![]() Mubadala’s commitment to a single financial modelling standard has resulted in its analysts understanding the importance of a common approach and honing their technical skills. We continue to work with Mubadala’s learning and development team in improving the design of the programme. This included the development of further case studies relevant to the work being undertaken (including a hotel and a satellite). Since running the early programmes, we have amended the course material to give it a more general appeal (suitable for analysts for whom modelling is not core but nonetheless important). Based on a real-world case study, the training took participants through all the requirements for financial modelling from basic skills and drills in Excel to the design and construction of a highly complex set of financing requirements. Our initial brief was to run a programme for Mubadala’s core modellers – those professionals who work with financial models of considerable complexity on a daily basis. At the final stage of the programme, participants are required to present to senior members of the organisation. Our training programme uses a variety of learning approaches: self-study backed up with support from our team of modellers, informal and formal assessments, instructor-led classroom sessions and webinars. We have taught over 200 analysts from a wide variety of sectors. ![]() We have been working with Mubadala to standardise their approach to financial modelling (“The Mubadala Way”) since 2012. The staff we have trained are now equipped to build better structured, more robust models and have direct access to F1F9 resource wherever it adds value. We recently supplemented this engagement with a ‘model clinic’ and remediation work to a tax model that the Energia Group identified as a priority for development.Īs a result of this work, the Energia Group has realised significant efficiencies in their modelling practices. In response to this we delivered a series of in-house training courses on best practice excel modelling to finance delegates from businesses across the Group. Subsequently, Energia Group expressed a desire to improve and standardise their range of financial reporting and forecasting models. In 2018, F1F9 created a best practice budgeting and reporting model for Power NI, followed by two smaller builds looking in more detail at specific elements of the business. Their parent is the Energia Group, a leading integrated Irish energy business with substantial businesses in both the Republic of Ireland and Northern Ireland. Power NI is the largest electricity company in Northern Ireland. The new process has been fully adopted within Virgin Trains and F1F9 are providing ongoing support. We worked in close collaboration with our Virgin Trains contacts and completed the work in 2 months. The reporting process is now simple and more accurate with a rapid turnaround. This dramatically increased budgeting and forecasting efficiency as well as reducing the risk of errors. Duplication of common inputs led to inconsistency.į1F9 created a new process for updating, analysing and communicating large financial datasets in 8 Excel financial models which take inputs from core non-Excel management information systems. Over 80 spreadsheets were in use across 11 departments for the budget process alone. The company’s previous process for budgeting, reporting actuals and updating forecasts was complex and time-consuming, with the complexity leading to errors in the final output. It operates long-distance passenger services on the West Coast Main Line between London, West Midlands, North West England, North Wales and Scotland. The cookie is used to store the user consent for the cookies in the "Other" category.Virgin Trains is a train operating company that has operated the InterCity West Coast franchise since 1997. ![]() This cookie is set by the GDPR Cookie Consent Plugin. The cookie is used to store the user consent for the cookies in the "Performance" category. The cookies are used to store the user consent for the cookies in the "Necessary" category. The cookie is set by GDPR Cookie Consent to record the user consent for the cookies in the "Functional" category. The cookie is used to store the user consent for the cookies in the "Analytics" category. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not the user has consented to the use of cookies. These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly. ![]()
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