Zaid A. Shafeeq Mohammed N. Al-Damluji Al-Ahliyya Amman University Amman - Jordan September 2015 1.
Amman, Jordan, 4 – 7 December 2006 Strategic Management – Part II Forecasting Lecture 5
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Transcript of Amman, Jordan, 4 – 7 December 2006 Strategic Management – Part II Forecasting Lecture 5
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 1
Amman, Jordan, 4 – 7 December 2006
Strategic Management – Part IIForecasting
Lecture 5
Fixed lines Forecasting
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 2
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
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 3
Definition of variables
Main linesin serviceML
New satisfied demandsSAT
CancellationsCAN
ML Dec year n = ML Dec year n-1 + SAT year n - CAN year n
WL Dec year n = WL Dec year n-1 + EXP year n - SAT year n
UD Dec year n = UD Dec year n-1 + UNX year n - EXP year 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
New expresseddemandsEXP
WaitinglistWL
Newunexpressed demandsUNX Unexpressed
demandsUD
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 4
Different methods depending on the network development
stage 1 stage 2stage 3
years
telephonedensity(%)
shortageof lines
networkextension
maturity
potential
in service
decline
stage 4
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 5
Stage 1 : shortage of lines
Main linesin serviceML
New Satisfied demandsSAT
CancellationsCAN
Potential Demand = POT = UN + WL +WLHigh unexpressed demand is caused by long waiting time and high tariffsHigh waiting list is caused by network saturation in some placesFew cancellations.The main issue is to optimize ML number with limited resources,Check occupancy rate in every local area for switches and outside plantImportance of localized demand for a right planning
New expresseddemandsEXP
WaitinglistWL
Newunexpressed demandsUNX Unexpressed
demandsUD
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 6
Stage 2 : network extension
Main linesin serviceML
New satisfied demandsSAT
Cancellations CAN
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 objectiveCancellation rate (CAN / ML) progressively increase.
Recommended method: forecast total demand ML+WL, and then split ML and WL.
New expresseddemandsDEM Waiting
listWL
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 7
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
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 8
Stage 2: Forecast of total demandwhen the waiting list is still high
Population P 2006
Population P in 2007,...2006
Total demand, ML+WL 2006
Density, D=(ML+WL)/P in 2006
Density, D=(ML+WL)/P in 2007,...2006
Total demand, ML+WLin 2007,...2006
= D * P
extrapolation
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 9
Stage 2 : continued
Main lines in service ML 2006
Main lines in service ML 2007,...2006
Total demand, ML+WL 2006
Satisfaction rate ML / (ML+WL) 2006
Satisfaction rate ML / (ML+WL) 2007,...2006
Total demand, ML+WL 2007,...2006
extrapolation
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 10
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 nSAT n = MLn - ML n-1 + CAN nDEM n= MLWLn - MLWL n-1 + CAN n
average waiting time (in months) = WL*12 / SAT
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 11
Stage 3 : demand satisfaction
Main linesin serviceML
New satisfieddemandsSAT
CancellationsCAN
ML Dec year n = ML Dec year n-1 + SAT year n - CAN year n
Delta ML = SAT year n - CAN year n
Network is fully available everywhere, average waiting time is so short that waiting list is ignoredNew expressed demands = New satisfied demands
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 12
Stage 3: Forecast of total demandwhen there is no waiting list
Population P 2006
Population P in 2007,...2006
Lines in service, ML 2006
Density, D= ML / P in 2006
Density, D= ML / P in 2007,...2006
Lines in service, ML= D * Pin 2007,...2006
extrapolation
Current situation at the base year
Forecast situationat everyfuture year
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 13
New jargon with the mobiles
• Churn = cancellationsCancellations 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
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 14
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
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 15
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
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 16
The « last mile » of the fixed lines:
Poor maintenance,Lack of competenciesNo compliance withengineering rules.Lack of tools andconnecting devicesLack 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.
The replacement of the fixed lines by cellularnetworks could be faster than expected !!!
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 17
Fixed lines : examples of evolution
0
2 000 000
4 000 000
6 000 000
8 000 000
10 000 000
12 000 000
14 000 000
16 000 000
18 000 000
1998 1999 2000 2001 2002 2003 2004
Algeria
Bahrain
Djibouti
Egypt
Iran
Iraq
Jordan
Kuw ait
Lebanon
Libya
Malta
Morocco
Oman
Palestine
Qatar
Saudi Arabia
Syria
Tunisia
U.A.Emirates
Yemen
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 18
0
500 000
1 000 000
1 500 000
2 000 000
2 500 000
3 000 000
1998 1999 2000 2001 2002 2003 2004
Algeria
Bahrain
Djibouti
Iraq
Jordan
Kuwait
Lebanon
Libya
Malta
Morocco
Oman
Palestine
Qatar
Syria
Tunisia
U.A.Emirates
Yemen
Fixed lines examples of evolution
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 19
Fixed lines examples of evolution
0
200 000
400 000
600 000
800 000
1 000 000
1 200 000
1 400 000
1 600 000
1998 1999 2000 2001 2002 2003 2004
BahrainDjiboutiIraqJordanKuwaitLebanonLibyaMaltaMoroccoOmanPalestineQatarTunisiaU.A.EmiratesYemen
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 20
Will the fixed lines decrease in long term ?(impact of high density of mobiles)
?
Telephonenumbers
fixed
mobiles
years
The logistic curve is no longer appropriate for fixed lines, but it should be used for total number of telephone: fixed +mobiles
Internet effect
Mobiles effectPrepaid effect
actualforecast
?
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 21
Percentage of mobiles / total subscribers (fixed+mobiles) 2004
0 20 40 60 80 100
Iraq
Egypt
Syria
Yemen
Lebanon
Malta
Algeria
Saudi Arabia
Qatar
Jordan
Palestine
Tunisia
Emirates
Djibouti
Oman
Bahrain
Kuwait
Morocco
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 22
Extrapolation methods
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.
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 23
Trends Formulafor 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) )
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 24
Trends Formula
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) ))
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 25
Adoption Probability over Time
Time (t)
Cumulative Probability of
Adoption up to Time t
Introduction of product
(a)
Time (t)
Density Function: Likelihood of Adoption
at Time t
(b)
1.0
F(t)
f(t) = d(F(t))dt
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 2628 February, 2006 Lecture 06 slide 11ITU/BDT/ HRD Marketing and Revenue Forecasts
Definition of the logistic curve
SD =
1 + e- k (T - T0)
Where :D = Telephone density at time TS = density saturation, (=asymptotic value of D at infinity)k = parameterT0 = parameter (symmetry center)
Telephone density
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 27
Definition of the logistic curve
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)
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 28
Use of the logistic curve (1)
The saturation is assumed to be : STwo 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 calculatedk = 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
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Use of the logistic curve (2)
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.
24 April 2023 ITU/BDT/ HRD Fixed lines forecasting Lecture 05 slide 30
Operator fixed F3
Operator fixed F2
General approach
Forecastsfor all fixed operators
Forecastsfor all mobiles operators
Operator fixed F1
Operator mobile M3
Operator mobile M2
Operator mobile M1
Potential demand at the national level for fixed and mobiles
Sharing between operators
1
2
churn