Budget Utilization For Improved Business

Contingency Planning In Service Delivery

Sreyash Kenkre

IBM India Research Lab

Krishnasuri Narayanam

IBM India Research Lab

Email:knaraya3@in.ibm.com

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

7978-1-4799-0530-0/13/$31.00 ©2013 IEEE

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)

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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.

REFERENCES

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