1 ppc- peramalan 2013

Departemen Teknik Industri FTI-ITB

TI-3003 Perencanaan dan Pengendalian Produksi

FORECASTING (Peramalan)

Laboratorium Sistem Produksi

www.lspitb.org ©2013


2

Hasil Pembelajaran

• Umum Mahasiswa mampu menerapkan model matematik,

heuristik dan teknik statistik untuk menganalisis dan merancang suatu sistem perencanaan dan pengendalian produksi

• Khusus Mampu menganalisis pola deman serta menerapkan

teknik-teknik peramalan


3

Tahapan PPC

Peramalan

Perencanaan

Agregat

Jadwal Produksi Induk

Perencanaan

Material

Order

Pembelian

Jadwal

Produksi

Penjadwalan

Ulang

Pengendalian Aktivitas Produksi di

Lantai Pabrik

Out-

sourcing

Rough Cut

Capacity

Planning

(RCCP)

Capacity

Requirement

Planning

(CRP)

Capacity Planning

Str

ateg

ic

pla

nn

ing

Peramalan (Forecasting)


Forecasting Horizons

• Long Term 5+ years into the future

R&D, plant location, product planning

Principally judgement-based

• Medium Term 1 season to 2 years

Aggregate planning, capacity planning, sales forecasts

Mixture of quantitative methods and judgement

• Short Term 1 day to 1 year, less than 1 season

Demand forecasting, staffing levels, purchasing, inventory levels

Quantitative methods


Short Term Forecasting: Needs and Uses

• Scheduling existing resources How many employees do we need and when?

How much product should we make in anticipation of demand?

• Acquiring additional resources When are we going to run out of capacity?

How many more people will we need?

How large will our back-orders be?

• Determining what resources are needed What kind of machines will we require?

Which services are growing in demand? declining?

What kind of people should we be hiring?


Types of Forecasting Models

• Types of Forecasts Qualitative --- based on experience, judgement,

knowledge;

Quantitative --- based on data, statistics;

• Methods of Forecasting Naive Methods --- eye-balling the numbers;

Formal Methods --- systematically reduce forecasting errors;

– time series models (e.g. exponential smoothing);

– causal models (e.g. regression).

Focus here on Time Series Models

• Assumptions of Time Series Models There is information about the past;

This information can be quantified in the form of data;

The pattern of the past will continue into the future.


Forecasting Examples

• Examples from student projects: Demand for tellers in a bank;

Traffic on major communication switch;

Demand for liquor in bar;

Demand for frozen foods in local grocery warehouse.

• Example from Industry: American Hospital

Supply Corp.

70,000 items;

25 stocking locations;

Store 3 years of data (63 million data points);

Update forecasts monthly;

21 million forecast updates per year.


Simple Moving Average

• Forecast Ft 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

nti

it

ntttt

Dn

F

DDDn

F

1

1

111

1

)(1


Simple Moving Average

• Include n most recent observations

• Weight equally

• Ignore older observations

weight

today

1 2 3 ... n

1/n


Moving Average Internet Unicycle Sales

0

50

100

150

200

250

300

350

400

450

Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-13

Month

Un

its

n = 3


Example:

Moving Average Forecasting


Exponential Smoothing I

• Include all past observations

• Weight recent observations much more heavily than very old observations:

weight

today

Decreasing weight given to older observations





weight

today


10





weight

today


10

)1(





weight

today


10

2)1(

)1(


Exponential Smoothing: Concept



weight

today


10

3

2

)1(

)1(

)1(


Exponential Smoothing: Math

21

2

2

1

)1()1(

)1()1(

tttt

tttt

DaDDF

DDDF



1)1( ttt FaaDF

21

2

2

1

)1()1(

)1()1(

tttt

tttt

DaDDF

DDDF



• Thus, new forecast is weighted sum of old forecast and actual demand

• Notes:

Only 2 values (Dt and Ft-1 ) are required, compared with n for moving average

Parameter a determined empirically (whatever works best)

Rule of thumb: < 0.5

Typically, = 0.2 or = 0.3 work well

• Forecast for k periods into future is:

1

2

2

1

)1(

)1()1(

ttt

tttt

FaaDF

DaaDaaaDF

tkt FF


20

DATA

SUMBER : Arsip perusahaan

Data pemerintah (laporan Biro Pusat Statistik, Departemen, dll)

• FAKTOR INTERNAL THD PENJUALAN Kualitas, harga, delivery time, promosi, discount, dll

• FAKTOR EKSTERNAL Indikator perekonomian : GNP, tingkat pertumbuhan

ekonomi, tingkat inflasi, nilai tukar valuta asing, dll


Exponential Smoothing

= 0.2

0

50

100

150

200

250

300

350

400

450

Jän.03 Mai.04 Sep.05 Feb.07 Jun.08 Nov.09 Mär.11 Aug.12

Un

its

Month

Internet Unicycle Sales (1000's)


Example:

Exponential Smoothing


Complicating Factors

• Simple Exponential Smoothing works well with data that is “moving sideways” (stationary)

• Must be adapted for data series which exhibit a definite trend

• Must be further adapted for data series which exhibit seasonal patterns


Holt’s Method: Double Exponential Smoothing

• What happens when there is a definite trend?

A trendy clothing boutique has had the following sales over the past 6 months:

1 2 3 4 5 6 510 512 528 530 542 552

480

490

500

510

520

530

540

550

560

1 2 3 4 5 6 7 8 9 10

Month

Demand

Actual

Forecast


Holt’s Method: Double Exponential Smoothing

• Ideas behind smoothing with trend:

``De-trend'' time-series by separating base from trend effects

Smooth base in usual manner using Smooth trend forecasts in usual manner using

• Smooth the base forecast Bt

• Smooth the trend forecast Tt

• Forecast k periods into future Ft+k with base and trend

))(1( 11 tttt TBDB

11 )1()( tttt TBBT

ttkt kTBF


ES with Trend = 0.2, = 0.4

0

50

100

150

200

250

300

350

400

450

Jän.03 Mai.04 Sep.05 Feb.07 Jun.08 Nov.09 Mär.11 Aug.12

Un

its

Month



Example:

Exponential Smoothing with Trend


Winter’s Method: Exponential Smoothing

w/ Trend and Seasonality

• Ideas behind smoothing with trend and seasonality: “De-trend’: and “de-seasonalize”time-series by

separating base from trend and seasonality effects

Smooth base in usual manner using Smooth trend forecasts in usual manner using Smooth seasonality forecasts using g

• Assume m seasons in a cycle 12 months in a year

4 quarters in a month

3 months in a quarter

et cetera




• Smooth the base forecast Bt


• Smooth the seasonality forecast St

))(1( 11

tt

mt

tt TB

S

DB

11 )1()( tttt TBBT

mt

t

tt S

B

DS )1( gg




• Forecast Ft with trend and seasonality


• Smooth the seasonality forecast St

mktttkt SkTBF )( 11

11 )1()( tttt TBBT

mt

t

tt S

B

DS )1( gg


ES with Trend and Seasonality


0

50

100

150

200

250

300

350

400

450

500

Jan-03 May-04 Sep-05 Feb-07 Jun-08 Nov-09 Mar-11 Aug-12

Month

Un

its

= 0.2, = 0.4, g = 0.6


Example:

Exponential Smoothing with

Trend and Seasonality


Forecasting Performance

• Mean Forecast Error (MFE or Bias): Measures average deviation of forecast from actuals.

• Mean Absolute Deviation (MAD): Measures average absolute deviation of forecast from actuals.

• Mean Absolute Percentage Error (MAPE): Measures absolute error as a percentage of the forecast.

• Standard Squared Error (MSE): Measures variance of forecast error

How good is the forecast?


Forecasting Performance Measures

)(1

1

t

n

t

t FDn

MFE

n

t

tt FDn

MAD1

1

n

t t

tt

D

FD

nMAPE

1

100

2

1

)(1

t

n

t

t FDn

MSE


• Want MFE to be as close to zero as possible -- minimum bias

• A large positive (negative) MFE means that the forecast is undershooting (overshooting) the actual observations

• Note that zero MFE does not imply that forecasts are perfect (no error) -- only that mean is “on target”

• Also called forecast BIAS

Mean Forecast Error (MFE or Bias)

)(1

1

t

n

t

t FDn

MFE


Mean Absolute Deviation (MAD)

• Measures absolute error

• Positive and negative errors thus do not cancel out (as with MFE)

• Want MAD to be as small as possible

• No way to know if MAD error is large or small in relation to the actual data

n

t

tt FDn

MAD1

1


Mean Absolute Percentage Error (MAPE)

• Same as MAD, except ...

• Measures deviation as a percentage of actual data

n

t t

tt

D

FD

nMAPE

1

100


Mean Squared Error (MSE)

• Measures squared forecast error -- error variance

• Recognizes that large errors are disproportionately more “expensive” than small errors

• But is not as easily interpreted as MAD, MAPE -- not as intuitive

2

1

)(1

t

n

t

t FDn

MSE


Fortunately, there is software...


Homework

• Text book

• Problem Chapter 2 Number 16, 17, 22, 30, 33

• Due date – next week 10 Sept 2013

40


41

JENIS POLA DATA

• Proses tetap (constant process)

Penjualan produk P

5.000

6.000

7.000

8.000

9.000

10.000

11.000

0 2 4 6 8 10 12

Bulan

Ju

mla

n (

10

00

bo


42

JENIS POLA DATA …. • Kecenderungan (Trend process)

Penjualan produk Q

8.000

9.000

10.000

11.000

12.000

13.000

14.000

1 2 3 4 5 6 7 8 9 10 11 12

Bulan

Un

it


43

JENIS POLA DATA …. • Siklus (Seasonal Process)

Penjualan produk perkantoran

-

100.000

200.000

300.000

400.000

500.000

600.000

700.000

1 2 3 4 5 6 7 8 9 10 11 12

Triwulan

Ju

ta R

p


44

Qualitative Forecasting

1. Market Survey

2. Expert Opinian and the Deplhi Technique

Please check the approproate boxes

I do not own a 35 mm camera

I own Single Lens Refelx (SLR 35 mm camera

I onw an autofocus 35 mm camera

I plan to purchase a new SLR 35 mm camera in the next two years

I plan to purchase a new autofocus 35 mm camera in the next two years

I do not plan to purchase a new 35 mm camera in the next two years


45

CAUSAL FORECASTING

1. SIMPLE LINIEAR REGRESSION

2. MULTI LINEAR REGRESSION

ntbhad ttt ,...,2,1

1bulan padan dikeluarka yg (IMB)bangunan mendirikanijin jumlah

bulan pada terjualyangset kitchen jumlah where

t-h

td

t

t

. 3322110 ttttt xbxbxbbd

termnoise

bulan padarusak yang phone-cellularjumlah

bulan pada phone-cellular harga

bulan pada potensial pembelijumlah

bulan pada terjualyang phone-cellularjumlah where

3

2

1

t

t

t

t

t

tx

tx

tx

td


46

TIME SERIES FORCASTING

1. CONSTANT PROCESS :

a) Simple methods :

b) Moving Average:

c) Simple Exponential smoothing:

2. TREND PROCESS:

• Double exponential smoothing

3. SEASONAL PROCESS:

tt ad

T

t

tTTkT dT

ddF

1

1 dimana

T

NTt

tTTkT dN

MMF

1

1 dimana

1)1( dimana TTTTkT SdSSF

tt btad

ttt acd


47

FORECAST ERROR

• Forecast Error = nilai actual – hasil peramalan

• Jenis Ukuran Forecast Error :

a) Mean Absolut deviatation (MAD) :

b) Mean Square Error (MSE) :

c) Mean absolut percentage Error (MAPE)

ttt Fde

T

t

teT

MAD

1

1

T

tt

eT

MSE

1

21

T

t t

t

d

e

TMAD

1

100.1

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