Why choose this Training Course?

The statistical analysis of numerical information is proven to be a powerful tool, providing everyday insight into matters like corporate finance, production processes and quality control. However, the advent of the Internet of Things, the consequential growth in Big Data, and the ever-increasing 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 statistical methods alone.

More and more companies are wrestling with complex modelling and simulation problems, addressing matters like trying to optimize production systems, to maximize performance efficiency, to minimize operating costs, to combat risk, to detect fraud and to predict future behavior and outcomes.

Prerequisite

This intensive training course is intended for delegates who already have a solid understanding of conventional data analysis methods. This advanced training course shows by example how to build on the method learned in the Data Analysis Techniques course and to create a variety of powerful modelling, simulation and predictive analytical methods.

The methods introduced include Bayesian models, Newtonian and genetic optimization methods, Monte Carlo simulation, Markov models, advanced What If analysis, Time Series models, Linear Programming, and more. The training course uses advanced features of Microsoft Excel throughout, and it is important that all delegates are fully competent in both Excel and the statistical analysis of data.

What are the Goals?

This training course aims to provide those involved in analyzing numerical data with the understanding and practical capabilities needed to convert data into meaningful information via the use of 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 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 optimization 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 is this Training Course for?

This training course has been designed for professionals whose jobs involve the manipulation, representation, interpretation and/or analysis of data. The training course involves extensive modelling and analysis using Excel 2010 (or higher) and therefore delegates must not only be numerate but must enjoy detailed working with numerical data to solve complex problems.

Full familiarity with Microsoft Excel (version 2007 or higher), and the ability to analyze data using common statistical methods, are fundamental prerequisites for attendance on this course. because without mastery of the capabilities taught in the Data Analysis Techniques course a delegate will not be able to succeed on this training course.

How will this Training Course be Presented?

This training course adopts a problem‐based learning approach, in which delegates are presented with a series of real problems drawn from the widest possible range of applications – they range from insurance to supply chain logistics, from chemistry to engineering, and from production optimization to financial risk assessment. Each problem presents and exemplifies the need for a different modelling or analytical approach.

The training course is entirely applications‐oriented, minimizing the time spent on the theory and mathematics of analysis and maximizing the time spent on the use of practical methods from within Excel, along with the understanding of how and why such methods work.

Delegates will spend almost all of their time exploring the use of modelling and simulation methods using Microsoft Excel, to develop solutions to the totally realistic problems that are presented.

Organizational Impact

Organizations which are able to make optimum decisions, and which can reliably predict future trends and behaviors, are able to enhance substantially their ability to compete on the global stage; as a result of sending their employees on this course, organizations can expect to benefit from:

• A shift from intuition-based to information-based decision making
• The provision of accurate solutions to complex problems
• Enhanced forecasting and future behavior prediction
• Advanced modelling and simulation of business processes
• More capable risk assessment and risk-informed decision making
• Improved capitalization on the wealth of information contained in Big Data

Personal Impact

Participants will each gain extensive understanding and lots of practical experience of a wide range of the more common modelling, simulation and predictive analytical techniques, all of which will have direct relevance to a wide range of business issues; specifically, delegates will acquire:

• New insights into the use of optimization, modelling and prediction using Microsoft Excel
• Experience of Linear Programming
• An understanding of how and when to use Newtonian and Genetic Optimization Methods
• Knowledge of Scenario Analysis, Markov Modelling and Monte Carlo Simulation
• The ability to recognize which types of analysis are relevant to particular types of issues
• Sufficient situational knowledge to judge when a technique will lead to incorrect conclusions

Outline

• DAY ONE: LINEAR PROGRAMMING
  • Introduction to optimization
  • Multi‐variate optimization 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 optimizing the products from a refinery, and minimizing the manufacturing and delivery costs for a complex supply chain (with and without batch manufacturing, and with and without warehousing)
• DAY TWO: NEWTONIAN AND GENETIC OPTIMISATION METHODS
  • Linear and non‐linear optimization problems; Stochastic search strategies
  • Introduction to genetic algorithms
  • Biological origins
  • Shortcomings of Newton‐type optimizers
  • How to apply genetic algorithms
  • Encoding
  • Selection
  • Recombination
  • Mutation
  • How to parallelize
  • Implementation using Solver in Excel
  • How to solve a range of optimization problems, culminating in the classic ‘travelling salesman problem’ by optimizing the motion trajectory of a large manufacturing robot, both with and without forced constraints
• DAY THREE: 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
• DAY FOUR: MARKOV MODELS
  • Understanding risk
  • Introduction to Markov models
  • 5 steps for developing Markov models
  • Manipulating arrays and matrices inside Excel
  • Constructing the Markov model
  • Analyzing 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
• DAY FIVE: 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
  • Analyzing 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