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In recent months, media outlets have notified the public about fluctuations in energy prices with headlines such as Australia’s High Electricity Prices the ‘New Normal’, Report Says (Hutchens, 2018), and Higher Energy Prices are Here to Stay — Here’s What We Can Do About It (Percival, 2018) and ‘No Likelihood of Relief Ahead’: Future Power Prices Continue to Rise (Latimer, 2018). These articles create a sense of concern due to the impact to Australian’s financial wellbeing. However, there is very little fact in these articles that are grounded in statistical evidence.

While these articles may have lacked academic rigour and rhetoric, the sentiment is still reflected in academic literature. Sardar (2015) justifies in his 2015 article entitled Research and Development, Welfare and Efficiency: An Australian Energy Perspective that increasing numbers of Australians are being driven to welfare as a direct result of Energy Prices. Moreover, in a 2017 article entitled Australian Energy Policy and Economic Rationalism, Horan et al. (2017) accuse the Australian Government of having irrational and inefficient energy policy, which is placing increasing and unnecessary financial pressure on Australian households and businesses. Furthermore, Lincoln (2012) proposes a succinct set of options for change which may curb this pressure, as articulated in the his article Options for Change in the Australian Energy Profile. As shown, the landscape of the Australian Energy Market environment is changing, and this trend may have dire consequences for the future of the Australian economy.

Therefore, with the intent to add some statistical rigour to the discourse around the Australian Energy Prices, this paper aims to model the aggregated monthly Energy Point Price in order to create a prediction of the for the future. The data is extracted from an Australian Government website, visualised, analysed, tested, and then forecast, in order to create such prediction. The resulting prediction will allow citizens to adequately plan for the future, and can also provide advice back to Governmental Agencies in order to advise future policy.

Within any large enterprise organisation, there is an IT department which works to service the rest of the company with any computer-related or technological enquiries. However, the process of requesting assistance from the IT department, by way of telephone enquiry or online ticket submission, can be somewhat convoluted, confusing, and even frustrating. One way to improve this is to have a search module, whereby the user can type a certain number of words, and the system will return a list of knowledge articles for the users’ consideration. This can be incredibly useful, and may even mean the user might not even need to submit an enquiry to the IT department. The details behind this program needs to be optimised for speed and accuracy, because ultimately the goal is to increase the user efficiency and usefulness of each employee within the organisation.

RYLA is a week-long experiential leadership program, aimed at empowering the next generation of leaders to achieve great things. The program is designed around a reflectively experiential approach to leadership, employing the abstraction of ‘ERLA’ to assist the participants through their journey, which is an abbreviation for: Experience, Reflection, Learning, Action (ERLA). This concept is continually reinforced throughout the course of the week. It is also steeped in academia, particularly in its liken to David Kolb’s work (1984), which conceptualises learning as a four-phase continuous cycle: Concrete Experience, Reflective Observation, Abstract Conceptualisation and Active Experimentation (Figure 1). This learning process, in other forms, has been referred to the ‘Plan-Do-Check-Act’ model, or the ‘Experience-Reflection-Conceptualise-Test’ model.

The ERLA concept is continually recycled for participants throughout the RYLA experience, and the week itself also acts as a standalone experience for future reflection and development. Two of the key objectives of RYLA are: 1) To empower and equip the next generations of leaders to be the best version of themselves that they can be; and 2) To provide a challenging, engaging, and safe environment for participants to try, test, apply and enable their own learning and development. Many researchers have attempted to synthesise the intricacies and nuances of leadership (eg: Avolio et al. 2009; Busse 2014; Jackson & Parry 2011; Northouse 2015); however, the resulting array of theories & conceptualisations are quite frankly diverse, convoluted, and in some instances inconsistent (Alveeson & Spicer 2012). What the discourse does agree on is the fact that leadership is inherently difficult and complex. Yet it is the capstone that is necessary in any successful business, and it is the fuel behind a highly effective and productive team. The environment created by RYLA, particularly resulting from the ERLA methodology, is one which can facilitate this learning for each individual, and can create a successful and beneficial leadership learning experience.

The Warehousing and Logistics Industry in Australia generates an enormous amount of data every day (DHL 2013), which creates great opportunities for businesses. However, this data is not currently being used to maximum efficiency (Zhong et al. 2015). Furthermore, if used correctly, data and statistical analysis provides immense opportunity for businesses and generates significant competitive advantage (Wang et al. 2016). By leveraging the data which already exists within the business, organisations can extract substantial value over multiple facets of the business, including guiding high-level strategic directions, assisting day-to-day tactical decisions, and reducing the cost-overhead caused when Service-Level Agreements (SLA’s) are not met. Jia, Wang & Wei (2015) discuss that the key to achieving this business success is through real-time data analytics and user-friendly visualisation. Moreover, Davenport & Harris (2007) emphasise the direct link between a business’s degree of competitive advantage and its degree of data intelligence. The use of data, and the realisation of in-depth statistical analysis is also an area where the warehousing and logistics industry is yet to achieve great outcomes (Bamberger et al. 2017).

GitHub is a vital tool for any Data Scientist, and is incredibly useful when properly integrated in the RStudio Integrated Development Environment (IDE). The default functionality allows for one to manage Code Sets seamlessly with Git through very user-friendly Stage, Commit, Push, Pull, and History buttons. This process is intentionally designed this way to ensure that the user properly follows the Git process to ensure that the appropriate Change Management, Code Merging, and Version Control practices are implemented.