Comprehensive course analysis
About the course
The statistical analysis of numerical information is proven to be a powerful tool, providing businesses with everyday insight into matters like corporate finance, manufacturing processes, service provision and product quality control.
However, the advent of the Internet of Things, the consequential growth in Big Data, and the ever-increasing business requirements to model and predict, mean that many of the analytical opportunities and needs of a modern, high performing company cannot be met using conventional data analysis methods alone.
More and more companies are wrestling with complex modelling and simulation problems, addressing matters like trying to optimise production systems, to maximise performance efficiency, to minimise operating costs, to combat risk, to detect fraud and to predict future behaviour and outcomes.
This Oxford seminar is 100% computer-based and shows by example how to use Microsoft Excel to solve a series of complex and realistic business problems. The problems are drawn from the widest possible range of applications – from robotics to refining, from supply chain logistics to production optimisation and from financial risk management to the efficient provision of healthcare. All the problems are different and all convey carefully designed learning objectives.
Delegates will learn how to code and simulate realistic problems and then how to use these simulations to understand system operation, to optimise performance, and to predict future behaviour. The seminar is intended for people who are experienced in conventional data analysis techniques, and who now want to become specialist in the modelling and simulation of complex business activities.
This Oxford Management seminar aims to provide those involved in monitoring, managing and controlling complex business processes with the understanding and practical capabilities needed to convert data into meaningful information via a range of very powerful modelling, simulation and predictive analytical methods. The specific objectives are as follows:
- To teach delegates how to solve a wide range of complex business problems which require modelling, simulation and predictive analytical approaches
- To show delegates precisely how to implement a range of modelling, simulation and predictive analytical methods using Microsoft Excel 2016 (or 365)
- To provide delegates with both a conceptual understanding and practical experience of advanced data analysis methods including: Bayesian models, conventional and genetic optimisation methods, Monte Carlo models, Markov models, What If analysis, Time Series models, Linear Programming, and more
- To engage delegates for the entire 3 days in the exploration and use of modelling and simulation methods within Microsoft Excel, to develop complete solutions to the 8 totally realistic business problems that are presented
- To enable delegates to make the shift from intuition-based to information-based decision making in complex situations, hence enabling them to enhance their forecasting and future behaviour predictions, increase their proficiency in risk assessment and risk-informed decision making, and to exploit to a much greater extent the wealth of information contained in Big Data
- To provide a clear understanding of why the best companies in the world see modelling, simulation and predictive analytics as being essential to delivering the right quality products and optimised services at the lowest possible costs
This course is aimed at professionals whose jobs involve the management of complex business processes, and the analysis and interpretation of data. It is a 100% hands-on seminar which involves extensive modelling and analysis using Excel 2016 (or 365); delegates must not only be numerate, but must enjoy detailed working with numerical data to solve a range of fully realistic complex business problems.
This Oxford seminar uses a series of 8 real world problems, drawn from a very wide range of subject areas, to convey the following understanding:
- Introduction to optimisation; single and multi‐variate optimisation; determining the objective function; use of constraints; graphical representations
- Using linear programming to solve production optimisation and product yield maximisation problems, including optimising the profit from a range of products in a petrochemical refinery, and minimising the delivery costs of variable sized packages for a complex supply chain
- Non‐linear optimisation; stochastic search strategies; exploration of the shortcomings of Newton‐type optimisers; introduction to genetic algorithms; how to apply genetic algorithm optimisers
- How to solve a range of complex optimisation problems, including contract financial penalty risk minimisation and the classic ‘travelling salesman problem’ by optimising the motion of a large manufacturing robot, both with and without forced constraints
- Introduction to Markov modelling and Monte Carlo simulation; discrete and continuous random variables; Monte Carlo building blocks in Excel; modelling the problem efficiently; building worksheet‐based simulations
- Monte Carlo Simulation solutions to problems exhibiting random behaviour, including dealing with uncertainty of customer behaviour in the sale of products, and simulating chained random events in a doctor’s surgery to maximise its cost effectiveness and efficiency.
Oxford e-Certificate will be provided for delegates who attend and complete the online training course
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.