Mba ii pmom_unit-1.3 forecasting a

Course: MBASubject: Production & Operation

ManagementUnit:1.3

Forecasting

Forecast

• Nature

• Types

• Factors affecting forecasting

• Models

What is Forecasting

Process of predicting a future event based on historical data

Educated Guessing

Underlying basis of all business decisions Production

Inventory

Personnel

Facilities

Importance of Forecasting in PM &OM

• Departments throughout the organization depend on forecasts to formulate and execute their plans.

• Finance needs forecasts to project cash flows and capital requirements.

• Human resources need forecasts to anticipate hiring needs.

• Production needs forecasts to plan production levels, workforce, material requirements, inventories, etc.

Importance of Forecasting in PM &OM

• Demand is not the only variable of interest to forecasters.

• Manufacturers also forecast worker absenteeism, machine availability, material costs, transportation and production lead times, etc.

• Besides demand, service providers are also interested in forecasts of population, of other demographic variables, of weather, etc.

Nature

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

Uses of Forecasts

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 systempast ==> future

• Forecasts rarely perfect because of randomness

• Forecasts more accurate forgroups vs. individuals

• Forecast accuracy decreases as time horizon increases

I see that you willget an A this semester.

Elements of a Good Forecast

Timely

AccurateReliable

Written

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

Step 6 Monitor the forecast

“The forecast”

Types of Forecasts

• Judgmental - uses subjective inputs

• Time series - uses historical data assuming the future will be like the past

• Associative models - uses explanatory variables to predict the future

Judgmental Forecasts

• Executive opinions

• Sales force opinions

• Consumer surveys

• Outside opinion

• Delphi method

– Opinions of managers and staff

– Achieves a consensus forecast

Time Series Forecasts

• Trend - long-term movement in data

• Seasonality - short-term regular variations in data

• Cycle – wavelike variations of more than one year’s duration

• Irregular variations - caused by unusual circumstances

• Random variations - caused by chance

Forecast Variations

Trend

Irregularvariation

Seasonal variations

90

89

88

Cycles

Types of Forecasts by Time Horizon

• Short-range forecast

– Usually < 3 months• Job scheduling, worker assignments

• Medium-range forecast

– 3 months to 2 years• Sales/production planning

• Long-range forecast

– > 2 years• New product planning

Quantitativemethods

QualitativeMethods

Detailed use ofsystem

Designof system

Forecasting During the Life Cycle

Introduction Growth Maturity Decline

Sales

Time

Quantitative models

- Time series analysis

- Regression analysis

Qualitative models

- Executive judgment

- Market research

-Survey of sales force

-Delphi method

Qualitative Forecasting Methods

Qualitative

Forecasting

Models

MarketResearch/Survey

SalesForceComposite

Executive Judgement

DelphiMethod

Smoothing

Qualitative Methods

Briefly, the qualitative methods are:

Executive Judgment: Opinion of a group of high level experts or managers is pooled

Sales Force Composite: Each regional salesperson provides his/her sales estimates. Those forecasts are then reviewed to make sure they are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast.

Market Research/Survey: Solicits input from customers pertaining to their future purchasing plans. It involves the use of questionnaires, consumer panels and tests of new products and services.

• .

Delphi Method

Delphi Method: As opposed to regular panels where the individuals involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members. The group involves individuals from inside as well as outside the organization.

Typically, the procedure consists of the following steps:Each expert in the group makes his/her own forecasts in form of statements

The coordinator collects all group statements and summarizes them The coordinator provides this summary and gives another set of

questions to each group member including feedback as to the input of other experts.

The above steps are repeated until a consensus is reached.

• .

Quantitative Forecasting MethodsQuantitative

Forecasting

RegressionModels

2. MovingAverage

1. Naive

Time SeriesModels

3. ExponentialSmoothing

a) simple

b) weighteda) level

b) trend

c) seasonality

Time Series Models

• Try to predict the future based on past data

– Assume that factors influencing the past will continue to influence the future

Time Series Models: Components

Random

Seasonal

Trend

Composite

Product Demand over Time

Year1

Year2

Year3

Year4

Dem

and

for

prod

uct o

r se

rvic

e

Product Demand over Time

Year1

Year2

Year3

Year4

Dem

and

for

prod

uct o

r se

rvic

e

Trend component

Actual demand

line

Seasonal peaks

Random

variation

Now let’s look at some time series approaches to forecasting…Borrowed from Heizer/Render - Principles of Operations Management, 5e, and Operations Management, 7e

Quantitative Forecasting Methods

Quantitative

Models

2. MovingAverage

1. Naive

Time Series

Models


a) simple

b) weighteda) level

b) trend

c) seasonality

1. Naive Approach

Demand in next period is the same as demand in most recent period

May sales = 48 →

Usually not good

June forecast = 48

Naïve Approach

• Simple to use

• Virtually no cost

• Quick and easy to prepare

• Data analysis is nonexistent

• Easily understandable

• Cannot provide high accuracy

• Can be a standard for accuracy

2a. Simple Moving Average

n

A+...+A +A +A =F 1n-t2-t1-tt

1t

• Assumes an average is a good estimator of future behavior

– Used if little or no trend

– Used for smoothing

Ft+1 = Forecast for the upcoming period, t+1

n = Number of periods to be averaged

A t = Actual occurrence in period t


You’re manager in Amazon’s electronics department.

You want to forecast ipod sales for months 4-6 using a

3-period moving average.

n

A+...+A +A +A =F 1n-t2-t1-tt

1t

MonthSales

(000)

1 4

2 6

3 54 ?5 ?6 ?


MonthSales

(000)Moving Average

(n=3)1 4 NA

2 6 NA

3 5 NA4 ?5 ?

(4+6+5)/3=5

6 ?

n

A+...+A +A +A =F 1n-t2-t1-tt

1t

You’re manager in Amazon’s electronics department.

You want to forecast ipod sales for months 4-6 using a

3-period moving average.

What if ipod sales were actually 3 in month 4

MonthSales

(000)Moving Average

(n=3)1 4 NA

2 6 NA

3 5 NA4 35 ?

5

6 ?


?

Forecast for Month 5?

MonthSales

(000)Moving Average

(n=3)1 4 NA

2 6 NA

3 5 NA4 35 ?

5

6 ?

(6+5+3)/3=4.667


Actual Demand for Month 5 = 7

MonthSales

(000)Moving Average

(n=3)1 4 NA

2 6 NA

3 5 NA4 35 7

5

6 ?

4.667


?

Forecast for Month 6?

MonthSales

(000)Moving Average

(n=3)1 4 NA

2 6 NA

3 5 NA4 35 7

5

6 ?

4.667(5+3+7)/3=5


• Gives more emphasis to recent data

• Weights

– decrease for older data

– sum to 1.0

2b. Weighted Moving Average

1n-tn2-t31-t2t11t Aw+...+Aw+A w+A w=F

Simple movingaverage models

weight all previousperiods equally

2b. Weighted Moving Average: 3/6, 2/6, 1/6

Month Weighted

Moving

Average1 4 NA

2 6 NA

3 5 NA4 31/6 = 5.167

56 ?

??


Sales(000)

2b. Weighted Moving Average: 3/6, 2/6, 1/6

Month Sales(000)

Weighted

Moving

Average1 4 NA

2 6 NA

3 5 NA4 3 31/6 = 5.167

5 76

25/6 = 4.16732/6 = 5.333


3a. Exponential Smoothing

• Assumes the most recent observations have the highest predictive value

– gives more weight to recent time periods

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

et

Ft+1 = Forecast value for time t+1

At = Actual value at time t

a = Smoothing constant

Need initial

forecast Ft

to start.

3a. Exponential Smoothing – Example 1

Week Demand

1 820

2 775

3 680

4 655

5 750

6 802

7 798

8 689

9 775

10

Given the weekly demand data what are the exponentialsmoothing forecasts for periods 2-10 using a=0.10?

Assume F1=D1

Ft+1 = Ft + a(At - Ft)i Ai

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



a =

F2 = F1+ a(A1–F1) =820+.1(820–820)

=820

i Ai Fi

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



a =

F3 = F2+ a(A2–F2) =820+.1(775–820)

=815.5

i Ai Fi

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


This process continues

through week 10


a =

i Ai Fi

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


What if the a constant equals 0.6


a = a =

i Ai Fi

Month Demand 0.3 0.6

January 120 100.00 100.00

February 90 106.00 112.00

March 101 101.20 98.80

April 91 101.14 100.12

May 115 98.10 94.65

June 83 103.17 106.86

July 97.12 92.54

August

September




a = a =

i Ai Fi

Company A, a personal computer producer

purchases generic parts and assembles them to

final product. Even though most of the orders

require customization, they have many common

components. Thus, managers of Company A need

a good forecast of demand so that they can

purchase computer parts accordingly to minimize

inventory cost while meeting acceptable service

level. Demand data for its computers for the past 5

months is given in the following table.


Month Demand 0.3 0.5

January 80 84.00 84.00

February 84 82.80 82.00

March 82 83.16 83.00

April 85 82.81 82.50

May 89 83.47 83.75

June 85.13 86.38

July ?? ??




a = a =

i Ai Fi

• How to choose α– depends on the emphasis you want to place on

the most recent data

• Increasing α makes forecast more sensitive to recent data

3a. Exponential Smoothing

Ft+1 = a At + a(1- a) At - 1 + a(1- a)2At - 2 + ...

Forecast Effects ofSmoothing Constant a

Weights

Prior Period

a

2 periods ago

a(1 - a)

3 periods ago

a(1 - a)2

a=

a= 0.10

a= 0.90

10% 9% 8.1%

90% 9% 0.9%

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

or

w1 w2 w3

• Collect historical data

• Select a model

– Moving average methods• Select n (number of periods)• For weighted moving average: select weights

– Exponential smoothing• Select a

• Selections should produce a good forecast

To Use a Forecasting Method

…but what is a good forecast?

A Good Forecast

Has a small error

Error = Demand - Forecast

Measures of Forecast Error

b. MSE = Mean Squared Error

n

F-A

=MSE

n

1=t

2

tt

MAD =

A - F

n

t tt=1

n

et

Ideal values =0 (i.e., no forecasting error)

MSE =RMSEc. RMSE = Root Mean Squared Error

a. MAD = Mean Absolute Deviation

MAD Example

Month Sales Forecast1 220 n/a

2 250 255

3 210 205

4 300 320

5 325 315

What is the MAD value given the forecast

values in the table below?

MAD =

A - F

n

t tt=1

n

55

20

10

|At – Ft|

FtAt

= 40

= 404

=10

MSE/RMSE Example

Month Sales Forecast1 220 n/a

2 250 255

3 210 205

4 300 320

5 325 315

What is the MSE value?

55

20

10

|At – Ft|

FtAt

= 5504

=137.5

(At – Ft)2

2525

400

100

= 550

n

F-A

=MSE

n

1=t

2

tt

RMSE = √137.5

=11.73

Measures of Error

t At Ft et |et| et2

Jan 120 100 20 20 400

Feb 90 106 256

Mar 101 102

April 91 101

May 115 98

June 83 103

1. Mean Absolute Deviation (MAD)

n

e

MAD

n

t 1

2a. Mean Squared Error (MSE)

MSE

e

n

t

n

2

1

2b. Root Mean Squared Error (RMSE)

RMSE MSE

-16 16

-1 1

-10

17

-20

10

17

20

1

100

289

400

-10 84 1,446

846

= 14

1,4466

= 241

= SQRT(241)=15.52

An accurate forecasting system will have small MAD, MSE and RMSE; ideally equal to zero. A large error may indicate that

either the forecasting method used or the parameters such as αused in the method are wrong. Note: In the above, n is the number of periods, which is 6 in our example

deviation absoluteMean

)(

=MAD

RSFE=TS

t

tt forecastactual

30

• How can we tell if a forecast has a positive or negative bias?

• TS = Tracking Signal

– Good tracking signal has low values

Forecast Bias

MAD

Quantitative Forecasting Methods

Quantitative

Forecasting

RegressionModels

2. MovingAverage

1. Naive

Time Series

Models


a) simple

b) weighteda) level

b) trend

c) seasonality

• We looked at using exponential smoothing to forecast demand with only random variations

Exponential Smoothing (continued)

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

Ft+1 = Ft + a At – a Ft

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

Exponential Smoothing (continued)

• We looked at using exponential smoothing to forecast demand with only random variations

• What if demand varies due to randomness and trend?

• What if we have trend and seasonality in the data?

Regression Analysis as a Method for ForecastingRegression analysis takes advantage of

the relationship between two variables. Demand is then forecasted based on the knowledge of this relationship and for the given value of the related variable.

Ex: Sale of Tires (Y), Sale of Autos (X) are obviously related

If we analyze the past data of these two variables and establish a relationship between them, we may use that relationship to forecast the sales of tires given the sales of automobiles.

The simplest form of the relationship is, of course, linear, hence it is referred to as a regression line.

Sales of Autos (100,000)

Formulas

xbya

22 xnx

yxnxyb

x

y

x

y

y = a + b x

where,

MonthAdvertising Sales X 2 XY

January 3 1 9.00 3.00

February 4 2 16.00 8.00

March 2 1 4.00 2.00

April 5 3 25.00 15.00

May 4 2 16.00 8.00

June 2 1 4.00 2.00

July

TOTAL 20 10 74 38

y = a + b XRegression – Example

22 xnx

yxnxyb xbya

General Guiding Principles for Forecasting

1. Forecasts are more accurate for larger groups of items. 2. Forecasts are more accurate for shorter periods of time.3. Every forecast should include an estimate of error.4. Before applying any forecasting method, the total system should

be understood.5. Before applying any forecasting method, the method should be

tested and evaluated.6. Be aware of people; they can prove you wrong very easily in

forecasting

Reference

• Forecasting-Mcgraw hill/irwin

• Operation management 8th edition by-William J

• POM-J.GALVAN

Education

Mba ii pmom_unit-1.3 forecasting a