Tenix Engineering Conference 06 Web Version
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Business process management
Improving a knowledge-intense business process using knowledge management
Dr Peter Dalmaris
Futureshock Research
Dr Bill Hall
Tenix Defence
peter.dalmaris@futureshock.com.au
About my project
Company C
A framework for the improvement of knowledge-intense business processes.
Validation of the framework.
Company A
Company BTheoretical Research (literature,
theory-building)
peter.dalmaris@futureshock.com.au
Research objective To develop the Knowledge-Based
Process Improvement framework. The development was based on
theoretical research and case-study based research.
Three case studies were completed. The first two were used to develop
and test the framework – the last was used to validate it.
peter.dalmaris@futureshock.com.au
Case study objective
To apply, test, and improve a framework for the improvement of knowledge-intense business processes using knowledge management.
peter.dalmaris@futureshock.com.au
What kind of companies?
Companies that are knowledge-intense.
Definition: A knowledge-intense organisation is one that depends on business processes that are high in knowledge intensity and complexity.
peter.dalmaris@futureshock.com.au
Overview of the KBPI
Based on Karl Popper’s evolutionary epistemology. Answers the question “what is knowledge”.
Describes a business process in terms of a formal ontology.
Uses an analytical methodology for identifying areas for potential improvement.
peter.dalmaris@futureshock.com.au
This presentation focuses on these components of the KBPI
Overview of the KBPI
Per
form
ance
Eva
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Per
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Ana
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sP
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odel
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Impr
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ynth
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Pro
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Aud
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IMPROVEMENT METHODOLOGY
PROCESS ONTOLOGY
EPISTEMOLOGY Fundamental assumptions about knowledge
Explicit specification of the concept of “Business Process”
A guide to the improvement process
Improvement methodology components
TOOLSAuditing and analysis tools facilitate process improvement tasks
peter.dalmaris@futureshock.com.au
KBPI Process Ontology
A formal language for describing a business process
Used to built a formal model of a business process
peter.dalmaris@futureshock.com.au
Normal Classes
The KBPI Process Ontology is composed of the top-level normal classes and eight abstract classes (next slide).
peter.dalmaris@futureshock.com.au
Abstract Classes
peter.dalmaris@futureshock.com.au
How is this ontology used?
Used to build tools that aid in capturing and analysing a business process instance.
Such a tool was build, based on Protégé, a free ontology editor from Stanford Medical Informatics
peter.dalmaris@futureshock.com.au
Improvement methodology
Designed as a “how-to” guide for improving business processes.
Defines the process improvement process.
Composed of the Audit, Analysis, and Design stages.
peter.dalmaris@futureshock.com.au
Improvement methodology
Knowledge Tools
Knowledge Paths
Knowledge Transactions
Identify potential improvement areas¦(desired process
performance)
Process Members
Environment: constraints, policies, targets
Audit:Probing, current state of the process (AS IS)
Design:Result (AS COULD)
Analysis:Improvement
improvement configuration of process
classes
Functions
Knowledge Objects
Knowledge Transformations
peter.dalmaris@futureshock.com.au
Two levels of improvement
FunctionsMembersKnowledge ObjectsKnowledge TransformationsKnowledge Tools
Knowledge PathsKnowledge ToolsKnowledge Transactions
Function level
Process level
peter.dalmaris@futureshock.com.au
Process level improvementKP1
KP2
KT TR
E
KX
KP1: Find all Knowledge Paths
KP2: Designate performance descriptors.KP3: Determine current performance.KP4: Determine desired performance.
For each Knowledge Path class instance:
For each of Knowledge Transaction and Knowledge Tool class instances :
KT: Define the Knowledge Transformation instance.
TR: Define the Knowledge Transaction instance.
For each of KT, TR, evaluate their current status and the impact of their performance on the Knowledge Path performance.
For each non-alignment:
E: Find the likely causes.
S: Design a possible solution.
Ope
ratio
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ath
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Ope
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Err
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KP3
KP4
peter.dalmaris@futureshock.com.au
Function level improvementF1
F4
F3
F2
PM KO
KT
KX
E
KX
F1: Find all knowledge intensive functions
F2: Designate performance descriptors.F3: Determine current performance.F4: Determine desired performance.
For each Function class instance:
For each of Process member, Knowledge Object, Knowledge Transformation and Knowledge Tool class instances :
KT: Define the Knowledge Tool instance.
KO: Define the Knowledge Object instance.
KX: Define the Knowledge Transformation instance.
PM: Define the Process Member instance. Determine their Critical Knowledge Success Factors.
For each of KT, KO, KX, PM, evaluate their current status and the impact of their performance on the Function performance.
For each non-alignment:
E: Find the likely causes.
S: Design a possible solution.
Op
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on
Fu
nctio
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inst
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sO
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sta
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Err
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esig
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peter.dalmaris@futureshock.com.au
Visual analysis
Label shows the actual format with which knowledge is encoded, the actual system utilised for its transport, and their general category
The red line encloses process tasks that are involved in the processing of the same knowledge object. This is generally called a “knowledge path”.
peter.dalmaris@futureshock.com.au
KBPI deliverables An AS IS process report
Provides a detailed description of the business process.
Identifies areas of potential improvement at the process level and function level.
An AS COULD process reportAddresses the areas of potential
improvement identified in the AS IS report.
Provides recommendations for improvement.
peter.dalmaris@futureshock.com.au
Importance of KBPI framework
Offers a systematic way for improving knowledge-intense business processes.
As part of the improvement process: the organisation gains detailed
knowledge of its own processesChanges to the process are rationalised
based on their impact to the process
peter.dalmaris@futureshock.com.au
Importance of KBPI framework For the first time (to the best of my
knowledge), process knowledge becomes a central resource and consideration for process improvement.
Execution is transparent and straight-forward.
Rule-based analysis: happy to get my self out of the job.
Predictable execution time.
peter.dalmaris@futureshock.com.au
How KBPI helped companies Company #1 (low-tech): Discovered and
documented numerous “knowledge bottlenecks” between company and contractors.
Company #2 (high-tech): Discovered and documented knowledge system redundancies leading to overly complicated knowledge processes.
Company #3 (Tenix, mid-tech): Discovered and documented poor utilisation of existing systems leading to waste of time and effort.
peter.dalmaris@futureshock.com.au
Thank you
Questions? Also visit http://www.futureshock.com.au
for a long paper of this presentation
Dr Peter Dalmaris is a lecturer and consultant based in Sydney. He has a PhD in Knowledge Management and Business Process Management, a Bachelors in Electrical Engineering, a Masters in Information Systems Engineering, and a Masters in Knowledge Management.
Recently he started Futureshock Research, a Sydney company that seeks to continue the development of the KBPI, introduce related products (especially software) to the market, and provide consultancy services.
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