Developing executive capability — banking and beyond

The transition to becoming a leader is perhaps the least understood and most difficult in business. This Portfolio of Exploration examines the development of conscious awareness and meaning complexity as key transformational requirements to operate competently at leadership level and to succeed in a work environment characterised by change and complexity. It recognises that developing executive leadership capability is not just an issue of personality increasing what we know or expertise. It requires development of complexity in terms of how we know ourselves, relate to others, construe leadership and organisation, problem solve in business and understand the world as a whole. The exploration is grounded in the theory of adult mental development as outlined by Robert Kegan (1982, 1994) and in his collaborations with Lisa Laskow Lahey (2001, 2009). The theory points to levels of consciousness which impact on how we make meaning of and experience the world around us and respond to it. Critically it also points to transformational processes which enable us to evolve how we make meaning of our world as a means to close the mismatch between the demands of this world and our ability to cope. The exploration is laid out in three stages. Using Kegan’s (1982, 1994) theory as a framework it begins with a reflection of my career to surface how I made meaning of banking, management and subsequently leadership. In stage two I engage with a range of source thinkers in the areas of leadership, decision making, business, organisation, growth and complexity in a transformational process of developing greater conscious and complex understanding of organisational leadership (also recognising ever increasing complexity in the world). Finally, in stage three, I explore how qualitative changes as a result of this transformational effort have benefitted my professional, leadership and organisational capabilities.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.