Quantitative Techniques for Business Decision Making

These Quantitative Techniques for Business Decision-Making sessions seek to provide you with ideas, skills, tools and techniques performing quantitative analysis in – and for – the business arena. The five sessions are:

Part 1: What If I Care?
There is a reason why we develop models in Excel. Never certain of the future, we like to consider as many alternatives as possible, covering off key risks and concerns. This session considers some of the simpler, more basic techniques as a pre-cursor to some of the more sophisticated analytical methods to come.
Using Excel examples, throughout, this session will cover:
• the difference between scenario and sensitivity analysis
• how to use Goal Seek to “force” a required business outcome
• simple optimisation using Excel’s built-in Solver tool
• the importance of breakeven analysis
• how to make variance analysis simpler.

Part 2: Introduction to Data Mining
The data may be “all mine” but what about the information? Data mining helps businesses find patterns and hidden relationships in their data. This may be used to uncover non-obvious trends or patterns to predict behaviour and make better-informed business decisions. For example, credit card companies have been using this approach for years to detect fraud, and aside from Financial Services, data mining is used heavily in direct marketing, electoral politics, cybersecurity and medicine.
This session will provide:
• an overview of data mining
• an explanation of the difference between descriptive and predictive analytics
• a walkthrough of the six steps of data mining
• a brief explanation of clustering, classification and regression techniques 
• examples of supervised and unsupervised learning.

Part 3: Introduction to Optimisation
Complex decision making requires a structured approached. Trial and error or intuitive approaches will not work. Only structured methods can identify the most likely optimal set of solutions to interrelated business decisions.
This session introduces optimisation modelling, i.e. provides a method of allocating resources efficiently and effectively across a number of what may be interrelated uses. To do this, we will provide examples that demonstrate the need to:
• build the decisions that need to be made
• model the objective that must be maximised (or minimised)
• develop the constraints on the potential solutions.
This will then find the values for the decision variables that satisfy the constraints whilst optimising the given objective.

Part 4: Introduction to Simulations Analysis
Real life is never simple. Businesses work in the real world. Forecasts and budgets may never be predicted accurately – so what may we do instead? Using simple ideas from mathematical probability, this session reviews some of the simple techniques that may be communicated to senior decision-makers and shareholders alike to more effectively determine the impact making decisions now has on future metrics.
This session will cover:
• why single point estimates are too simplistic
• risk-adjusted analysis may be dangerously simplistic
• the difference between risk and uncertainty
• introduce the concept of probability distributions – and what to do when they are unknown
• identify simulations analysis techniques that may be adopted
• consider complicating factors such as correlations between variables.

Part 5: Bringing It All Together
It’s all very well understanding the need for optimal sensitivity, scenario, simulations, breakeven and variance analysis, but what objectives should you be setting and what decisions are relevant? This final session considers the need to use different metrics at different times using a decision tool commonly known as Strategic Options Analysis.
This session considers:
• why more than one metric is required to measure business performance
• how the Balanced Scorecard approach can help
• ensuring you can identify operating activities
• the difference between Critical Success Factors and Key Performance Indicators
• the difference between variables, constraints and decisions
• the four-stage Strategic Options Analysis process
• why Post Implementation Review is important.

Upon satisfactory completion of these recorded webinars, you will be able to:

• Use Excel to undertake scenario analysis
• Practically explain how data mining works
• Develop simple optimisation models to allocate resources
• Produce simulations using tools available in Excel

Benefits:

Aimed at all finance professionals / decision-makers involved in strategic planning, this course shows you how to improve the quality of your decision-making and recommendations by using quantitative analysis.