Role-Driven Knowledge Management Implementation

Lessons Learned

Alexander Smirnov

1,2

and Nikolay Shilov

SPIIRAS, 39, 14 Line, 199178, St.Petersburg, Russia

ITMO University, 49, Kronverkskiy pr., 197101, St. Petersburg, Russia

Keywords: Knowledge Management, Role, Ontology, Lesson.

Abstract: Today, companies have to deeply transform both their product development structure and the structure of

their business processes. Knowledge management has shown its efficient applicability in this area.

However, implementation of such complex changes in large companies faces many difficulties. The paper

presents lessons learned from implementing knowledge management in two collaboration experiences with

industrial partners. The role-based knowledge management approach used in these collaboration

experiences is described. It relies on the ontological knowledge representation for its sharing and considers

the workflows from perspectives of different roles. The major steps of the approach are described. The

observations made during the implementation of the approach address problems related to the

implementation and generic principles that helped to overcome the problems.

1 INTRODUCTION

Modern market opportunities require companies to

introduce new strategic objectives and tools. They

have to build strategies that provide maximum

flexibility and can optimally respond to changes in

their environment (Gunasekaran et al., 2008;

Gunasekaran and Ngai, 2005; Christopher and

Towill, 2001). In order to cope with these

requirements, companies need to deeply transform

both their product development structure and the

structure of their business processes.

Knowledge management has shown its efficient

applicability in this area. It is a complex cooperative

network-centric process to support multi-object and

multi-disciplinary areas including modelling, design,

knowledge representation and acquisition, decision

support and supporting environment (Liu et al.,

2004).

Due to the modern trends in knowledge-

dominated economy from “capital-intensive

business environment” to “intelligence-intensive

business environment” and from “product push”

strategies to a “consumer pull” management

companies accumulate large volumes of knowledge

usually referred to as corporate knowledge. An

efficient approach was required in order to provide a

mechanism which allows for decision maker to have

required knowledge “at hand” in an appropriate form

for making correct and timely decisions, what in turn

will make possible for a manufacturing system to

quickly react on changes in its environment and to

be flexible enough.

A number of efforts have been done in the area

of sharing information and processes between

applications, people and companies. However

knowledge sharing / exchange required more than

this. It required information coordination and

repository sharing with regard to semantics. This has

led to appearance of the Corporate Knowledge

Management (CKM) that can be defined as a

complex set of relations between people, processes

and technology bound together with the cultural

norms, like mentoring and knowledge sharing.

However, implementation of such complex

changes in large companies faces many difficulties:

business process cannot be stopped to switch

between old and new workflows; old and new

software systems have to be supported at the same

time; the range of products, which are already in the

markets, has to be maintained in parallel with new

products, etc. Another problem is that it is difficult

to estimate in advance which solutions and

workflow would be efficient and convenient for the

employees. Hence, just following existing

knowledge management implementation guidelines

Smirnov A. and Shilov N..

Role-Driven Knowledge Management Implementation - Lessons Learned.

DOI: 10.5220/0005034700360043

In Proceedings of the International Conference on Knowledge Management and Information Sharing (KMIS-2014), pages 36-43

ISBN: 978-989-758-050-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

is not possible (e.g. Oluikpe, 2012), and this process

has to be and iterative and interactive.

The paper presents lessons learned from

implementing knowledge management in two

collaboration experiences. The first one is a result of

a long-term joint work with Festo AG&Co KG, an

industrial company that has more than 300 000

customers in 176 countries supported by more than

52 companies worldwide with more than 250 branch

offices and authorised agencies in further 36

countries (Oroszi et al., 2009; Smirnov et al.,

2013a). Some early steps of this collaboration

related to implementation of the product codification

system have been reported in (Smirnov et al., 2011).

The other one is a result of project carried out for

Ford Motor Company aimed to describing

production processes and production facilities (Golm

and Smirnov, 2000a; Golm and Smirnov, 2000b).

The paper is structured as follows. Section 2

describes the role-driven approach to knowledge

management. Section 3 introduces the

implementation of the approach. The lessons learned

are concluding the paper.

2 ROLE-BASED KNOWLEDGE

MANAGEMENT

Efficient knowledge management assumes deriving

and processing not only internal knowledge but also

knowledge from various sources including (adapted

from Botkin, 1999):

 customer needs, perceptions, and motivations,

etc.;

 expertise within and across the supply chain;

 best practices, technology intelligence and

forecasting, systemic innovation, etc.;

 products in the marketplace, who is buying

them and why, what prices they are selling at;

 what competitors are selling now and what they

are planning to sell in the future.

Knowledge management in a global companies

requires interoperability at both technical and

semantic levels. The interoperability at the technical

level is addressed in a number of research efforts. It

is usually represented by such approaches as e.g.,

SOA (Service-Oriented Architecture) (SOA, 2014)

and is based on the appropriate standards like

WSDL and SOAP (Web services explained, 2014).

The semantic level of interoperability in the

production network is also paid significant attention.

As an example (probably the most widely known),

the Semantic Web initiative is worth mentioning

(Semantic Web, 2014). The Semantic Web relies on

application of ontologies for knowledge and

terminology description.

The approach used in the presented work

(Smirnov, Levashova and Shilov, 2009) relies on the

ontological knowledge representation for its sharing.

The ontology describes common entities of the

company’s knowledge and relationships between

them. Besides, the dynamic nature of the company

requires considering the current situation in order to

provide for actual knowledge or information. For

this purpose, the idea of contexts is used. Context

represents additional information that helps to

identify specifics of the current transaction. It

defines a narrow domain that the user of the

knowledge management platform works with. One

more important aspect covered by the approach is

the competence profiling. Profiles contain such

information as the network member’s capabilities

and capacities, terminological specifics, preferred

ways of interaction, etc.

The overall conceptual model of the knowledge

management platform would be formed as follows.

The approach is based on the idea that knowledge of

the company can be represented by two levels for

the purposes of its processing in information

systems. The knowledge of the first level (structural

knowledge) is described by a common ontology.

The ontology forms the core of the platform. In

order for the ontology to be of reasonable size it

includes only most generic common entities.

Ontologies provide a common way of knowledge

representation for its further processing. They have

shown their usability for this type of tasks (e.g.,

Bradfield et al., 2007; Chan and Yu, 2007; Patil et

al., 2005). The common ontology is used to solve

the problem of knowledge heterogeneity and enables

interoperability between heterogeneous information

sources due to provision of their common semantics

and terminology (Uschold and Grüninger, 1996). It

describes all the products (produced and to be

produced), their features (existing and possible),

production processes and production equipment.

This ontology is used in a number of different

workflows. The tools are interoperable due to the

usage of the common ontology and database.

Knowledge map connects the ontology with

different knowledge sources of the company.

Knowledge represented by the second level is an

instantiation of the first level knowledge.

For modern decision support systems,

personalized support is important. Usually, it is

based on application of the profiling technology.

Each user (human or an information system) works

on a particular problem or scenario represented via a

context that may be characterised by a particular

customer order, its time, requirements, etc.

Role-DrivenKnowledgeManagementImplementation-LessonsLearned

Figure 1: Role-based perspectives of the common ontology.

Figure 2: Approach illustration.

The second idea of the approach is to consider

the workflows from perspectives of different roles.

Research efforts in the area of information

logistics show information and knowledge needs of

a particular emploee depend on his/her tasks and

responsibilities (Lundqvist, 2007). This is also

confirmed in other works, e.g.: “Information demand

depends on the role and tasks an entity has within a

larger organization. If the role and/or the tasks

change, so too will the demand" (Persson and Stirna,

2009).

Role-based approaches have shown their

efficiency in such adjacent areas as ontology

modelling (Fox et al., 1995), competence modelling

(Tarasov and Sandkuhl, 2011), etc.

Based on the experiences from two industrial

case studies, the following perspectives have been

identified: product manager & product engineer

(from the first case study), and production manager

and production engineer (from the second case

study). Each of them works with his/her part of the

User 1

(product manager)

User 2

(product engineer)

User 3

(production engineer)

User 4

(production manager)

Workflow 1

Workflow 2

Workflow 3

Workflow 4

Workflows

Problem

domain

Common

ontology

User roles

Tasks

Knowledge-based

workflows

Reference

Information / knowled

e flow

Knowledge /

information

storages

Knowledge

map

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

common ontology, with the ontology parts

overlapping (Figure 1).

The approach assumes the following steps for

knowledge management implementation (Figure 2):

1. Structural information about workflows and

the problem domain is collected and described

in the common ontology.

2. User roles are identified and their relevant

parts of the common ontology are defined.

3. Tasks assigned to the identified roles are

defined.

4. Knowledge required for performing identified

tasks is defined.

5. Based on the identified roles, tasks and

knowledge, new knowledge-based workflows

are defined.

6. Corresponding role-based knowledge support

of the workflows is provided based on the

usage of the common ontology and

knowledge / information storages.

This process repeats for each particular role,

with some knowledge being reused between roles.

The next section describes the implementation of

the developed approach.

3 IMPLEMENTATION

The first step of the approach implementation is

creation of the ontology. This operation was done

automatically based on existing documents and

defined rules of the model building. The resulting

ontology consists of more than 1000 classes

organized into a four level taxonomy, which is based

on the VDMA (Verband Deutscher Maschinen – und

Anlagenbau, German Engineering Federation)

classification (VDMA, 2014). Taxonomical

relationships support inheritance that makes it

possible to define more common attributes for

higher level classes and inherit them for lower level

subclasses. The same taxonomy is used in the

company's PDM and ERP systems.

For each product family (class) a set of

properties (attributes) is defined, and for each

property, its possible values and their codes are

defined as well. The lexicon of properties is

ontology-wide, and as a result, the values can be

reused for different families.

Then, based on the developed ontology, the

complex product modelling design and system has

been implemented. Complex product description

consists of two major parts: product components and

rules. Complex product components can be the

following: simple products, other complex products,

and application data. The set of characteristics of the

complex product is a union of characteristics of its

components. The rules of the complex products are

union of the rules of its components plus extra rules.

Application data is an auxiliary component, which is

used for introduction of some additional

characteristics and requirements to the product (for

example, operating temperatures, certification,

electrical connection, etc.). They affect availability

and compatibility of certain components and

features via defined rules.

At the second step, the major roles, whose

workflows were addressed by knowledge

management implementation, have been identified.

As it was mentioned earlier, the roles are product

manager, product engineer, production manager, and

production engineer.

Then, at steps 3 and 4, their tasks and needs are

analysed. The product manager works with

customers and their needs. Usually, the parameters

and terminology the customer operates with differ

from those, operated by product engineers. For this

reason, a mapping between the customer needs and

internal product requirements is needed. Based on

these requirements new products, product

modifications or new product systems can be

engineered for future production.

For the goal of production process description

the approach distinguishes between virtual and real

modules. In accordance with the approach, the

virtual modules are used for grouping technological

operations from the production engineer’s point of

view. The real modules represent actual production

equipment (machines) at the level of production

manager.

At steps 5 and 6 the knowledge-based workflows

are defined and corresponding supporting tools are

built.

A system (called DESO) has been developed for

a structured storage of the knowledge about data

domain, and for its further processing. Depending on

the particular tasks it can be supplemented by other

components (tools) intended for solution of specific

problems using the knowledge, contained in the

common storage. In the time being the tools for the

enterprise production program planning (Goal), for

the production modules designing (Module), and for

the industrial resources distribution and planning

(Goal and Module) have been developed.

The system supporting the levels of production

engineer and production manager was originally

focused on the early stages of planning procedure of

investment calculation and determination for the

Role-DrivenKnowledgeManagementImplementation-LessonsLearned

(a) derivation of production scenarios,

(b) determination of investment cost, (c) assignment

of locations and (d) estimation of product variable

cost. The system aims at providing a knowledge

platform enabling manufacturing enterprises to

achieve reduced lead time and reduced cost based on

customer requirements through customer satisfaction

by means of improved availability, communication

and quality of product information. It follows a

decentralized method for intelligent knowledge and

solutions access. Configuring process incorporates

the following features: order-free selection, limits of

resources, optimization (minimization or

maximization), default values, freedom to make

changes in global production network model.

This system distinguishes between virtual and

real modules. In accordance with the approach, the

virtual modules are used for grouping technological

operations from the production engineer’s point of

view. The real modules stand for the real equipment

used for the actual production. The production

engineer sets correspondences between the

technological operations of virtual modules and

machines of real modules.

It also includes a tool for sequences of

operations for a part production, possible

alternatives of production distribution etc. This tool

supports inheriting subordinate objects, what allows

creating of complex hierarchical systems of objects,

and using templates automating the user’s work.

The main entities of the approach

implementation and identified roles are presented in

Figure 3. The figure also identifies tools

implemented in the first case study.

The developed so far integrated knowledge

management workflow for the first case study

(addressing roles of product engineer and product

manager) is presented in Figure 4 and is described in

detail in (Smirnov et al., 2013b). At the first stage,

the major product ontology is filled with generic

classifications of products and their components.

This is done via two tools (NOC and CONCode)

since recently developed order code scheme differs

from that used before. However, since multiple

customers are used to operate with the old

classification it has to be maintained.

At the next stage, the product managers and

product engineers design new products and solutions

based on existing products and components (the

CONSys tool). If a new product or component is

needed, its implementation can be requested from

the order code structure team. Together with new

products and solutions, the appropriate rules and

conditions are designed as well (e.g., acceptable

load, size, compatibility constraints, etc.).

When the configuration model is finished it is

proposed to the customers so that they could

configure required products and solutions

themselves or with assistance of product managers

(the CONFig tool).

4 LESSONS LEARNED

During the work on the mentioned case studies the

following observations related to knowledge

management implementation in companies have

been made:

 Engineers and managers are concentrated on

their work and cannot pay enough attention to

additional tasks related to trying new

knowledge-based workflows. This was in a

higher degree applicable to the product

managers and product engineers. At the levels

of production engineers and production

managers, this issue was less obvious, because

the “experimental” knowledge-based

production planning could be done in parallel

with the actual one.

 A potential target knowledge management

group has to be formed. It has to consist of

people volunteering to assist in implementing

knowledge management in the company.

These people have to be experts in their roles

and in several other roles, which would re-use

some of the knowledge of this role. They will

be involved into the processes of building the

initial common ontology and implementing

knowledge-based workflows for their role and

several other roles thus slowly involving other

roles into the process of knowledge

management implementation.

 Role-based approach makes it possible to

implement knowledge management

incrementally, with initiative coming from

employees. E.g., an experimental knowledge-

based support of one workflow could be

implemented for one user role letting the users

estimate its efficiency and convenience. Then,

workflows reusing some of the knowledge of

the experimental workflow can be added, etc.

Representatives of other roles seeing the

improvements of the implemented knowledge-

based workflows also wish to join and actively

participate in the identification of the

knowledge needed for their workflows and

further turning their workflows into the

knowledge-based ones.

KMIS2014-InternationalConferenceonKnowledgeManagementandInformationSharing

Figure 3: Role-driven knowledge management implementation.

Role-DrivenKnowledgeManagementImplementation-LessonsLearned

Figure 4: Integrated knowledge-based workflow.

ACKNOWLEDGEMENTS

The research was supported partly by projects

funded by grants # 13-07-13159, # 13-07-12095,

# 14-07-00345, # 12-07-00298, and # 12-07-00302

of the Russian Foundation for Basic Research,

project 213 (program 15) of the Presidium of the

Russian Academy of Sciences, and project #2.2 of

the basic research program “Intelligent information

technologies, system analysis and automation” of the

Nanotechnology and Information technology

Department of the Russian Academy of Sciences.

This work was also partially financially supported

by Government of Russian Federation, Grant 074-

U01.

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