Budget Utilization For Improved Business
Contingency Planning In Service Delivery
Sreyash Kenkre
IBM India Research Lab
Krishnasuri Narayanam
IBM India Research Lab
Email:[email protected]
Vinayaka Pandit
IBM India Research Lab
Abstract—Service delivery using geographically distributeddelivery locations has emerged as a mature methodology ofservice delivery. The fragile nature of business environments atthe delivery locations has resulted Business Continuity Planningmethodology in becoming a key differentiator between servicedelivery organizations. Increasingly, these organizations are ac-tively seeking to allocate funds for improving their BCP postureby procuring resources. However, the conventional techniques ofcost benefit analysis while allocating budget for the procurementof resources do not take into account the special need of factoringfor resumption plans while procurement of resources for BCP.In this paper we explore the issues faced in utilizing budgets forBCP, and suggest a broad methodology that may be used foroptimal budget utilization.
I. INTRODUCTION
Business continuity entails a commitment of the service
provider to the client that at least certain critical services will
be delivered round the clock irrespective of the uncertainties in
the remote operational environment [10], [2]. The procedure
of identifying the critical services, disruption scenarios, and
formulating plans for resumption of the critical services under
the disruptive scenarios is called business contingency plan-
ning (BCP). Usually, the procurement of resources for BCP is
done in a per resource manner, where a fixed percentage of
each resource is over provisioned, so as to build redundancy in
the system which could then be used for contingency planning
related activities. However, with the next generation of tech-
nological change taking place in the service engineering area,
more and more organizations are seeking to actively pursue
methods for intelligent resource procurement to augmenting
BCP infrastructure. Any procurement of resources needs a
cost benefit analysis of the procurement (see [7] and the
references therein). However, the cost benefit analysis need to
take into account the manner win which the procured resources
will be utilized for business continuity planning. Without this
analysis, the utilization of the resources for the purpose of
BCP may be limited. In this paper we deal with the problem
of integrating the resumption plans for different scenarios, and
the resource procurement for implementing them, in the cost
benefit analysis. We give a broad methodology that can be
adopted and specialized by service delivery organizations.
A. BCP Posture of Service Delivery Organization
Service delivery organizations make use of different re-
source types in the process of business service delivery to
the clients. Disruption refers to non-availability of some of
these resource types. In such a situation, the client may
want at least a pre-identified set of critical services to be
delivered without interruption. For providing this business
continuity to the client, the organization reallocates resources
from the pool of the currently unused resources, and ensure
that the critical service delivery is not interrupted. Such a
reallocation of resources for business continuity is called a
mitigation plan or resumption plan. The resumption plan is
prepared by the business organization in advance, for the pre-
identified high risk scenarios for the organization. However, it
may not be possible to resume the critical service delivery
of all the projects affected under a scenario, due to the
lack of unused resources. Thus the organization may seek to
improve its ability to provide uninterrupted service delivery to
more projects by augmenting its inventory by procuring more
resources. We refer to this as the process of Improving the
BCP posture of the organization.
B. Budget utilization for improved BCP Posture
The inventory of resources of business organizations is
highly dynamic, due to its large size, short life times of
the resources, urgent procurement requirements due to the
fast changing business requirements, and the need to con-
stantly maintain a buffer of unused resources to be used
when any resource malfunctions (i.e. redundancy for business
contingency planning). Thus, old resources are sold off and
new resources procured at a steady rate. A key step in
this procurement process is budget allocation, which is done
periodically (monthly, or every quarter). The budget utilization
usually follows a per resource pattern, where in, an estimate of
the requirements of each type of resource is estimated based
on business forecasts, the likelihood of new customers, the
ability to buy financial instruments in the resource commodity
markets etc. However, from the point of BCP, such a per
resource plan is suboptimal, since the BCP plans have an
intrinsic dependence on the different types of resources that
are used in the plans. The procurement plan needs to take
into account the different scenarios that organization faces, and
the resumption plans that are in place, and those that can be
implemented after the procurement of the resources. Thus, the
procurement process is intrinsically tied to the process of the
formulation of resumption plans. Thus, for utilizing a budget
for improving the BCP posture, what resources to procure is
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itself a key step of the procurement process. Conventional
cost benefit analysis of budget utilization usually calculate
the benefit the organization gets by the procurement, and
then weigh it against the absolute cost of the procurement.
The benefit and cost may not be just the actual realized
cost or benefit, but may also be an expected cost or benefit,
and may also include human sentiment for the benefit/cost,
which may not be tangible [7]. For the case of BCP, the
benefit translates to an expected savings due to the ability
to delivery service in spite of a disruption, and the cost refers
to the cost of procurement. For calculating the benefit, the
organization needs to a priori compute the possible scenarios
and the resumption plans for the scenarios, if the procurement
is realized. We outline the methodology that can be adopted
for this analysis in the case of budget utilization for BCP.
II. DEFINITIONS AND NOTATIONS
For formulating our problem we need to model the orga-
nization mathematically. In [5], [6] an accurate model for
service delivery organizations is provided, and we shall use
a this model. In the context of a service delivery system,
there are three important components to be modelled. They
are resource infrastructure network, service accounts and sce-
narios. The resource infrastructure network is used to model
the set of all the resources that a service delivery organization
uses to deliver its services. The service accounts represent
the different services being delivered to different customers.
Essentially, service accounts represent the customer accounts
in the business world and model the resource requirements of
the customer accounts. The scenarios are used to model the
different possible disruptions that may occur to the resource
infrastructure network.
Formally, the resource infrastructure network comprises
of resources belonging to a finite set of resource types
T = {T1, T2, . . . , Tr}. The resources in the organization
are distributed geographically over a set of locations L ={L1, L2, . . . , Lm}. Associated with each location Li is its
capacity profile given by (ci,1, ci,2, . . . , ci,r) where ci,t denotes
the capacity of the resource type Tt available at location Li.
Furthermore, for each pair Li1, Li2 ∈ L, we are also given
di1,i2, the distance between Li1 and Li2. We assume that the
cost of movement between Li and Lj are same in both the
directions. However, these assumptions can easily be relaxed.
The service accounts in the system are given by
the set J = {J1, J2, . . . , Jn}. Each service account
Jh is specified by its resource requirement profile:
((uh,1, lh,1), (uh,2, lh,2), . . . , (uh,r, lh,r)). uh,t is the ”normal
requirement” and means that Jh requires lh,t units of resource
type Tt to ensure continuity of service delivery; furthermore
lh,t ≤ uh,t.
For business continuity planning, a key input is the set
of scenarios which model different disruptions that could
happen. Formally, the set of scenarios is given by S ={S1, S2, . . . , Sp}. Each scenario Sk is a subset of L. The
meaning of Sk is that the resources located at the locations in
Sk are not available. Therefore, for a given scenario Sk, the set
of service accounts that need to be rerouted are those that were
delivered from locations in Sk. They need to be rerouted to one
of the locations in L\Sk. When a job Jh is rerouted to location
Li it means that a resource profile of (lh,1, lh,2, . . . , lh,r)is allocated to Jh at location Li. This model corresponds
to the two stage planning approaches studied in stochastic
optimization [8], [3].
III. BUSINESS PROBLEM AND ANALYSIS
The set of scenarios of interest to the organization be
S = {S1, S2, . . . , Sk}. Let the set of customer accounts
impacted due to the scenario be {Jk1, Jk2, . . . , Jkb}. Suppose
the organization needs to know the best set of resources
to procure for utilizing a fixed budget, B. We first need
to quantify the benefit for the organization, by utilizing the
budget. After that we need to identify the groups of resources
that should be procured.
A. Quantifying BCP Posture Improvement
Every service account (project) comes with three types
of costs. If the project Ji is disrupted under scenario Sj ,
then there is a fixed cost Fi to be paid. This corresponds
to the fixed penalty for disruption. For some accounts, this
may be zero. Then there is a cost ci corresponding to the
cost paid per unit time, for the time duration for which
the project is unable to deliver the service. However, if the
organization is able to resume service delivery for some of
those components of the project that are identified as critical,
then the organization pays a cost of c∗i per unit time, till normal
service delivery is attained. c∗i is usually much smaller than ci,
and the ci and c∗i are pre specified in the contract between the
organization and the client account. For resuming the critical
service delivery, the organization has to reroute the critical
services to a location, unaffected by the scenario. For this, the
organization has to pay a cost called recourse cost, and denoted
by rik. The recourse cost corresponds to the cost of enabling
resources at the new locations, arranging for the transportation
of employees to the new locations etc.
Let C(i, j) be the cost the organization pays for project Jiunder scenario Sj . Thus, if the disruption lasts a duration of
τj , the organization pays the following cost:
C(i, j) = Fi + c∗i τj + rij Disrupted and resumed (1)
= Fi + ciτj Disrupted. Not resumed (2)
= 0 Not disrupted (3)
We will assume that the time taken to implement contin-
gency plans is zero. This is a reasonable assumption, since
the scenarios correspond to problems serious enough to take a
much longer time to recover, than to implement contingency
plans. Thus the total cost that the organization faces under
scenario Sj is
C(Sj) =
i=n∑
i=1
C(i, j) (4)
8
Now suppose that the organization uses the budget B to
procure some resources and updated the resumption plans to
take into account the new resources that have been procured.
Thus more projects are able to resume under the scenarios,
and hence the total cost of disruption faced by the organziation
islikely to reduce. Let us denote this new cost under scenario
Sj by Cnew(Sj). Note that the set of scenarios after the
utilization of the budget will be a subset of the current set of
scenarios (since the resources present before the utilization of
the budget are still present after the utilization of the budget,
and no new scenarios are created). Hence, without loss of
generality, we assume that the set of scenarios remains the
same.
For each scenario Sj , let wj be a set of weights that the
organization specifies (this may correspond to the importance
that the organization gives to that scenario, or it may be the
likelihood of the occurrence of the scenario). Consider the
following quantity.
SF = 1−
∑Si
wiCnew(Si)∑
SiwiC(Si)
(5)
The numerator of the fraction is the sum of the costs across
the scenarios after the budget is utilized, while the denominator
is the sum of the costs before the budget is utilized. Since the
disruption cost can only go down by procuring new resources,
SF lies in [0, 1]. Further, if it is one, then it corresponds
to the case where the organization is not disrupted at all.
So SF is an indication of how well the organization does,
after the budget is utilized. In fact, it is the savings that the
organization gets by utilizing the budget, as a fraction of the
total initial cost (hence the terms SF i.e. savings factor). Thus,
for optimal budget utilization, we should try to maximize SF ,
and the effectiveness of the budget utilization can be seen
by calculating the corresponding SF . So for optimal budget
allocation, we try to minimize the sum of the new costs across
all scenarios that the organization sees. However, note that
the calculation of the numerator requires information of the
scenarios the organization will face, and the resumption plans
it will implement under each scenarios, after it has done the
procurement. For this, we first need to identify the set of
feasible procurements.
B. Identifying Feasible Procurements
To utilize the budget, we need to first identify the feasible
set of resources that we can procure. For example, spending
a large part of the budget on a rarely used resource may not
improve SF . Further, even if we identify resources that the
organization makes heavy use of, it may not be possible for
the organization to use the resources effectively, since they
need to be utilized in the contingency plans of the scenarios.
There may be business constraints (like only one project will
use the server, no sharing etc) which may lead to sub-optimal
utilization. Further, the cost faced by the organization goes
down only if all the critical services are delivered. Partial
resumption of the critical services still faces a very large
cost. Thus, either all the critical services of a project are
resumed, or none are resumed. Since there is a high utilization
of the resources in the ”cost cutting” driven organizational
processes, it makes sense to resume the critical services
of a project completely, rather than partially. Thus, simply
procuring heavily used resources may not improve the SF .
This makes the case for identifying groups of resources
that can be procured, and pre planning their use for particular
projects under various scenarios. Typically, for each project,
it is possible by inspection, or prior experience to identify
under each scenario, possible resource groups that can be
procured, for resuming the critical services. This involves
surveying under each scenario, what resources could have
been procured at each location, so that the project could be
resumed. Thus, we identify, locally, (at a project level, or
a location level), a set of resources that may be procured,
and how they can be used under different scenarios. We call
these as LocalProcurementPlans (denoted by LPP). We let
LPPi, LPP2, ..., LPPp denote the set of local procurement
plans that the organization identifies. Thus, when the organi-
zation procures resources, it has to aggregate the resources in
the local procurement plans that it wants to implement, and ac-
quire those resources. After that it has to allocate the resources
to the corresponding locations as specified by the chosen local
procurement plans, and then update its resumption plans to
use the newly acquired resources. Thus the concept of LPP’s
integrates the process of business contingency planning and the
process of resource procurement. It ensures that the resources
that we acquire are efficiently utilized in resumption planning,
which is faced by the usual procurement planning cycle. Next,
we need to quantify the cost/benefit of each individual LPPi
to be able to analyze the budget utilization.
IV. PROCEDURE FOR BUDGET UTILIZATION
In the previous section we have described a procedure of
identifying sets of resources that the organization can procure,
which integrates the process of business contingency planning
with the plans for procurement. However the expenditure for
implementing all local resumption plans (LPPs) may exceed
the allocated budget B. In this case, we need to initiate a cost
benefit analysis of each LPP.
A. Cost Benefit Analysis Of LPP
Given the cost of procurement of each resource, it is easy to
calculate the cost of implementing an LPP, by simply adding
up the cost of each resource instance in the LPP. Any discounts
for bulk purchases can be accounted for by using average
prices, and allowing for a small margin of error. Once we
get the cost of the LPP, we need to know the benefit of the
LPP, simply because spending the budget on procuring LPPs
which are utilized only for a very rarely occurring scenario
may not be beneficial when averaged over several scenarios.
Thus we need to capture the average savings that a particular
LPP leads to.
Consider a local procurement plan LPPi. Suppose that we
choose to implement only LPPi and no other plan. Let SFi
be the savings factor, SF , we observe after we implement
9
only LPPi. Let EXPDi be the expenditure for implementing
only LPPi. Now, SFi represents the benefit we get by
implementing LPPi, and EXPDi is the corresponding cost
of procuring the resources for LPPi. Thus, we can define the
cost/benefit rank of LPPi, denoted by CBRi as
CBRi =SFi
EXPDi
(6)
The idea behind the cost/benefit rank is that if we have
LPPi and LPPj , and CBRi is greater than CBRj , then
procuring CBRi leads to better cost savings averaged over
all scenarios, per unit of money spent. It should be noted
that the combined savings of procuring two plans LPPi and
LPPj may be better than the sum of the savings as calculated
for each of them individually. This is because, under some
scenarios, it may be possible to aggregate the resources of
LPPi and LPPj to resume projects that could not be resumed
only when one of the LPPs was implemented. However, our
definition of cost/benefit rank serves as a good lower bound
on the cost, there by ensuring its utility.
We outline the procedure we have given so far as follows.
1) Based on a survey of the current set of projects, the
resources and their utilization, and procedure used for
formulating contingency plans, identify sets of resources
and how they can be integrated in the contingency plans.
Call these local resumption plans LPP1, . . . , LPPp.
2) Based on the cost of procuring the resources, identify the
expenditure each LPP entails EXPD1, . . . , EXPDp.
3) Simulate the procurement of each LPP individually, and
get the savings factor SF1, . . . , SFp
4) compute the cost/benefit ranks CBR1, . . . , CBRp.
B. Selecting the Best LPP Set
At this point we have captured all the information about
the ability of utilizing the procured resources, the expenditure
entailed, and the benefit in terms of cost savings it results in,
in the cost/benefit ranks, and we now have the combinatorial
optimization problem of selecting the best LPPs, subject to
the following budget constraint.∑
i∈SEL
EXPDi ≤ B SEL is set of selected LPPs (7)
This is a knapsack constraint [9], [1], and most combinatorial
optimization problems with these type of constraints are NP-
Hard. In fact, if we simply have to maximize the sum over
the selected LPPs of the CBRi, then our problem is the same
as the knapsack problem which isknown to be NP-complete
[1]. Once we define a good optimization function, we may
now encode the problem as an Integer Program, and solve it
directly using a solver like CPLEX [4]. However, we present
simpler heuristics which may also be used.
1) Minimize Penalty Cost (MPC): Under this heuristic,
we rank the LPPs based on highest penalty of the
projects it allows resumption for. Then we keep selecting
the projects with the highest ranks till our budget is
exhausted. This corresponds to greedily selecting the
LPPs with thehighest SFi. Since SFi appears in the
numerator of CBRi, for LPPs with similar expenditures,
we effectively choose those LPPs with highest CBRs.
2) Maximize Number Of Services Resumed (MNSOR): Un-
der this heuristic, we rank the LPPs based on the number
of projects that they allow for resumption, and keep
selecting the projects with the highest ranks till our
budget is exhausted. For LPPs with similar expenditures,
and projects with similar penalties, this corresponds to
selecting the LPPs based on high CBRs.
3) Minimize Penalty Cost Over Expenditure (MPCOE) Un-
der this, we simply rank the LPPs based on the CBRs
and keep selecting till our budget is exhausted.
For a given organization, based on the cost profiles of the
projects, and the expenditures of the LPPs, we may select any
one of the above heuristics for selecting the LPPs. In the next
section, we compare each of the above heuristics strategies,
with the naive strategy of augmenting the resources at each
location by a fixed amount.
V. EXPERIMENTAL RESULTS
We conduct an empirical study of the benefits of the
resource procurement strategies Minimize Penalty Cost, Max-
imize Number Of Services Resumed, Minimize Penalty Cost
On Expenditure in comparision to the traditional resource
procurement process of Capacity Increase By Fixed Target
which we have already explained.
A. Simulation Engine
We have built a simulation engine that can simulate dis-
tributed service delivery organizations. Most remote service
delivery organizations can be conceptualized hierarchically.
For example, an organization can be thought as distributed
in multiple cities, each city consists of multiple campuses,
each campus consists of multiple buildings, each building has
multiple floors, and finally each floor consists of office spaces.
So, the distance between various points have a hierarchical
nature. Our simulation engine can generate such organizational
structure. But, for simplicity, we present results with a flat
organization in which there is only one layer of geographical
locations. This is a convenient abstraction if we treat all the
distances below a hierarchical level, say a campus, as equal
to zero. In this case, the flat representation just considers a
distance metic over all the campuses. In most settings, the flat
representation is good enough to the hierarchical one.
One naive way of implementing a simulator of service
delivery would be to independently and randomly generate
each of the three major components: infrastructure, service
accounts, and scenarios. But, the resulting data would be quite
meaningless as the service accounts that an organization de-
cides to serve typically depends on its infrastructure. Similarly,
whether a scenario is of interest or not depends on what
impact it has on the overall infrastructure network. Therefore,
our simulator is designed to reflect the correlations between
various components as briefly described below.
10
We observe that there are a few resource types (example:
WAN, Power Systems), referred to as common type which are
required by most service accounts. There are some resource
types, referred to as special type which are required by only
a few service accounts (example: LANs with limited access
and security features, secured seats, etc.). At each location, the
simulator generates instances of all the common type resources
and sets high capacity for them. As for the special type
resources, it only generates a subset of them and sets relatively
lower capacity. Moreover, not all the resource types of the
organization are required by all service accounts (example:
purely call handling account may not need printers). Further,
it is important to note that the service accounts taken on by
the organization is highly correlated with its infrastructure
network. So, the simulator generates the resource requirements
of the service accounts as follows. Each service account has an
associated inherent size. It picks small subsets of both common
and special type resources. Its requirement for the common
type resources is set proportional to its inherent size. For the
special type resources, its requirement is set as per a normal
distribution whose mean is determined by its inherent size.
We have verified that the profile of the infrastructure networks
and the service accounts generated this way are quite similar
to some confidential real-life datasets.
B. Scenario Generation
We construct rule based scenarios and scenarios which are
intended to create specific pattern of the available capacity
of resource types. An example of rule based scenarios is
one S1 = {{L1}, {L2}, . . . , {Lm}}, i.e, set of all possible
scenarios in which exactly one location is not available. In
real-life, rule based scenarios help an organization to test its
preparedness for contingency. We consider two types of rule
based scenario sets S1, S2 where S1 is defined as above and
S2 consists of all possible scenarios in which exactly two
locations are not available.
C. Geographical Distribution of the Locations
We generate locations on a map by just locating them at
random locations on a grid. With out loss of generality, this
method of identifying the locations from a grid captures the
way how different locations of an organization are distributed
geographically.
D. Penalty Costs for Service Accounts
There are 2 types of penalty costs associated with the re-
sumption of any service account affected due to a scenario. If it
is possible to resume the critical service delivery of a service to
its clients, then the penalty PenaltyForJustCriticalServiceDe-
livery is to be charged by the client from the service provider.
And if it is not possible to resume at least the critical service
delivery of a service account to its clients after the project gets
affected by a scenario, then the penalty PenaltyForNotEvenRe-
sumingCriticalServiceDelivery is to be charged by the client
from the service provider. And if the critical service delivery
is resumed, there is also a recourese cost in resuming the
critical service delivery to the alternate location. The value
of PenaltyForNotEvenResumingCriticalServiceDelivery for a
service account is generated such that it is much higher than
the sum of the costs PenaltyForJustCriticalServiceDelivery
and recourse cost for that service account.
E. Budget Utilization during Procurement Process
After the initial allocation of the resources to the service
accounts (i.e., projects), there is some surplus of resources
at each location. But by just using these set of unallocated
resources, it may not be possible to plan for the reallocation
of services affected under a scenario. Which means that the
resumption planning for all the services affected due to a
scenario is not possbile by just using the unallocated resources
at each location. In other words, the resumption planning is
possible only for few of the service accounts affected due to a
scenario using the existing unallocated resources. The services
for which the resumption is not possible incur penalty costs to
the organization for not providing business contingency during
crisis (a scenario affecting the organization). To reduce these
penalty costs, the organization could acquire new resources
so that it can resume few more service accounts which are
affected due to a scenario. There are 3 different strategies
proposed in this paper for identifying the set of service
accounts (projects) to be resumed during a scenario, so that
the resources needed for resuming these service accounts are
procured using the budget for resource procurement: Minimize
Penalty Cost (MPC), Maximize Number Of Services Resumed
(MNOSR) and Minimize Penalty Cost On Expenditure (MP-
COE). Once the set of projects for which business contingency
is provided using the budget by acquiring new resources is
identified, it plans to implement the optimal resumption plan
possible under the scenario due to which the project is affected,
and the resources are procured to implement the optimal
resumption plan using the budget. Once all the projects for
which there was no resumption plan before procuring new
resources are resumed, and if still there is some more budget
left, we use that budget in acquiring resources to implement the
optimal resumption plans for the projects which are affected
due to a scenario and already some resumption plan exists
with the unused resources at the locations (only catch here is
that the existing resumption plans may not be optimal; and
since we are left with more budget it is logical to use that
budget in procuring new resources to implement the optimal
resumption plans for those projects).
F. Experimental Comparisons
We conducted experiments on small and large organizations
to verify if the cost benefits of procuring the new resources fol-
lowing the proposed procurement strategies vary with size. The
small sized organizations consisted of roughly half a dozen
locations and up to 250 jobs. The large sized organizations
consisted of one or two dozen locations and in the range of
500 jobs.
Along with the procurement strategies proposed earlier, we
also consider the traditional resource procurement strategy of
11
increasing the resource capacity at each location by a fixed
size. Traditionally the organizations adopt this strategy of
increasing the resource capacity at the different locations of the
organization by a fixed percentage constrained by the budget
allocated in that procurement cycle. As already introducted,
this strategy is referred to as Capacity Increase By Fixed Target
(CIBFT).
We generated large number of instances of small and large
sized organizations on which all the 4 resource procurement
strategies were run. Due to lack of space, we will only
present a summary of the experimental comparison of the
four methods. What we have captured in these summaries
is representative of the results observed across all the ex-
periments. We run these experiments exercising all the 4
different resource procurement methods with varying budgets.
We observed similar penalty cost savings on both small and
large organizations.
Table in Figure 1 shows the comparison of the different
procurement strategies assuming the budget is equivalent to
the expenditure to procure a flat 5% of capacity increase
at all the locations. The penalty cost under the scenarios of
interest with the increase of the capacity of resources at each
location by 5% following the CapacityIncreaseByFixedTarget
(CIBFT) strategy is computed first. Now the equivalent budget
is invested in acquiring the new resources at different locations
following the 3 different proposed strategies in the paper
namely MinimizePenaltyCost (MPC), MaximizeNumberOfSer-
vicesResumed (MNOSR) and MinimizePenaltyCostOnExpen-
diture (MPCOE). If we compare the penalty costs of MPC
strategy against the CIBFT strategy of resource acquisition
under the ’Rule: 1 loc’, there is a penalty cost saving of
almost 13% if we follow the MPC procurement strategy
over the CIBFT resource procurement strategy. Similarly the
penalty cost saving following the MNOSR strategy of resource
acquision over the CIBFT strategy is 12% under the ’Rule: 1
loc’. Under the “Scenario Type” column, “Rule: x loc” refer to
the rule based scenarios which include all scenarios in which
exactly x location(s) are not available.
Similarly, Table in Figure 2 shows the comparison of
different resource procurement strategies assuming the budget
is equivalent to the expenditure to procure a flat 10% of
capacity increase at all the locations.
From the tables in Figure 1 and 2, we see that all the
3 proposed strategies MPC, MNOSR and MPCOE perform
consistently better than the traditional strategy CIBFT. And
among these 3 newly proposed strategies, we could see that the
MPCOE strategy performs slitely better than other 2 strategies.
VI. CONCLUSIONS
We addressed the problem of budget utilization for im-
proving the BCP posture of an organization. In particular,
we showed how to integrate a priori, an analysis of the
scenarios and resumption plans that may be implemented post
procurement, in the cost benefit analysis. A key idea was the
Scenario Capacity Minimize Maximize Minimize
Rule Increase Penalty Num of Penalty
By Fixed Cost Services Cost/
Target (MPC) Resumed Expd
(CIBFT) (MNOSR) (MPCOE)
1 loc 1.0 0.87 0.88 0.85
2 loc 1.0 0.9 0.88 0.86
Fig. 1. Comparison w.r.t. the budget equivalent to flat 5% of resource capacityincrease expenditure
Scenario Capacity Minimize Maximize Minimize
Rule Increase Penalty Num Of Penalty
By Fixed Cost Services Cost/
Target (MPC) Resumed Expd
(CIBFT) (MNOSR) (MPCOE)
1 loc 1.0 0.93 0.93 0.92
2 loc 1.0 0.89 0.86 0.85
Fig. 2. Comparison w.r.t. the budget equivalent to flat 10% of resourcecapacity increase expenditure
concept of LPPs that capture the dependency of the procure-
ment and the resumption planning. The ideas presented give
a broad methodology that may be customized and optimized
by various service delivery organizations.
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