Improving a knowledge- intense business process using knowledge management

In 2005, as part of my PhD research in the area of knowledge management and business process improvement, I was given the opportunity to conduct a case study in Tenix during which I evaluated the KBPI framework: a framework for Knowledge-Based Process Improvement.

The KBPI is an approach to business process improvement that considers process knowledge and its flow within the business process as the focal element of the improvement effort. It is based on the logical conjecture that in knowledge-intense business processes, improvements in the various knowledge processes will yield improvement in the various business process performance indicators.

The Tenix study was the third study, in a series of three. By that time, the KBPI had matured over the course of the previous year and thus the Tenix study consisted a “real life” application of the framework. As such, it yielded interesting lessons that informed the latest version of the KBPI based on which my PhD research was concluded

The proposed presentation will have two parts. In the first one, I will present the KBPI, making a quick reference to its underlying philosophical assumptions, and placing more emphasis in its methodology and relevant tools. In the second part, I will talk about how the KBPI was applied on a Tenix business process; the lessons I learned; the results and implications to the business process; and speculate on future directions for the framework.

Transcript

1. Business process management Improving a knowledge- intense business process using knowledge management Dr Peter Dalmaris Futureshock Research Dr Bill Hall Tenix Defence
2. 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 B Theoretical Research (literature, theory-building)
3. peter.dalmaris@futureshock.com.au Research objective n  To develop the Knowledge-Based Process Improvement framework. n  The development was based on theoretical research and case-study based research. n  Three case studies were completed. n  The first two were used to develop and test the framework – the last was used to validate it.
4. 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.
5. peter.dalmaris@futureshock.com.au What kind of companies? n  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.
6. peter.dalmaris@futureshock.com.au Overview of the KBPI n  Based on Karl Popper’s evolutionary epistemology. Answers the question “what is knowledge”. n  Describes a business process in terms of a formal ontology. n  Uses an analytical methodology for identifying areas for potential improvement.
7. peter.dalmaris@futureshock.com.au This presentation focuses on these components of the KBPI Overview of the KBPI Performance Evaluation Performance Analysis Process Modelling Improvement Synthesis Process Auditing 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 TOOLS Auditing and analysis tools facilitate process improvement tasks
8. peter.dalmaris@futureshock.com.au KBPI Process Ontology n  A formal language for describing a business process n  Used to built a formal model of a business process
9. 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).
10. peter.dalmaris@futureshock.com.au Abstract Classes
11. peter.dalmaris@futureshock.com.au How is this ontology used? n  Used to build tools that aid in capturing and analysing a business process instance. n  Such a tool was build, based on Protégé, a free ontology editor from Stanford Medical Informatics
12. peter.dalmaris@futureshock.com.au Improvement methodology n  Designed as a “how-to” guide for improving business processes. n  Defines the process improvement process. n  Composed of the Audit, Analysis, and Design stages.
13. 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
14. peter.dalmaris@futureshock.com.au Two levels of improvement Functions Members Knowledge Objects Knowledge Transformations Knowledge Tools Knowledge Paths Knowledge Tools Knowledge Transactions Function level Process level
15. peter.dalmaris@futureshock.com.au Process level improvement KP 1 KP 2 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. Operations on Knowledge Path class instances Operations on other class instances Error discovery and solution design KP 3 KP 4
16. peter.dalmaris@futureshock.com.au Function level improvement F1 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. Operations on Function class instances Operations on other class instances Error discovery and solution design
17. 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”.
18. peter.dalmaris@futureshock.com.au KBPI deliverables n  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. n  An AS COULD process report ¨ Addresses the areas of potential improvement identified in the AS IS report. ¨ Provides recommendations for improvement.
19. peter.dalmaris@futureshock.com.au Importance of KBPI framework n  Offers a systematic way for improving knowledge-intense business processes. n  As part of the improvement process: ¨ the organisation gains detailed knowledge of its own processes ¨ Changes to the process are rationalised based on their impact to the process
20. peter.dalmaris@futureshock.com.au Importance of KBPI framework n  For the first time (to the best of my knowledge), process knowledge becomes a central resource and consideration for process improvement. n  Execution is transparent and straight- forward. n  Rule-based analysis: happy to get my self out of the job. n  Predictable execution time.
21. peter.dalmaris@futureshock.com.au How KBPI helped companies n  Company #1 (low-tech): Discovered and documented numerous “knowledge bottlenecks” between company and contractors. n  Company #2 (high-tech): Discovered and documented knowledge system redundancies leading to overly complicated knowledge processes. n  Company #3 (Tenix, mid-tech): Discovered and documented poor utilisation of existing systems leading to waste of time and effort.
22. peter.dalmaris@futureshock.com.au Thank you n  Questions? n  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.