Generic Control Phase for Project Risk Management

This chapter elaborates a generic tactic for the DMAIC Control phase for Project Control. This is required because the projects presented in the book are not implemented, so there are no data available for analysis. Also, the generic Project Control phase reuses the associated scenario elaborated in Ch. 6. Moreover, the Project Control phase is applicable to the applications of the method in Chapter 3, Chapter 4, Chapter 6 and Chapter 7, which is appropriately referenced in the respective individual chapters. Finally, by elaborating the Project Control phase in a dedicated chapter, avoids repetitions in the book.

Retail Ordering Policy Risk

The method is applied to Retail Ordering Policy to manage the associated risk. DMAIC framework applies stochastic techniques. Stochastic optimisation determines the optimal retail ordering policies to maximise profit. Simulate every determined optimal ordering policy and calculate profits, risks, and Six Sigma metrics to measure against specified target limits. Analyse simulation results and identify and quantify the main contributors to the profits variability by using sensitivity analysis. The optimal retail ordering policies are ranked based on their profits and associated risk factors. The technically best optimal retail ordering policy is recommended to the management for implementation. Control stage is elaborated by reusing the data and presented stochastic optimisation and simulation models for ongoing management of the optimal strategy. Some changes are applied to the data and models however, in order to emulate the scenario of an implemented strategy.

Petroleum Exploration Risk in Prospect Portfolio Selection

Presented method is applied to petroleum exploration for prospect portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to risk management. Optimisation resolves Efficient Frontier of portfolios for desired range of expected return with initially defined increment. Simulation measures Efficient Frontier portfolios calculating mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target limits. Analysis considers mean return, Six Sigma metrics and Sharpe Ratio and selects the portfolio with maximal Sharpe Ratio as initially the best portfolio. Optimisation resolves Efficient Frontier in a narrow interval with smaller increments. Simulation measures Efficient Frontier performance including mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target. Analysis identifies the maximal Sharpe Ratio portfolio, i.e. the best portfolio for implementation. Selected prospects in the portfolio are individual projects. So, Project Management approach is used for control.

The Novel Approach

This chapter presents the novel Six Sigma DMAIC generic approach to Risk Management. The method is introduced first. In The Generic Approach and Algorithms section, generic mathematical concepts are elaborated. Also, four generic classes of applications of the proposed method are identified including: 1) Portfolio Management; 2) Quality Management; 3) Project Management; and 4) Income Management. Furthermore, four generic algorithms are elaborated for the respective four classes of application of the method. The generic algorithms include description and process flow of the applications. Finally, the modelling tools used in the book's elaborations are detailed, as well as references for how to use these tools and run Simulation and Stochastic Optimisation step-by-step.

Agricultural Planning Risk

The method is applied to Farm Planning to manage the associated risk. DMAIC framework applies stochastic techniques. Stochastic optimisation determines the optimal land allocation strategies to maximise profit. Once the optimal strategies are resolved, every determined strategy is simulated. So, the profit and associated risk factors are stochastically calculated. Six Sigma process capability metrics are also calculated to measure the process performance of the mean profit distribution. Simulation results are analysed and sensitivity analysis is used to identify and quantify the main contributors to the profit variability. The optimal strategies are ranked and prioritised for management's attention based on their gain and associated risk factors. This will facilitate the management decision making in order to select the best strategy for implementation. The execution of the optimal plan is considered as an individual implemented project. Therefore, Project Management approach is used to control the plan execution.

Research and Development Risk in Projects Selection

Elaborated method is applied to R&D for project portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to risk management. Optimisation resolves Efficient Frontier of portfolios for desired range of expected return with initially defined increment. Simulation measures Efficient Frontier portfolios calculating mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target limits. Analysis considers mean return, Six Sigma metrics and Sharpe Ratio and selects the portfolio with maximal Sharpe Ratio as initially the best portfolio. Optimisation resolves Efficient Frontier in a narrow interval with smaller increments. Simulation measures Efficient Frontier performance including mean return, variance, standard deviation, Sharpe Ratio, and Six Sigma metrics versus pre-specified target. Analysis identifies the maximal Sharpe Ratio portfolio, i.e. the best portfolio for implementation. Selected projects in the portfolio are individual projects. So, Project Management approach is used for control.

Risk Management Future Directions

This chapter outlines the future directions for the Risk Management research. Eminent Risk Mangement scholars and practitioners are referenced considering the future direction for Risk Management in general across industries. Also, references to the future directions for specific industries, disciplines and corporate businesses are provided including: 1) Financial Risk Management; 2) Information Systems Security; 3) Energy Sector; 4) Project Management; 5) Construction Industry; 6) Supply Chain; 7) Agriculture; and 8) Six Sigma.

Information Systems Security Risk

Proposed method is applied to Software Engineering for security software quality management. DMAIC framework applies stochastic techniques to risk management. Hypothetical software project is considered with specified delivery target date and quality goal. Testing project is analysed uncompleted with weeks of testing remaining. Simulation considers testing defects and predicts the number defects at the end of test. If simulation confirms that the quality goal will be met, testing continues as is. Simulation regularly checks quality goal as testing progresses. If the predicted quality is missing targets, simulation predicts when the target will be achieved. There are two options, either more resources should be allocated to the project to rectify the problem, or the project should be delayed. An improvement project is defined to rectify the problem. Control is shown by using a very similar scenario with data for Quality Control, which applies slightly different models.

Project Management Risk

Presented method is applied to Project Management by using PERT-CPM critical paths to manage project risk. DMAIC framework applies stochastic techniques. Stochastic optimisation determines the critical paths. Every critical path is simulated and associated risks are calculated. Six Sigma process metrics are calculated against specified targets. Simulation results are analysed and sensitivity analysis is used to identify and quantify the main contributors to the variability of the project duration time. The critical paths are ranked and prioritised for management's attention based on their associated risk factors. The project was not implemented, so there no data for analysis. However, assuming that the project was implemented, a generic Project Control phase is applied.

Investment Management Risk

Proposed method is applied to Investment Management for portfolio selection to achieve investment objectives controlling risk. DMAIC framework applies stochastic techniques to investment risk management. Optimisation constructs Efficient Frontier of optimal portfolios with expected return in a predefined range with a determined increment. Simulation calculates and measures the portfolio return, Variance, Standard Deviation, Value at Risk (VAR), Sharpe Ratio and Beta of Efficient Frontier portfolios; Six Sigma capability metrics of investment process are calculated versus specified limits. Analysis allows for selection of the best Efficient Frontier portfolio with maximum Sharpe Ratio. Simulation sensitivity analysis identifies the riskiest asset. Portfolio revision considers options to improve the portfolio and replaces the asset with an option to reduce risk. Portfolio execution implements the revised portfolio. Ongoing portfolio management evaluates portfolio performance on regular basis and if required, revises the portfolio considering changes in the market and investor's position.