INTRODUCTION: 

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 Target online 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. 

Course objectives: 

• To teach delegates how to solve a wide range of business problems which require modelling, simulation and predictive analytical approaches
• To show delegates how to implement a wide range of the more common modelling, simulation and predictive analytical methods using Microsoft Excel 2010 (or higher) and in particular the Solver tool
• To provide delegates with both a conceptual understanding and practical experience of a range of the more common modelling, simulation and predictive analytical techniques, including Bayesian models, conventional and genetic optimisation methods, Monte Carlo models, Markov models, What If analysis, Time Series models, Linear Programming, and more
• To give delegates the ability to recognize which modelling, simulation and predictive analysis methods are best suited to which types of problems
• To give delegates sufficient background and situation experience to be able to judge when an applied technique will likely lead to incorrect conclusions
• 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 

WHO SHOULD ATTEND? 

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. 

Course Outline:

Linear Programming: 

• Introduction to Optimisation; Multi‐variate Optimisation Problems; Determining the Objective Function; Constraints to Problems; Sign Restrictions; The ‘feasibility region’; Graphical Representation; Implementation using Solver in Excel
• Using Linear Programming to Solve Production and Supply Chain / Logistics Problems, such as optimising the products from a refinery, and minimising the manufacturing and delivery costs for a complex supply chain (with and without batch manufacturing, and with and without warehousing) 

Newtonian and Genetic Optimisation Methods: 

• Linear and Non‐linear Optimisation Problems; Stochastic Search Strategies; Introduction to Genetic Algorithms; Biological Origins; Shortcomings of Newton‐type optimisers; How to Apply Genetic Algorithms; Encoding; Selection; Recombination; Mutation; How to Parallelise; Implementation using Solver in Excel
• How to Solve a range of Optimisation Problems, Culminating in the classic ‘travelling salesman problem’ by optimising the motion trajectory of a large manufacturing robot, both with and without forced constraints 

Scenario Analysis: 

• Introduction to Scenario Analysis; A What-If example in Excel; Types of What-If analysis; Performing manual what-if analysis in Excel; One Variable Data Tables; Two-variable data tables
• Using Scenario Manager in Excel; Using scenario analysis to predict business expenses and revenues for an uncertain future 

Markov Models: 

• Understanding Risk; Introduction to Markov Models; 5 Steps for Developing Markov Models; Manipulating Arrays and Matrices inside Excel; Constructing the Markov Model; Analysing the Model; Roll Back and Sensitivity Analysis; First-order Monte Carlo; Second-order Monte Carlo
• Decision Trees and Markov Models; Simplifying Tree Structures; Explicitly Accounting for Timing of Events
• Using Markov Chains to simulate an insurance no claims discount scheme, and Modelling the Outcomes of a Healthcare System 

Monte Carlo Simulation: 

• Introduction to Monte Carlo Simulation; Monte Carlo building blocks in Excel; Using the RAND() function; Learning to model the problem; Building worksheet‐based simulations; Simple problems; How many iterations are enough?; Defining complex problems; Modelling the variables; Analysing the data; Freezing the model; Manual recalculation; "Paste Values" function; Basic statistical functions; PERCENTILE() function
• Monte Carlo Simulation solutions to problems of traffic flow in a city, dealing with uncertainty in the sale of product, predicting market growth and assessing risk in currency exchange rates