Metode Peramalan Permintaan

CHAPTER

3

Forecasting

Rahmi Yuniarti,ST.,MT Anni Rahimah, SAB,MAB

FIA - Prodi Bisnis Internasional Universitas Brawijaya

Pengelolaan Order Pesanan

2

Permintaan

Penawaran

Negosiasi

Manajemen Permintaan

3

Order Pesanan

Ramalan Permintaan Manajemen

FORECAST

:

 _{A statement about the future value of a variable of}

interest such as demand.

 _{Forecasts affect decisions and activities throughout}

an organization

 _{Accounting, finance}  _{Human resources}  _Marketing

 _MIS

 _Operations

 _{Product / service design}

Accounting Cost/profit estimates

Finance Cash flow and funding

Human Resources Hiring/recruiting/training

Marketing Pricing, promotion, strategy

MIS IT/IS systems, services

Operations Schedules, MRP, workloads

Product/service design New products and services



Assumes causal system

past ==> future



Forecasts rarely perfect because of

randomness



Forecasts more accurate for

groups vs. individuals



Forecast accuracy decreases

as time horizon increases

I see that you will

get an A this semester.

Steps in the Forecasting Process

Step 1 Determine purpose of forecast Step 2 Establish a time horizon

Step 3 Select a forecasting technique Step 4 Gather and analyze data

Step 5 Prepare the forecast

Forecasting Models

Forecasting Techniques Qualitative

Models Time Series _Methods

Causal Methods

Delphi Method

Jury of Executive Opinion_{Sales Force}

Composite Consumer Market Survey Naive Moving Average Weighted

Model Differences



Qualitative – incorporates judgmental &

subjective factors into forecast.



Time-Series – attempts to predict the future by

using historical data.

Qualitative Forecasting Models

 _{Delphi method}

 _{Iterative group process allows experts to make forecasts}  _{Participants:}

 _{decision makers: 5 -10 experts who make the forecast}

 _{staff personnel: assist by preparing, distributing, collecting, and}

summarizing a series of questionnaires and survey results

 _{respondents: group with valued judgments who provide input to decision}

Qualitative Forecasting Models

(cont)

 _{Jury of executive opinion}

 _{Opinions of a small group of high level managers, often in combination} with statistical models.

 _{Result is a group estimate.}

 _{Sales force composite}

 _{Each salesperson estimates sales in his region.}  _{Forecasts are reviewed to ensure realistic.}

 _{Combined at higher levels to reach an overall forecast.}

 _{Consumer market survey.}

 _{Solicits input from customers and potential customers regarding future} purchases.

Metode Peramalan Deret Waktu

(Time Series Methods)

 _{Teknik peramalan yang menggunakan data-data historis}

penjualan beberapa waktu terakhir dan

mengekstrapolasinya untuk meramalkan penjualan di masa depan

 _{Peramalan deret waktu mengasumsikan pola}

kecenderungan pemasaran akan berlanjut di masa depan.

 _{Sebenarnya pendekatan ini cukup naif, karena}

mengabaikan gejolak kondisi pasar dan persaingan

Langkah-langkah Peramalan Time Series

(Deret Waktu)

 _{Kumpulkan data historis penjualan}

 _{Petakan dalam diagram pencar (scatter diagram)}  _{Periksa pola perubahan permintaan}

 _{Identifikasi faktor pola perubahan permintaan}  _{Pilih metode peramalan yang sesuai}

 _{Hitung ukuran kesalahan peramalan}

 _{Lakukan peramalan untuk satu atau beberapa periode}

mendatang

UKURAN AKURASI HASIL PERAMALAN

Ukuran akurasi hasil peramalan yang merupakan

ukuran kesalahan peramalan merupakan ukuran

tentang tingkat perbedaan antara hasil peramalan

dengan permintaan yang sebenarnya terjadi.

Ukuran yang biasa digunakan, yaitu:

1. Rata-rata Deviasi Mutlak (Mean Absolute Deviation = MAD)

2. Rata-Rata Kuadrat Kesalahan (Mean Square Error = MSE)

UKURAN AKURASI HASIL PERAMALAN

1. Rata-rata Deviasi Mutlak (Mean Absolute Deviation = MAD)

MAD merupakan rata-rata kesalahan mutlak selama periode tertentu tanpa memperhatikan apakah hasil peramalan besar atau lebih kecil dibandingkan kenyataannya.

2. Rata-Rata Kuadrat Kesalahan (Mean Square Error= MSE)

MSE dihitung dengan menjumlahkan kuadrat semua kesalahan peramalan pada setiap periode dan membaginya dengan jumlah periode peramalan.

3. Rata-rata Peramalan Kesahalan Absolut (Mean Absolute

Percent Age Error + MAPE)

MAPE merupakan kesalahan relatif. MAPE menyatakan persentase kesalahan hasil peramalan terhadap permintaan aktual selama

Forecast Error

 _{Bias - The arithmetic sum of}

the errors

 _{Mean Square Error - Similar to}

simple sample variance

 _{MAD - Mean Absolute}

Deviation

 _{MAPE – Mean Absolute}

Percentage Error

t t F

A Error

Forecast  

T F

A

MAD T _{t t}

t / | | /T | error forecast | 1 T 1 t       T A F A

MAPE T _{t t t}

Controlling the Forecast

 _{Control chart}

 _{A visual tool for monitoring forecast errors}  _{Used to detect non-randomness in errors}

 _{Forecasting errors are in control if}

 _{All errors are within the control limits}

Quantitative Forecasting Models

 _{Time Series Method}

 _Naïve

 _{Whatever happened recently}

will happen again this time (same time period)

 _{The model is simple and}

flexible

 _{Provides a baseline to}

measure other models

 _{Attempts to capture seasonal}

Naive Forecasts

Uh, give me a minute....

We sold 250 wheels last

week.... Now, next week

we should sell....



Simple to use



Virtually no cost



Quick and easy to prepare



Easily understandable



Can be a standard for accuracy



Cannot provide high accuracy

Techniques for Averaging



Moving average



Weighted moving average

Moving Averages

 _{Moving average – A technique that averages a}

number of recent actual values, updated as new values become available.

 _{The demand for wheels in a wheel store in the past 5}

weeks were as follows. Compute a three-period moving average forecast for demand in week 6.

83 80 85 90 94

MA

_n

=

n

A

_i

Moving Averages

 _{Weighted moving average – More recent values in a}

series are given more weight in computing the forecast.

 Assumes data from some periods are more important than data

from other periods (e.g. earlier periods).

 Use weights to place more emphasis on some periods and less on

others.

Example:

 _{For the previous demand data, compute a weighted}

average forecast using a weight of .40 for the most recent period, .30 for the next most recent, .20 for the next and .10 for the next.

 _{If the actual demand for week 6 is 91, forecast}

Exponential Smoothing

 _{ES didefinisikan sebagai:}

Keterangan:

F_t+1 = Ramalan untuk periode berikutnya D_t = Demand aktual pada periode t

F_t = Peramalan yg ditentukan sebelumnya untuk periode t  = Faktor bobot

  besar, smoothing yg dilakukan kecil

  kecil, smoothing yg dilakukan semakin besar   optimum akan meminimumkan MSE, MAPE

t t

t D F

Exponential Smoothing



Weighted averaging method based on previous

forecast plus a percentage of the forecast error



A-F is the error term,



is the % feedback

Trend , Seasonality Analysis



Trend

- long-term movement in data



Cycle

– wavelike variations of more than one

year’s duration



Seasonality

- short-term regular variations in

data



Random variations

- caused by chance

Techniques for Trend



Develop an equation that will suitably describe

trend, when trend is present.



The trend component may be linear or nonlinear

Common Nonlinear Trends

Parabolic

Exponential

Linear Trend Equation

 F_t = Forecast for period t

 _{t = Specified number of time periods}  a = Value of F_t at t = 0

 _{b = Slope of the line}

F

_t

= a + bt

0 1 2 3 4 5 t

Example



Sales for over the last 5 weeks are shown below:

Week: 1 2 3 4 5

Sales: 150 157 162 166 177



Plot the data and visually check to see if a linear

trend line is appropriate.

Calculating a and b

b

=

n (ty)

-

t y

n t

2 - (

t)

2

a

=

y

-

b t

n











Linear Trend Equation Example

t y

Week t2 Sales ty

1 1 150 150

2 4 157 314

3 9 162 486

4 16 166 664

5 25 177 885

 _{t = 15} _t2 _{= 55}  _{y = 812}  _{ty = 2499}

Linear Trend Calculation

F

t

= 143.5 + 6.3t

a =

812 - 6.3(15)

5

=

b = 5 (2499) - 15(812)

5(55) - 225 =

12495-12180

275 -225 = 6.3

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