The final phase of the quantitative risk assessment process is implementation of stan-dardized risk mitigation tools to enable manufacturing teams to manage risk once it is identified. Chapter 5 discussed the methodology behind risk management and its connection to learning in aerospace. A learning curve framework to a dynamic tool that can be used to manage risk mitigation efforts of any scope from cost reduction of a single manufacturing cell to risk management of an entire engine program.
The four main trends compared in managing risk are theoretical learning, action
plan, actual cost, and target cost. In the context of employing the tool to manage high level risk for a whole engine program, these four values tracked over production of each engine sheds light into Pratt & Whitney learning rate against industry learning as well as feasibility of hitting a target when compared against theoretical learning.
The theoretical learning curve is generated as given in Equation 5.3, and the user interface is shown in Figure 6-9. The final tool implemented the log-linear methodology, and used target cost to back-calculate the current total impact as the dependent variable. Thereby, the team would be able to benchmark actual impact against the theoretical impact given the process follows industry standard learning trends.
Figure 6-9: User interface for generation of the theoretical learning curve. Variable learning curve parameters are displayed in tan colored cells. Parameters are laid out in a context aligned to performance goals of manufacturing teams for ease of use.
While costs do decrease because of gradual learning in a manufacturing process, often these cost decreases are results of operations improvement projects that provide step decreases in cost per engine. The risk mitigation tool tracks intervention projects and projected improvements alongside natural learning to display a prediction of cost progression taking planned projects into account.
Figure 6-10 shows the tool input for action plan that later feeds into the final comparison output of all curves.The final data input is actual data which is simply the actual measured impact of each engine after production. This set of data is used for retroactive analysis to determine how close the program came to meeting the action plan, and how the program compares to an industry standard theoretical learning trajectory. During the course of risk mitigation management, this tool easily
Figure 6-10: Action plan input including cost impact, timeline, and unit incorporated.
This sample action plan shows interface that manufacturing management can use to plan and track improvement efforts on high risk parts. Ownership, timeline, and unit incorporation all provide visibility and accountability in implementation of the risk mitigation effort.
receives inputs from the risk assessment dashboard to allow tracking of risk per part or engine produced.
Figure 6-11 depicts the final output of the risk mitigation tool comparing all input data side by side to give a comprehensive understanding of risk mitigation progress.As shown in Figure 6-11, risk management is done through comparison of the four curves in the risk mitigation chart. Theoretical learning shows expectation of natural cost decrease per engine given the industry standard learning rate. Since the industry standard rate is 85%, this curve represents a trajectory such that each successive engine is 15% less risky to produce than the previous unit.
The action plan curve includes natural learning benefits and integrates the discrete improvement projects input into the action plan portion of the tool. Risk decreases due to learning are generally a result of manufacturing teams becoming better at producing parts due to repetition and practice. The learning curve expects the first unit to be the most difficult to produce while each successive unit becomes easier as the processes become more familiar to manufacturing teams. Process improvement projects provide discrete improvements in risk on top of natural learning. For exam-ple, the procurement of a new machine to improve quality in a manufacturing process will provide value on top of learning and is documented separately as an action plan.
Actual impact values are plotted around these curves to track performance of the program in meeting risk mitigation targets. Finally, the target final cost is shown to give an understanding of feasibility of meeting program target. This tool provides a political capital to manufacturing teams in negotiating reasonable production goals
as quantitative evidence of discrepancy between targets and theoretical learning rate.
Figure 6-11: Final risk mitigation curve output with all four benchmarks represented.
Comparison of the learning curve, program targets, action plan, and actual data gives a comprehensive understanding of risk mitigation progress. Action plan and actuals can be benchmarked against risk reduction due to standard learning, and targets can be continuously re-evaluated based on current state.
Implementation of the learning rate framework based risk mitigation tool is both part of the quantitative risk assessment process laid out in this thesis and the overall standardization of tools focus of the MRL team. This phase of the process specifically strengthens the connection between the MRL team and manufacturing teams, as the risk output directly feeds into improvement management which is owned by each individual team.