Amman, Jordan,  4 – 7 December 2006
Download
1 / 30

Amman, Jordan, 4 – 7 December 2006 Strategic Management – Part II Forecasting Lecture 5 - PowerPoint PPT Presentation


  • 68 Views
  • Uploaded on

Amman, Jordan, 4 – 7 December 2006 Strategic Management – Part II Forecasting Lecture 5 Fixed lines Forecasting. Fixed lines forecasting. Forecasting methods for fixed lines demand depend on several factors: Satisfaction rate (waiting list, network capacity)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Amman, Jordan, 4 – 7 December 2006 Strategic Management – Part II Forecasting Lecture 5' - owen-ramsey


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Amman, Jordan, 4 – 7 December 2006

Strategic Management – Part II

Forecasting

Lecture 5

Fixed lines Forecasting

ITU/BDT/ HRD Fixed lines forecasting


Fixed lines forecasting
Fixed lines forecasting

  • Forecasting methods for fixed lines demand depend on several factors:

    • Satisfaction rate (waiting list, network capacity)

    • Competition level between fixed operators

  • Global approach fixed + mobiles + Internet is necessary taking into account different interaction effects:

    • Substitution

    • Stimulation

    • Complementary role with converged services

ITU/BDT/ HRD Fixed lines forecasting


Definition of variables

New

unexpressed

demands

UNX

New

expressed

demands

EXP

New satisfied

demands

SAT

Cancellations

CAN

Main lines

in service

ML

Unexpressed

demands

UD

Waiting

list

WL

MLDec year n= ML Dec year n-1+ SAT year n- CANyear n

WLDec year n= WL Dec year n-1+ EXPyear n- SATyear n

UDDec year n= UD Dec year n-1+ UNXyear n- EXPyear n

New demands, satisfied demands, cancellations are flows data :

given for a period, annual value = sum of 12 monthly values

Main lines in service and waiting list are stock data :

given for a precise date, annual value = last monthly value

ITU/BDT/ HRD Fixed lines forecasting


Different methods depending on the network development
Different methods depending on the network development

telephone

density

(%)

stage 3

stage 4

stage 2

stage 1

potential

in service

years

shortage

of lines

decline

maturity

network

extension

ITU/BDT/ HRD Fixed lines forecasting


Stage 1 : shortage of lines

New

unexpressed

demands

UNX

New

expressed

demands

EXP

New Satisfied

demands

SAT

Cancellations

CAN

Main lines

in service

ML

Unexpressed

demands

UD

Waiting

list

WL

Potential Demand = POT = UN + WL +WL

High unexpressed demand is caused by long waiting time and high tariffs

High waiting list is caused by network saturation in some places

Few cancellations.

The main issue is to optimize ML number with limited resources,

Check occupancy rate in every local area for switches and outside plant

Importance of localized demand for a right planning

ITU/BDT/ HRD Fixed lines forecasting


Stage 2 : network extension

New expressed

demands

DEM

New satisfied

demands

SAT

Cancellations

CAN

Main lines

in service

ML

Waiting

list

WL

New demands and cancellations characterize customers behavior,

Operator attract new demands by better tariffs.

Unexpressed demand disappears and waiting list is decreasing.

Satisfaction rate = ML / (ML + WL) is a strategic objective

Cancellation rate (CAN / ML) progressively increase.

Recommended method:

forecast total demand ML+WL, and then split ML and WL.

ITU/BDT/ HRD Fixed lines forecasting


Stage 2 continued
Stage 2 : (continued)

New satisfied demands is controlled by operator depending on the extension of the network capacity (concept of total system ready to sale, usual bottleneck in outside plant).

A continuous monitoring of waiting list for every elementary area is necessary, with the root of the problem: switch, main cable, distribution.

Coordination between commercial and technical units is crucial.

Waiting time (in months) = Waiting list * 12 / Annual new satisfied demand

Objective: to increase: Delta ML = ML Dec year n – ML Dec year n-1

ITU/BDT/ HRD Fixed lines forecasting


Stage 2 forecast of total demand when the waiting list is still high
Stage 2: Forecast of total demandwhen the waiting list is still high

Population P 2006

Total demand, ML+WL 2006

Density, D=(ML+WL)/P in 2006

extrapolation

Density, D=(ML+WL)/P

in 2007,...2006

Population P

in 2007,...2006

Total demand, ML+WL

in 2007,...2006

= D * P

ITU/BDT/ HRD Fixed lines forecasting


Stage 2 continued1
Stage 2 : continued

Main lines in service ML 2006

Total demand, ML+WL 2006

Satisfaction rate ML / (ML+WL) 2006

extrapolation

Satisfaction rate

ML / (ML+WL) 2007,...2006

Total demand, ML+WL

2007,...2006

Main lines in service ML 2007,...2006

ITU/BDT/ HRD Fixed lines forecasting


Stage 2 continued other future data
Stage 2 : continued : other future data

WL = waiting list = (ML+WL) - ML

percentage of cancellation at the base year = PCCAN :

extrapolation of the value PCCAN n at future years

CAN n= ML n * PCCAN n

SAT n = MLn - ML n-1 + CAN n

DEM n= MLWLn - MLWL n-1 + CAN n

average waiting time (in months) = WL*12 / SAT

ITU/BDT/ HRD Fixed lines forecasting


Stage 3 demand satisfaction
Stage 3 : demand satisfaction

New satisfied

demands

SAT

Cancellations

CAN

Main lines

in service

ML

MLDec year n= ML Dec year n-1+ SAT year n- CANyear n

Delta ML= SAT year n- CANyear n

Network is fully available everywhere,

average waiting time is so short that waiting list is ignored

New expressed demands = New satisfied demands

ITU/BDT/ HRD Fixed lines forecasting


Stage 3 forecast of total demand when there is no waiting list
Stage 3: Forecast of total demandwhen there is no waiting list

Current situation at the base year

Population P 2006

Lines in service, ML 2006

Density, D= ML / P in 2006

extrapolation

Forecast situation

at everyfuture year

Density, D= ML / P

in 2007,...2006

Population P

in 2007,...2006

Lines in service, ML= D * P

in 2007,...2006

ITU/BDT/ HRD Fixed lines forecasting


New jargon with the mobiles
New jargon with the mobiles

  • Churn = cancellations

    Cancellations are much higher in competitive markets

    (sometimes > 15%)

  • Net adds = Delta lines, increase of mobiles in service

  • Gross adds = New satisfied demands or new mobiles put in service

    Gross adds = Net adds + churn

ITU/BDT/ HRD Fixed lines forecasting


Churn
Churn

  • Churn means the percentage of subscribers who cancel their subscription for a service,

  • either they give up this service

  • or they move to another supplier:

    • for a better quality

    • for a lower price

    • for a better image / reputation.

  • Churn becomes higher :

    • when the global customer density increases

    • when the effective competition increases.

  • Churn is higher:

    • for new services

    • for some categories of customers

  • ITU/BDT/ HRD Fixed lines forecasting


    Stage 4 decline churn becomes higher than new satisfied demands
    Stage 4: declineChurn becomes higher than new satisfied demands

    Factors to be investigated

    • Impact of connection fee and monthly rental fee

    • Substitution effect (mobiles instead of fixed lines)

    • Competition effect (aggressive competitors with new technologies, quality of service, brand image)

    • Saturation of the whole market

    • New demand for Internet access and applications

    ITU/BDT/ HRD Fixed lines forecasting


    The last mile of the fixed lines
    The « last mile » of the fixed lines:

    The replacement of the fixed lines by cellularnetworks could be faster than expected !!!

    Poor maintenance,

    Lack of competencies

    No compliance withengineering rules.

    Lack of tools andconnecting devices

    Lack of control by themanagement

    It is necessary to improve skills and to ensure an effective field management

    before constructing new networks in order to avoid to get the same results.Important factor for the evaluation during the privatisation process.

    ITU/BDT/ HRD Fixed lines forecasting


    Fixed lines examples of evolution
    Fixed lines : examples of evolution

    ITU/BDT/ HRD Fixed lines forecasting


    Fixed lines examples of evolution1
    Fixed lines examples of evolution

    ITU/BDT/ HRD Fixed lines forecasting


    Fixed lines examples of evolution2
    Fixed lines examples of evolution

    ITU/BDT/ HRD Fixed lines forecasting


    Will the fixed lines decrease in long term impact of high density of mobiles

    actual

    forecast

    Will the fixed lines decrease in long term ?(impact of high density of mobiles)

    The logistic curve is no longer appropriate for fixed lines, but it should be used for total number of telephone: fixed +mobiles

    ?

    Telephonenumbers

    mobiles

    Prepaid effect

    Mobiles effect

    Internet effect

    fixed

    ?

    years

    ITU/BDT/ HRD Fixed lines forecasting


    Percentage of mobiles total subscribers fixed mobiles 2004
    Percentage of mobiles / total subscribers (fixed+mobiles) 2004

    ITU/BDT/ HRD Fixed lines forecasting


    Extrapolation methods
    Extrapolation methods 2004

    • Extrapolation of numbers of subscribers is carried out by using the penetration rate of a socio-demographic group, which is:

      • population : very general

      • households : for residential subscribers

      • employees : for business subscribers

    • The choice of the formula to use depends on

      • the market segment,

      • the level of development

      • the specific constraints in the local environment.

    ITU/BDT/ HRD Fixed lines forecasting


    Trends formula for density extrapolation
    Trends Formula 2004for density extrapolation

    Linear formula y = M+ a * t

    Parabolic formula y = M+ a * t + b * t2

    Exponential formula y = M+ a * ebt

    Logistic curve y = S / (1 + e –k * ( t – t0) )

    Exponential logistic curve y = S / (1 + a * e b* t )m

    Gompertz curve y = S / (1 + e –e ( a + b* t) )

    ITU/BDT/ HRD Fixed lines forecasting


    Trends formula
    Trends Formula 2004

    • Formula used for monthly forecasts, at short term

    • Linear formula y = M+ a * t

    • Parabolic formula y = M+ a * t + b * t2

    • Exponential formula y = M+ a * ebt

    • Formula for fixed lines at medium and long term

    • Logistic curve y = S / (1 + e –k * ( t – t0) )

    • Exponential logistic curve y = S / (1 + a * e b* t )m

    • Gompertz curve y = S / (1 + e –e ( a + b* t) )

    • Formula for mobiles

    • Bass curve N(t) = N(t-1) + p * (M - N(t-1) ) + q * (N(t-1) /M) * (M-N(t-1) ))

    ITU/BDT/ HRD Fixed lines forecasting


    Adoption probability over time
    Adoption Probability over Time 2004

    (a)

    1.0

    Cumulative Probability of Adoption up to Time t

    F(t)

    Introduction of product

    Time (t)

    (b)

    f(t) = d(F(t))dt

    Density Function: Likelihood of Adoption at Time t

    ITU/BDT/ HRD Fixed lines forecasting

    Time (t)


    Definition of the logistic curve

    S 2004

    D =

    1 + e

    - k (T - T0)

    Definition of the logistic curve

    Where :D = Telephone density at time T

    S = density saturation, (=asymptotic value of D at infinity)

    k = parameter

    T0 = parameter (symmetry center)

    ITU/BDT/ HRD Fixed lines forecasting

    ITU/BDT/ HRD Marketing and Revenue Forecasts

    28 February, 2006

    Lecture 06 slide 11


    Definition of the logistic curve1
    Definition of the logistic curve 2004

    • The formula of the logistic curve corresponds to the differential equation :

    • dD k * D * (S – D)

    • dT S

    • Where dD/ dT represents the growth of the density D,

    • It means this growth is proportional both

    • to the number of people already equipped (D)

    • (pulling effect of the existing subscribers)

    • and to the number of people not yet equipped (S – D)

    • (when all people are equipped, saturation)

    ITU/BDT/ HRD Fixed lines forecasting


    Use of the logistic curve 1
    Use of the logistic curve (1) 2004

    • The saturation is assumed to be : S

    • Two points are necessary to define the parameters of the curve

    • the initial point : year T1, density D1

    • The target point : year T2, density D2

    • The parameters k and T0 can be calculated

    • k = LN((S/T1 – 1) / (S/T2 – 1)) / (T2 – T1)

    • T0 = T1 + LN (S/T1 – 1) / k

    • The intermediary points between T1 and T2 are carried out with the formula of the logistic curve

    ITU/BDT/ HRD Fixed lines forecasting


    Use of the logistic curve 2
    Use of the logistic curve (2) 2004

    Logistic curve is not suitable for specific services in a decline stage when churn is high.

    Use logistic curve for an overall service at the national level or for a high level for all operators, taking into account the potential demand and the Internet effect.

    Estimate the substitution effect.

    Then split forecasts between fixed operators depending on assumptions of their respective attractiveness for new customers and the loyalty of their respective current customers.

    ITU/BDT/ HRD Fixed lines forecasting


    General approach
    General approach 2004

    Potential demand at the national level for fixed and mobiles

    1

    churn

    Forecasts

    for all fixed operators

    Forecasts

    for all mobiles operators

    2

    Sharing between operators

    Operator fixed F3

    Operator mobile M3

    Operator fixed F2

    Operator mobile M2

    Operator fixed F1

    Operator mobile M1

    ITU/BDT/ HRD Fixed lines forecasting


    ad