The advantages to using knowledge representation to model industrial engineering tasks and artifacts are: KBE can have a wide scope that covers the full range of activities related to Product Lifecycle Management and Multidisciplinary design optimization.
KEE started on Lisp and added frames, objects, and rules, as well as powerful additional tools, such as hypothetical reasoning and truth maintenance.
One of the issues that Simkit faced was a common issue for most early KBE systems developed with this method: The Lisp knowledge-based environments provide very powerful knowledge representation and reasoning capabilities; however, they did so at the cost of massive requirements for memory and processing that stretched the limits of the computers of the time.
CATIA started with products for CAD and other traditional industrial engineering applications and added knowledge-based capabilities on to them; for example, their KnowledgeWare module.
Like expert systems, it relied on what at the time were leading edge advances in corporate information technology such as PCs, workstations, and client-server architectures.
In the case of KBE, the interest was perhaps strongest in the business-to-business type of electronic commerce and technologies that facilitate the definition of industry standard vocabularies and ontologies for manufactured products.
A natural area of emphasis is on the production process; however, lifecycle management can cover many more issues such as business planning, marketing, etc.
KBE supports the decision processes involved with configuration, trades, control, management, and a number of other areas, such as optimization.
Essentially KBE extends, builds on, and integrates with the CAx domain typically referred to as Computer Aided Design (CAD).
In the case of the 777, the project got to where influences to changes in the early part of the design/build stream (loads) could be recomputed over a weekend to allow evaluation by downstream processes.
Knowledge management provides the various group support tools to help diverse stake holders collaborate on the design and implementation of products.
[11] The development of KBE applications concerns the requirements to identify, capture, structure, formalize, and finally implement knowledge.
In order to limit the risk associated with the development and maintenance of KBE application, there is a need to rely on an appropriate methodology for managing the knowledge and maintaining it up to date.
Two critical issues for the languages and formalisms used for KBE are: A fundamental trade-off identified with knowledge representation in artificial intelligence is between expressive power and computability.
As Levesque also demonstrated, the closer a language is to First Order Logic, the more probable that it will allow expressions that are undecidable or require exponential processing power to complete.
[15] Genworks GDL, a commercial product whose core is based on the AGPL-licensed Gendl Project,[16] addresses the issue of application longevity by providing a high-level declarative language kernel which is a superset of a standard dialect of the Lisp programming language (ANSI Common Lisp, or CL).