The use of goal models may have the capacity to influence the way people approach the relations between ideas, and thus, creative performance. Ample research suggests that cognitive properties, such as thinking styles and motivation, can extend from one domain to another (see Baumann and Kuhl, 2005; Förster et al., 2008; Trope and Liberman, 2010). For instance, studies showed that properties in the physical domain can spill over to the social domains: perceptions of spatial distance increased feelings of emotional distance (Williams and Bargh, 2008) and attention to global features in pictures increased the use of social stereotypes to evaluate a target person (McCrea et al., 2012). Likewise, the properties of the way people visualize the relations among their goals (as a function of their goal models) may extend to affect mental processes involved in the domain of creative problem-solving in general.
The LTP is a graphical representation of cause-effect logical relationships. It was conceived in the early 1990s by an Israeli physicist named E.M. Goldratt as a way to analyze the performance of complex organizational systems. Its foremost value is as a policy analysis and business decision tool. As such, it is very much a qualitative, not a quantitative tool. The thinking process is designed to provide the answers to the only three questions any manager needs to know: a) what to change, b) what to change to, and c) how to make the change happen. The thinking process is comprised of five distinct logic trees:
The Logical Thinking Process A Systems Approach To Complex Problem Solving By H William Dettmer.pdfl
Despite its origins as a manufacturing approach (Goldratt & Cox, The Goal: A process of Ongoing Improvement, 1992), Goldratt's Theory of Constraints (TOC) methodology is now regarded as a systems methodology with strong foundations in the hard sciences (Mabin, 1999). Through its tools for convergent thinking and synthesis, the "Thinking processes", which underpin the entire TOC methodology, help identify and manage constraints and guide continuous improvement and change in organizations (Dettmer H. , 1998).
Abstract:The world is at the threshold of the fourth industrial revolution, which has already begun. It requires enterprises and even sectors to move toward Industry 4.0. Innovative systems (IS) play an important role in this process. In the article, the innovative systems in Industry 4.0 are considered to be complex systems whose components have a lot of tasks; in particular, to produce innovative policy; to provide the subjects of innovative activity with the necessary resources; to participate directly in the process of creation, commercialization, and the practical use of new knowledge; to implement integration approaches between these processes, etc. The complexity of the innovation system leads to the development of modern approaches to their modeling, as a tool for further designing, creating, and modifying real innovative systems of different levels of organization under the conditions of Industry 4.0. In the simulation of IS under the conditions of Industry 4.0, the description of the subsystems by a number of sets is proposed. The model is described by the graph of relationships, including the abstract level of the hierarchical model of IS, its elements, indicators and their values, functions, actions and operations, their states and efficiency, and the tree of goals. In order to make optimal solutions, using the mathematical apparatus of the theory of Markov chains to study the dynamic and static characteristics of the states of the IS is proposed. This approach can be widely used in the simulation, designing, development, and rebuilding of IS at different levels of an organization.Keywords: Industry 4.0; graph; relationship; innovative system; set; model 2ff7e9595c
Comments