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This assignment has been designed to address the following subject learning objectives:

•be able to critically interpret the risk management concept;
•be able to interpret risk through modelling techniques;
•be able to determine the probability, frequency and consequences of a risk event occurring.

Theory - Please evaluate the following question. Your evaluation requires you to prepare and defend a chosen position:

'Knowledge can be divided into uncertainty and perfect knowledge. Where does risk fit into this spectrum?'

(Hint: Evaluate the measurable component of risk and where it sits on the spectrum)

Workplace application - Identify how your work case problem would benefit if more reliable quantitative analysis and forecasts were available. (Ideally you should the same work case problem from the first assignment, but if it does not lend itself well to this assignment you may choose another problem from your workplace).

Your research requires you to:

1. Collect as much relevant data about the work case problem as possible. Analyse the data using descriptive statistics and then plot the data in appropriate graphical forms.

Assess the diagrams by determining the key characteristics of the data - i.e. summarise the patterns and interpret what these patterns are likely indicate about the data to date.

2. Use one quantitative forecasting techniques to develop forecasts from the data - for example naive, moving average, exponential smoothing and/or regression.

3. Explain your key findings from the quantitative analysis. In doing so, apply the predictions to your work case problem and determine how these affects might change the future outcomes for your workplace.

- Under what circumstances are qualitative techniques suitable for forecasting? Explain how qualitative techniques could be applied to your chosen work case problem to improve future outcomes.

My project is about ERP (SAP Implementation)

Risk management Assignment: Sample data for Task

Month

Purch-Orders

Purch-Inv

MiscInv

Pmnts

Relative Frequency %

Jan

122

250

190

280

10.8

Feb

220

170

55

121

7.26

Mar

150

150

45

89

5.56

Apr

160

160

66

89

6.09

May

105

170

50

90

5.32

Jun

190

169

59

129

7.01

Jul

150

170

58

128

6.49

Aug

130

150

49

118

5.73

Sep

260

180

45

79

7.23

Oct

111

190

180

200

8.73

Nov

202

290

220

350

13.62

Dec

210

350

250

450

16.16

Statistics and Probability, Statistics

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