2. forecast demand

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FORECAST DEMAND



FORECAST IS DIFFICULT

“Prediction is very difficult, especially if it's about thefuture”

-Niels Bohr-

" It is often said there are two types of forecasts ... luckyor wrong!!!! “

-in "Control" magazine published by Institute ofOperations Management-

"Forecasting is the art of saying what will happen, andthen explaining why it didn't! “

-Anonymous (communicated by Balaji Rajagopalan)-



FORECASTING

Prediction, projection, estimationoccurrence of uncertain future events or levels activity



FITTING FORECASTING TO THE PURPOSE

What? Aggregat? Product? Item? Sparepart?

Where? Which geographic area should include?

When? Forecast horizon?

How? Divide forecast into period? Frequency to

update? Accuracy needed?

DEFINE

PURPOSE

DEFINE

FORECAST



CHARACTERISTICS OF GOOD FORECAST

ACCURACY

COST

RESPONSE

SIMPLICITY



PRINCIPLES OF FORECASTING

• Forecast involves error >>> they are usually

wrong

• Family forecast are more accurate than item

forecast. Aggregate forecasts are more

accurate.

• Short-range forecasts are more accurate than

long-range forecasts

• A good forecast is more than a single number



DEMAND INFLUENCE FACTORS

1. Business condition and state of the economy

2. Competitor actions and reactions

3. Governmental legislative action4. Market trend

Product life cycles

Style and fashion

Changing consumer demands

5. Technological innovations



DEMAND MANAGEMENT

A

Independent Demand(finished goods and spare parts)

B(4) C(2)

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

Dependent Demand(components)

Where possible, calculate demand rather than forecast.If not possible...



DemandEstimates

SalesForecast

ProductionResource

Forecast



Examples of Production Resource Forecasts

Forecast

Horizon Time Span Item Being Forecast

Units of

Measure

Long-Range Years

• Product lines

• Factory capacities

• Planning for new products

• Capital expenditures

• Facility location or expansion

• R&D

Dollars, tons,

etc.

Medium-

RangeMonths

• Product groups

• Department capacities

• Sales planning

• Production planning and

budgeting

Dollars, tons,

etc.

Short-Range Weeks

•

Specific product quantities• Machine capacities

• Planning

• Purchasing

• Scheduling

• Workforce levels

• Production levels

•Job assignments

Physical units

of products



FORECAST METHODS

• QUALITATIVE

– Sales force composite

– Market Research

– Management Decision (Jury of executive opinion)

– The Delphi Method

• QUANTITATIVE

– Time series



Time Series Analysis

Selalu menggunakan data historis

(Naïve methods)

Komponen time series: – Trend

– Seasonality

– Cycles

– Randomness



Simple Time Series Models

• Moving Average (Simple & Weighted)

• Exponential Smoothing (Single)

•

Double Exponential Smoothing (Holt’s)• Winter’s Method for Seasonal Problems



Simple Moving Average•

Forecast F t is average of n previous observations or actuals Dt :

• Note that the n past observations are equally weighted.

• Issues with moving average forecasts:

– All n past observations treated equally;

– Observations older than n are not included at all;

– Requires that n past observations be retained;

– Problem when 1000's of items are being forecast.

t

nt i

it

nt t t t

Dn

F

D D Dn

F

1

1

111

1

)(1



Example of Simple Moving Average

Week Demand 3-Week 6-Week

1 650

2 678

3 720

4 785 682.675 859 727.67

6 920 788.00

7 850 854.67 768.67

8 758 876.33 802.00

9 892 842.67 815.33

10 920 833.33 844.00

11 789 856.67 866.50

12 844 867.00 854.83



Weighted Moving Average

)( 11111 nt nt t t t t t Dw Dw Dw F

Forecast is based on n past demand data, each

given a certain weight. The total weight must equal

to 1.

Re-do the above example, using 3 past data, each given

a weight of 0.5, 0.3, and 0.2 (larger for more recent data)



Exponential Smoothing

• New Forecast = α (current observation of demand) +(1-α) (last forecast)

• Or

Ft = α(Dt-1) + (1-α)Ft-1

And

Ft-1 = α(Dt-2) + (1-α)Ft-2, dst

Sehingga pada model ini, semua data historis terwakilipada forecast terakhir dengan bobot yang semakinkecil (untuk data yang semakin lama)




• Include all past observations

• Weight recent observations much more heavily than

very old observations:

weight

today

Decreasing weight givento older observations

0 1

( )

( )

( )

1

1

1

2

3




• Example:

– Exponential smoothing and a constant model are

being used for forecasting. The smoothed average

at the end of period zero was 80. The actualdemand in period 1 was 104. The smoothing

constant is 0,1. What is the forecast for period 2

made at the end of period 1?



Persamaan MA dan ES

• Sama-sama mengasumsikan demand bersifat

stationary

• Keduanya tergantung pada 1 nilai parameter,

N pada MA dan α pada ES.

• Kalau ada trend, kedua-duanya terlambat

dalam merespon



Perbedaan MA dan ES

• MA mengakomodasikan lebih banyak data

• ES hanya menyimpan dua data: forecast

terakhir dan actual demand terakhir, sedang

MA menyimpan N data demand terakhir

2. forecast demand

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