Collier 11

Embed Size (px)

DESCRIPTION

Notes

Citation preview

  • 7/18/2019 Collier 11

    1/61

    1Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Operations Management, 2e/Ch. 11 For

    ecasting and Demand Planning

    2007 Thomson o!th"#estern

    Forecasting and

    Demand Planning

    CHAPTER 11

    DAVID A. COLLIER

    AND

    JAMES R. EVANS

    OPERATIONS

    MANAGEMENTGoods, Services and Value Chains

  • 7/18/2019 Collier 11

    2/61

    2Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 Learning Objectives1. To understand the need for forecasts andthe implications of information technologyfor forecasting in the value chain.

    2. To understand the basic elements offorecasting, namely, the choice ofplanning horizon, dierent types of data

    patterns, and ho! to calculate forecastingerrors.

    ". To be a!are of dierent forecasting

    approaches and methods.

    #. To understand basic time$seriesforecasting methods, be a!are of more

    advanced methods, and use spreadsheetmodels to ma%e forecasts.

  • 7/18/2019 Collier 11

    3/61

    3Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    &.To learn the basic ides and method of regression

    analysis.

    '.To understand the role of human judgment inforecasting and !hen judgmental forecasting is

    most appropriate.

    (.To %no! that judgment and )uantitative forecastmethodologies can complement one another,and therefore improve overall forecast accuracy.

    Chapter 11 Learning Objectives

  • 7/18/2019 Collier 11

    4/61

    4Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    - Forecasting is the process ofprojecting the values of one or morevariables into the future.

    - Poor forecastingcan result in poorinventory and stang decisions,resulting in part shortages,

    inade)uate customer service, andmany customer complaints.

  • 7/18/2019 Collier 11

    5/61

    5Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning- /any 0rms integrate forecasting !ithvalue chain and capacity

    management systems to ma%ebetter operational decisions. oodforecasting and demand planningsystems result in

    higher capacity utilization,

    reduced inventories and costs,

    more ecient process

    performance, more e3ibility,

    improved customer service,and

    increased ro0t mar ins.

  • 7/18/2019 Collier 11

    6/61

    6Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    - 4ccurate forecasts are neededthroughout the value chain, andare used by all functional areas of

    the organization, includingaccounting, 0nance, mar%eting,operations, and distribution.

  • 7/18/2019 Collier 11

    7/617Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    - One of the biggest problems !ith

    forecasting systems is that theyare driven by dierentdepartmental needs and

    incentive systems.- +emand planning soft!are

    systems integrate mar%eting,

    inventory, sales, operationsplanning, and 0nancial data.

  • 7/18/2019 Collier 11

    8/618

    Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.1 The $eed %or Forecasts in a &al!e Chain

  • 7/18/2019 Collier 11

    9/619

    Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    SAP Demand Planning moduleenablescompanies to integrate planning information from

    dierent departments or organizations into a singledemand plan. The soft!are oers these %eycapabilities5

    - /ultilevel lanning

    - +ata 4nalysis- 6tatistical *orecasting

    - Trade romotion 6upport

    - Collaborative +emandlanning

    Collaborative demand planning is information$sharing across the entire value chain.

  • 7/18/2019 Collier 11

    10/6110Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.2 'mpact o% Colla(orati)e Demand Planning

  • 7/18/2019 Collier 11

    11/6111

    Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Basic Concepts in Forecasting

    - Theplanning horizon is the lengthof time on !hich a forecast is based.

    This spans from short$rangeforecasts !ith a planning horizon ofunder " months to long$rangeforecasts of 1 to 17 years.

  • 7/18/2019 Collier 11

    12/6112

    Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Basic Concepts in Forecasting

    - 4 time series is a set ofobservations measured at successivepoints in time or over successive

    periods of time. 4 time seriespattern may have one or more of thefollo!ing 0ve characteristics5

    Trend Seasonal Cyclical Random Variation

    Irregular (one time) Variation

  • 7/18/2019 Collier 11

    13/6113

    Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.3 *inear Trend o% 'nd!strial

    Photographic +!ipment

    4 trendis the underlying pattern of

    gro!th or decline in a time series.

  • 7/18/2019 Collier 11

    14/6114

    Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.4 +-ample o% *inear and $onlinear Trend Patterns

  • 7/18/2019 Collier 11

    15/6115

    Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.5 easonal Pattern o% ome $at!ral as sage

    Seasonal patternsare characterized byrepeatable periods of ups and do!ns over short

    periods of time.

  • 7/18/2019 Collier 11

    16/6116

    Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.6 Trend and !siness Ccle Characteristics

    3each data point is 1 ear apart4

    Cyclical patternsare regular patterns in a

    data series that ta%e place over long periodsof time.

  • 7/18/2019 Collier 11

    17/61

    17Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Basic Concepts in Forecasting

    Random variation8sometimescalled noise9 is the une3plaineddeviation of a time series from apredictable pattern, such as a trend,seasonal, or cyclical pattern.

    :ecause of these random variations,forecasts are never 177 percentaccurate.

  • 7/18/2019 Collier 11

    18/61

    18Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Basic Concepts in Forecasting

    Irregular variationis one$timevariation that is e3plainable. *or

    e3ample, a hurricane can cause asurge in demand for buildingmaterials, food, and !ater.

    The ne3t e3ample sho!s a timeseries of data representing callvolumes over 2# )uarters from a callcenter at a major 0nancialinstitution. The data is lotted in

  • 7/18/2019 Collier 11

    19/61

    19Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit

    7.7

    Exhibit 11.7

    Call Center

    &ol!me

  • 7/18/2019 Collier 11

    20/61

    20Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.8 Chart o% Call &ol!me

    There is an increasing trend over the si3

    years along !ith seasonal patterns !ithineach year.

  • 7/18/2019 Collier 11

    21/61

    21Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning- Forecast error 8et9 is the dierence bet!een the

    observed value of the time series and theforecast,or 4t= *t.

    - Mean Square Error (MSE)

    - Mean Absolute Deviation Error (MAD)

    - Mean Absolute Percentage Error (MAPE)

    2

    Mean Square Error

    ( n

    t=1t

    =

    e

    n

    Mean A!solu"e Error

    # #n

    t

    t=1

    e

    =n

    Mean Avera$e Percen" Error

    # #n

    t=1t

    =

    pe

    n

  • 7/18/2019 Collier 11

    22/61

    22Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.9 Forecast +rror o% +-ample Time eries Data

  • 7/18/2019 Collier 11

    23/61

    23Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Forecast Errors and Accurac

    - 4 major dierence bet!een /6; and /4+is that /6; is inuenced much more bylarge forecasts errors than by small errors8because errors are s)uared9.

    - /4; is dierent in that the measurementscale factor is eliminated by dividing theabsolute error by the time$series valuedata. This ma%es the measure easier to

    interpret.- The selection of the best measure of

    forecast accuracy is not a simple matter>

    indeed, forecasting e3perts often disagreeon !hich measure should be used.

  • 7/18/2019 Collier 11

    24/61

    24Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning!pes of Forecasting Approac"es

    - Judgmental forecasting relies upon

    opinions and e3pertise of people indeveloping forecasts.

    - Statistical forecasting is based on the

    assumption that the future !ill be ane3trapolation of the past.

    - /any commercial soft!are pac%ages and

    general statistical analysis programs, suchas 666, /initab, and 646, haveforecasting features or modules. ?ariousother stand$alone soft!are pac%ages e3ist

    that automate some of these tas%s.

  • 7/18/2019 Collier 11

    25/61

    25Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.10 Classi%ication o% asic Forecasting Methods

  • 7/18/2019 Collier 11

    26/61

    26Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Statistical Forecasting Models

    The follo!ing list e3plains some of the basic andmore popular statistical forecasting models.

    - 6ingle /oving 4verage- @eighed /oving 4verage

    - 6ingle ;3ponential 6moothing@hen trend or seasonal factors e3ist, several othermethods are used. These models include5

    - +ouble /oving 4verage- +ouble ;3ponential 6moothing- 6eason 4dditive or

    /ultiplicative- Aolt$@inters 4dditive

    - Aolt$@inters /ultiplicative

  • 7/18/2019 Collier 11

    27/61

    27Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Single Moving Average

    - 4 moving average 8/49 forecast is anaverage of the most recent B%observations in a time series.

    - /4 methods !or% best for short planning

    horizons !hen there is no major trend,seasonal, or business cycle patterns.

    - 4s the value of B% increases, the forecastreacts slo!ly to recent changes in the

    time series data.

    - 4 !eighted moving average allo!s aforecaster to put more !eight on recent

    observations than older observations.

  • 7/18/2019 Collier 11

    28/61

    28Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.11 as"Mart Mil5 ales Time"eries Data

    E hibit 11 12 % 6 M th

  • 7/18/2019 Collier 11

    29/61

    29Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.12 !mmar o% 6"Month

    Mo)ing")erage Forecasts

    E hibit 11 13 Mil5 l F t + l i

  • 7/18/2019 Collier 11

    30/61

    30Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.13 Mil5"ales Forecast +rror nalsis

    E hibit 11 14 8 lt % + l M i T l 3 t

  • 7/18/2019 Collier 11

    31/61

    31Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.14 8es!lts o% +-cel Mo)ing )erage Tool 3note

    misalignment o% %orecasts 9ith the time series4

    E hibit 11 15 C i % 6 M th M i d

  • 7/18/2019 Collier 11

    32/61

    32Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Exhibit 11.15 Comparison o% 6"Month Mo)ing )erage and

    #eighted Mo)ing )erage Models

  • 7/18/2019 Collier 11

    33/61

    33Operations Management, 2e/Ch. 11 Forecasting and Demand Planning

    2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Single E#ponential Smoot"ing

    - This is a forecast techni)ue thatuses a !eighted average of pasttime$series values to forecast thevalue of the time series in the ne3tperiod.

    - The forecast Bsmoothes out the

    irregular uctuations in the timeseries.

  • 7/18/2019 Collier 11

    34/61

    34Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    Single E#ponential Smoot"ing

    - 4s the number of data pointsincreases, the !eights associated!ith older data get progressively

    smaller.- CBPredictor is an ;3cel add$on

    for forecasting. C:redictor !ill

    run each forecasting method youselect and !ill recommend the onethat best forecasts your data.

    E hibit 11 16 mmar o% ingle + ponential moothing Mil5

  • 7/18/2019 Collier 11

    35/61

    35Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.16 !mmar o% ingle +-ponential moothing Mil5"

    ales Forecasts 9ith : ; 0.2

    Exhibit 11 17 raph o% ingle +-ponential moothing

  • 7/18/2019 Collier 11

    36/61

    36Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.17 raph o% ingle +-ponential moothing

    Mil5"ales Forecasts 9ith : ; 0.2

    Exhibit 11 18 CBPredictor 'np!t Data Dialog

  • 7/18/2019 Collier 11

    37/61

    37Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.18 CBPredictor'np!t Data Dialog

    Exhibit 11 19 CBPredictor Methods aller Dialog

  • 7/18/2019 Collier 11

    38/61

    38Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.19 CBPredictorMethods aller Dialog

    Exhibit 11 20

  • 7/18/2019 Collier 11

    39/61

    39Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.20

    Portions o%

    CBPredictor

    Report

    #or5sheet

    Exhibit 11 21 Data ttri(!tes Ta( o% CBPredictor Dialog

  • 7/18/2019 Collier 11

    40/61

    40Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.21 Data ttri(!tes Ta( o%CBPredictor Dialog

    Exhibit 11 22

  • 7/18/2019 Collier 11

    41/61

    41Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.22

    CBPredictor

    8es!lts

    Chapter 11 *orecasting and +emand lanning

  • 7/18/2019 Collier 11

    42/61

    42Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning- Regression analysis is a method for

    building a statistical model that de0nes a

    relationship bet!een a single dependentvariable and one or more independentvariables, all of !hich are numerical.

    Yt= a !t

    - 6imple linear regression 0nds the bestvalues of a and b using the method ofleast s)uares.

    - ;3cel provides a very simple tool to 0ndthe best$0tting regression model for a timeseries by selecting theAdd Trendline

    option from the Chart menu.

    Exhibit 11 23

  • 7/18/2019 Collier 11

    43/61

    43Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.23

    Call Center

    &ol!me

    Forecasts %or

  • 7/18/2019 Collier 11

    44/61

    44Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.24 Factor +nerg Costs

    Exhibit 11 25 dd Trendline Dialog

  • 7/18/2019 Collier 11

    45/61

    45Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.25 dd Trendline Dialog

    Exhibit 11 26 dd Trendline Options Ta(

  • 7/18/2019 Collier 11

    46/61

    46Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.26 dd Trendline Options Ta(

    Exhibit 11 27 *east"!ares 8egression Model %or

  • 7/18/2019 Collier 11

    47/61

    47Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.27 *east !ares 8egression Model %or

    +nerg Cost Forecasting

    Exhibit 11 28 asoline ales Data

  • 7/18/2019 Collier 11

    48/61

    48Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.28 asoline ales Data

    Exhibit 11.29 Chart o% ales &ers!s Time

  • 7/18/2019 Collier 11

    49/61

    49Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.29 Chart o% ales &ers!s Time

    Exhibit 11.30 M!ltiple 8egression 8es!lts

  • 7/18/2019 Collier 11

    50/61

    50Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.30 M!ltiple 8egression 8es!lts

    Chapter 11 *orecasting and +emand lanning

  • 7/18/2019 Collier 11

    51/61

    51Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning$udgmental Forecasting

    - @hen no historical data is available,only judgmental forecasting ispossible.

    - The "elphi approach consists offorecasting by e3pert opinion bygathering judgments and opinions of%ey personnel based on their

    e3perience and %no!ledge of thesituation.

    Chapter 11 *orecasting and +emand lanning

  • 7/18/2019 Collier 11

    52/61

    52Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanning

    $udgmental Forecasting

    - 4nother common approach to gatheringdata is a survey. 6ample sizes are usuallymuch larger than !ith +elphi> ho!ever, thecost of such surveys can be high.

    - The major reasons for using judgmentalmethods are5

    reater accuracy,

    4bility to incorporate unusual or one$time events, and

    The dicultly of obtaining the datanecessary for )uantitative techni)ues.

    Chapter 11 *orecasting and +emand lanningForecasting in Practice

  • 7/18/2019 Collier 11

    53/61

    53Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanningForecasting in Practice

    - /anagers use a variety of

    judgmental and )uantitativeforecasting techni)ues.

    - 6tatistical methods alone cannot

    account for such factors as salespromotions, competitive strategies,unusual economic disturbances, ne!

    products, large one time orders,natural disasters or laborcomplications.

    Chapter 11 *orecasting and +emand lanning

  • 7/18/2019 Collier 11

    54/61

    54Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 *orecasting and +emand lanningForecasting in Practice

    - The 0rst step in developing apractical forecast is to understandthe purpose, time horizon, and levelof aggregation.

    - +ierent forecasting methodsre)uire dierent levels of technicalability and understanding ofmathematical principles andassumptions.

    Chapter 11 6olved roblem D1

  • 7/18/2019 Collier 11

    55/61

    55Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    p

    +evelop a three$period and four$periodmoving$average forecasts and singlee3ponential smoothing forecasts. Computethe /4+, /4;, and /6; for each. #hichmethod provides a !etter forecast$

    PeriodDeman

    d PeriodDeman

    d

    1

  • 7/18/2019 Collier 11

    56/61

    56Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 6olved roblem D1

    :ased on the three error metrics 8/4+, /6;,/4;9 the "$month moving average is the bestmethod among these three.

    =0

    =2

    =>

    =?

    ==

    @0

    @2

    @>

    @?

    @=

    1 2 6 > A ? 7 = @ 10 11 12

    Period

    Movin

    Aver!e

    "ore#!$t$

    Chapter 11 6olved roblem D2

  • 7/18/2019 Collier 11

    57/61

    57Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 6olved roblem D2

    4verage attendance 0gures at a majoruniversityFs home football games havegenerally been increasing as the teamGsperformance and popularity has beenimproving5

    Hear 4ttendance1 2',7772 "7,777" "1,&77# #7,777& "",777' "2,277

    ( "&,777

    Chapter 11 6olved roblem D2Solution

  • 7/18/2019 Collier 11

    58/61

    58Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Chapter 11 6olved roblem D2SolutionThe forecast for the ne3t year 8Hear

  • 7/18/2019 Collier 11

    59/61

    59Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    Exhibit 11.32 +-ample Call &ol!me Data ( Da %or an5

  • 7/18/2019 Collier 11

    60/61

    60Operations Management, 2e/Ch. 11 Forecasting and Demand Planning2007 Thomson o!th"#estern

    p

    3see the %ile an5 Forecasting Case

    Data.-ls on the t!dent CD"8OM4

    Exhibit 11.33 elp Des5 'n!ir &ol!mes ( o!r o% Da 34

  • 7/18/2019 Collier 11

    61/61

    3 4