Demand Forecasting - Principles and methods

8/13/2019 Demand Forecasting - Principles and methods

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FORECASTING


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What does Production planning and control deal with

PRODUCTION that transformation of raw materials to finished goods.

PLANNING looks ahead, anticipates possible difficulties and decidesin advance as to how the production, best, be carried out.

CONTROL This phase makes sure that the programmed production isconstantly maintained.


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Demand Management

Qualitative & Quantitative Forecasting Methods

Simple & Weighted Moving Average Forecasts

Simple Exponential Smoothing

Winters trend model

Topics to be discussed in this chapter are


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Before making an investment decision, many questions will ariselike

1. What should be the size or amount of capital required ?2. How large should be the size of work force ?

3. What should be the capacity of plant?

4. What should be the size of the order and safety stock?


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Many factors influence the demand for a product. Some of them are:

1. General business and economic conditions.

2. Competitive factors.

3. Market trends.

4. The firms own plans for advertising, promotion, pricing, and product

changes.


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Demand ManagementDemand management is the process of recognizing and managing all demands

for products. If material and capacity resources are to be planned effectively,

all sources of demand must be identified.

Demand management includes four major activities:

1. Forecasting.

2. Order processing.

3. Making delivery promises.

4. Interfacing between manufacturing planning and control and the

marketplace


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Demand ManagementA company can :

1. Can take an active role to influence demand:

I. Apply pressure on sales personnel

II. Incentives to sales personnel or customers

2. Take a passive role and simply respond to demand

I. Market may be fixed & static

II. Powerless to change demand (heavy expense for advt.)

I. Demand beyond control


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Forecasting as defined by American Manufacturing Association is :

An estimate of sales in physical units for a specified future period

under proposed marketing plan or programme and under the assumed set of

economic and other forces outside the organization for which the forecast is

made .


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Forecasting is Prelude to planning. Before making plans, an estimate must be

made of what conditions exist over some future period.

In other words demand for a product must be known to the firm or companyto reduce the delivery time to the customer.

Firm must plan to provide the capacity and resources to meet that demand.

Firms that make to order cannot begin making a product before a customer

places an order but must have the resources of labor and equipment available

to meet demand.


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Principles Of ForecastingForecasts have four major characteristics or principles.

1. Forecasts are usually wrong. Forecasts attempt to look into the unknownfuture and so errors are inevitable.

2. Every forecast should include an estimate of error. Since forecasts are

expected to be wrong, the real question is, By how much?

3. Forecasts are more accurate for families or groups. The behavior of

individual items in a group is random even when the group has very stable

characteristics. For eg., the marks for individual students in a class are

more difficult to forecast accurately than the class average.


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4. Forecasts are more accurate for nearer time periods. Near future

holds less uncertainty than the far future. Most people are more

confident in forecasting what they will be doing over the next weekthan a year from now

Principles Of Forecasting


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Forecasting period:

1. Short term: up to one year

2. medium term: 1-3 years

3. Long term: > 5 years


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Forecasting Techniques:There are many forecasting methods, but usually classified into 3 categories:

qualitative, extrinsic, and intrinsic.

Qualitative(Judgmental) techniquesare projections based on judgment,

intuition, and informed opinions.

Estimating the demand the for a new product by

1. market survey2. Data from salesperson

3. Based on demand of a similar product already in the market

4. Advise from a group of experts.


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Quantitative methods

(i) Extrinsic forecasting techniquesare projections based on external

(extrinsic) indicators which relate to the demand for a companys products.

The theory is that the demand for a product group is directly proportional, or

correlates, to activity in another field. Examples of correlation are:

1. Sales of bricks/cement are proportional to housing stats.

2.Sales of automobile tires are proportional to sale of automobiles.

3. Sales of appliances and disposable income.


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Intrinsic forecasting techniques use historical data to forecast. These data are

usually recorded in the company and are readily available. Intrinsic forecasting

techniques are based on the assumption that what happened in the past will

happen in the future.


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Components of Demand


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Trend: An average or general tendency of a series of data points to move in a

certain direction over time, represented by a line on a graph.

The trend in the above case is a upward linear one.

It is the long run historical component of the time series which indicates

overall growth or decline of the business over time.


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Seasonal variations:

Patterns of change in demand within a year. These patterns tend to repeatthemselves each year.

The result of the weather, holiday seasons, or particular events that take place on

a seasonal basis. Seasonality is usually thought of as occurring on a yearly basis,

but it can also occur on a weekly or even daily basis.


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Random variations: were factors influence the demand randomly.

Cyclical variation:

The rise and fall of demand (a time series) over periods longer

than one year.

Over a span of several years, wavelike increase and decrease inthe economy influence demand.


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Time series forecasting models

1. Simple moving average

2. Weighted moving average3. Simple Exponential smoothing

4. Winters Trend model


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Simple Moving Average Formula

F =A + A + A +...+A

nt

t-1 t-2 t-3 t-n

The simple moving average model assumes an average

is a good estimator of future behavior

The formula for the simple moving average is:

Ft= Forecast for the coming period

N = Number of periods to be averagedA t-1= Actual occurrence in the past period for up to n

periods

15-24


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Simple Moving Average Problem (1)

Week Demand

1 6502 678

3 720

Question: What is the 3-

week moving averageforecast for demand datashown in the table?

15 24

Moving average (MA) = (Sum of old demand forlast n periods) (No. of periods used in themodel)

15-25


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Simple Moving Average Problem (1)

Week Demand

1 650

2 678

3 720

4 785

5 859

6 920

Question: What is the 6-weekmoving average forecast fordemand?

15 25

n

D-DMA=MA

n-ttt 1-t


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Week Demand 3-Week 6-Week

1 650

2 678

3 720

4 785 682.67

5 859 727.67

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

F4=(650+678+720)/3

=682.67

F7=(650+678+720

+785+859+920)/6

=768.67


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12 844 867.00 854.83

500

550

600

650

700

750

800

850

900

950

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

De

mand

Week

Demand

3-Week

6-Week

Plotting the moving averages and comparing them shows how the

lines smooth out to reveal the overall upward trend in this example

Note how the

3-Week issmoother than

the Demand,

and 6-Week is

even smoother


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Example Problem1.

(a) Demand over the past three months has been 120, 135, and 114 units. Using

a three-month moving average, calculate the forecast for the fourth month.

Ans: 123

(b) If the actual demand for the fourth month turned out to be 129. Calculate

the forecast for the fifth month.

Ans: 126


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Weighted Moving Average Formula

F = w A + w A + w A + ...+ w At 1 t -1 2 t - 2 3 t -3 n t - n

w = 1i

i=1

n

While the moving average formula implies an equal weight being placed oneach value that is being averaged, the weighted moving average permits an

unequal weighting on prior time periods

wt = weight given to time period t occurrence (weightsmust add to one)

The formula for the moving average is:


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Weighted Moving Average Problem (1) Data

Weights:(t-1) .5

(t-2) .3

(t-3) .2

Week Demand

1 650

2 678

3 720

4

Question: Given the weekly demand and weights, what is

the forecast for the 4thperiod or Week 4?

Note that the weights place more emphasis on the

most recent data, that is time period t-1


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Weighted Moving Average Problem (1) Solution

Week Demand Forecast

1 650

2 678

3 720

4 693.4

F4= 0.5(720)+0.3(678)+0.2(650)=693.4


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Weighted Moving Average Problem (2) Data

Weights:

(t-1) .7

(t-2) .2(t-3) .1

Week Demand

1 820

2 775

3 680

4 655

Question: Given the weekly demand information and

weights, what is the weighted moving average forecast

of the 5thperiod or week?


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Weighted Moving Average Problem (2) Solution

Week Demand Forecast

1 820

2 775

3 680

4 655

5 672

F5= (0.1)(775)+(0.2)(680)+(0.7)(655)= 672


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EXPONENTIAL SMOOTHING FORECAST

Premise: The most recent observations might have thehighest predictive value

Therefore, we should give more weight to the more recenttime periods when forecasting

Ft= Ft-1 + a(Dt-1 - Ft-1)

constantsmoothingAlpha

periodpast timerecentmostfor thedemandActualA

periodpast timerecentmostfor thealueForecast vFt''periodtimecomingfor thelueForcast vaF

:Where

1-t

1-t

t

a


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Exponential Smoothing Average

Premise: The most recent observations might havethe highest predictive value

Therefore, we should give more weight to the morerecent time periods when forecasting

Ft= Ft-1 + a(Dt - Ft-1)

constantsmoothingAlpha

periodmeCurrent tifor thedemandActual

periodpast timerecentmostfor thealueForecast vFt''periodtimecomingfor thelueForcast vaF

:Where

Dt

1-t

t

a


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SIMPLE EXPONENTIAL SMOOTHING

A special type of weighted moving average Include all past observations

Use a unique set of weights that weight recent observations

much more heavily than very old observations:

a

a a

a a

a a

( )

( )

( )

1

1

1

2

3

weightDecreasing weights

givento older observations

0 1 a

Today


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SIMPLE ES: THE MODEL

New forecast = weighted sum of last period

actual value and last period forecast

a: Smoothing constant

Ft : Forecast for period t

Ft-1: Last period forecast

Yt-1: Last period actual value

321

3

2

21

)1()1()1()1(

tttt

tttt

YaYYF

YYYF

aaaa

aaaaa

11 )1( ttt FYF aa


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SIMPLE EXPONENTIAL SMOOTHING

Properties of Simple Exponential Smoothing

Widely used and successful model

Requires very little data

Formulating an exponential model is relatively easy

Little computation is required to use the model

Largera, more responsive forecast; Smaller a, smoother forecast

Computer storage requirements are small because of the limited use

of historical data

Suitable for relatively stable time series


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EXPONENTIAL SMOOTHING PROBLEM (1) DATA

Question: Given the

weekly demand

data, what are the

exponentialsmoothing

forecasts for

periods 2-10 using

=0.10 and=0.60?

Assume F1=D1

Week Demand

1 820

2 775

3 680

4 655

5 750

6 802

7 798

8 689

9 775

10


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Week Demand 0.1 0.6

1 820 820.00 820.00

2 775 820.00 820.00

3 680 815.50 793.00

4 655 801.95 725.20

5 750 787.26 683.08

6 802 783.53 723.23

7 798 785.38 770.49

8 689 786.64 787.00

9 775 776.88 728.20

10 776.69 756.28

Answer: The respective alphas columns denote the forecast values. Note

that you can only forecast one time period into the future.


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EXPONENTIAL SMOOTHING PROBLEM (1) PLOTTING

500

550

600

650

700

750

800850

1 2 3 4 5 6 7 8 9 10

Demand

Week

Demand

0.1

0.6

Note how that the smaller alpha results in a smoother line in

this example


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EXPONENTIAL SMOOTHING PROBLEM (2) DATA

Question: What are

the exponential

smoothing forecasts

for periods 2-5 using

a =0.5?

Assume F1=D1

Week Demand

1 820

2 775

3 680

4 655

5


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EXPONENTIAL SMOOTHING PROBLEM (2) SOLUTION

Week Demand 0.5

1 820 820.00

2 775 820.00

3 680 797.50

4 655 738.75

5 696.88

F1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75


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WINTERS TREND MODEL


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MONTH Demand Month Demand

January 89 July 223

February 57 August 286

March 144 September 212

April 221 October 275

May 177 November 188

June 280 December 312


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Trend implies a pattern of change over time.


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PATTERN-BASED FORECASTING SEASONAL

Once data turn out to be seasonal, deseasonal ize

the data.

Make forecast based on the deseasonalized data

Reseasonalizethe forecast Good forecast should mimic reality. Therefore, it is

needed to give seasonality back.


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Deseasonalize

Forecast

Reseasonalize

Actual data Deseasonalized

data


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Deseasonalization

Deseasonalized data = Actual / SI

Reseasonalization

Reseasonalized fo recast

= deseasonal ized fo recast * SI


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CALCULATING SEASONAL INDICES

Quick method of calculating SI For each year, calculate average demand

Divide each demand by its yearly average

This creates a ratio and hence a raw indexFor each quarter, there will be as many raw indices

as there are years

Average the raw indices for each of the quarters

The result will be fourvalues, one SI per quarter


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CLASSICAL DECOMPOSITION

Start by calculating seasonal indices Then, deseasonalizethe demand

Divide actual demand values by their SI values

y = y / SIResults in transformed data (new time series)

Seasonal effect removed

Forecast

Reseasonalize with SI

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Demand Forecasting - Principles and methods