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# Forecasting - PowerPoint PPT Presentation

Forecasting. August 29, Wednesday. Course Structure. Introduction. Operations Strategy & Competitiveness . Quality Management . Strategic Decisions (some) . Design of Products and Services. Process Selection and Design. Capacity and Facility Decisions. Forecasting.

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### Forecasting

August 29, Wednesday

CourseStructure

Introduction

Operations Strategy & Competitiveness

Quality Management

Strategic Decisions (some)

Design of Products

and Services

Process Selection

and Design

Capacity and

Facility Decisions

Forecasting

Tactical & Operational Decisions

• Predict the next number in the pattern:

a) 3.7, 3.7, 3.7, 3.7, 3.7, ?

b) 2.5, 4.5, 6.5, 8.5, 10.5, ?

c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?

• Predict the next number in the pattern:

a) 3.7, 3.7, 3.7, 3.7, 3.7,

b) 2.5, 4.5, 6.5, 8.5, 10.5,

c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5,

3.7

12.5

9.0

• What is forecasting?

• Types of forecasts

• Time-Series forecasting

• Naïve

• Moving Average

• Exponential Smoothing

• Regression

• Good forecasts

• Process of predicting a future event based on historical data

• Educated Guessing

• Underlying basis of all business decisions

• Production

• Inventory

• Personnel

• Facilities

In general, forecasts are almost always wrong. So,

Throughout the day we forecast very different things such as weather, traffic, stock market, state of our company from different perspectives.

Virtually every business attempt is based on forecasting. Not all of them are derived from sophisticated methods. However, “Best" educated guesses about future are more valuable for purpose of Planning than no forecasts and hence no planning.

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.

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.

Quantitative

methods

• 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

Detailed

use of

system

Design

of system

Qualitative

Methods

Introduction

Growth

Maturity

Decline

Quantitative models

Qualitative models

- Executive judgment

- Market research

• Survey of sales force

• Delphi method

- Time series analysis

- Regression analysis

Sales

Time

Qualitative

Forecasting

Models

Sales

Force

Composite

Delphi

Method

Executive

Judgement

Market

Research/

Survey

Smoothing

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: 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

Regression

Time Series

Models

Models

2. Moving

3. Exponential

1. Naive

Average

Smoothing

a) simple

b) weighted

a) level

b) trend

c) seasonality

Quantitative

Forecasting

Regression

Time Series

Models

Models

2. Moving

3. Exponential

1. Naive

Average

Smoothing

a) simple

b) weighted

a) level

b) trend

c) seasonality

• Try to predict the future based on past data

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

Random

Trend

Seasonal

Composite

Demand for product or service

Year

2

Year

3

Year

4

Year

1

Trend component

Seasonal peaks

Demand for product or service

Actual demand line

Random variation

Year

2

Year

3

Year

4

Year

1

• 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

Time Series

Models

Models

2. Moving

3. Exponential

1. Naive

Average

Smoothing

a) simple

b) weighted

a) level

b) trend

c) seasonality

1. Naive Approach

• Demand in nextperiod is the same as demand in most recentperiod

• May sales = 48 →

• Usually not good

June forecast = 48

• 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.

Sales

(000)

Month

1

4

2

6

3

5

4

?

5

?

6

?

You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average.

Sales

(000)

Moving Average

Month

(n=3)

NA

1

4

NA

2

6

NA

3

5

(4+6+5)/3=5

4

?

5

?

6

?

2a. Simple Moving Average

Sales

(000)

Moving Average

Month

(n=3)

NA

1

4

NA

2

6

NA

3

5

5

?

4

3

5

?

6

?

2a. Simple Moving Average

Sales

(000)

Moving Average

Month

(n=3)

NA

1

4

NA

2

6

NA

3

5

5

4

3

(6+5+3)/3=4.667

5

?

6

?

2a. Simple Moving Average

Sales

(000)

Moving Average

Month

(n=3)

NA

1

4

NA

2

6

NA

3

5

5

4

3

4.667

?

5

7

6

?

2a. Simple Moving Average

Sales

(000)

Moving Average

Month

(n=3)

NA

1

4

NA

2

6

NA

3

5

5

4

3

4.667

5

7

(5+3+7)/3=5

6

?

• Gives more emphasis to recent data

• Weights

• decrease for older data

• sum to 1.0

Simple moving

average models

weight all previous

periods equally

Weighted

Moving Average

Month

Sales

(000)

NA

1

4

NA

2

6

NA

3

5

31/6 = 5.167

4

?

5

?

6

?

Weighted

Moving Average

Month

Sales

(000)

NA

1

4

NA

2

6

NA

3

5

31/6 = 5.167

4

3

25/6 = 4.167

5

7

6

32/6 = 5.333

• Assumes the most recent observations have the highest predictive value

• gives more weight to recent time periods

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

et

Need initial

forecast Ft

to start.

Ft+1 = Forecast value for time t+1

At = Actual value at time t

 = Smoothing constant

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

Given the weekly demand

data what are the exponential

smoothing forecasts for

periods 2-10 using a=0.10?

Assume F1=D1

i

Ai

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

i

Ai

Fi

a=

F2 = F1+ a(A1–F1)

=820+.1(820–820)

=820

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

i

Ai

Fi

a=

F3 = F2+ a(A2–F2)

=820+.1(775–820)

=815.5

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

i

Ai

Fi

a=

This process continues

through week 10

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

i

Ai

Fi

a=

a=

What if the

a constant equals 0.6

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

i

Ai

Fi

a=

a=

What if the

a constant equals 0.6

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.

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

i

Ai

Fi

a=

a=

What if the

a constant equals 0.5

• 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

Forecast Effects ofSmoothing Constant 

Weights

=

Prior Period

2 periods ago

(1 - )

3 periods ago

(1 - )2

= 0.10

= 0.90

Ft+1 = Ft +  (At - Ft)

or

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

w1

w2

w3

8.1%

9%

10%

90%

9%

0.9%

• Collect historical data

• Select a model

• Moving average methods

• Selectn (number of periods)

• For weighted moving average: selectweights

• Exponential smoothing

• Select

• Selections should produce a good forecast

…but what is a good forecast?

A Good Forecast

• Has a small error

• Error = Demand - Forecast

et

• MAD = Mean Absolute Deviation

• MSE = Mean Squared Error

• RMSE = Root Mean Squared Error

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

= 40

4

=10

What is the MAD value given the forecast values in the table below?

At

Ft

|At – Ft|

Month

Sales

Forecast

1

220

n/a

5

2

250

255

5

3

210

205

20

4

300

320

5

325

315

10

= 40

= 550

4

=137.5

What is the MSE value?

RMSE =

√137.5

=11.73

At

Ft

|At – Ft|

(At – Ft)2

Month

Sales

Forecast

1

220

n/a

5

25

2

250

255

5

25

3

210

205

20

400

4

300

320

5

325

315

10

100

= 550

84

= 14

6

-16

16

2a. Mean Squared Error (MSE)

1

1

-1

100

-10

10

1,446

= 241

17

17

289

6

-20

20

400

2b. Root Mean Squared Error (RMSE)

-10

84

1,446

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 α used in the method are wrong.

Note: In the above, n is the number of periods, which is 6 in our example

= SQRT(241)

=15.52

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

• TS = Tracking Signal

• Good tracking signal has low values

30

Quantitative

Forecasting

Regression

Time Series

Models

Models

2. Moving

3. Exponential

1. Naive

Average

Smoothing

a) simple

b) weighted

a) level

b) trend

c) seasonality

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

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

Ft+1 = Ft + a At – a Ft

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

• 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 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.

y = a + b x

where,

y = a+ b X

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.

• Before applying any forecasting method, the total system should be understood.

• 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

FOR JULY 2nd MONDAY