Abstract

Many large-scale endeavors, especially the ones that need to be performed on an occasional basis, are performed on a de novo basis. Whether one is designing a spacecraft or a dam, or launching a sales campaign, each endeavor tends to have a life of its own. Very little knowledge, if any, is transferred from one business or engineering design endeavor to a successive one. This paper describes an approach that involves the acquisition of raw information from the keystrokes entered by the user while performing the primary activity. Such information is distilled to produce knowledge for future use, without imposing additional burden on human users involved in the first endeavor or the succeeding endeavors. The paper also describes a complementary approach that significantly reduces the time and effort spent by human beings in articulating their requirements, and specifically, in terms of delineating the indifference points among a set of feasible alternatives. Both these approaches have been validated in a complex multi-organizational situation related to spacecrafts. The approaches can be applied, either singly or jointly, to a broad class of business and engineering scenarios. They incorporate the notion of automated knowledge acquisition, discovery, management, and dissemination. The proposed approach is discussed in the context of supporting work-teams that are distributed both on geographic and temporal basis, thereby creating a “24-Hour Knowledge Factory” environment.

Keywords: Business process management; Knowledge engineering; Multi-attribute tradespace; Design rationale; Utility function

Article Outline

1. Introduction

1.1. Goals

1.2. Context

1.3. Proposed paradigm

1.4. Related efforts

1.5. Structure of paper

2. Knowledge engineering approach

3. SSPARCy

4. MIST

4.1. Software implementation

4.1.1. From properties to attributes

4.1.2. Information visualization

4.1.3. Converging the automata

4.1.4. From attributes to stakeholder utility and expense

4.1.5. Single attribute utility functions

4.1.6. Multi-attribute utility and expense calculations

4.2. Design rationale and history

4.2.1. Knowledge acquisition

4.2.2. Knowledge management

4.2.3. Knowledge discovery

4.2.4. Knowledge dissemination

5. Test deployment

6. Conclusions and future directions

Acknowledgements

References

Vitae