BUSINESS PROCESS ENTERPRISE MODEL

Operations Research for Managing Business Process Communication

and Performance

Yves Caseau

Bouygues Telecom, 82 rue H. Farman, 92447 Issy-les-Moulineaux, France

Keywords: Enterprise modelling, Business process, Simulation, Communication flows, Organization theory.

Abstract: This paper presents a computational model of a generic enterprise (BPEM, which stands for Business

Process Enterprise Model), based upon the core concept of business process. BPEM may be seen as a bridge

between two worlds of “Enterprise Models”, the world of mathematical models, formal and fully

operational for optimization purposes and the world of conceptual models (boxes & arrows type) for

management science, for reasoning and communicating about what a company is. Our model was built as

the minimal and most elegant model that is detailed enough to investigate difficult management science

issues such as the influence of hierarchical organization on performance, the optimal usage of various

communication channels or the benefits of lean-management-style control of processes. BPEM is organized

around four concepts: business processes, capabilities that encapsulate resource management, hierarchical

and transverse management organization, as well as information flows that are required to run business

processes.

1 INTRODUCTION

Operations Research has a long tradition of

successes to improve the performance of enterprises.

The traditional approach is to define a business

problem with a mathematical model and to use

optimization techniques to provide an optimal or an

improved solution that translates into better business

performance. The goal of this paper is different,

since we aim to use mathematical modelling and

optimization techniques to provide insights about the

intrinsic performance of business processes.

The contribution of this paper is to propose a

computational model of a generic enterprise, which

describes its business processes, its organization and

its information flows. Because of its generic nature,

such a model cannot be used to “solve” business

problems, but it is a tool for better understanding,

through analysis or simulation, a number of hard

questions from management science. For instance,

we may assess the benefit of lean management

applied to business processes, evaluate the impact of

organizational architecture or study the impact of the

amount of time spent during meetings, which is

often criticized in today’s large organizations.

The search for a realistic enterprise model is

nothing new. It is at the heart of management

science. Without a model, entreprises are left with

trials and errors, with empiric studies of what works

and what does not, as far as organizations and

reorganizations are concerned. The difficulty is that

simple models that are adequate for paper studies

leave too many aspects of corporate life aside, while

intricate computational models tend to be too

complex to understand, hence the results obtained

though simulation leave most practical managers

skeptical. Our aim, with the model that we propose

in this paper, is to find a balance bewteen the two.

This paper is organized as follows. Section 2

discusses the motivations behind introducing a

computational model for enterprise performance.

We relate our approach with a number of pre-

existing enterprise models, and with classical

theories of the enterprise. We define the objective

assigned to this model, which is to evaluate short-

term performance – the long-term issues of learning

and structure evolution are left aside – with respect

to organization – that is, the way decisions and

communications are handled –, business processes

and capability management. Section 3 provides a

description of BPEM (Business Process Enterprise

Caseau Y..

BUSINESS PROCESS ENTERPRISE MODEL - Operations Research for Managing Business Process Communication and Performance.

DOI: 10.5220/0003718200110020

In Proceedings of the 1st International Conference on Operations Research and Enterprise Systems (ICORES-2012), pages 11-20

ISBN: 978-989-8425-97-3

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

Model). This model may be seen as the combination

of business process, value creation, organization and

communication models. This model is the fruit of

many years of simulation, trying to reach the afore-

mentioned compromise between simplicity and the

ability to look at complex aspects of enterprise

efficiency. Section 4 demonstrates this claim with

various examples of applying BPEM to different

sides of management science. We first use this

model for a study of the use of different

communication channels. We have also used this

approach to characterize some of the benefits of lean

management (Womack, Jones and Root, 2004). Last

we explain how this model may be used to evaluate

the impact of organization on performance. Section

5 concludes with some perspectives about new

applications and future work.

2 MOTIVATIONS

2.1 Enterprise Theories

Our goal is to build a computational model –

suitable for simulation and optimization –, but any

model reflects a theory of the enterprise. Our work

is, therefore, rooted in the tradition of describing and

understanding the inner working of a company. The

first pillar of our approach is none other than F.

Taylor’s scientific organization of companies from

(Shafritz and Ott, 2001), based upon business

processes, break-down of activities and

specialization. Although one of our goal is to

challenge the benefits of “breakdown & specialize”,

business processes are still a powerful tool to

describe a company.

A second key concept of “Enterprise Theory” is

“transaction costs”, as defined by Ronald Coase and

further developed by O. Williamson. One of the

main benefits of a company is to reduce transaction

costs. Thus, it is necessary to take transactions and

communication into account in our model. Our work

is equally influenced by the SCP model of E. Mason,

which separates structure, conduct and performance.

The importance of communication is a

cornerstone of our approach, as will be illustrated in

Section 4. We follow in the footsteps of March and

Simon who wrote “The capacity of an organization

to maintain a complex, highly interdependent

pattern of activity is limited in part by its capacity to

handle the communication required for

coordination” in (March and Simon, 1993). Their

book is focused on decision making, and the flow of

information within organizations that instructs,

informs, and support decision making processes.

Performance is defined as valued creation, as

defined by M. Porter in (Porter, 1980). Value

analysis is a common technique that is jointly used

with business process decomposition (for instance

with lean management). Starting with a value chain

that defines the position of a company within its

industrial ecosystem, value creation may be

attributed to business processes, through the

definition of work units (services, products, etc.).

Our work is strongly influenced by Mintzberg

(Mintzberg, 2009), who is famous for proposing

different model of enterprises and organizations.

Mintzberg has characterized different types of

organization (from hierarchical to matrix- or

networked-organization). Our overall model (cf.

Figure 2) is quite close in its structure with

Mintberg’s organizational model in (Gabarro, 2005).

2.2 Enterprise Models

Modelling the enterprise is necessary for the design

of information systems, as well as the formalization

of frameworks for total quality management (TQM).

Therefore, there already exist a number of semi-

formal models that describe what an enterprise is

and (partially) how it operates. Since our goal is to

propose a computational model which may also be

used for explanation and communication, we tried to

inherit as many traits as possible from existing

“enterprise models”. Here is a list of models which

are fully compatible with BPEM:

 A traditional view of a company is the

function/ business process matrix (Galbraith

1998). In this model, the company is seen as a

set of functional units, which operate business

processes. Each unit is responsible for a given

activity, the combination of which makes

processes that deliver value to customer.

 CEISAR is a research center dedicated to

Enterprise Architecture, which has developed

over the years a complete and elegant

“enterprise model” (CEISAR, 2008). The

cornerstone of this model is the business

process, a sequence of actions that produce

value (to the end customer). Business

Processes are operated by actors, who rely on

resources (managed with their own processes).

The CEISAR model describes the

organization of roles, actors and various

resources including information.

 BAPO is a model developed at Philips (Van

der Linden et all, 2004) for an ITEA project

ICORES 2012 - 1st International Conference on Operations Research and Enterprise Systems

related to software management. BAPO stands

for Business, Architecture, Processes and

Organization. The BAPO model provides with

ways to characterize the maturity of a

company across those four dimensions (in a

way similar to CMMI (Chrissis, Konrad and

Schum, 2003)).The process characterization

(predictability, repeatability, quantification) is

somehow similar to the model of Section 3.3.

The organizational model, although focused

on software organization, carries the key traits

that we use in BPEM.

 IDEA (Ludwig and Farcet, 2010) is a system

engineering methodology which includes an

“Enterprise Architecture model” based on

processes, capabilities, and roles. An

enterprise is a collection of capabilities and

roles that execute processes that rely on

services. The introduction of capabilities in

BPEM (3.2) is directly inspired from IDEA.

IDEA is itself inspired from the NATO

Architecture Framework (NAF).

 Similarly, the British Ministry of Defense

produced an Architecture Framework called

MODAF which includes an enterprise model

that is also based on capabilities, roles and

activities (MODAF, 2008).

 The French “club of business process owners”

produced in their collective book a rich

“enterprise model” that goes further than the

previously mentioned ones (Club de Pilotes de

Processus, 2008). In an approach that is

similar to CEISAR’s, the core of the model is

built around business processes and

information systems, but this core is itself

placed in a continuous improvement loop.

This loop models the reaction of the enterprise

according to its current performance and its

strategy, using the transformation levers such

as learning, innovation and re-engineering.

These models are conceptual models, which easily

lead, for instance, to UML models. They define

precisely the concepts which are necessary to

describe and understand how an enterprise works.

One of the most thorough efforts to produce an

“Enterprise Architecture Model” that includes an

“Enterprise Model” is the PRAXEME method which

is related in (Bonnet, Detavernier and Vauquier,

2009). A computational model relies on a conceptual

model, but goes further, to fully specify “how things

work”.

2.3 A Computational Model of

Enterprise Efficiency

A computational model allows the simulation of a

company’s internal working. To specify a

computational model, it is necessary to understand

which aspects of the functioning are deemed to be

interesting. BPEM has evolved from a number of

computational studies, aimed at characterizing issues

from management science. BPEM may be defined as

the “simplest common model” that supports these

kinds of studies. Namely, here are some of the issues

that we want to address through computer

simulation:

 value creation (especially with respect to SLA

– service level agreements), in the spirit of

(Reinertsen, 2009),

 reactivity to events and load distributions,

 lean management (pull vs. push, focus on lead

time reduction, WIP – work in progress –

management),

 management of communication flows,

 shape of the management organization (shape

of the hierarchical pyramid, process-function

matrix).

On the other hand, we tried to make BPEM “just

right”, using “Occam’s razor principle”, in order to

deliver computational experience that as close to

self-explanatory as possible. This requires to avoid

“generic efficiency parameter” (we shall see later

that there remains a few) and to keep away from

parts that are really difficult to model (in an

operational way). This is why BPEM is only

concerned with “short-term efficiency” and why we

leave aside issues such as:

 learning (as well as the capitalization of

knowledge, although BPEM shares many

concepts with (Nonaka, Toyama and Hirata,

2008)),

 long-term evolution & self-organization,

 resource management optimization (we shall

assume later on that resources are used

optimally).

Figure 1: BPEM perimeter from an EFQM perspective.

Leadership

Process

KPI

people

Policy &

Strategy

Partnership &

resources

Leadership

Customer

results

Society

results

Innovation & Learning

enablers

results

Left out

(long term)

Perimeter of

BPEM

BUSINESS PROCESS ENTERPRISE MODEL - Operations Research for Managing Business Process Communication and

Performance

To illustrate what is left aside, we use the EFQM

framework in the previous figure (EFQM is the

European Foundation for Quality Management).

The background of Figure 1 is the EFQM model of

enterprise functions. We see that BPEM (dashed

ellipse) is only concerned with the core of the

company’s activities. This approach is similar to

“enterprise simulation” models that are produced for

“serious games” software (Datar, 2000). Indeed, as

we shall see in section 5, a possible outcome of

BPEM is a scenario-exploration tool which helps

understanding the impact of organizational

architecture.

3 THE “BPEM” MODEL

3.1 Enterprise Model

The BPEM model is defined as the combination of

four components:

 The core of the enterprise is a set of business

processes that are triggered by external events

which represent customer requests. Business

processes entail a sequence of activities

supported by the enterprise’s capabilities.

 Each process run consumes a quantity of

resources and takes a certain amount of time,

both of which are explicitly modelled (cf. 3.3).

The value that is created by a successful

process termination is a function of time.

There exists an explicit SLA (service level

agreement) with an associated time window.

A delivery after the maximum allowed time

brings no value.

 The teams that combine human resources

(skills and time) and material resources are

glued together by a management organization

that performs the necessary decision-making.

This organization is the juxtaposition of two

common forms: a hierarchical pyramid that

links the CEO to all team leaders, as well as a

transverse “process” organization which is

dedicated to “horizontal” communication

(Galbraith, 1998).

 BPEM associates two kinds of information

flows to business processes, horizontal

(synchronization & transfer) and vertical

(reporting and management). Information

flows are measured with time (the time it

takes to process/understand a given piece of

information) and are generated according to

the business processes. Communication flows

are supported by a central component called

the “communication matrix” which represents

the sum of all communication channels (face-

to-face, phone, email, meetings, etc.).

This model is summarized by the figure below.

Notice that we have represented the information

system explicitly, but that it does not play any

specific role in the operational semantic that we

shall develop, where it is seen both as a resource and

part of the communication matrix. Making it visible

on this figure is useful for communication purposes

(cf. the link with Enterprise Architecture models

such as those of Section 2.2).

Figure 2: Overview of the BPEM model.

3.2 Organization Model

The next figure is a close-up on the organizational

model. The hierarchical part is a traditional

management pyramid which is defined by its height

and the average span (the number of subordinates

for each manager). These dimensions have a direct

impact on the propagation of information through

the hierarchical channel.

Figure 3: Organizational Model.

Communication Matrix

…

Information System

Business Processes

Market

Competitors

Requests

delivery

Management

Capabilities

Functional

mapping

…

Hierarchical

Management

…

n-1

Process (Transverse)

Management

Functional

mapping

ICORES 2012 - 1st International Conference on Operations Research and Enterprise Systems

The process management component is simply a

set of available man.hour of management to convey

information from one part of the process to the

other. It supports a transmission that is simpler

(hence faster) than going through hierarchical

management. This is a simple yet effective way of

testing the influence of matrix-style management.

There is no assumption about the matching

between processes and teams (units) which are the

leaf nodes of the hierarchical organization. From a

business process perspective, the enterprise is

organized into capabilities, which represent the

combination of resources and skills necessary to

perform a given activity. Figure 4 represents the

concept of capability, which may be seen as the

combination of functional domain (there are n

functional capabilities here) and resources (there are

p resources associated with the first capability). A

resource is an abstraction that covers human as well

as material resources. It is described with

skills/competencies, with an associated level. Hence,

each resource is a tuple (here, there are q skills). The

skills determine which resources may be used for

which activity (cf. next section). If a resource

possesses the right skills at the appropriate level, the

efficiency (the time it takes to perform the activity)

depends on the level difference (a high level

represents a form of “mastery”). This decomposition

of organizational units with skills is very similar to

(Nelson and Winter, 1982).

Figure 4: Capability Model.

The concept of “functional mapping” (the

correspondence matrix between units and

capabilities) is not part of the operational semantics

since we assume that resources may be located and

requested optimally (this is another instance of the

“Occam’s razor” principle: we found that

introducing an extra layer of complexity to represent

this correspondence was of no value since not

enough is known in the “real world of companies” to

calibrate such an extension to the BPEM model).

3.3 Business Process Modelling

Business Processes are one of the most common

concepts of management literature (Burlton, 2001).

We distinguish the concept of process pattern, which

is a model for how the work is executed, and process

instance, which is the actual sequence of activities

that produce value. A process pattern is a sequence

of activity pattern (this is a simplified view of what a

process is, but sufficient to our purpose here). The

activity pattern tells which capability is exercised,

which are the necessary skills and their associated

levels.

A process instance is generated by a customer’s

request. A request has a type (the process pattern),

an expected value V, and a quantitative indication of

how much work is required. It may be generic (the

amount of work is a property of the business pattern)

or specific (each request comes with a set of units

that tell how much work is required for each skill of

each activity – the unit is time, such as man.hour).

BPEM uses a stochastic generator to produce such

requests, with the ability to generate all types of

incoming work distribution, as well as all types of

workload distribution. This is a way to evaluate

companies’ flexibility and reactivity.

The value produced by a process is a simple

linear function (see Figure 5) defined by the time

window that defines the SLA of the customer’s

request. The maximal value V is obtained if the

service is delivered before the minimum date. It is

null if the maximum date has occurred and decreases

linearly between these two values.

Figure 5: Business Process Communication Model.

Another major simplification of BPEM

(Occam’s razor) is to evaluate quality with respect to

…

Capabilities

, .. , l

)

Set of Ressources

Activity

, .. , l

)

, .. , l

)

WBS

Σ(skill, level, units)

Efficiency= duration

= f( skill match)

Σ(skill,level)

Process pattern

Stochastic

Request

Model

Process Instance

value

time

WBS

Σ (units)

WBS

Σ (units)

WBS

Σ (units)

Variation in rate &

load

request

BUSINESS PROCESS ENTERPRISE MODEL - Operations Research for Managing Business Process Communication and

Performance

one unique dimension: time. It would be quite

logical to introduce a “quality” dimension to the

evaluation of business process execution. Quality

could actually depend on skill match and impact the

value that is being produced. We use a single-

versus-dual dimension approach because we found

that an additional dimension only adds complexity,

arbitrary equations and factors, and does not provide

any additional expressive power. On the other hand,

focusing of COD (Cost of Delay) is justified by

(Reinertsen, 2009) as the most salient metric for

business processes.

3.4 Business Process Communication

Model

Following the insights of March & Simon that were

presented in Section 2.1, a distinctive feature of

BPEM is to represent communication flows, which

come in two flavors: horizontal and vertical (cf.

Figure 5). BPEM does not represent inter-unit

communication flows, since it may be included in

the activity model, as one of the time-consuming

activities. It only focuses on enterprise-wide

information flows which interplay with the

company’s organization. The links between the

communication architecture and the structure of that

the company produces was pointed out a long time

ago by Melvin Conway in a famous article (Conway,

1968).

The importance of information flows vary

according to the enterprise’s domain.

Communications are more important with valued-

added immaterial services, such as software

development, that they are with industrial factory

production. In order to use BPEM as a

production/simulation model, we need to introduce

communication in a quantitative form with explicit

effect on performance and output.

Horizontal flows represent information that

needs to be exchanged between two consecutive

activities of one business process. A major feature of

modern work is that a significant amount of context

information must be exchanged between process

participants. This is precisely one of the trends that

goes against the principles of “break-down and

specialization” from Frederick Taylor. BPEM

associates a “synchronization and transfer flow” to

each pair (A,B) of consecutive activities within a

process, with the constraint that B cannot be

completed until the (A→B) transfer has been

completed.

Vertical flows represent the exchange of

information that is necessary between the teams and

their management, for reporting and decision

making. These “monitoring and management” flows

are associated to each activity from the business

processes.

Flows are generated at the same time business

processes requests are generated. A flow is mostly

characterized by the amount of time it takes to

process the information. This amount is a linear

function of the activity completion time (the

coefficient is a parameter of the model – cf. Section

4.1). We make no assumption about the

communication channel that will be use to support

the flow, but we also qualify the “span”, which is an

abstract indication of how many persons need to

receive the information.

Decision making in BPEM occurs in two forms,

which are related to two kinds of events (represented

with short arrows in the following figure). The first

kind represents a “production event”, when a given

activity requires significantly more resources than

what was initially anticipated. The model assumes

that the reaction (which requires a decision from the

management) occurs with the latency of the

associated vertical flow. The second type of event is

a change to the value of a process instance that is

currently run. The valuation change reflects an

“environmental change” (from the customer/market

or from the competition). The decision is a re-

prioritization of the process instance, which also

occurs after a delay (latency) which is derived from

the associated vertical flow. In other words, BPEM

generates a vertical flow associated to an activity.

The simulation software (cf. Section 4.1) schedules

this flow which produces a latency (the time to

process the associated information) which is taken as

the time it takes to react to events.

Figure 6: Business Process Communication Model.

Management

Process

Monitoring &

Management

Transfer &

Synchronize

WBS

Σ (units)

WBS

Σ (units)

WBS

Σ (units)

Environment

Event: Value

Variation

Event :

production

variation

ICORES 2012 - 1st International Conference on Operations Research and Enterprise Systems

4 APPLICATIONS

4.1 Managing Information Flows

The first application of BPEM is a simulation tool

designed to study the impact of communication

channels. We define four categories of

communication channels:

 Synchronous one-to-one communications,

such as face-to-face meetings or telephone

calls.

 Meetings, which support many-to-many

communications but require scheduling.

 Asynchronous communication methods, such

as email, blogging, micro-blogging, Intranet

document sharing, etc.

 Hierarchical scheduled communication, which

uses the manager-employee relationship and

the regularly scheduled face-to-face meetings.

We use a “channel communication model”

which defines how communication flow units may

be scheduled though each type of communication

channel. More precisely, each channel is described

though a number of parameters and equations that

define its latency (information propagation time), the

average number of recipients (when relevant), the

average loss factor (from which we derive the

average number of times that a message needs to be

sent to be understood). Our goal is to study under

which hypothesis which channels should be used

preferably, for a given company context.

The focus of our interest is a matrix which tells

the frequency of use of each communication channel

type (including the typical amount of time spent in

meetings), called the “channel policy”. The

assembly with the BPEM model is described in the

following figure. For a given company model

(processes, organization, context = request profile),

BPEM generates a load of work to be processed,

which comes from activities derived from the stream

of requests and communication flows associated to

these activities. These tasks are fed to a scheduler,

which assigns each task to the best matching

resource. It is possible to play with various

assignment schemes, but we usually simply select

the first available resource, with the better skill

match to separate ties. To schedule a communication

unit, the first step is to look into the “channel policy

matrix” to find which channel is used, and then use

the “communication channel model” to find when

the actual communication may take place. This

model is not an actual scheduler (where each hour of

each agent would be represented), it is a set of

equations that provide an approximate formula for

the latency that is observed for each communication

channel.

Rather than guessing the best channel policy, it

is easy to compute it as a fixed-point of a learning

process, using a simple local-optimization-loop such

as described in (Caseau, Silverstein and Laburthe,

2001). We incrementally modify the “channel

policy” matrix in order to maximize the value

generated by business processes. The result of the

simulation is, therefore, the best communication

channel usage, given the company description (using

BPEM) and the channel characterization.

Figure 7: Simulation of Information Flows.

The BPEM company description is itself the

combination of the company’s BPEM instance (its

processes, its capabilities, its organization) and the

“scenario” that contains the parameters that govern

the stochastic load generation as well as the event

generator. The BPEM instance usually does not

change, while we use different scenarios to evaluate

how the company reacts to changes in its

environment (more about this in the next section).

The following figure shows an example of the

output of such simulation. We used this simulation

tool extensively a few years ago to evaluate the

importance of various communication channels.

Figure 8: Typical result of simulation.

BPEM

Results

(value)

Learning

(optimization)

Activities to be

assigned to

resources

Communication

Channel

Model

Channel

Policies

Communication

Flow units to be

scheduled

Scheduler

=== Experiment E1 ====

done on Mon May 29 03:47:34 2006

scenario = S1 x 10 iterations

context = Telephony

-------------- summary for scenario S1 @ 12463s -------------------

chanel ASYNC : (17% of info) 13% usage [9%] 114839 load -> 134096 used (85%)

chanel SYNC : (28% of info) 19% usage [13%] 110938 load -> 217216 used (51%)

chanel MEET : (44% of info) 46% usage [12%] 245355 load -> 342275 used (71%)

chanel HF2F : (8% of info) 16% usage [13%] 41170 load -> 67112 used (61%)

unit CRM : 40% usage [5%]

unit IT : 44% usage [5%]

unit Mkt : 48% usage [3%]

unit Sales : 45% usage [3%]

unit Network : 46% usage [2%]

unit Com : 45% usage [5%]

process BillUsage : 1750k$ [95%]

process SellService : 565k$ [85%]

process LaunchService : 635k$ [73%]

process PromoteService : 1068k$ [84%]

total value = 2277k$ [dev 19%]

average rate of return = 140%$

average diameter = 28 [10%]

BUSINESS PROCESS ENTERPRISE MODEL - Operations Research for Managing Business Process Communication and

Performance

What we have shown (on one practical company

example) is that:

 Email is an efficient communication channel

(which does not mean that alternate electronic

tools cannot do an even better job). Removing

email and returning to slower forms of

asynchronous communication (as well as all

the classical synchronous ones) produces a

significant decrease in performance.

 Meetings, which are often criticized and/or

abused, play a key role. There seems to exist

an “optimal meeting rate”, too few meetings

represents missed opportunities, while too

many places a burden on the time that is left

open to do actual work. Obviously, but this is

worth repeating, this simulation places no

value on the creative and collaborative

opportunities that a meeting represents. It

simply evaluates meetings as one possible

form of communication.

Since these preliminary findings raise a lot of

questions, it became necessary to transform our

simulation platform into a “white box” (see the

following “perspectives” discussion).

4.2 Lean Management of Business

Processes

Before giving a second example of using BPEM to

investigate the benefits of lean management of

business processes, it is important to state that there

is much more in lean management than control

strategy. The part of lean management (Liker, 2001)

that we are able to address is only a tiny fraction of

what may be described as a work philosophy

(ranging from human resource principles, routines,

learning, to control, visual management, etc.). This

being said, one of the intriguing principles of lean

management is to “reduce the lead time” (the time it

takes to execute a process instance) to its minimal

value. Using the BPEM model, it is easy to contrast

two situations:

 A “regular situation”, where most resources

are optimized in such a way that their “usage

ratio” is close to 90%. This is what most

people consider to be a well-run company. In

the world of Information Systems, it is also a

desirable goal to demonstrate a high usage

ratio which shows that critical assets are

delivering as much value as possible.

 A “lean situation”, where the SLA are much

tighter (the allowed completion time is closer

to the optimal lead time), which requires more

resources. A “lean organization” is less

intuitive, since it keeps operating critical

resources at lower “usage ratio”. In this

experiment, the level of resource availability

(e.g. staffing level if we consider people) is

determined through simulation so that we

achieve the same level of SLA satisfaction

(say, 98%) in both cases. Obviously, finding

the optimal SLA satisfaction level is business-

dependant (each 1% gained brings incremental

value – cf. our value model in Section 3.3 –

but at a cost since more resources are

required).

We have made a number of computing

experiments, using both the afore-mentioned

simulation platform, as well as the simulation tool

described in (Caseau, 2005). Being able to use one

or the other is the consequence of the fact that

BPEM includes a generic BP evaluation model

(hence it is a useful tool to evaluate BPM – business

process management – strategies).

What we did is what was described earlier in the

paper: we subjected both “companies” (i.e., BPEM

instances) to different types of load: irregular, burst

of different kinds, as well as a “failure” scenario

when one resource is temporarily unavailable. The

following table indicates the results that we have

obtained in both cases (the result is the time

percentage when the SLA are met). The interesting

conclusion is that a BPEM model is able to

demonstrate in a spectacular way the reactivity and

adaptability benefits that have been claimed by

proponents of lean management. Somehow, this is

counter-intuitive since the “lean SLAs” are much

tighter (hence, one could think that they are harder

to keep)

Table 1: SLA satisfaction in lean/non-lean cases.

Scenario Non-lean Lean setting

Default 98% 98%

Irregular 84% 97%

Burst 80% 96%

Failure 78% 87%

4.3 Impact of Organizational

Dimensions onto Performance

A third application of BPEM comes from the ability

to evaluate the impact of organizational features on

performance. A similar warning may be given to the

one about lean management: the impact of

organizational architecture on performance comes

from more than the structural dimension of

management, which is one of four in (Bolman and

ICORES 2012 - 1st International Conference on Operations Research and Enterprise Systems

Deal, 1991). However, a BPEM model of a company

makes it easy to play with organizational parameters

and see the effects of the following:

 Flattening the hierarchical pyramid. This is

common advice amongst management

consultant, and computer simulation agrees

with them. For a complete discussion of the

impact of the span of control see (Perrow,

1986). Simulation shows that reducing the

depth shortens the communication paths and

increases the reactivity. This actually implies

that the hierarchical management

communication channel plays an important

role.

 Increasing or decreasing the number of

managers. Simulation shows that managers

play a key role in passing the necessary

information around, which is precisely what

the quote from March & Simon said. A

consequence is that, when the hierarchy is

flattened, the number of “transverse

managers”, attached to projects or processes,

should be increased.

 Specialization, defining many capabilities and

skills. Another interesting factor of the BPEM

model is that we may decide the level of

granularity with which skills are defined. The

same company may be described with the use

of a handful of capabilities, or with a much

more detailed analysis. Depending on the

communication load hypothesis (remember

that the amount of communication flow units

that are generated for each process is governed

by a parameter), we may observe the “cost of

specialization” and see that over-segmenting

creates a communication burden that washes

away the “benefits of specialization”.

These results are not generic (they are dependent on

the BPEM company configuration) but they

illustrate the claim made in Section 2 that BPEM is

capable of supporting management science analyses.

5 PERSPECTIVES

The software platform that was mentioned in Section

4.1 is called SIFOA (Simulation of Information

Flows and Organizational Architecture). The first

generation of the SIFOA simulation software was

able to produce interesting results (cf. previous

section) but its “black box” design made it very

difficult to communicate and explain these results.

Our goal is to build a “white box” version of this

“management simulation toolbox”. Making BPEM a

self-explanatory Enterprise Model is part of this

endeavor. The next step is to release the source code

that implements BPEM. The scheduler which we

mentioned in Section 4.1 is a key component since it

supports the investigation of various queuing

disciplines (Caseau, 2005), different type of flow

priorization and WIP constraints, such as kanban

(Reinertsen, 2009). Because of its stochastic request

model, BPEM is a suitable tool to explore all these

aspects of business process flow performance.

The communication model that we have used a

few years ago is quite simple (a few equations for

each communication channel) and raises many

questions. A follow-up project has been the study of

the influence of social networks (the underlying

structure of the communication channels) on

performance. For instance, we consider the

efficiency of meetings as a communication channel.

Meetings define an affiliation network (Wasserman

and Faust, 1994), the structure of which has a direct

influence on communication characteristics such as

latency, bandwidth, and loss (Nardi, 2005). Our

approach is to generate random graphs that represent

communication needs, and study which patterns of

meeting does a better job of handling these

communications. Our computational model is thus

composed of three parts: a random graph generator

(which is tuned to generate graphs with the

appropriate characteristics, since quite a few

characteristics of social networks are known), a “set-

coverage algorithm” which covers edges with hyper-

edges, and a simulation tool that measures

communication performance. Using this

computational model, we were able to characterize

latency (a useful finding for the simpler model of

4.1) and establish a few rules about the optimal

structure of a “set of meetings” (Caseau, 2011). A

next step is to use BPEM to generate communication

requests that reflect more closely the needs of a

company.

6 CONCLUSIONS

The contribution of this paper is an ongoing

computational model of the enterprise. By

construction, such a model is an open-ended

proposal, but we have found that BPEM is a reliable

and powerful core for many computational projects

that aim at shedding light on management science

issues. The conclusion of this work is threefold:

 There is a need for generic enterprise models

to bridge the fields of Operations Research

and Management Science. These models also

BUSINESS PROCESS ENTERPRISE MODEL - Operations Research for Managing Business Process Communication and

Performance

play a key role for Information Systems

(Winosky and Vogel, 2004). They provide a

foundation to lay out what Information

Systems are expected to do.

 Among those, we need computational models,

with complete operational semantics. Our

claim is that many of management sciences

issues are complex and will benefit from the

kind of analysis that one may perform through

simulation. As it was said in the introduction,

no such problems may be “solved” using a

computer model (each company is different

and too many critical factors are left aside in

such a model), but our experience shows that

insights may be gained about the role of the

structure of organization (Nadler, Gerstein and

Shaw, 1992).

 Managing information flows is a key part of

management science. This is an old idea

(March and Simon, 1993), but which strength

has increased in the 21

century, with the

increase of information overload and the

advent of the “Enterprise 2.0”.

Such a model may also be used for training

managers, using a “serious game” software

approach, both within the enterprise itself and in a

management school setting.

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ICORES 2012 - 1st International Conference on Operations Research and Enterprise Systems