Engineering Knowledge Management and Decision Support
Authors: Aftahi Mehdi 
Published: April 2012

One of the keys to making effective, accurate decisions is the extent of thedecision-maker's knowledge with respect to the subject matter. Indeed, theprecision of a decision's outcome correlates directly with the amount ofinformation provided about the problem at hand, as well as with the detail andaccuracy of the information about the various available solutions.

In engineering environments, knowledge and expertise in understanding problemsand potential solutions is typically limited to a handful of subject-matterexperts (SMEs), regardless of the size of the organization. This isparticularly true given the complex and multi-faceted nature of problems andsolutions in this domain. These SMEs, while often extremely knowledgeable intheir respective fields of expertise, may have neither the time nor the mandateto participate in every project that actually requires their expertise.

To date, knowledge management tools have focused on capturing and reusing SMEknowledge in different projects via the use of documents related to previousprojects, predicated on the project owner's awareness of the existence of suchprojects or documentation. These documents may also contain outdatedinformation, which typically renders them unusable, since document updates canoften be as time-consuming and resource-intensive as conducting the pertinentresearch from scratch.

This paper discusses a knowledge management and decision support platform thatcaptures the expertise of the SMEs in a structured, accessible, and flexibleformat. The platform provides SMEs with a user-friendly means of retainingcontrol of the data, enabling them to maintain responsibility for makingchanges and updates. These characteristics of this platform allow SMEs tomanage data related to their fields of expertise, while the end users of thesystem—the project managers—can define their needs and project specifics, andintegrate disparate silos of expert opinion into a single decision model.

Introduction

One of the keys to making effective, accurate decisions is the extent of thedecision-maker's knowledge with respect to the subject matter. Indeed, theprecision of a decision's outcome correlates directly with the amount ofinformation provided about the problem at hand, as well as with the detail andaccuracy of the information about the various available solutions.

In engineering environments, knowledge and expertise in understanding problemsand potential solutions are typically limited to a handful of subject-matterexperts (SMEs), regardless of the size of the organization. This isparticularly true given the complex and multi-faceted nature of problems andsolutions in this domain. These SMEs, while often extremely knowledgeable intheir respective fields of expertise, may have neither the time nor the mandateto participate in every project that actually requires their expertise.

To date, knowledge management tools have focused on capturing and reusing SMEknowledge in different projects via the use of documents related to previousprojects, predicated on the project owner's awareness of the existence of suchprojects or documentation. These documents may also contain outdatedinformation, which typically renders them unusable, since document updates canoften be as time-consuming and resource-intensive as conducting the pertinentresearch from scratch.