The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Modeling with System Archetypes: A Case Study
Abstract
This chapter examines the application of system archetypes as a systems development methodology to create simulation models. Rapid organizational change and need to adapt to new business models limits the lifespan of both the databases and software applications. With the information representation permitted by archetypes, diagnostic analysis and can help to evolve generic classes and models for representing the real world. Archetypes do not describe any one problem specifically. They describe families of problems generically. Their value comes from the insights they offer into the dynamic interaction of complex systems. The case of a healthcare system is discussed here. As part of a suite of tools, they are extremely valuable in developing broad understandings about the hospital and its environment, and contribute more effectively to understanding problems. They are highly effective tools for gaining insight into patterns of strategic behavior, themselves reflective of the underlying structure of the system being studied. Diagnostically, archetypes help hospital managers recognize patterns of behavior that are already present in their organizations. They serve as the means for gaining insight into the underlying systems structures from which the archetypal behavior emerges. In the casemix model of the hospital, the investigation team discovered that some of the phenomena as described by these generic archetypes could be represented. The application of system archetypes to the strategic business analysis of the hospital case reveals that it is possible to identify loop holes in management’s strategic thinking processes and it is possible to defy these fallacies during policy implementation as illustrated by the results of the archetype simulation model. In this research study, hospital executives found that policy modification with slight variable changes helps to avoid such pitfalls in systems thinking and avoid potentially cost prohibitive learning had these policies been implemented in real life.
Related Content
|
Bhargav Naidu Matcha, Sivakumar Sivanesan, K. C. Ng, Se Yong Eh Noum, Aman Sharma.
© 2023.
60 pages.
|
|
Lavanya Sendhilvel, Kush Diwakar Desai, Simran Adake, Rachit Bisaria, Hemang Ghanshyambhai Vekariya.
© 2023.
15 pages.
|
|
Jayanthi Ganapathy, Purushothaman R., Ramya M., Joselyn Diana C..
© 2023.
14 pages.
|
|
Prince Rajak, Anjali Sagar Jangde, Govind P. Gupta.
© 2023.
14 pages.
|
|
Mustafa Eren Akpınar.
© 2023.
9 pages.
|
|
Sreekantha Desai Karanam, Krithin M., R. V. Kulkarni.
© 2023.
34 pages.
|
|
Omprakash Nayak, Tejaswini Pallapothala, Govind P. Gupta.
© 2023.
19 pages.
|
|
|