Chapter 3: Conceptual Model
3.2 Theoretical framework
This study is based on the relationship between three key concepts derived from four main theories.
The first is the concept of complexity of interactions and information flow in the nuclear power plant operational readiness programs. The second is the concept of network analysis, a method for evaluating and understanding complexity and dynamic interactions. The third is the application of the network analysis on nuclear power plant operational readiness programs which creates a means to examine aspects of Influence and Interdependencies towards achieving the Operating License requirements. These concepts are discussed fully in chapter 2 and illustrated in the theoretical framework displayed in figure 3.1.
Figure 3. 1 Theoretical framework
3.2.1 Complexity in nuclear power plant operational readiness
Life cycle stages of a nuclear power plant (NPP) include Design, Construction, Commissioning (Operational Readiness), Operations, and decommissioning. The commissioning phase is one of the most interesting and important phases in the lifetime of a nuclear power plant (NPP). It is a short but very intense and complex period, typically encompassing 1–2 years in the total lifetime of an NPP. However, the way the commissioning process is managed across different industries and sectors is yet seen as ad hoc and lacks integration (Lawry and Pons 2013, IAEA 2018). The current practice of managing the commissioning of new nuclear power plants by only using the project management methodology might result in unpredictable outcomes (Lawry and Pons 2013).
During the commissioning phase, the inability to address complex issues and take timely and effective decisions based on a comprehensively understanding of all involved stakeholders and interfaces usually leads to errors and delays (IAEA 2014, Cagno, Caron, and Mancini 2002).
During the commissioning phase, unnecessary activities are carried out because of lacking the comprehensive system view of the commissioning (Kirsilä, Hellström, and Wikström 2007).
Nuclear commissioning (Operational Readiness) shares the characteristics of complex adaptive systems (CAS). Operational Readiness is a dynamic phase of any nuclear new build that is governed by the influence (Feedback) from one activity, milestone, or stakeholder to another Operational readiness milestones are interdependent and the Operational readiness programs adapt based on the progress and experience (Daniel and Daniel 2019, IAEA 2018, IAEA 2014, AERB 1998, STUK 2003). The US Ministry of Energy standard for the Planning and Conducting Readiness Reviews (2010) emphasized on the importance of identifying and evaluating the complexity of the startup or restart of any nuclear power plant. Understanding and effectively managing the complexity of the commissioning programs is critical to the success of plant safe
48
and reliable signed off for commercial operation (Rosenergoatom 2017). Complexity theory describes the uncertainty created by dynamic interactions in non-linear systems, where cause and effect are not proportional. Graph theory complements the complexity theory by supplying a vocabulary for denoting interactions and information structural properties (Nodes, edges, Hubs, clusters, Ego networks, etc.). also, graph theory provides statistical and mathematical tests and models to quantify these network properties. Furthermore, graph theory (through network analysis) establishes a method for proving theorems about networks that help in drawing reliable insights on how well ident fed network properties network represent the measured systems.
3.2.2 Information use in the nuclear power plant operational readiness
Increasing information interdependence during the commissioning life cycle is also an essential feature of its complexity. The complex and interdependent information flow between different processes, programs, and systems during commissioning cuts through different project’s stages, process and involve multiple internal and external stakeholders. Fiatech’s Capital Facilities Information Handover Guide (2006) emphasized on the critical role of information flow and handover for the success of commissioning activities and developed a methodology for defining full life cycle e information requirements as a prerequisite for managing information flow and implementing interphases information handover. Understanding the information flow between different actors of the nuclear power plant operational readiness program through modeling the interaction between those actors and linking it to achieving regulatory requirements and other safety-related commitments is vital to the success of such programs. Galbraith explained how evolvement in organizational design emerges from efforts for enhancing decision-makers and
49
middle management pre-planning ability by equipping them with the right information at the right time to achieve the strategic outcomes for organizations (Galbraith, 1973). Commissioning effective information management is a critical enabler for the success of the nuclear power plant's operational readiness. Effective use of commissioning data enables commissioning engineers to identify design and construction non-conformances and take the required action of repair or modification accordingly (IAEA 2016, AERB 1998).
3.2.3 Network analysis
Network analysis enables researchers to comprehend and illustrate information flow structures by exploring relational data and investigating the nature and attributes of those relations (Hanneman, 2001; Wasserman & Faust, 1994). Network analysis views actors (Nodes) and actions as interdependent rather than autonomous units. It helps in understanding relational links between actors (Nodes) as channels for information flow (Newman 2013, Valente 2010, Epstein 2006).
When network analysis is applied in an organizational context, it's usually referred to as organizational network analysis. The network analysis method takes into account the Intra organizational perspective as well (the relationship between organizational actors and the external operating environment). Network Analysis demonstrates relations between different actors within a dynamic system (Stakeholders (People), groups, organizations), represent through nodes and ties, and uses a wide range of graphical and statistical models to display the network and the nature of the relationships between system’s actors. (Newman 2013, Valente 2010). The results produce knowledge and insights through network statistics ( like centrality measures, clustering and modularity, network density, path lengths, and ego networks) into systems’ behavior and complexity comprehension.