continued.

)FA(FF 111 tttt

10where

)FITF(TT 11 tttt

)FITA(FITF 111 tttt

ttt TFFIT


12/2012

Measuring Accuracy, Forecast Errors

To compare different time series techniques or to select thebest set of initial values for the parameters, use a combinationof the the following four metrics:

Mean Absolute Deviation Most popular but

Mean Absolut Percent Error Should be used in tandem with MAD

Mean Square Error

Root Mean Square Error

n

FA

=MAD1

n

i

ii

n

i i

ii

n 1 A

FA100=MAPE

n

FA

=MSE 1

2

n

i

ii

MSERMSE


13/2013

Tracking Signal

The Tracking Signal or TS is a measure that indicateswhether the forecast average is keeping pace with anygenuine upward or downward changes in demand.

Depending on the number of MADs selected, the TS can beused like a quality control chart indicating when the modelis generating too much error in its forecasts.

TS is a monitoring system.

The TS formula is:

DeviationAbsoluteMean

ErrorsForecastofSumRunning=TS


14/2014

Regression analysis

Identify factors (independent variables) that can be used to

predict the values for the forecast variable (e.g., sales).

Regression applied to causal data requires different kinds ofdata

Regression applied to time series data is also know astrend line analysis

We will use Excel (Tools/Data analysis) to obtain theregression line and all relevant statistics.


15/2015

A simple regression example

The first example applies regression to time series data.

Whenever possible, plot and observe the data.

The scatter plot shows a linear relation between advertising andsales. So the following regression model is suggested by the data,which refers to the true relationship between the entire population ofadvertising and sales values.

Other common formats are:

ii 110i XY

tbaYXbaY


16/2016

Decomposition of a Time Series

Demand has both trend and seasonal components.

View data via Excel.

1. Compute overall average

2. Compute average of the same seasons of each cycle (e.g., year)

3. Compute seasonal indexes (seasonal averages / overall avg.)

4. Deseasonalize data (actual values /seasonal indexes)

5. Apply regression to deseasonalized data6. Compute (project) deseasonalized forecasts using the regressionequation

7. Reseasonalize the forecasts by multiplying them with theseasonal indexes.

Excel time.

Problem 21.


17/2017

Multiple regression

Most regression problems involve more than one independent

variable. If each independent variables varies in a linear manner with Y, the

estimated regression function in this case is:

Where b0 is the intercept (also called constant)

The optimal values for the bi (slopes) can again be found using theleast squares method

kkbbbb XXXY

22110


18/2018

Steps in multiple regression analysis

1. Hypotheses for testing whether a general linear model is useful ispredicting Y:

1. Ho :1 = 2 = 3 = ... = k = 0 (means there is NOTHING useful)

2. HA : At least one of the parameters in Ho is nonzero.

2. Test statistic: F-statistic = MSR / MSE

3. If the model is deemed adequate (passes the F-test; rejected H0 )

then go to step 4 (otherwise, none of variables have any impact on Y )4. Conduct t-tests (significance tests) on parameters (slopes).

5. Remove the most insignificant independentvariable, re-run theregression, and go to step 4.

6. Repeat steps 4 & 5 until all remaining independent variable

parameters (slopes) are significant, then go to step 77. If the intercept (0 ) is insignificant then remove it, run regression one

more time.

Excel time!


19/2019

What Forecasters Should Do

Determine what elements of historical data provide repeatable

patterns and utilize this to make extrapolations. Make a list of the possible independent variables that may have

influenced the historical data and may influence futureoutcomes.

Statistically correlate the independent variables to the outcome

history using regression analysis to validate their importanceand to calibrate their effects.

Make estimates of forecast error wherever possible using MADor standard deviation measures.

Make clear presentations of the results and assumptions andlisten to feedback.


20/2020

Forecasting

Always remember that you (managers) are decision makers and

sound decisions are based on good forecasts

Suggested problems:

2, 3, 4, 7, 11, 12, 14, 17, 20, 21, 27

ch 15 demand manmagement & forecasting-hk

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