6.0-Topic 6_ Forecasting

8/4/2019 6.0-Topic 6_ Forecasting

http://slidepdf.com/reader/full/60-topic-6-forecasting 1/39

4/21/2012 NY - KJP 585 2009 1

Operations Management

Topic 6 – Forecasting

UiTM Shah Alam Lecturer: Pn. Noriah Yusoff T1-A16-6C



4/21/2012 NY - KJP 585 2009 2

What is Forecasting?

Process of predicting afuture event

Underlying basis of all business decisions Production

Inventory

Personnel

Facilities

Hmm…. you

gonna get an A forthis subject



4/21/2012 NY - KJP 585 2009 3

Short-range forecast Up to 1 year, generally less than 3 months

Purchasing, job scheduling, workforce levels, jobassignments, production levels

Medium-range forecast 3 months to 3 years

Sales and production planning, budgeting

Long-range forecast

3+ years New product planning, facility location, research and

development

Forecasting Time Horizons



4/21/2012 NY - KJP 585 2009 4

Seven Steps in Forecasting

Determine the use of the forecast

Select the items to be forecasted

Determine the time horizon of the forecast

Select the forecasting model(s)

Gather the data

Make the forecast Validate and implement results



4/21/2012 NY - KJP 585 2009 5

Types of Forecasts

Economic forecasts

Address business cycle – inflation rate, money

supply, housing starts, etc.

Technological forecasts

Predict rate of technological progress

Impacts development of new products

Demand forecasts Predict sales of existing products and services



4/21/2012 NY - KJP 585 2009 6

Strategic Importance of

Forecasting

Human Resources – Hiring, training, laying off

workers Capacity – Capacity shortages can result in

undependable delivery, loss of customers,loss of market share

Supply Chain Management – Good supplierrelations and price advantages



4/21/2012 NY - KJP 585 2009 7

The Realities!

Forecasts are seldom perfect

Most techniques assume an underlying stability in the system

Product family and aggregated forecasts are more accurate than individual product forecasts



4/21/2012 NY - KJP 585 2009 8

Forecasting Approaches

Used when situation is vague and

little data exist New products

New technology

Involves intuition, experience

e.g., forecasting sales on Internet

Qualitative Methods



4/21/2012 NY - KJP 585 2009 9

Forecasting Approaches

Used when situation is ‘stable’ and

historical data exist Existing products

Current technology

Involves mathematical techniques

e.g., forecasting sales of color televisions

Quantitative Methods



4/21/2012 NY - KJP 585 2009 10

Overview of Quantitative

Approaches1. Naive approach

2. Moving averages3. Exponential

smoothing

4. Trend projection5. Linear regression

Time-SeriesModels

AssociativeModel



4/21/2012 NY - KJP 585 2009 11

Set of evenly spaced numerical data

Obtained by observing response variable at

regular time periods

Forecast based only on past values, no

other variables important

Assumes that factors influencing past andpresent will continue influence in future

Time Series Forecasting



4/21/2012 NY - KJP 585 2009 12

Components of Demand

D e m a

n d f o r p r o d u c t o r

s e r v i c e

| | | |

1 2 3 4

Year

Average demand

over four years

Seasonal peaks

Trendcomponent

Actualdemand

Randomvariation

Figure 4.1



4/21/2012 NY - KJP 585 2009 13

Persistent, overall upward or

downward pattern

Changes due to population,technology, age, culture, etc.

Typically several years duration

Trend Component



4/21/2012 NY - KJP 585 2009 14

Regular pattern of up and down

fluctuations

Due to weather, customs, etc. Occurs within a single year

Seasonal Component

Number of Period Length Seasons

Week Day 7Month Week 4-4.5Month Day 28-31Year Quarter 4Year Month 12

Year Week 52



4/21/2012 NY - KJP 585 2009 15

Repeating up and down movements

Affected by business cycle, political, and

economic factors Multiple years duration

Often causal or

associativerelationships

Cyclical Component

0 5 10 15 20



4/21/2012 NY - KJP 585 2009 16

Erratic, unsystematic, ‘residual’

fluctuations

Due to random variation or unforeseenevents

Short duration and

nonrepeating

Random Component

M T W T F



4/21/2012 NY - KJP 585 2009 17

Naive Approach

Assumes demand in nextperiod is the same as

demand in most recent period e.g., If January sales were 68, then

February sales will be 68

Sometimes cost effective and efficient

Can be good starting point



4/21/2012 NY - KJP 585 2009 18

Moving average

Weighted moving average

Exponential smoothing

Techniques for Averaging



4/21/2012 NY - KJP 585 2009 19

MA is a series of arithmetic means

Used if little or no trend

Used often for smoothingProvides overall impression of data over

time

Moving Average Method

Moving average =∑ demand in previous n periods

n



4/21/2012 NY - KJP 585 2009 20

January 10

February 12 March 13 April 16May 19

June 23July 26

Actual 3-Month Month Shed Sales Moving Average

(12 + 13 + 16)/3 = 13 2/3

(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 1/3

Moving Average Example

10

12 13

(10 + 12 + 13)/3 = 11 2/3



4/21/2012 NY - KJP 585 2009 21

Graph of Moving Average

| | | | | | | | | | | |

J F M A M J J A S O N D

S h e d S a l e s

30 –

28 –

26 –

24 –

22 –

20 –

18 –

16 –

14 – 12 –

10 –

Actual

Sales

Moving Average Forecast



4/21/2012 NY - KJP 585 2009 22

Used when trend is present

Older data usually less important

Weights based on experience andintuition

Weighted Moving Average

Weighted moving average =

∑ (weight for period n ) x (demand in period n )

∑ weights



4/21/2012 NY - KJP 585 2009 23

January 10 February 12 March 13

April 16May 19June 23July 26

Actual 3-Month Weighted

Month Shed Sales Moving Average

[(3 x 16) + (2 x 13) + (12)]/6 = 141/3 [(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 201/2

Weighted Moving Average

10 12 13

[(3 x 13) + (2 x 12) + (10)]/6 = 121

/6

Weights Applied Period

3 Last month 2 Two months ago 1 Three months ago

6 Sum of weights



4/21/2012 NY - KJP 585 2009 24

Moving Average AndWeighted Moving Average

30 –

25 –

20 –

15 –

10 –

5 –

S a l e s d e m a n

d

| | | | | | | | | | | |

J F M A M J J A S O N D

Actual sales

Moving average

Weighted moving average

Figure 4.2



4/21/2012 NY - KJP 585 2009 25

Increasing n smooths the forecast but

makes it less sensitive to changes Do not forecast trends well

Require extensive historical data

Potential Problems With

Moving Average



4/21/2012 NY - KJP 585 2009 26

Form of weighted moving average

Weights decline exponentially

Most recent data weighted most

Requires smoothing constant ( )

Ranges from 0 to 1

Subjectively chosen Involves little record keeping of past data

Exponential Smoothing



4/21/2012 NY - KJP 585 2009 27

Exponential Smoothing

New forecast = Last period’s forecast + (Last period’s actual demand

– Last period’s forecast )

F t = F t – 1 + (At – 1 - F t – 1)

where F t = new forecast F t – 1 = previous forecast

= smoothing (or weighting) constant (0 ≤ ≤ 1)

Remember This!!!!!!!!



4/21/2012 NY - KJP 585 2009 28

Choosing

The objective is to obtain the most accurate forecast no matter the

technique We generally do this by selecting the model that gives us the lowest forecast error

Forecast error = Actual demand - Forecast value

= At - F t



4/21/2012 NY - KJP 585 2009 29

Common Measures of Error

Mean Absolute Deviation (MAD )

MAD = ∑ |Actual - Forecast| n

Mean Squared Error (MSE )

MSE = ∑ (Forecast Errors )2

n



4/21/2012 NY - KJP 585 2009 30

Exponential Smoothing Example

Predicted demand = 142 Ford Mustangs Actual demand = 153

Smoothing constant = .20



4/21/2012 NY - KJP 585 2009 31




New forecast = 142 + .2(153 – 142)



4/21/2012 NY - KJP 585 2009 32




New forecast = 142 + .2(153 – 142)

= 142 + 2.2= 144.2 ≈ 144 cars



4/21/2012 NY - KJP 585 2009 33


2Demand for the last four months was:

Predict demand for July using each of these methods:(A)1) A 3-period moving average2) exponential smoothing with alpha equal to .20 (use naïve to

begin).(B)3) If the naive approach had been used to predict demand for April

through June, what would MAD have been for those months?



4/21/2012 NY - KJP 585 2009 34

Exponential Smoothing Example 2

Month Demand Forecast

March 6 -

April 8 6May 10 6 + 0.2(8 – 6) = 6.4

June 8 6.4 + 0.2(10 – 6.4) = 7.12

7.12 + 0.2(8 – 7.12) = 7.296

A) 1. (8+10+8)/3 = 8.33 (July Forecast)2. Use naïve to begin

B)

Month March April May JuneDemand 6 8 10 8

Naïve - 6 8 10

Error - +2 +2 -2

MAD 6/3 = 2.0



4/21/2012 NY - KJP 585 2009 35

Exponential Smoothing with Trend

AdjustmentWhen a trend is present, exponential smoothing must be modified

Forecast including (FIT t ) = trend

Exponentially Exponentially smoothed (F t ) + (T t ) smoothed forecast trend



4/21/2012 NY - KJP 585 2009 36

Exponential Smoothing with Trend

Adjustment

F t = (At - 1) + (1 - )(F t - 1 + T t - 1)

T t = b(F t - F t - 1) + (1 - b)T t - 1

Step 1: Compute F t Step 2: Compute T t

Step 3: Calculate the forecast FIT t = F t + T t



4/21/2012 NY - KJP 585 2009 37

Moving Average

Weekly sales of ten-grain bread at the local organic food market are in the

table below. Based on this data, forecast week 9 using a five-week moving

average.

Other Examples

Week 1 2 3 4 5 6 7 8

Sales 415 389 420 382 410 432 405 421

(382+410+432+405+421)/5 = 410.0



4/21/2012 NY - KJP 585 2009 38

Exponential Smoothing & MAD Jim's department at a local department store has tracked the sales of a productover the last ten weeks. Forecast demand using exponential smoothing withan alpha of 0.4, and an initial forecast of 28.0. Calculate MAD.

Other Examples

Period Demand1 24

2 23

3 26

4 36

5 26

6 30

7 32

8 26

9 25

10 28



4/21/2012 NY KJP 585 2009 39

Period Demand Forecast Error Absolute

1 24 28.00

2 23 26.40 -3.40 3.40

3 26 25.04 0.96 0.96

4 36 25.42 10.58 10.58 5 26 29.65 -3.65 3.65

6 30 28.19 1.81 1.81

7 32 28.92 3.08 3.08

8 26 30.15 -4.15 4.15

9 25 28.49 -3.49 3.49 10 28 27.09 0.91 0.91

Total 2.64 32.03

Average 0.29 3.56

Bias MAD

Other Examples – Exponential Smoothing

6.0-Topic 6_ Forecasting

Documents