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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 l.jpg

Forecasting

August 29, Wednesday


Slide2 l.jpg

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


Forecasting3 l.jpg
Forecasting

  • 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, ?


Forecasting4 l.jpg
Forecasting

  • 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


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Outline

  • What is forecasting?

  • Types of forecasts

  • Time-Series forecasting

    • Naïve

    • Moving Average

    • Exponential Smoothing

    • Regression

  • Good forecasts


What is forecasting l.jpg
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


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Why do we need to forecast?

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.


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Importance of Forecasting in 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.


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Importance of Forecasting in 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.


Types of forecasts by time horizon l.jpg
Types of Forecasts by Time Horizon

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


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Forecasting During the Life Cycle

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 methods l.jpg
Qualitative Forecasting Methods

Qualitative

Forecasting

Models

Sales

Force

Composite

Delphi

Method

Executive

Judgement

Market

Research/

Survey

Smoothing


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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.

  • .


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Qualitative Methods

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 methods l.jpg
    Quantitative Forecasting Methods

    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 methods16 l.jpg
    Quantitative Forecasting Methods

    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


    Time series models l.jpg
    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 l.jpg
    Time Series Models: Components

    Random

    Trend

    Seasonal

    Composite


    Product demand over time l.jpg
    Product Demand over Time

    Demand for product or service

    Year

    2

    Year

    3

    Year

    4

    Year

    1


    Product demand over time20 l.jpg
    Product Demand over Time

    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 forecasting methods21 l.jpg
    Quantitative Forecasting Methods

    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 l.jpg
    1. Naive Approach

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

      • May sales = 48 →

    • Usually not good

    June forecast = 48


    2a simple moving average l.jpg
    2a. Simple Moving Average

    • 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


    2a simple moving average24 l.jpg
    2a. Simple Moving Average

    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

    ?


    2a simple moving average25 l.jpg
    2a. Simple Moving Average

    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

    ?


    What if ipod sales were actually 3 in month 4 l.jpg
    What if ipod sales were actually 3 in month 4

    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

    ?


    Forecast for month 5 l.jpg
    Forecast for Month 5?

    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

    ?


    Actual demand for month 5 7 l.jpg
    Actual Demand for Month 5 = 7

    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

    ?


    Forecast for month 6 l.jpg
    Forecast for Month 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

    ?


    2b weighted moving average l.jpg
    2b. Weighted Moving Average

    • Gives more emphasis to recent data

    • Weights

      • decrease for older data

      • sum to 1.0

    Simple moving

    average models

    weight all previous

    periods equally


    2b weighted moving average 3 6 2 6 1 6 l.jpg
    2b. Weighted Moving Average: 3/6, 2/6, 1/6

    Weighted

    Moving Average

    Month

    Sales

    (000)

    NA

    1

    4

    NA

    2

    6

    NA

    3

    5

    31/6 = 5.167

    4

    ?

    5

    ?

    6

    ?


    2b weighted moving average 3 6 2 6 1 632 l.jpg
    2b. Weighted Moving Average: 3/6, 2/6, 1/6

    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


    3a exponential smoothing l.jpg
    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

    Need initial

    forecast Ft

    to start.

    Ft+1 = Forecast value for time t+1

    At = Actual value at time t

     = Smoothing constant


    3a exponential smoothing example 1 l.jpg
    3a. Exponential Smoothing – Example 1

    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


    3a exponential smoothing example 135 l.jpg
    3a. Exponential Smoothing – Example 1

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

    i

    Ai

    Fi

    a=

    F2 = F1+ a(A1–F1)

    =820+.1(820–820)

    =820


    3a exponential smoothing example 136 l.jpg
    3a. Exponential Smoothing – Example 1

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

    i

    Ai

    Fi

    a=

    F3 = F2+ a(A2–F2)

    =820+.1(775–820)

    =815.5


    3a exponential smoothing example 137 l.jpg
    3a. Exponential Smoothing – Example 1

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

    i

    Ai

    Fi

    a=

    This process continues

    through week 10


    3a exponential smoothing example 138 l.jpg
    3a. Exponential Smoothing – Example 1

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

    i

    Ai

    Fi

    a=

    a=

    What if the

    a constant equals 0.6


    3a exponential smoothing example 2 l.jpg
    3a. Exponential Smoothing – Example 2

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

    i

    Ai

    Fi

    a=

    a=

    What if the

    a constant equals 0.6


    3a exponential smoothing example 3 l.jpg
    3a. Exponential Smoothing – Example 3

    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.


    3a exponential smoothing example 341 l.jpg
    3a. Exponential Smoothing – Example 3

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

    i

    Ai

    Fi

    a=

    a=

    What if the

    a constant equals 0.5


    3a exponential smoothing42 l.jpg
    3a. Exponential Smoothing

    • 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 of smoothing constant l.jpg
    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%


    To use a forecasting method l.jpg
    To Use a Forecasting Method

    • 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 l.jpg
    A Good Forecast

    • Has a small error

      • Error = Demand - Forecast


    Measures of forecast error l.jpg
    Measures of Forecast Error

    et

    • MAD = Mean Absolute Deviation

    • MSE = Mean Squared Error

    • RMSE = Root Mean Squared Error

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


    Mad example l.jpg
    MAD Example

    = 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


    Mse rmse example l.jpg
    MSE/RMSE Example

    = 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


    Measures of error l.jpg
    Measures of Error

    1. Mean Absolute Deviation (MAD)

    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


    Forecast bias l.jpg
    Forecast Bias

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

    • TS = Tracking Signal

      • Good tracking signal has low values

    MAD

    30


    Quantitative forecasting methods51 l.jpg
    Quantitative Forecasting Methods

    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


    Exponential smoothing continued l.jpg
    Exponential Smoothing (continued)

    • 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


    Exponential smoothing continued53 l.jpg
    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 forecasting l.jpg
    Regression Analysis as a Method for Forecasting

    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.


    Formulas l.jpg
    Formulas

    y = a + b x

    where,



    General guiding principles for forecasting l.jpg
    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.

    • 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 2 nd monday l.jpg
    FOR JULY 2nd MONDAY

    • READ THE CHAPTERS ON

      • Forecasting

      • Product and service design