1. Introduction to modelling Basic concepts and simple modelling techniques 7/12/20151

2. What does modelling involve? How are models built? What are the components of a model and how do we identify these? Are there different types of model? How do models deal with time? How do models deal with certainty, uncertainty and risk? 7/12/20152

3. 3 Benefits of Producing Models Having a model to use is beneficial but the process of producing a model is equally if not more beneficial. Helps you to understand the problem. – Need to be explicit about your goals. – Need to quantify the variables which affect the goals. – Need to identify constraints and relationships between variables. – Facilitates communication and understanding.

4. 4 What does Modelling involve? variables are identified by analysing the problem relationships among them are established. Simplifications are made through assumptions. (untested beliefs or predictions). A decision-maker can test assumptions using what-if or sensitivity analysis. Simpler models are cheaper but don’t model reality so closely.

5. How are models built? Models can be built using statistical packages, forecasting software, modelling packages and end-user software tools like Excel. 7/12/20155

6. Components of a Model Mathematical Models are made up of 3 basic components, decision variables, uncontrollable variables(and/or parameters) and result (outcome) variables. Mathematical formulae link these variables together. 7/12/20156

7. Decision variables represent alternative courses of action. The level of these is determined by the decision-maker e.g. the amount to invest. Result variables indicate the outcome of the decision, they are dependent on the occurrence of the decision and the uncontrollable or independent variables. 7/12/20157

8. Uncontrollable variables are factors which are not under the control of the decision-maker. These can be fixed or variable e.g. interest rates. Intermediate result variables reflect intermediate outcomes e.. if salary is a decision variable, employee satisfaction is an intermediate variable and productivity is the result variable. 7/12/20158

9. Example : Simple profit model 7/12/20159

10. Example : Printer model Number of pages = pages per week* nweeks + oneoffs Total cost per page = fixed cost + var costs for year number of pages Goal : minimise cost per page (tells us what we need to count). Decision variable = cost of printer Result variable = cost per page Uncontrollable variable = cost of ink (given you’ve decided on that printer) Assumptions : that the printer will work! That we’ll print x many pages per week etc., That the college price will stay fixed. 7/12/201510

11. Look at the cash flow model and identify – Decision variables – Uncontrollable variables – Result variables. 7/12/201511

12. 7/12/201512

13. 7/12/201513 Cash Flow Calculations JulyAugSeptOctNovDec Income Product Cash Sales Product Credit Sales Sale of Vehicle Total Outgoings Materials Labour Variable Overheads Fixed Costs Corporation Tax Purchase of Vehicle Total Receipts Less Payments Balance Brought Forward Balance Carried Forward

14. Aspects of Modelling Normative vs descriptive models Static vs dynamic models Treating certainty, uncertainty and risk – What if analysis – Sensitivity analysis – Scenario analysis 7/12/201514

15. Types of Model Normative (Optimisation) the chosen alternative is demonstrably the best of all possible alternatives. e.g. Linear Programming 157/12/2015 Descriptive Describe things as they are, or as they are believed to be. Checks the outcome of a given set of alternatives not of all alternatives. No guarantee of an optimal solution. e.g. simulation models which are used to explore different solutions and relationships between variables

16. Optimisation Examples Get the highest level of goal attainment from a given set of resources e.g. max profit from €1000000 investment. Find alternative with highest ratio of goal attainment to cost. Find alternative with the lowest cost that wll achieve acceptable level of goals. 7/12/201516

17. Example : Optimisation 7/12/201517

18. Good Enough or Satisficing decision-maker sets up an aspiration, goal or desired level of performance and searches the alternatives until one is found which achieves this level. This involves:- Generating Alternatives Predicting the outcome of each alternative Measuring outcomes –value in terms of goal attainment e.g. profit,customer satisfaction – no. complaints, level of loyalty to product, ratings found by surveys. 7/12/201518

19. Descriptive Model Examples Scenario analysis Environmental impact analysis Simulation Waiting line (queue) management Narratives. 7/12/201519

20. Static Analysis Static models take a single snapshot of a situation. During this, everything occurs in a single interval or fixed time frame. During a static analysis stability of the relevant data is assumed. e.g. buy or make. 7/12/201520

21. Dynamic Analysis - time-dependent models scenarios that change over time e.g. 5 year profit and loss projection, in which the input data such as costs, prices and quantities change over time. e.g. in determining how many checkouts need to be open in a supermarket the time of day must be considered. Dynamic simulation represents when conditions vary from the steady state over time:- there may be variations in the raw materials, or unforeseen events. Dynamic models are important because they use, represent or generate trends and patterns over time. e.g. 5 year profit projection where input data such as costs, prices and quantities change over time. Can be used to create averages per period or moving averages and to prepare comparative analysis. May facilitate the development of business plans, strategies,tactics. 7/12/201521

22. Certainty, Uncertainty and Risk Certainty models- easy to develop and solve- models constructed under assumed certainty e.g. many financial models. Uncertainty –the decision maker does not know, or can’t assess the probability of occurrence of certain outcomes. More information increases certainty. Risk analysis involves estimates of risk. Risk can thus be estimated. Uncertainty and risk can be examined using what-if and sensitivity analysis. It is a good idea when identifying variables to assess certainty. 7/12/201522

23. What if analysis The end user makes changes to variables or relationships between variables and observes the resulting change in the values of other variables. Example Change a revenue amount (variable) or a tax rate formula in a simple financial spreadsheet model, and recalculate all the affected variables. A manager would be interested in observing and evaluating any changes in values that occurred e.g. net profit after taxes. In may cases this is the “bottom line” i.e. a key factor in making many types of decisions. What would happen to sales if we cut advertising by 10%? 7/12/201523

24. Sensitivity analysis the value of one variable is changed repeatedly and the resulting changes on the other variables are observed e.g. value of revenue is changed incrementally in small increments and the effects on other spreadsheet variables noted and evaluated. e.g. Cut advertising by 10% repeatedly and note effect on sales. 7/12/201524

25. Scenario Analysis Examine the best case, worst case, most likely and average case scenarios. 7/12/201525

26. Examine the Wilmington example. How does this model deal with uncertainty? What is the best case here for Wilmington? What is the worst case? What other factors do we need to consider in scenario analysis? 7/12/201526

27. Excel Techniques: Goal Seeking Finds a value for a variable and links it to an outcome. E.g. How many pages do we have to print to make it worth buying a printer? 7/12/201527

28. 7/12/2015Source Turban 200328 Model categories Optimisation of Problems with few alternatives Find best solution from small number of alternatives Decision tables, trees Optimisation via algorithm Find best solution from large number of alternatives using a step- by-step improvement process Linear and other mathematical programming models, network models Optimisation via analytic formula Find best solution using formula Some inventory models SimulationUse experimentation HeuristicsFind good enough solution using rules Heuristic programming, expert systems Other ModelsSolve a what-if case using a formula Financial modelling, waiting lines Predictive modelsPredict future for given scenario Forecasting models