Proactive Knowledge Distribution for Agile Processes

1. Proactive Knowledge Distribution forAgile Processes Dr. Rosina WeberCollege of Information Science & Technology Drexel University, Philadelphia, USA

2. Outline • Knowledge Distribution and Knowledge Management (KM) • Technological Process Oriented KM • Motivation for Monitored Distribution (MD) • MD is an approach for proactive distribution of knowledge artifacts • Direct and Indirect MD • MD and Agile Organizations • Future Work

3. Knowledge Distribution and KM • Learning from the past • Managing intellectual assets • Organizations manage through communication • Organizations attain their objectives by communication and coordination as a means of learning, exchanging and accumulating knowledge (Atwood, 2002) • Knowledge distribution is an enabler of knowledge sharing and thus of KM

4. Motivation for Process Oriented KM • KM solutions should be integrated to existing processes (Aha et al., 1999) • Role-based organization in agile methods • KM solutions for agile methods for software development should be incorporated in the programming language environment

5. Motivation for TechnologicalProcess Oriented KM • Large and distributed organizations • Highly automated organizations • Whose processes are modeled in enterprise wide information systems • Real world problems require leveraging power

6. Motivation for Monitored Distribution • KM solutions that are technologically supported and process-oriented • KM solutions have to include people, technology and processes (Abecker, Decker, Maurer, 2000). • The impact of knowledge in resulting processes has to be measured (Ahn & Chang, 2002) • Knowledge should be distributed when and where it is needed because this is when users reuse it • Existing knowledge repositories are not being used

7. Monitored Distribution (MD) is an organizational approach for the proactive distribution of knowledge artifacts

8. MD: characteristics • Focuses on distribution and reuse steps in a POKM approach • Distribution of knowledge artifacts • Tightly integrated to targeted processes • Measurable knowledge • Measurable impact • Proactive distribution • Shifts burden from user to the system • Standalone tools place the distribution burden on the user discouraging sharing • Distributes knowledge when and where it is needed with applicability-oriented retrieval

9. Basic Knowledge Cycle Weber & Kaplan, 2003

10. MD: distribution & reuse • Focuses on distribution and reuse steps in a POKM approach

11. MD: distribution & reuse • Focuses on distribution and reuse steps in a POKM approach

12. Monitored Distribution Processes

13. MD: distributes knowledge artifacts • Distribution of knowledge artifacts • Knowledge artifact most used today:lessons-learned

14. Some organizations that adopt lessons-learned Weber, Aha, Becerra-Fernandez, 2001

15. Lessons-learned: definition A lesson learned is knowledge gained by experience. The experience may be positive or negative. A lesson must have an impact on operations. A lesson must be applicable by identifying a specific design or decision that generates a real or assumed impact in its applicable task or process. (By Secchi et al., 1999 )

16. Regular Process Expected decision process i Expected impact

17. decision 1 decision 2 decision 3 decision n lesson 1ip lesson 2p lesson 3p lesson np positive negative lesson 1in lesson 2n lesson 3n lesson nn neutral no lesson no lesson no lesson no lesson Lesson, process, impact process i process i process i process i impact

18. Distribution of lessons-learned • Measurable knowledge • Measurable impact • Tightly integrated to targeted processes

19. Lessons-learned • Applicable task • To which task/process is it applicable? • Preconditions: • Do conditions really match to make lesson applicable? • Lesson suggestion • What do repeat or avoid • Rationale • How was it learned • What is the expected impact • Why should I reuse it?

20. Proactive distribution • Pushing lessons to the users shifts burden from user to the system • Standalone tools place the distribution burden on the user discouraging sharing • Know about system’s existence • Skills to use it • Believe its usefulness

21. When and Where Needed • Distributes knowledge when and where it is needed with applicability-oriented retrieval • Where: in the screen of the targeted system • When: when a lesson is applicable to the current process 15

22. Applicability-Oriented Retrieval • MD keeps track of a user’s context to assess similarity between contexts and lessons in the LL base • Similarity-based retrieval that gives high weight to the applicable process to the extent that lessons are only retrieved if applicable to the current process

23. Lessons as Cases • Case retrieval retrieves cases with this structure: • Advantages associated with using CBR indexing elements reuse elements applicable task preconditions lesson suggestion rationale

24. Proactive Distribution Goal Improve Process Quality

25. Process Quality • Locally determined • Depends upon target organization • Associated with organizational culture • Variable • Difficult (impossible?) to collect computationally • Typically requires collection from humans

26. Evaluation: Weber & Aha, 2003 no lessons with lessons variation NEO plan total duration* 32h48 18 % 39h50 duration until medical assistance* 24h13 18 % 29h37 casualties among evacuees 24 % 11.48 8.69 casualties among friendly forces 6.57 30 % 9.41 casualties among enemies -2 % 3.08 3.14

27. Direct and Indirect MD • MD can distribute knowledge artifacts directly to the user • MD can distribute knowledge artifacts to an intelligent system that performs decision making and thus distributing indirectly to the user 20

28. capture processes reuse understand user distribute Direct MD

29. capture understand user reuse distribute Indirect MD Intelligent decision- making system processes

30. Example Direct MD

31. And the user is notified of a lesson RATIONALE: TYPE: advice Clandestine SOF should not be used alone WHY: The enemy might be able to infer that SOF are involved, exposing them. RATIONALE: TYPE: advice Clandestine SOF should not be used alone WHY: The enemy might be able to infer that SOF are involved, exposing them. RATIONALE: TYPE: advice Clandestine SOF should not be used alone WHY: The enemy might be able to infer that SOF are involved, exposing WHY: The enemy might be able to infer that SOF are involved, exposing

32. Example Indirect MD CBKMST MD case base case base RETAIN case base RETRIEVE case base CI-tool REVISE REUSE NN GA CS

33. MD and Agile Organizations 25

34. impact organization’s result Process PROCESS decision data, information output knowledge

35. impact organization’s result Agile Process PROCESS NEW PROCESS decision data, information output knowledge

36. MD can support agility • If evolving parameters are distributed through lessons-learned • Decisions -> lesson suggestion • Data/info -> preconditions • Impact -> rationale • Process -> applicable task

37. AP are no obstacle for MD • MD can be used as a knowledge distribution method for agile processes because if processes change, lessons will incorporate such changes when captured • A lesson that does not find its applicable process is no longer distributed

38. MD and Agile Org. • If agile organizations (AO) are: • Highly automated • Virtually paperless • Then AO are highly appropriate for MD • Lessons as means to responding to change (evolution & adaptation)

39. Requirements/limitations for MD • Processes delivered computationally • Processes modeled computationally • Flexible target systems that allow integration of MD • Knowledge capture that allows lessons-learned be represented in LL base • Capture of org. processes, impact • Maintenance • PRIME an extension for training

40. Future Work • Investigate the extent of the difficulties and challenges of the actual integration of MD when processes are agile • Integrate MD with methods that dynamic recognize agile processes • Develop knowledge capture for agile processes

41. References (i) • Abecker, A., Decker, S., Maurer, F. (2000). Organizational Memory and Knowledge Management. Guest editorial. Information Systems Frontiers, 2, 3-4, 251-252. • Aha, D.W. Becerra-Fernandez, I. Maurer, F. and Muñoz-Avila, H. eds., Exploring Synergies of Knowledge Management and Case-Based Reasoning: Papers from the AAAI 1999 Workshop (Tech. Rep. WS-99-10). Menlo Park, CA: AAAI Press, 1999. • Ahn, J.H & Chang, S. G. (2002). Valuation of Knowledge: A Business Performance-Oriented Methodology. Proc. Of the 35th Annual Hawaii International Conference on System Sciences. IEEE. • Atwood, M. (2002). Organizational Memory Systems: Challenges For Information Technology Proceedings of the 35th Hawaii International Conference on System Sciences.

42. References (ii) • Secchi, P. (Ed.) (1999). Proceedings of Alerts and LL: An Effective way to prevent failures and problems (Technical Report WPP-167). Noordwijk, The Netherlands: ESTEC. • SELLS (2003). Proceedings of the Society for Effective Lessons Learned Sharing (SELLS) Meetings. In U.S. Department of Energy Lessons Learned Information Services. [http://www.tis.eh.doe.gov/ll/proceedings/] Last visited 05-05-2003 • Weber, R. & Aha, D.W. (2003) Intelligent Delivery of Military Lessons learned. Decision Support Systems, 34, 3, 287-304. • Weber, R. & Kaplan, R. (2003). Knowledge-based knowledge management. Innovations in Knowledge Engineering, Editors: Colette Faucher, Lakhmi Jain, and Nikhil Ichalkaranje. Physica-Verlag, forthcoming. • Weber, R., Aha, D.W., Becerra-Fernandez, I. (2001). Intelligent lessons learned systems. Int. J. Expert Systems Research and Applications, 20, 1, 17–34.

43. Acknowledgements • David W. Aha • National Institute for Systems Test and Productivity at USF under the USA Space and Naval Warfare Systems Command grant no. N00039-02-C-3244