Leonie Hallo

Developing a system of systems management framework for the Fukushima Daiichi Nuclear disaster recovery

Systems are becoming more complex due to uncertainties arising from rapid global technological progress and the growing involvement and interconnectedness of the human factor. Thus new management approaches are needed in order to improve system management effectiveness and efficiency. This research addresses critical issues in management decision-making processes for complex systems, and proposes a System of Systems (SoS) management framework to assist system engineers to assess continuously a complex situation and develop systemic solutions to problems. This paper demonstrates the usefulness of an SOS management framework using modelling and simulation. A case study examines the application of the new approach in the emergency recovery management of the Fukushima Daiichi Nuclear Power Plant disaster in Japan. Based on research related to this incident and the recovery efforts following, the paper shows that the SoS approach can have benefits for complex system management, providing flexibility while at the same time retaining the ability to regulate the degree of control of the system.

Systems engineering decision-making: Optimizing and/or satisficing?

Not all systems are the same: consequently they need different management approaches. The varied typology of systems is not always universally recognized. One way in which systems can vary is in terms of complexity; and there is increasing awareness about growing complexity in systems across a number of literature domains. Complexity is now an evolving topic in systems engineering (SE). However, traditional SE has tended to address all systems with the same approach, moving from the premise that activities and their interrelationships are linear and measurable. Recently there has been a paradigm shift in the way systems are understood, with the recognition of their nonlinear and emergent properties. Traditionally optimization has been the primary method of decision-making. When dealing with complexity, true optimization is not always possible: therefore satisficing was introduced. Selecting and applying these two very different approaches is proving to be a challenging task for engineering practitioners. This paper explores SE decision-making in dealing with different types of systems with various degrees of complexity and proposes a decision-making methodology which can assist engineering managers with the selection of optimizing versus satisficing in a given situation or problem space.

Entrepreneurship in Mid-career

This chapter addresses the situation of those who begin an entrepreneurial venture in their mid-career. Defining exactly when mid-career begins and ends is a challenging exercise as it seems to be mostly understood as a period without specific boundaries that lies somewhere between the first few years of employment, when one is considered to still be a relative newcomer, and the final few years prior to retirement. One attempt by Saunders (2008) placed it as not less than ten years’ working experience and not less than ten years prior to retirement. Others (e.g. Reining 2004; Winkle man 2007) have defined it largely in terms of a state of mind, associated with a period of stagnation or career plateau in between stages of career advancement. More recently Lindstrom (2011) classified career stages based on earlier work by Super (1957), and a combination of previous empirical studies have divided careers into four stages largely underpinned by age, though to a lesser extent still associated with the conditions underpinning upward progress. These stages consist of new career entrants up to age 20, early career people in the 20–34 range, mid-career 35–49, and late career people 50–65. Equating mid-career with age would place mid-career employees in the broad 35–50 range, but these categories are only guidelines given that chronological age is a convenient and easy way to select which people fall into the category of mid-career incumbents.