Toward overcoming accidental complexity in organisational decision-making

Abstract

This paper takes a practitioner's perspective on the problem of organisational decision-making. Industry practice follows a refinement based iterative method for organizational decision-making. However, existing enterprise modelling tools are not complete with respect to the needs of organizational decision-making. As a result, today, a decision maker is forced to use a chain of non-interoperable tools supporting paradigmatically diverse modelling languages with the onus of their co-ordinated use lying entirely on the decision maker. This paper argues the case for a model-based approach to overcome this accidental complexity. A bridge meta-model, specifying relationships across models created by individual tools, ensures integration and a method, describing what should be done when and how, and ensures better tool integration. Validation of the proposed solution using a case study is presented with current limitations and possible means of overcoming them outlined.

Publication DOI: https://doi.org/10.1109/MODELS.2015.7338268
Divisions: College of Engineering & Physical Sciences
Additional Information: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Event Title: 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS)
Event Type: Other
Event Location: Ottawa, ON, Canada
Event Dates: 2015-09-30 - 2015-10-02
ISBN: 978-1-4673-6908-4
Last Modified: 01 Apr 2024 07:52
Date Deposited: 10 Feb 2020 14:35
Full Text Link:
Related URLs: https://ieeexpl ... cument/7338268/ (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2015-11-30
Authors: Clark, Tony (ORCID Profile 0000-0003-3167-0739)
Kulkarni, Vinay
Barat, Souvik
Barn, Balbir

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