By Kate Le Grand at November 09 2018 10:30:14
In the last few years a lot has been written about Business Process Management, and about technologies supporting it such as BPMS, SOAP and Web Services. Most of these theories, tools and techniques refer to processes of a highly structured nature. Typically, BPM theorists and practitioners have focused on highly structured processes, like back-office processes of industrial or administrative nature. These processes are highly standardized and repeatable, produce a consistent output and are likely to be automated in part or end-to-end (STP).
It is usual that knowledge processes take the form of projects to manage their execution. If the output of the process is a unique product, managing work as a project will result in obvious advantages. There are certain guidelines that can help an organization willing to improve their knowledge processes: Provide process description on how to approach work Try to figure out the best way to carry out a knowledge process, by making the best practices existing in your organization (or in your industry) explicit. Publish process definitions in a format that is easy to consult and understand. Provide tools that facilitate and standardize work Decide which tools are best to help knowledge workers carry out their work. Involving all affected knowledge workers in the process of deciding which tools will be used is very convenient, in order to obtain user buy in.
All process instances are executed in a very similar way and it is easy to draw a flowchart detailing the sequence in which tasks are executed. It is also possible to formalize the business rules that guide decisions, normally based on the evaluation of some process variables. But recently other kinds of processes have caught the attention of process management specialists. They are known as knowledge processes, or knowledge-based processes. Knowledge processes can be defined as "high added value processes in which the achievement of goals is highly dependent on the skills, knowledge and experience of the people carrying them out". Some examples could be management, R&D, or new product development processes.
Current research in information processing deals with programs that enable a computer to understand written or spoken information and to produce summaries, answer specific questions, or redistribute information to users interested in specific areas of this information. Essential to such programs is the ability of the system to generate grammatically correct sentences and to establish linkages between words, ideas, and associations with other ideas. Research has shown that whereas the logic of language structure-its syntax-submits to programming, the problem of meaning, or semantics, lies far deeper, in the direction of true AI.