KNOWLEDGE MANAGEMENT SUPPORT FOR SYSTEM
ENGINEERING COMMUNITY
Olfa CHOURABI, Pr. Mohamed BEN AHMED
RIADI Research Laboratory, ENSI University, la Manouba, Tunis,TUNISIA
Yann POLLET
Chaire d’intégration de systèmes, CNAM, Paris, France
Keywords: Knowledge Management, Corporate Memory, Project Memory, Knowledge Processes, System Engineering
Abstract: Knowledge is recognized as a crucial resource in today’s Knowledge Intensive Organizations. Creating
effective Knowledge Management systems is one of the key success factors in Process Improvement
initiatives like the CMMI, SPICE etc. This contribution aims to provide a starting point for discussion on
how to design a Knowledge Management System to support System Engineering activities. After
motivating the problem domain, we introduce a conceptual model supporting continuous learning and reuse
of all kinds of experiences from the System Engineering domain and we present the underlying
methodology.
1 INTRODUCTION
In today’s highly dynamic environment the effective
use of all available Corporate Knowledge is
indispensable for success. Knowledge Management
(KM) tries to tackle this problem by providing
methods and tools to support the creation,
acquisition, capitalization, sharing and effective use
of knowledge in social settings. To this end, a
promising approach is the development of a
Corporate Memory (CM).
A Corporate Memory is an explicit, disembodied
and
persistent representation of knowledge and
information in an organization, in order to facilitate
their access and reuse by members of the
organization, for their tasks (Rabarijaona et al.,
2000). The main objective of building a Corporate
Memory Management System is the coherent
integration of this dispersed knowledge in a
corporation with the objective to promote
knowledge growth, promote knowledge
communication and in general preserve knowledge
within an organization (Steels, 1993).
System Engineering (SE) is a knowledge-intensive
pr
ocess encompassing requirement gathering,
design, development, testing, deployment,
maintenance, and project coordination and
management activities. The System Engineering
process could be defined as an iterative process of
problem resolution aiming at transforming user’s
requirements into a solution satisfying the
constraints of: functionality, cost, time and quality
(Meinadier, 2002).
It is highly unbelievable that all members of a
devel
opment team possess all the knowledge
required for the activities mentioned above. This
underlies the need for KM support to enable SE
organizations to:
-Effectively share domain expertise within
devel
opment team;
- Identify the requirements of a system;
-Reuse non-externalized knowledge of the
devel
opment team members;
-Bring together knowledge from distributed
i
ndividuals to form a repository of organizational
knowledge;
-Retain knowledge that would otherwise be lost due
t
o the loss of experienced staff; and Improve
organizational knowledge dissemination to support
quality and process improvement initiatives.
Meeting these demands requires fast, continuous
l
earning and reuse of past experiences within project
teams or Communities of Practice (
Wenger, 1998).
339
CHOURABI O., BEN AHMED M. and POLLET Y. (2005).
KNOWLEDGE MANAGEMENT SUPPORT FOR SYSTEM ENGINEERING COMMUNITY.
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 339-342
DOI: 10.5220/0002520803390342
Copyright
c
SciTePress
We here focus on system process improvement
through transfer of experience, to create what we
could call “learning System Engineering
organizations”.
In this paper we argue that an interesting approach
for System Engineering activity support lies in the
adoption of a process-oriented Knowledge
Management strategy (POKM) A POKM strategy
aims to provide project members with task-relevant
knowledge in corporate operational processes (
Maier
et al., 2001).. As opposed to a purely document-
centric perspective for KM, it examines what
processes create knowledge assets and how to
represent this contextual knowledge in a Corporate
Memory.
The paper is structured as following: the first section
introduces System Engineering domain and presents
our motivations for supporting SE Community with
a Knowledge Management System. The second
section gives an overview of existing Corporate
Memory construction approaches that are relevant
for SE domain. Based on existing approaches and
industrial needs, we propose a KM System for SE
community in section three. Finally potential issues
for future research directions are outlined.
2 CONTEXT AND MOTIVATION
2.1 System engineering domain
A cartography of SE processes according to the
Standard ISO 15288 includes:
Agreement Processes: Acquisition, and Supply
Enterprise Processes: Enterprise Environment
Management, Investment Management, System Life
Cycle Process Management, Resource Management,
and Quality Management
Project Processes: Project Planning, Project
Assessment, Project Control, Decision-making, Risk
Management, Configuration Management, and
Information Management
Technical Processes: Stakeholder Requirements
Definition, Requirements Analysis, Architectural
Design, Implementation, Integration, Verification,
Transition, Systems Analysis, Validation, Operation,
Maintenance, and Disposal
2.2 Motivation for KM in SE
We constantly gain experience with each
development project. “Knowledge emerges in work
practices, often being defined by the first project to
address the issue involved”. (Henninger, 2003). In
this section, we discuss the advantages of KM
support for SE community.
Various attempts have been made to Quality
Improvement in SE domain. According to the
standard IEEE 1220, continuous process
improvement in SE could be addressed through:
The application of an auto-evaluation program that
determines the degree of maturity in SE practices
like CMMI
Capitalization and transfer of experiences gained
with each development project (Meinadier, 2002).
We here focus on se Process Improvement through
knowledge capitalization. We propose a Corporate
Memory structure to support SE activities by
arguments that create a result. Expressions can be
used as values in any command. providing pieces of
relevant experiences, competencies, tools,
methodologies, process models and reusable
components.
3 RELATED WORKS AND
POSITIONING IN THE STATE
OF THE ART
Knowledge Management is a very complex problem
and can be tackled from several viewpoints: socio-
organizational, financial and economical, technical,
human and legal (Barthes, 1996)
One approach for managing knowledge in
organizations is to set up a corporate memory. The
corporate memory is in charge of insuring persistent
storage, indexing and diffusion of relevant pieces of
knowledge within the organization. (Gandon, 2002)
In the following, we present an overview of some
relevant methodologies to construct Corporate
Memories. (Dieng et al., 1998)
Non Computational Corporate Memory ;
Document-based Corporate Memory (Dieng et
al., 1998); Competence Management systems
(Rus et al., 2001) ; Knowledge-based Corporate
Memory (Strohmaier, 2003) ; Case-based
Corporate Memory ; Process-oriented Corporate
Memory (Abecker et al., 2001),
4 CONCEPTUAL MODEL FOR
SYSTEM ENGINEERING
KNOWLEDGE MANAGEMENT
SYSTEM (SEKMS)
4.1 Basic principles of SEKMS
This section discusses basic principles guiding the
design of our approach
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Process centered approach for KM: the
hypothesis stating that Knowledge is tied to action
leads us to consider knowledge capture inside
processes. We argue that knowledge management
activities cannot be separated from other business
processes, because knowledge emerges within
business processes (Maier, 2001
). Thus, knowledge
that emerges in a process can be stored together with
its process context.
Multi-perspective approach: typically, corporate
memories are supported by centralized approaches.
Thus, knowledge about people, knowledge about
processes, and domain knowledge are represented
and maintained as information in global repositories
which serve as sources to meet a knowledge
worker’s knowledge needs.(Abecker, 2004). We
propose to better balance between the needs of
smaller organizational units and the more global KM
concerns, by providing each community the
capability of managing its own knowledge. We
propose to design a distributed organizational
memory as a constellation of set specialized OMs.
Proactive delivery of relevant knowledge: a
proactive knowledge delivery methodology
addresses the dissemination of knowledge assets in a
just-in- time manner in the context of its applicable
targeted processes. (Abecker,2004) It allows
knowledge retrieval when it is applicable to the task
in which a user is currently engaged. To achieve this
goal, we propose to adopt intelligent agent
technology to procure crucial knowledge to users.
4.2 Architecture of SEKMS
In the context of SE activities, both informal
knowledge (such as documents) and formal
knowledge (such knowledge represented by a
knowledge model) are needed. Therefore our
approach aims to design a CM architecture where
the CM can be composed of different sorts of
memories: documents, knowledge bases etc. And
thus, combining several techniques for CM
construction is needed. Figure 1 shows the different
components of the SE Knowledge Management
System (SEKMS). We distinguish three principal
perspectives.
Business process perspective: provides the
description of the real business process.
In a business process, knowledge exists in the form
of data and information in combination with
experience, communication, reflection, expertise,
techniques and cognitive abilities.
Our approach begins with a careful analysis of the
existing business processes, in order to identify what
kind of knowledge is created/used by whom in the
context of his daily work. This analyze includes both
the explicit and the tacit form of knowledge. It
includes the identification of tools, documents,
models, -defined roles, and codified procedures. But
it also includes all the implicit relations, the tacit
conventions, the recognizable intuitions, the specific
perceptions, the embodied understandings, the
underlying assumptions, the shared worldviews, the
sequences of collaboration, which may exist in
implicit form, connected to the activities of
employees in the context of running activities. The
output of this business process analyze, is a set of
raw experiences captured and then enriched with
context attributes.
Knowledge structure perspective: Provides a
formal structure to describe knowledge assets. A
common approach to model knowledge in corporate
memory is to take document-centric approach. As
SE experiences are complex and diverse, our CM is
defined in term of knowledge components (KC)
instead of documents. A KC can be a process model
fragment, a best practice, a lesson learned, a product
model, a requirement, or comment on a business
process et. Providing that we work on different KC
structures, we propose to rely on several knowledge
representation techniques for representing KC in the
CM. Thus, we propose using: conceptual graphs,
lattices, semi-structured documents etc., in order to
provide more flexibility and consisted ness in KC
representation.
Meta Knowledge Model perspective The Meta
knowledge model is a unified view that structures
the content of the CM. It provides explicit formal
specifications of the concepts and terms in the SE
domain and the relationships among them. This meta
knowledge model is not yet well specified, we plan
to generate it through synergies between standards
Meta models proposed in SE literature.
KNOWLEDGE MANAGEMENT SUPPORT FOR SYSTEM ENGINEERING COMMUNITY
341
Business process analysis / Assessment of Knowledge
Needed/generated in the context of SE activities
Business
p
rocess
p
ers
p
ective
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Figure 1: Conceptual model of SEKMS
Raw ex
p
erience ca
p
tured
Context modeling: (phase V cycle, activity, process, role, out put, input,
constraints)
S
y
stem En
g
ineerin
g
unified ontolo
gy
(
meta-model
)
Meta knowledge perspective
Model perspective
Categorize knowledge
components
Explicit: technical documents,
process fragment, lessons
learned, requirement statement
Tacit: know how, design
rationale, informal
collaboration
Formalize knowledge
Conceptual graphs, lattices,
semi structured documents,
cases. Semantic annotations,
process models,
Knowledge Mining
Clustering
/Generalizing/
Refinement
Knowledge
Structure
perspective
KC
C
KC
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