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Introduction

- Lecture 6. Forecasting

- Course content
- Demand Management
- Components of demand
- Qualitative technique in forecasting
- Time series analysis
- Causal relationship forecasting

Forecasting

- Forecast plans It is not possible to make

decisions in production scheduling, purchasing,

and inventory levels until forecasts are

developed that give reasonably accurate views of

demand over the forecasting horizon. - Forecasting is a prediction of future events used

for planning process - Management needed accurate forecast to ensure

supply chain management

Forecasting

- Accurate forecast allows schedulers to use

capacity efficiently, reduce customer response

times and cut inventories. - Managers need forecast to anticipate changes in

prices or costs or to prepare for new laws or

regulations, competitors, resource shortages or

technologies.

Demand Management

- Demand Management
- - Demand management is to coordinate and control

all sources of demand so the productive system

can be used efficiently and the product delivered

on time. - Demand are of two types
- Dependent Demand
- If the demand can be tabulated from the end item

configuration. For developing a Car, number of

tires, wheel barrow etc can be known. Usually

sub-components of the final product. - 2. Independent Demand
- It cannot be derived directly from that of other

products. E.g sales prediction

Handling Demand

- Handling Demand
- Holistic approach or active approach to influence

demand - (Developing aggressive marketing strategy to

influence demand) - 2. Passive role or simply respond to demand
- (Firm accept the demand and runs with it

passively accepting it) - Our primary interest is in forecasting for

independent demand

Components of Demand

- Demand for product or service can be broken down

to six components - Average demand for the period
- Trend
- Seasonal elements
- Cyclical elements
- Random variation
- Autocorrelation

Components of Demand

- Trend are the usual pattern of demand. Trends are

of 4 types. Trend is a systematic increase or

decrease over time.

Linear Trend It is a straight continuous

relationship

Asymptotic trends starts with the highest demand

growth at the beginning but then tappers off.

Objective of capturing the market and gradually

saturating it.

An exponential curve is common in products with

explosive growth. The explosive trend suggests

that sales will continue to increase.

S-Curve is typical of product growth and maturity

cycle. Important point in S-Curve is a point

where the trend change from slow to fast growth

sales

quarters

Components of Demand

- 1. Seasonal Elements Seasonal fluctuation in

demand - 2. Cyclical Elements Cyclical influence on

demand comes from macroeconomic factors as

political, war, sociological pressure etc. Time

span is unknown and cause of cycle may not be

considered. - 3. Random variations are caused by chance events.

The cause for the this type of demand is unknown.

- 4. Autocorrelation It denotes the persistence of

occurrence. The value expected at any point is

highly correlated to its own past.

Historical product demand consisting of growth

trend and seasonal demand

Number of units demanded

Trend

Average demand

seasonal

Types of Forecasting

- Forecasting is classified into four basic types
- Qualitative Technique
- Time series analysis
- Causal Relationship
- Simulation

Quantitative Technique

Qualitative Techniques of Forecasting

- Qualitative analysis are subjective, judgmental

and based on estimate and opinions - Grass Roots
- As per this, the person near to the customer

knows the market more better. His information is

taken as a base for further forecasting. - 2. Market Research
- Market survey, personal calling, data collection

etc are done to collect information from market

and then test the hypothesis to better understand

the management decision problem (MDP). - 3. Panel Consensus
- Group discussion to exchange the ideas. The

problem is the lower management staff may not

fully participate in idea sharing. - 4. Historical analogy
- Grouping of the customer based on product

category purchased. If an person has bought a

DVD, then it is likely that he is interested in

purchasing DVD movies. Also people who had

previously purchase some items before are

interested in new product of same category.

Qualitative Techniques of Forecasting

- 5. Delphi method
- Delphi method is a form of panel consensus but

the identity of the individual participating is

not open. The view of everyone has same weight. - View of individual is known through open end and

closed end questionnaire. - The result from all the participant is

summarized. - Distribute the final result to participants.

Time series Analysis

- Predict the future based on past data. The

analogy of past weeks data can be used to predict

the future data. - Short term forecast under 3 mts
- Medium term forecast 3 mts 2 years
- Long term forecast greater than 2 years.
- Short term compensate for tackling, problem in

hand, - Medium term compensate for seasonal effects
- Long term compensate for identifying change in

trends and consumer habits - Forecasting models available for firm are
- Time Horizon to forecast
- Data Availability
- Accuracy required
- Size of forecasting budget
- Availability of qualified personals

Time Series Analysis

- Calculation of the forecasting based on time

series analysis can be done on following basis - Simple Moving Average
- Weighted Moving Average
- Exponential Smoothing

- Simple Moving Average
- The demand for the product or service is

relatively constant, neither growing nor

declining, with no seasonal slump, then in such

scenario, a moving average is preferred. - The simple moving average method is used to

estimate the average of a demand time series and

thereby remove the effects of random fluctuation.

Simple Moving Average

- Applying a moving average model simply involves

calculating the average demand for the n most

recent periods and using it as the forecast for

the next time period.

Where D actual demand in period t n total

number of periods in the average

Forecast for period t1

With the moving average method, the forecast of

next periods demand equals the average

calculated at the end of this period

Simple Moving Average

- a. Compare a three-week moving average forecast

for the arrival of medical clinic patients in

week 4. The number of arrivals for the past

three weeks were

Weeks Patient arrival

1 400

2 380

3 411

b. If the actual number of patient arrivals in

week 4 is 415, what is the forecast for week 5

Simple Moving Average

- Solution
- The moving average forecast at the end of week 3

is

b. The forecast for week 5 requires the actual

arrivals from weeks 2-4, the three most Recent

weeks of data

The forecast at the end of week 3 would have been

397 patients for week 4. The forecast For week 5,

made at at the end of week 4, would have been 402

patients. In addition, at the end of week 4, the

forecast for week 6 and beyond is also 402

patient.

Simple Moving Average

- Determining the value of n
- The stability of the demand series generally

determines how many periods to include (i.e the

value of n). Stable demand series are those for

which the average (to be estimated by the

forecasting method) only infrequently experiences

changes. - Large values of n should be used for demand

series that are stable and small values of n for

those that are susceptible to change in

underlying average. - If the underlying average in the series is

changing, however, the forecasts will tend to lag

behind the changes for a longer time interval

because of the additional time required to remove

the old data from the forecast.

Simple Moving Average

Example Exhibit 13.5 Pg. 546 Eleventh Edition

Chase/ jacob

Weighted Moving Average

- In simple moving average, each demand has the

same weight in the average. But in the weighted

moving average method, each historical demand in

the average can have its own weight. - The sum of the weights equal to 1.
- The Formula for a weighted moving average is

Where W1weight to be given to the actual

occurrence for the period t-1 W2weight to be

given to the actual occurrence for the period

t-2 Wn weight to be given to the actual

occurrence for the period t-n ntotal number of

periods in the forecast

Weighted Moving Average

- A department store may find that in a four month

period, the best forecast is derived by using 40

of the actual sales for the most recent month,

30 for two months ago, 20 for three month ago

and 10 of four months ago. If actual sales

experience was

January February March April May

100 90 105 95 ?

0.1 0.2 0.3 0.4

Find the forecast for month 6 if the sales for 5

mts turned out to be 110 (Ans 102.5)

Weighted Moving Average

- Using the weighted moving average method to

estimate average Demand - a. The analyst for the medical clinic has

assigned weights of 0.70 to the most recent

demand, 0.2 to the demand one week ago, and 0.10

to the demand two weeks ago. Use the data for the

first three weeks from the table below to

calculate the weighted average for week 4. (Ans

403) - b. If the actual demand for 4th week is 415

Patients, what would be the forecast for week 5.

(Ans. 410)

Weeks Patient Arrival

1 400

2 380

3 411

Exponential Smoothing

- Exponential Smoothing is method is actually a

weighted moving average method that calculates

the average of the time series by giving recent

demands more weights than earlier demands. - It is most frequently used for forecasting due to

its simplicity and the amount of data needed to

support it. - Weighted moving average requires n periods of

past demand and n weights, whereas exponential

smoothing requires only three items to calculate

demand - The last periods forecast
- The demand for this period
- Smoothing parameter alpha (a) (Value of a is

between 0 and 1)

Exponential Smoothing

- The equation for forecast is

Smoothing constant a is the level of smoothing

and the speed of reaction between forecasts and

actual occurrences. Value for smoothing constant

can be taken from organization requirement as per

their volume of demand. Or mathematically it can

be taken as 2/(n1)

The equation for exponential smoothing

highlights, the old forecast error portion

between previous forecast and what actually

occurred.

Exponential Smoothing

- E.g In the given table below, consider the

arrival of patients, at the end of 3 weeks, using

a0.10, calculate the exponential smoothing for

week 4. Assume initial forecast as 390

Weeks Patient Arrival

1 400

2 380

3 411

Exponential Smoothing

- In the above example, if the demand for 4th week

becomes 415, the new forecast for week 5 would be

as follow

Conclusion Using the exponential smoothing

model, the analysts forecasts would have been

392 patients for week 4 and then 394 patients for

week 5 and beyond. As Soon as the actual demand

for week 5 is known, then the forecast for week6

will be Updated.

Trend Effect in Exponential Smoothing

- Exponential smoothing has an advantages of

simplicity, minimal data requirement, inexpensive

and attractive to firm. - But its simplicity is a disadvantage if the

underlying average is changing, as in case of

demand series with a trend. - Higher values of Smoothing constant (a) may help

to reduce forecast error to some extent, when

there is a change in the average of the time

series however, the lag will still be there if

the average is changing systematically.

Trend Effect in Exponential Smoothing

- Assume that actual demand is steadily increasing

at 10units per period. Forecast using exponential

smoothing with a0.3

As we see, forecast using exponential smoothing

with a0.3 will lag severely behind the actual

demand even if the first forecast is perfect. To

improve the forecast, we need to calculate an

estimate of the trend, we start by calculating

the current estimate of the trend which is the

difference between the average of the series

computed in the current period and the average

computed last period. Another smoothing constant

delta (?) is added to reduce impact of error

Trend Effects in Exponential Smoothing Model

FITt Ft Tt Ft FITt-1 a(At-1 - FITt-1) Tt

Tt-1 ? (Ft - FITt-1 )

Ft the exponentially smoothened forecast for

period t Tt the exponentially smoothened trend

for period t FITt the forecast including trend

for period t FITt-1 the forecast including

trend made in prior period or period t-1 At-1

actual demand for prior period or period t-1 a

,? smoothing constants

Assume a initial starting Ft of 100 units, a

trend of 10 units, an alpha of 0.20 and a delta

of 0.30. If actual demand turned out to be 115

rather then the forecast 100, calculate the

forecast for the next period.

Hence, the forecast for next period turned out to

be 121.3 with a trend of initial 100 units.

Mean Absolute Deviation (MAD)

- MAD is the average error in forecasts, using

absolute values. - MAD is computed using the differences between the

actual demand and the forecast demand without

regard to sign. - It equals the sum of the absolute deviation

divided by the number of data points or stated in

equation as follow - Where
- tperiod number
- Aactual demand for the period
- F forecast demand for the period
- Ntotal number of period

Mean Absolute Deviation (MAD)

Month Motorcycle sales

Jan 9

Feb 7

March 10

April 8

May 7

June 12

July 10

August 11

Sept 12

Oct 10

Nov 14

Dec 16

- Compute a 3 month moving average forecast of

demand for April through January (of the next

year) - Compute a 5 months average for June through

January - Compare the two forecasts computed in parts a and

b using MAD. Which one should the dealer use of

January of the next year.

- e.g

Mean Absolute Deviation (MAD)

Mean Absolute Deviation (MAD)

- MAD is often use to forecast errors.
- When errors that occurs in the forecast are

normally distributed, the mean absolute deviation

relates to the standard deviation as - Standard deviation

Conversely, 1 MAD 0.8 Standard Deviation

- The ideal MAD is zero which would mean there is

no forecasting error - The larger the MAD, the less the accurate the

resulting model

Mean Absolute Deviation (MAD)

- The value of MAD to forecast in case of

exponentially smoothing is as follow

Measurement of Error

- Tracking Signal
- It is a measurement that indicates whether the

forecast average is keeping pace with any genuine

upward or downward changes in demand. - Tracking signal is the number of mean absolute

deviations that the forecast value is above or

below the actual occurrence. - Tracking signal (TS) RSFE/ MAD
- RSFE running sum of forecast error, considering

the nature of the error

Measurement of Error

- Computing MAD and Tracking signal

In a perfect forecasting model, the sum of actual

forecast errors would be zero the error that

results in overestimates should be offset by

errors that are underestimate. The tracking

signal would then be also zero, indicating an

unbiased model, neither leading nor lagging the

actual demand.

Linear Regression Analysis

- Regression is a functional relationship between

two or more correlated variables. - It is used to predict one variable to other. Or

more precisely, relation of dependent and

independent variables. - Linear regression line is of the form Ymx C
- Where Y is the value of dependent variable that

we are solving for, C is the intercept and m is

slope, x is the independent variable. - ? In linear regression forecasting, the past data

and future projection are assumed to fall about a

straight line.

Linear Regression Analysis

- Linear regression is used in for both time series

forecasting and for casual relationship

forecasting. - When the dependent variable changes as a result

of time, it is time series analysis. - If one variable changes because of the change in

another variable, this is called casual

relationship. E.g Death of lung cancer increasing

with the increase in number of people smoking. - Casual Method provides the most sophisticated

forecasting tools and are very good for

predicting turning points on demand and preparing

long range forecast.

Linear Regression Analysis

- Least square method fits the line to the data

that minimizes the sum of the squares of the

vertical distance between each data point and its

corresponding point on the line. - Equation of st. line is Yabx

Standard Error of Estimate

Linear Regression Analysis

- Example
- Following are the sales and advertising data for

past five months. The marketing manager says that

the next month, the company will spend 1750 on

advertising of product. Use linear regression to

develop an equation and forecast for this

product.

Month Sales (Y) Thousands of unit Advertising Thousand of

1 264 2.5

2 116 1.3

3 165 1.4

4 101 1.0

5 209 2.0

Linear Regression Analysis

Month Sales (Y) Thousands of unit Advertising(X) Thousand of X.Y X2

1 264 2.5 660 6.75

2 116 1.3 150.8 1.69

3 165 1.4 231 1.96

4 101 1.0 101 1

5 209 2.0 418 4

Correlation Coefficient for regression

- Correlation coefficients shows the strength

between the dependent and independent variable. - The value of correlation coefficient lies between

-1 to 1. - If r-1, it shows, negatively correlated
- If r0, there is not linear relationship
- If r1, highly correleted

Casual Relationship Forecasting

- Casual relationship forecasting is the one in

which the causing element is known enough in

advance, it can be used as a basis for

forecasting. - E.g increase in rain will increase sales of

umbrella - Increase in car accidents, increase in number of

insurance - Identify the occurrence that are really the

cause. Often leading indicators are not the

casual relationship, but in some indirect way,

they may suggest that some other things might

happen. - Other non casual relationships just seem to exist

as a coincidence.

Casual Relationship Forecasting

Important Questions discussion

- PU 2003 Fall
- 5.a) From the choice of a simple moving average,

weighted moving average, exponential smoothing,

and linear regression analysis, which forecasting

technique would you consider the most accurate?

Why? (7) - 4.a)what is the difference between dependent

demand and independent demand. Why do firms keep

inventory? (5) - 6.c) Explain the features of a good forecasting

technique. (5) - 2.B What do you mean by demand management?

Differentiate between dependent demand and

independent demand. (5)

End of Lecture