Paper: Defining Project Success, a Systems Thinking Approach

Paul W.M. Cuypers

HYVES Project Research, Næroset, Norway

Proceedings of The 4th International Conference on Applied Research in Business, Management and Economics

Year: 2022

DOI: https://www.doi.org/10.33422/4th.bmeconf.2022.03.01

[Fulltext]

ABSTRACT

Although research is inconclusive, a consensus exists that project success is a subjective, multidimensional, time, context, and stakeholder-dependent construct. A definition of project success is a prerequisite to assessing the success rate of the parent organization and demonstrating the effectiveness of project management methods. This research uses a systems thinking approach to develop a generic model of a project’s achievement. It is shown that project success or failure can be viewed as the extreme values of system states, and complex system behavior can be visualized and evaluated using cause-and-effect diagrams. The relevance of the model is demonstrated by corroborating five key findings from previous research on project success.

Computer Science & Information Technology (CS & IT)

ISSN : 2231 - 5403

Volume 15, Number 15, August 2025

https://airccse.org/csit/V15N15.html

Paper: Next-Generation Agile Methods: Agile Rolling Wave Planning

Paul W.M. Cuypers

HYVES Project Research, Næroset, Norway

Computer Science & Information Technology (CS & IT)

ISSN : 2231 - 5403

Volume 15, Number 15, August 2025

https://airccse.org/csit/V15N15.html

ABSTRACT

Project planning is subject to diminishing returns and contradictory demands. While management requires a comprehensive overview of activities, costs, and benefits, detailed planning for an uncertain future can be wasteful, as reality quickly catches up with the plan. Agile rolling wave planning is a multi-level, multi-horizon planning approach designed to address this challenge. This paper outlines the theoretical foundation and practical application of agile rolling wave planning during a ServiceNow implementation project. A systems thinking model of project uncertainty is presented, along with four fundamental counter strategies. Based on the model, it is demonstrated how agile rolling wave planning reduces uncertainty by providing relevant information and facilitating horizontal coordination between teams. A roadmap for AI-enabled project management software is outlined to support the method. Agile rolling wave planning techniques enable organizations to transition from risk to uncertainty management, applying agile principles beyond the software development realm.

Paper: A Systems Thinking Theoretical Foundation for Project Uncertainty and Reduction Strategies.

Paul W.M. Cuypers

HYVES Project Research, Næroset, Norway

Project Management and Scheduling 2024, the 19th International Workshop, Bern, initiated by the EURO Working Group on Project Management and Scheduling.

ABSTRACT

Uncertainty permeates every aspect of project work. As one cannot control what is not defined, and the definition of uncertainty is uncertain, this paper presents a generic project uncertainty model using a systems thinking approach. Uncertainty can be described as a lack of information awareness or information availability. The uncertainty matrix visualizes this principle, forming the quadrants:

– Known-known refers to available information, and its relevance is understood

– Known-unknown is desired, but unavailable information

– Unknown-known, information is present, but one is unaware of its existence

 – Unknown-unknown, relates to unavailable information and unawareness of its pertinence. A project can be modelled as a conceptual system, with principal elements and sub-components being the assignment, context, decisions, method, and scenarios. Elemental strategies for managing project uncertainty are identified based on the model, and techniques are suggested for:

1. Raising information awareness
2. Increase information availability
3. Improve the effective use of information
4. Maximize information efficiency.

The fundamental differences between risk and uncertainty management, as well as the need for a scientifically rigorous approach, are highlighted.