A System Dynamics Strategic Planning Model For A Municipal Water Supply Scheme
Ezekiel A. Adeniran*
Civil & Environmental Engineering Department
University of Lagos, Nigeria
engrea@yahoo.com
Olufemi A. Bamiro
Mechanical Engineering Department,
University of Ibadan Nigeria
femi.bamiro@skannet.com
* Corresponding Author
Abstract
This paper reports the development of a System Dynamics model for the Strategic planning of a
municipal water supply scheme. The model is capable of handling the critical variables that impact
on the operations of a water supply scheme. The model was developed using the Dynamo
programming software to handle the process dynamics. Other supporting software - Fortran,
Clipper 5, and Epiglue are used to handle data input, analysis and presentation of results in a user-
friendly environment. The model was validated by applying it to the simulation of the University of
Ibadan Water Supply Scheme for which extensive data of operations were collected over a period of
ten years. The results obtained were found to be realistic and useful for the planning of plant
operations. It is concluded that the model developed is sensitive to variable changes and has the
capability of answering various operational ‘what-if’ questions. As a strategic planning tool, the
present model generates, for different scenarios, important operational information for the
management of the water supply scheme. It is concluded that SD model is a useful tool to assist
water mangers and policy makers in making decisions and evolving strategic planning for water
supply.
Key Words: Water Supply, System Dynamics, Strategic Planning, Policy Decisions.
Introduction
The city of Ibadan has been faced with water scarcity since the late 1980s. The scarcity of water in
the city affected the University of Ibadan (UI) culminating in a fire incident that gutted a female
undergraduate hostel. The management and operation of a Water Supply Scheme in an African
metropolis is a complex and challenging problem that has cultural dimensions and demands a high
level of strategic planning. A tool that can facilitate the performance of these functions is a step in
the right direction.
The management of the scarce water in UI requires the development and operation of such a system
and in this paper, a System Dynamic (SD) model is used to represent the structure of the UI water
supply system and tested as tool for making strategic planning decisions using simulations to
replicate various scenarios. The objective of such a Strategic Planning Model for a Municipal Water
Supply Scheme (SPMWSS) was to develop a strategic planning tool that would give the water
mangers, planners, policy and decision makers, flexibility in assembling and testing water
management strategies for the University and similar settlements. A strategy may include structural
measures to increase supply capacity as well as managerial options to manage water supply, reduce
costs and to allocate water supply to various demand units at varying quantities and time (A deniran,
2007).
1
The simulation system is designed with the appreciation that there are several limitations on the
processing and computing abilities of human managers and decision makers (Simon, 1979). A
model for water supply management must necessarily focus attention on the flow of information in
a complex water supply management system, and the use of such information in the decision
making process. It should also focus attention on the role that simulation can play in gaining
insights into current and future water management policies and strategies (World Bank, 1993).
Three types of information can be used in the development of a strategic planning model viz:
mental, written and numeric (Forrester; 1961, 1992). In the development of the SPMWSS model,
the mental information relied on the mental data base of the author and his r colleagues in the water
unit of the University and it seemed to reliably capture the structure and policies governing
management of the water supply scheme in UI. On the other hand, Written data on water supply
problem and modelling strategies abound. Several articles and conferences have tried to focus
attention on paradigm shift and the need to apply modelling techniques to strategic planning in the
water supply industry. The principles of SD are well suited for modeling and application to water
resources and environmental problems (Fletcher et al., 1998, Ford 1999) and (Deaton and
Winebrake, 2001). This fact is corroborated by Nirmalakhandan (2002). The behaviour in space can
also be simulated using SD framework (Tangirala et al, 2003). Huang and Chang (2003) described
SD as an emerging tool with great potentials for improved understanding of environmental systems.
Numerical data used in building the model were collected and reviewed. For our problems, these
included data from 1991 to 2003 kept by the UI water unit and unpublished reviews from
consultants engaged at different times for improvement to the University’s water supply.
Concept, Structure and Development of the SPMWSS
The simulation model was formulated base on System Dynamics theory. System dynamics, as an
aspect of system theory, is a method for understanding the dynamic behavior of complex systems. The
goveming equations represented by finite difference expressions used for modeling different elements
in a system are solved using standard numerical schemes (Bamiro, 1995). System Dynamics
modelling has attracted considerable attention over the past few decades. In a particular sense, System
Dynamics is concerned with the use of models and modelling techniques to analyse complex systems
and policy issues with a view of getting the right combination of scenarios to accomplish an efficient
strategic planning for the system being modelled (Adeniran, 2007).
In the SPMWSS model, DYNAMO, FORTRAN, CLIPPER 5 and EPIGLUE programming
languages are combined to develop a user-friendly and menu-driven environment to drive the
model. The simulation model consists of four major sectors.
The finance module includes details of the capital, operational and maintenance financial analysis. It
is further sub-divided into (i) total cost in a 12-month cycle which can be used for budgeting and (ii)
production and unit cost analysis which can be used for tariff setting mechanisms. The production
module includes the model representation of population, per capita demand and supply and demand
management options. The operation and maintenance module captures the pumping operations,
availability, spare parts and maintenance schedules. It also includes store and inventory schedules.
The distribution module lists the various water uses and demand nodes and their usage potentials. It
incorporates the reservoirs and the pumping stations. Each module is a stand alone but is interlinked
with other module. Each of the modules also represents an aspect of water management from the point
of view of sectoral interdependency. Figure 1.0.
PRODUCTION
\
OPERATION &
MAINTENANCE
FINANCE
DISTRIBUTION
Figure 1.0: Interconnectivity of the Major Components of the SPMWSS Model
Source: Adeniran (2007)
The four modules are then housed in the SPMWSS model as interactive and interdependent
components for the strategic planning of a Municipal Water Supply constituting the global system.
The concept of the SPMWSS model is as illustrated in Figure 2.0 The total model is segregated
into three stages viz: (i) Supporting Data Interface (ii) Main SPMWSS Simulation
Model and (iii) Report Generator Systems. The three-stage model concept is based
on the identified interdependencies.
v4 PRODUCTION] \/
\/
INTERACTIVE DATA INPUT Yap
*volUME
re A
PARAMETER] CAM FINANCE BIBL OfoRs ON |
N v
* SUPPLY AT NODES,
OST
WAILABILITY
DN
ava
DISTRIBUTION
Figure 2.0: Concept of the SPMWSS Model
Source: Adeniran (2007)
In the SPMWSS, the DYNAMO language was adopted to model the various components and
variables identified. The Equations are based on the mathematical philosophy of System Dynamics,
the interconnectivity of the Water Supply System and the Syntax of DY NAMO modelling. The
complete DY NAMO program listing (UIWATER.DYN) for the SPMWSS and the exogenous data
file (UIWATER.ASC) is presented in (Adeniran, 2007).
Model Validation
The SPMWSS model is validated based on the rendition that the model must have the ability to
replicate the real life system being modeled under the same parametric conditions. It is the
validation of the basic underlying processes of the model (Design Validity). Selected endogenous
variables, which are representative of the water supply system’s behaviour, which have been
collected from 1991 to 2003, are used to validate the model over a 12-month planning horizon each.
Comparison of the model output with actual historical data over the same time period provides the
required validation.
Figure 3.0 show the comparison of the Actual Production for the years 1992 to 2002 under the real
life operating conditions as available in the Water Works records as compared with the simulated
outputs of the SPMWSS model for the same period. A comparison of simulated versus actual data
shows that the variance is less than 5% and hence considered within limits.
Figure 4.0 compare the Actual Monthly production as available in the Water works records with the
SPMWSS simulated Production under the same operating conditions for the year 2002.
It is concluded from the two examples that the model replicates the system fairly well.
Actual Production Vs Simulated Production
1,400,000.00 +
= 1,200,000.00
é = 2 eee ae
5 1,000,000.00 =: B= == =
< 800,000.00 == Fz a
B 600,000.00 | Rey
3 400,000.00
g - - Actual Production
200,000.00 —a- Simulated Production
0,00 (= FE
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Years
Fig. 3.0 Chart of Actual Production vs. Simulated Production
90000 SSS SSSI
80000 —e— Actual Production Lt
70000 K[--= - Simulated Production | 7° NL
3 60000 Ne
40000 RN J
Production (cu.m)
Ww u
So o
S =)
So So
So So
1
||
|
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Months
Figure 4.0 Year 2002 Actual Production vs. Simulated Production
Application of SPMWS: Scenario Investigations
Since the primary objective in building SPMWSS is the conduct of scenario experiments for
strategic planning, considerable thought is given to the types of experimental features that must be
built into SPMWSS to ensure effective and efficient scenario simulations can be carried out. The
utilization of SPMWSS is oriented toward scenario experiments to production, finance and
maintenance situations that will assist Management in short term and long-range and strategic
planning of the water supply system. Scenario investigations allow the model users to ask “What if”
questions. The following strategic “what if” questions were investigated using the model and the
results presented.
Scenario 1: | What if population should increase but per capita demand is constant?
Scenario 2: | What if the plant efficiency should depreciate?
Scenario 3: | What if there is a decline in raw water supply?
Scenario 4: | What if the cost of energy (fuel and power) should increase?
Scenario 5: | What if the plant efficiency should decrease?
Scenario 6: | What if cost of raw water input from Eleyele intake, (one of the raw water intake
point), should increase?
Scenario 7: What if level of maintenance of the production plant should increase in order for
production to remain constant?
Scenario 8: | What if certain chemical dosage is applied, how much chemical?
Scenario 9: | What if the production budget is required?
Scenario 1: Population Increase But C onstant Per Capita Demand
The simulation results of the above scenario are presented below in Figure 5.0. The population is
varied from 20,000 to 55,000 while the per capita water demand is kept at the WHO minimum of
110 litres. The cumulative annual productions are compared with the cumulative required
production for each of the simulated situation.
Actual and Required Water Production
2500000 4
: -@- Actual Production
3 2000000 —— Required Production
g Lett,
§ 1500000 pare =< Soo See > |
5 1000000 ae
3 ae
500000
5
0
20000 22000 24000 26000 28000 30000 35000 40000 45000 55000
Population
Fig. 5.0: Actual Production Compared with Water Demand with Varying Population
It is observed from Fig. 5.0 that the Water Supply Scheme is capable of meeting the per capital
demand of 110 litres per person per day up to a population of about 35,000 after which it can no
longer cope.
Scenario 2: Effect of Depreciating Plant Efficiency
One of the major factors militating against the ability of a water supply scheme to meet with the
demand of the consumers is equipment depreciation and/or lack of maintenance resulting in
decrease in Plant Efficiency. The effect of plant efficiency on water production is examined vis-a-
vis the demand for water by the population. For this case, the population and per capita demand are
kept constant while the plant efficiency is varied. The observations are presented in graphical form
as Fig. 6.0.
Effect of Efficiency on Water Production
1400000 5
1200000 ll d d J
1000000
800000
600000
400000 +4
200000
0 T 1 T T t 1
85 80 75 70 65 60 55 50 45 40
Efficiency (%)
—— Actual Production
=m - Demanded Production
Water Production (Cum)
Fig. 6.0: Effect of Plant Efficiency on Water Production
It is observed from Fig. 6.0 that the demanded quantity of water is the same as the quantity of water
produced when the efficiency of the Plant is higher than 65%. In other words, the plant is capable of
meeting the water demand of the population provided the efficiency is not below 70%. However at
lower efficiency, the plant would not be able to cope with the demanded water by the population. It
should be noted that for this scenario investigation, the population and the per capita demand are
kept constant.
The Strategic Planning implication of this is that regular maintenance activities should be carried
out to ensure that the efficiency of any the components of the system does not drop below 70%.
This can be achieved by conducting regular reliability tests on the equipment.
Scenario 3: Declining Raw Water Supply
At present, the UI Water supply system modelled obtains raw water from both Eleyele and Oba
Dam. While Eleyele is an external supply source, the Oba Dam is an internal supply source. A
situation that can arise is for Eleyele source may not be available or the Oyo State Government who
is the owner of the dam could come up with an exclusion policy and the University is stopped from
the abstraction of raw water from Eleyele Dam. Presently 60% of the University’s raw water is from
Eleyele. In order to simulate the above condition, the scenario in which the raw water input of
Eleyele dam is reduced gradually from 60% to 0% is simulated. This is done by reducing the hours
of operation of the Eleyele pumps form 20 hours a day to zero hours a day. For this scenario, the
population is assumed to be 30000 and per capita demand is 110 litres. The effect of this scenario
on water production is as shown in Fig. 7.0.
Effect of Declining Raw Water Supply
Pe
—e— Limiting Factors
60 55 50 45 40 35 30 25 20 15 10 5 0
% Contribution From Eleyele Source
Fig 7.0: The Effect of Declining Raw Water supply on Production
From Fig. 7.0, the limiting factor becomes 1.0 when the raw water contribution from Eleyele
becomes 12.5%. Any further reduction implies that the water demand of the population can not be
met. In fact, at 0% contribution from Eleyele, the limiting factor is 0.77. In other words, Oba Dam
raw water source alone can cope with only 77% of the water demand of the population.
The Strategic planning implication is for Management to plan ahead and look for alternative source
of raw water. For instance, the long proposed Ona river dam (see Fig. 8.0) could be a feasible
option to counteract the effect of declining raw water supply either from Eleyele or Oba dam.
7
+ _ Thadan Settlement
AVA ‘280s Ibadan Express
"AY Major Road
Acess roads
4\/,
ae | /N/ Railway Line
/NY River
Proposed Dam Lake
‘= UI Water Treatment Plant
/\/250mm Raw Water Pipe
‘A
Fig. 8. Map showing the Locations of Water Supply Reservoirs to University of Ibadan
Source: Works and Maintenance Department, University of Ibadan.
Scenario 4: Increase in cost of Fuel and Energy.
Some of the major costs of production include the cost of Fuel and Energy (PHCN). From 1992 to
2005, the Nigerian Economy witnessed many fluctuations in the cost of energy and fuels. The
effects of these changes are examined using the model. First, the effect of changes in the cost of fuel
alone is examined, then the effect of the cost of energy (PHCN) alone is examined, and then the
combined effects of these changes are examined together. The simulation results of these effects on
the cost of Production and the Unit cost of production are presented in the following sections.
(a) Effect of Fuel Price on Unit Cost of Production
Table 1.0 below shows the effect of the various changes in the petroleum product prices between
1992 and 2005. It is seen that the unit cost of production rose from N55.68 to N97.17.
Table 1.0: The Effect of Changes in F uel Prices on Cost of Production
PMS Price
=N= 0.70 | 3.23 | 11.00 | 25.00 | 20.00 | 22.00 | 26.00 | 34.00 | 49.90 | 50.50
AGO Price
=N= 0.50 | 2.50 | 7.50 | 15.00 | 12.50 | 22.00 | 27.50 | 42.00 | 52.00 | 65.00
Unit Cost
=N= 55.68 | 57.09 | 60.80 | 66.65 | 64.66 | 70.10 | 73.63 | 82.53 | 89.99 | 97.17
(b) The Effect of Cost of Energy on Unit C ost of Production
Table 2.0 below shows the effect of the various changes in the unit cost of (PHCN) energy N/kWh
between 1992 an 2005. It is seen that the unit cost of production rose from N59.12 to N85.48. This
is a single factor simulation while the other factors are kept constant.
Table 2.0 Effect of Changes in Unit Cost of Energy on Unit Cost of Production
Changing Corresponding
Year PHCN Price Unit Cost
=N=/kWh =N=/cu.m
1992 1.50 59.12
1993 3.00 62.89
1998 4.50 66.65
1999 6.00 70.43
2000 7.50 74.18
2001 7.50 74.18
2005 12.00 85.48
(c) The Combined Effect of Changes in C ost of Fuel and Energy
Usually, the changes in a factor lead to changes in other factors in real life. Especially, whenever
there is a change in the cost of petroleum products there is usually a correspondence change in the
cost of energy and vice versa. Fig. 9.0 shows the changes in the costs of energy and fuel from 1992
to 2005 with the simulated unit costs of production. It is seen that the cost of production increases
with increases in cost of fuel and energy. This result is expected, but with the SPMWSS model, it is
now possible to quantify the effect of any slight changes in any of these important factors. It should
be noted that these results are for a fixed population and fixed production quantity. The model is
also capable of examining the effect of the economy of scale with varying production quantities.
140.00 1 1
Effect of Fuel and Energy Costs on Unit Cost of Production
120.00 Tes Erectricny
a Pevolpms)
100.00 —*iesetiacoy
J s000
j
£6000 |_waseeeree |
20.00
\\
Figure 9.0: Effect of Fuel and Energy Costs on Unit Cost of Production
Scenario 5: Declining Plant Efficiency on Unit C ost of Production
One of the major factors in cost consideration is the efficiency of the production plant. The
reduction in the plant efficiency does not only affect the production output it also affect the cost of
production. In order to observe the effects of the efficiency on the cost of production, the efficiency
is gradually reduced from the normal operating efficiency of 85% to 5%. The results of simulation
are presented in the Figure 10.0.
It is seen from simulation results that the unit cost of production increases exponentially with linear
declining of Plant Efficiency. It is obvious that it is not wise to operate the plant at low efficiency.
The Strategic plan implication of this is for adequate attention to be placed on maintenance such
that efficiency is not allowed to drop. Any equipment that is operation below 70% efficiency should
either be overhauled or replaced.
Unit Cost (=N=/cu.m)
1,200.00
1,100.00
1,000.00 H
900.00 |-e— Unit Cost (=N=/cu.m) |
800.00
700.00 + vi
600.00 f
500.00
400.00 Low
300.00
200.00
100.00
Unit Cost (=N=cu.m.)
85.00
80.00
75.00
70.00
60.00 }
Plant Efficiency (%)
Fig. 10.0: The Effect of Declining Plant Efficiency on Unit Cost of Production
Scenario 6: Cost of raw water input from Eleyele Intake
Raw water is a major production input. At present, the University abstracts raw water from two
sources, Eleyele Dam belonging to the Water Corporation of Oyo State (WCOS) and the Oba dam
which belongs to the University. Payment of raw water to the WCOS has been very controversial
from the inception of the University Scheme in 1990. The WCOS has insisted on payment for raw
water and has increased the raw water tariff from N1.50, to N3.0 and now N6.0 per cu.m. In order
to examine the effect of increase of raw water input from Eleyele, a scenario is created in which the
cost of raw water from Eleyele is increased from N3.0 to N100.00. The results are presented in
Figure 11.0
It is seen that the production cost increases as the cost of raw water input from Eleyele increases.
The Strategic Planning implication is evident from this graphic example: Suppose the WCOS
decides to increase the cost of raw water from the present N6.00 per cubic meter to say =N=100.00
per cubic meter, the unit cost of production will change from N92.11/m3 to N234.53/m3. This type
of information can be used as argument against an arbitrary increase in the cost of raw water. It can
also be used to justify the need for the University to look for its own internal sources of raw water.
10
100 |
50 SE] ein cost of Production | FF
Unit Cost of Production (=Ne/cum)
Bb
a
°
neg g 8288288 8
Cost of Raw Water (Eleyele) =N=/cu.m
Fig. 11.0: Effect of Cost of Raw Water From Eleyele on Unit Cost of Production
Scenario 7: Increase in Level of Required Maintenance to Sustain Expected Production
The plant capacity is usually fixed relative to the level of maintenance input. If the plant is to be
expected to have a constant production level, then the maintenance activities on the plant must be
kept regular. Managers and operators are often requested to keep the plant running without
corresponding supply of spare parts to ensure the optimum performance of the plant. Effective and
cost effective maintenance also involve keeping reasonable level of staff for the running of the plant
as well as evolving control policies as to the level of fuel consumptions and running cost expenses.
Strategic planning in the area of maintenance, therefore, places primary emphasis on cost control
with respect to the Level of Maintenance for constant production output.
The required level of maintenance of a system is a function of the maintenance input into the
system. A new system requires a relatively low level of maintenance whereas a system that is
tending towards unserviceability requires a very high level of maintenance. The aggregate of spare
part usage, manpower attendance and down times are captured in terms of percentage. The required
level of maintenance is varied from 10% to 100% while the production parameters are kept
constant. The unit cost of production is observed for each of the simulated situation. The results are
presented in Figure 12.0 and Table 3.0
Maitenance Vs Unit Cost
160 5
140
120
J 100 -—A——e tT
: 80
Y 604 ost
4 40
20
0
Oo 10 20 30 40 50 60 70 80 90 100
% Level of Maintenance Required
Fig. 12.0: Effect of Increased Level of Maintenance on Unit Cost of Production
11
It is seen that the unit of production cost increases as the required level of maintenance increases. It
is noted that when the level of maintenance is 0% , the unit cost is =N=97.46 whereas when the
level of maintenance required rises up to 100% i.e. requiring shut down maintenance, the unit cost
of production is =N=140.96.
Table 7.0: Effect of Increased Level of Maintenance on Cost of Production
% Level of Total Production Cost Unit Cost of
Required =N=k Production
Maintenance =N=k
0 117,716,000.00 97.46
10 122,977,000.00 101.82
20 128,218,000.00 106.16
30 133,468,000.00 110.51
40 138,711,000.00 114.85
50 143,976,000.00 119.21
60 149,242,000.00 123.57
70 154,486,000.00 127.91
80 159,727,000.00 132.25
90 164,979,000.00 136.59
100 170,251,000.00 140.96
It is also noted from Table 3.0 that if the level of required maintenance should increase from 0% to
only 10%, the University has to look for an additional sum of =N=5,000,000.00 in order to maintain
production. The situation becomes worse with higher level of required maintenance. If the level of
required maintenance is allowed to reach 100%, the University must look for as much as
=N=52,535,000.00 for tum around maintenance. The Strategic Planning implication is that
Management must evolve and sustain a maintenance policy that ensures routine maintenance in
order to limit the production cost. Under no condition must equipment be allowed to depreciate to
the level of breakdown maintenance.
Scenario 8: Total Chemical Usage Given Chemical Dosage
As an Expert System, the SPMWSS can be used as an oracle to tell the total quantities of Chemical
expected to be used if the level of chemical dosages at each period of the planning horizon is
known. The model can thus be used to monitor chemical usage of the Treatment Plant and thus
prevent and or detect theft. In this example, Alum alone is used. However, other chemicals can be
equally monitored. The dosage of Alum is varied from 80 ppm to 180 ppm based on the turbidity
profile of the raw water which varies from season to season. The best Alum dosage is about 80 ppm
while the worse is about 180 ppm at the peak of the raining season.
Table 4.0: Total Alum Usage Given Dosage Level
Alum Dosage 80 90 130 140 150 160 170 180
(ppm)
100 110 120
Alum Usage 157,020 | 169,080 ] 181,121 | 193,260 | 205,320 | 217,380
(Kg)
96,627 | 108,702 | 120,780 | 132,840 | 144,960
12
Table 4.0 shows the expected Total Annual Alum usage at a given dosage. This information can be
used to intelligently figure out the quantity of Alum that Management should purchase if the
average dosage in a year is known.
Scenario 9.0: Expert System for Production Budget
One of the attributes of the SPMWSS model is its budgeting capability. In a public establishment,
budgets are often required within a short time frame. Such budgets are usually prepared using the
tule of the thumb. SPMWSS model is an Expert System for preparing production budget because all
the financial components for preparing a production budget have been built into the model. In actual
sense, the total cost under certain conditions can be given as a model output as shown in Table 5.0
below.
It is seen from this table that cumulative costs of each of the vital production item for the simulated
condition are presented. Thus it is known that about =N=6,161,200 is required for Chemicals while
about =N=511,000.00 is required for spare parts and a total of =N=117,716,000 is the total money
required for the water sector during the planning year.
Table 9.0: Cumulative Costs of Various Production Items
CUMMULATIVE COSTS OF VARIOUS PRODUCTION ITEMS
PERIOD = TOTAL EXP. CHEM. COST SPARE PARTS INCIDENTALS SALARY POWER COST TRANSPORT
aie. =NF =NF NF =NF =NF =N IF
1 9723000.00 454400.00 5000.00 5000.00 940000.00 3081000.00 4067000.00
2 18901000.00 873500.00 16000.00 10000.00 1820000.00 5963000.00 7872000.00
3 26645000.00 1335900.00 42000.00 15000.00 2620000.00 3044000.00 — 11939000.00
A 38120000.00 1773800.00 74000.00 20000.00 3760000.00 1202 6000.00 15875000.00
5 47879000.00 2264000.00 79000.00 25000.00 4700000.00 15107000.00 19942000.00
6 57396000.00 2773200.00 84000.00 30000.00 5640000.00 18089000.00 23878000.00
7 68156000.00 3370800.00 260000.00 35000.00 6730000. 00 21125000.00 28559000.00
8 78916000.00 3968400.00 436000.00 40000.00 7820000.00 24161000.00 33240000.00
9 88497000.00 4546900.00 436000.00 4000.00 8760000.00 27143000.00 — 37176000.00
10 96370000.00 5144500,00 440000.00 50000, 00 9700000.00 30224000.00 41243000.00
11 10730000.00 5671000.00 479000.00 55000.00 10640000. 00 33206000.00 45179000.00
12 117716000.00 6161200.00 511000.00 60000.00 11580000. 00 36287000.00 49246000.00
Conclusions
In this paper, the development of a Computer based Strategic Planning model for a Water Supply
Scheme has been reported. The model is developed using the DY NAMO modeling software.
However, in order to create a user friendly environment, other application programs such as
FORTRAN, CLIPPER 5 DBASE, EXCEL and EPIGLUE have also been used in an integrated
manner. The model was validated using existing data that spanned ten (10) years from the
University of Ibadan Water Supply Scheme. The model was then used to carry out scenario
experimentations for Strategic Planning. The results presented indicate that an SPMWSS model has
been successfully developed to handle the operation of the University of Ibadan water supply
scheme.
13
The potential of greater understanding of a system vide a System Dynamic approach, which is still
in its infancy, is shown to be not only feasible but also exciting. The model is found useful to
managers and planners for its strategic planning capability.
The SPMWSS model integrates the production and financial elements; in addition to achieving true
total cost perspective, this particular capability introduces the potential flow paths from production
to consumption. The SPMWSS provides a mechanism wherein the modeled system can be
examined under scenario experiments. In addition, many other aspects of system management as
they relate to water supply can be tested in an effort to evaluate existing policies and principles.
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IBRD
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