3201~ w1 & 2 - mrp forecasting from ts 20100722

8/8/2019 3201~ W1 & 2 - MRP Forecasting From TS 20100722

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Controlling the system

Production controlInventory controlLabor controlCost control

I. FORECASTING AS A PREREQUISITESTEP FOR MOST PLANNING ACTIVITIES

Information on most recent demand and production

Demand forecast for operations

Planning the System(designing)Product designProcess designEquipment investmentand replacementCapacity Planning

Scheduling the system

Aggregate ProductionPlanningOperations scheduling

Output of goods and services


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Forecasting Vs. Prediction

3 Most social and nature phenomena carry with them aninertia. Forecasting intends to cast the historical pattern intothe future.

Comparisons Forecasting Prediction

Goal Estimating a futureevent (Usually infigures)

Estimating a future event

M ethod A predetermined way(a defined model)

Subjective considerations

Information Bas

ePast data Knowledge, intuition, preference, emotion ...


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II. THE UNDERLYINGPATTERN OF THE DATA

Season(Cyclical)

Linear trend

ConstantProduction Demand(units)

Time


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Production demand(units)

Time

Low NoiseHigh Noise

Demand Patternwith trend andseasonalcomponents

Noise in demand


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3 (4)Cyclical:5 similar to a seasonal pattern, but the length of a

cycle is generally longer than a year.5 For example:

x Number of housing startsx price of metals

x GNP


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III. THE CRITERIA IN EVALUATINGFORECASTING PERFORMANCE

(FORECAST ERROR)For a single period, the forecast error is the

difference between actual data and forecasteddata.

E t = F t - D t3 where

5 E t: error 5 F t: forecast for period t (made prior to period t)

5 D t: actual demand of period t


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IV. FORECAST ERROR MEASURES

3 When multiple periods are involved,usually we derive some single measures toreflect forecasting performance.


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(A) Mean Absolute Deviation(MAD)

3 If E t ~ N ( U , )3 i.e.,forecast errors follow a normal distribution

with zero mean and a variance,3 then3 TheoreticallyTheoretically

MAD

F D

n

t t t

n

=

=

1

e2

e MAD= 1 25.

E MADe( ) =

2

2


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(B) Bias

( ) Bias

F D

n

t t t

n

=

=

1


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(C) Mean Square Error (MSE)

( )M E S F Dn

t t t

n

=

=2

1


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IV-1. Comparisons:

3 MADMAD measures the absolute magnitude of errors.

3

Bias Bias reflects the direction of errors (positiveor negative).3 MSE MSE intends to amplify large errors.


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IV-2. A thought question :

3 Question : Which one is the best measure?3 Answer:


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IV-3. Computation of forecasterror measures

3 Example:

5 MAD = 5.755 Bias = 3.255 MSE = 39.75

Period Forecast Demand E t=F t-Dt

1 100 90 102 110 105 53 120 117 34 130 135 -5


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V. FORECASTING TECHNIQUESCOMPARISONS

3 Qualitative vs. Quantitative

Comparisons Qualitative Quantitative

Structure Problem is notwell structured

Problem is wellstructured

Past Data Past data is notavailable

Past data isavailable

Implementation Throughindividual orgroup judgment

Through models


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Time Series vs. Causal

Comparisons Time Series CausalPlanning

Horizon

Short-run

planning

Long-term planning

Purpose Operations processes

Strategic consideration

Assumption Inertia exists Cause-effect relationship

Implementation Empirical studies Theoretical


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Regression Analysis vs.Econometric Modeling

Y = a 0 + a 1X1 (Simple Regression)

Y = b 0 + b 1x1 + b 2x2 (Multiple Regression)Y1 = C 1X1 + C 2X2Y2 = D 0 + D 1X1 + D 2X2 (An econometric model)Y3 = E 0 + E 1X2 + E 2X1 (A system of equations)


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Forecasting Techniques3 Qualitative

5Delphi Method

5 Market Research5 Historical Analogy

3

Time Series5 Moving Average5 Exponential Smoothing5

Box-Jenkins3 Causal

5 Regression5

Econometric Models


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Forecasting Techniques

3 Question: What is the naive model?3 Answer:

5

In Forecasting, "using the most recent demandas the forecast for next period" is referred to asthe naive model.


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VI. USEFUL FORECASTINGMODELS FOR OPERATIONS


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VI-1. Simple Average (Mean)

3 Use the mean of historical demand to identifythe constant level of demand pattern.


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VI-2. Linear Regression

3 Use regression analysis to identify the increaserate or decrease rate of trend in the demand

pattern.


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VI-3. Simple Moving Average

3 for n period moving average3 t = 1 is the oldest period3 t = n is the most recent period

MA D

n

t t n m

n

=

=


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VI -4. Simple ExponentialSmoothing

3 F t: Forecast for period t3 D t-1 :Demand of period t-1

3 F t-1 :Forecast for period t-13 The weight assigned to the most recent

demand,

( ) F D F t t t = + 1 11

0 1


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Why is this model called exponential smoothing?

3 Substituting Ft-n-1

into Ft-n

, continuing the procedure, wehave an expanded form as follows:

3 The weights , (1- ), . . ., assigned to the past datadecrease exponentially. " Smoothing " means " averagingout " errors by using more than one period's data.

( ) F D F t t t = + 1 11

( ) F D F t t t = + 1 2 21

( ) F D F t t 2 13 3= +

( ) ( ) ( ) F D D D D F t t t t n

t n

n

t n= + + + + +

1 2

2

3

11 1 1 1.... ( )


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VI-5. Weighted Moving Average(WMA)

3 C t is the weight assigned to D t3 where , and

WMA C Dt t

n

t ==

1

0 1 C t C t t

n

= =

11


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VI -6. Adaptive Model

3 The parameter value of the model isallowed to change, and the procedure isdesigned in the forecasting model to changeautomatically when the model detects theneed to change.


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VII. EXAMPLE:

3 Time series data for monthly sales :

Jan 460

Feb 440Mar 460Apr 510May 520June 495July 470


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VII-1.Use the three-month movingaverage model to forecast the demand

for May, June, and July.

MA April = (460 + 440 + 460)/3 = 453

MA May= (440 + 460 + 510)/3 = 470

MA June= (460 + 510 + 520)/3 = 497

MA July= (510 + 520 + 495)/3 = 508


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VII-2.Use simple exponential

smoothing model to forecast thedemand for May, June and July.

Question:What do you need to know to make

the forecast?


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Answer:

3 (1) the initial forecast for March (assume it is440)

3 (2) the parameter value (assume = 0.6)FApril = (0.6)(460)+(0.4)(440) = 452

FMay = (0.6)(510)+(0.4)(452) = 487

FJune = (0.6)(520)+(0.4)(487) = 507FJuly= (0.6)(495)+(0.4)(507) = 500


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VII-3.Use a weighted moving averagemodel to forecast the demand for May,

June and July.

3 Question: What do you need to know toimplement the forecast?

3 Answer:

5 The relative weights of selected number of periods.


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Assuming C 1 = .2, C 2 = .3, C 3 = .5

WMA April = (.2)(460)+(.3)(440)+(.5)(460) = 454

WMA May = (.2)(440)+(.3)(460)+(.5)(510) = 481

WMA June = (.2)(460)+(.3)(510)+(.5)(520) = 505WMA July = (.2)(510)+(.3)(520)+(.5)(495) = 506

VII 4 U Bi MAD & MSE


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VII-4.Use Bias, MAD & MSE toevaluate the performance of these

models.

x *:the best performance for each measurex In this example, exponential smoothing is the best

model.

Demand 3-month MA Exp. Smoothing Weighted MAForecast Error Forecast Error Forecast Error

Aprial 510 453 -57 452 -58 454 -56

May 520 470 -50 487 -33 481 -39June 495 497 2 507 12 505 10July 475 508 33 500 25 506 31

BIAS = -18.0 -13.5* -13.5*MAD = 35.5 32.0* 34.0MSE = 1,710.5 1,305.5* 1,429.5


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VIII. SUGGESTED READING

3 Chapter 13 Forecasting pp.497-510 (Background & Time Series

Forecasting) pp.513-516 (Forecast Error) pp.529-530 (Choosing A Forecasting

Method)3 Study Solved Problem 1, pp.537-538

3201~ w1 & 2 - mrp forecasting from ts 20100722

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