Knowledge-Based Engineering (KBE) is a part of intelligent systems that are used across a wide range of problem-solving areas including Design Automation, Standardization, Rapid prototyping, Decision Making, Quality Control, and Verification among many others. These systems vary in complexity from well-defined Engineering Tasks in Design Automation to Human thought emulation problem-solving processes. To develop KBE applications there are many methods and are used as per requirements with knowledge acquisition and knowledge Modelling processes, with the help of domain experts and professionals. This process involves capturing Knowledge (Data), Knowledge Structuring, Representation of Data, coding to develop application and Testing, implement this system, and controlling and continuous Enhancement. There are different challenges faced by the organization while developing the KBE system, whether it is developed in-house or with the service providers who are the SMEs in this area.
Capturing the Required Knowledge
Organizations must adopt the right strategy in capturing the required knowledge to build their KBE Application or System. This knowledge capturing task involves understanding all the processes, data sets, and domain elements such as Design standards, CAD templates, Modeled vs. Borrowed parts, etc. This data forms the base of the whole KBE system, as from this data the KBE implementation consultation will design an application to meet the customer requirements. For Organization it will be an arduous task to update and transfer knowledge to KBE professionals and sometimes missing that data which is conventional and non-documented.
For any Organization, Structuring and storing of the captured data is a substantial challenge as the knowledge base should provide an easy way to access the data when necessary which includes standards, templates, CAD models, and documents. KBE initiatives in organizations are always taken-up with a roadmap to extend it to multiple product lines and divisions. Many a time the data can be related and sharing of data also becomes a key element of the decision-making process regarding data structuring. KBE consultant also has the additional responsibility to safeguard the organization’s confidential data and not expose everything to everyone. KBE systems also should be robust and fail-safe, which means data storage devices should be secure from external usage and hack.
Due to a lack of required skill sets, organizations engage external service providers or KBE consultants who are SME in this area. The challenge here is to identify how skilled these professionals are to manage and implement the KBE System as per the requirements. The ideal scenario is if the professional has experience working in a similar industry. KBE professionals should also be able to clearly quantify the Return of Investments and benefits of developing a proper KBE system. Most of the time, the consultant should be able to work alongside a software development team in defining the requirements and acceptance criteria of the system to be developed.